diff --git a/CLAUDE.md b/CLAUDE.md index 14b8aca..edd9d89 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -135,7 +135,7 @@ sentinel/ evaluate/ ← accuracy + latency benchmarks (K8s Job) retrain/ ← fine-tuning scripts, called by Airflow drift/ ← PySpark PSI/JSD jobs (spark-submit, own pyproject.toml) - dags/ ← Airflow DAG definitions (orchestrates pipelines/) + orchestration/ ← Airflow DAG definitions (orchestrates pipelines/) datasets/ ← shared data loading utilities infra/ ← Terraform + Helm charts ``` diff --git a/infra/explanation.md b/infra/explanation.md index a46bf1c..8d6f095 100644 --- a/infra/explanation.md +++ b/infra/explanation.md @@ -1,8 +1,12 @@ # Infra — Overview The `infra/` directory contains everything needed to run Sentinel's supporting -infrastructure. All services run inside a local k3d Kubernetes cluster managed -entirely by Terraform — no `docker-compose.yml`, no manual `kubectl apply`. +infrastructure. All services — including the classifier and stream processor +themselves, as of Phase 5 — run inside a local k3d Kubernetes cluster managed +entirely by Terraform. No `kubectl apply` files exist outside of what +Terraform generates; `docker-compose.yml` at the repo root is a secondary, +lightweight alternative for running just the classifier + Prometheus + Grafana +without a full cluster (see its own comments for the tradeoffs). --- @@ -13,11 +17,18 @@ infra/ terraform/ local/ — Terraform workspace for the k3d dev cluster providers.tf — kubernetes + helm provider config - variables.tf — all tuneable knobs (passwords, sizes) - main.tf — every K8s resource (namespaces → all services) + variables.tf — all tuneable knobs (passwords, sizes, keys) + main.tf — data/monitoring/app layer resources + airflow.tf — Airflow (Phase 7 orchestration) + mlflow.tf — MLflow tracking server (Phase 7 experiment tracking) + label-ui.tf — manual-labelling UI (Phase 7 retraining loop) outputs.tf — port-forward commands printed after apply + mlflow/ + Dockerfile — extends ghcr.io/mlflow/mlflow with psycopg2 + boto3 prometheus/ - prometheus.yml — global config, scrape jobs, rule file references + prometheus.yml — k8s-path scrape config (rule_files: rules/) + prometheus.compose.yml — docker-compose-path scrape config (different + classifier target — see its own comment) rules/ classifier.yml — alert + recording rules for the classifier grafana/ @@ -30,21 +41,48 @@ infra/ ## Component explanations -Each sub-directory has its own detailed explanation file: +Each sub-directory (and several outside `infra/`) has its own detailed +explanation file: -- [`terraform/local/explanation.md`](terraform/local/explanation.md) — all Terraform - resources: namespaces, PostgreSQL, MongoDB, MinIO, Prometheus, Grafana, Kafka, - Jaeger, OTel Collector. Covers every provider block, variable, resource type, - and the patterns used throughout (StatefulSet vs Deployment, wait_for_rollout, - lifecycle hooks, etc.). +- [`terraform/local/explanation.md`](terraform/local/explanation.md) — every + Terraform resource: namespaces, PostgreSQL, MongoDB, MinIO, Prometheus, + Grafana, Kafka, Jaeger, OTel Collector, spark-operator, Airflow, MLflow, + and the label-ui service. Covers every provider block, variable, resource + type, and the patterns used throughout (StatefulSet vs Deployment, + `wait_for_rollout`, ConfigMap-as-file mounting, etc.) — plus a growing + list of live-debugged gotchas. + +- [`mlflow/explanation.md`](mlflow/explanation.md) — why MLflow needs a + custom image on top of the official one, and how to bump its version. - [`prometheus/explanation.md`](prometheus/explanation.md) — global Prometheus - config, the k3d host scraping trick, scrape job relabeling, recording rules, - alert thresholds, and how to add new services to monitoring. + config, scrape job relabeling, recording rules, alert thresholds, and how to + add new services to monitoring. + +- [`grafana/explanation.md`](grafana/explanation.md) — Grafana provisioning + system, datasource proxy model, editable vs managed resources, and how to + build and persist dashboards. + +- [`../pipelines/drift/explanation.md`](../pipelines/drift/explanation.md) — + PySpark-based drift detection (PSI/JSD), the SparkApplication CRD, and how + it's scheduled to run. + +- [`../pipelines/evaluation/explanation.md`](../pipelines/evaluation/explanation.md) + — the model quality gate: benchmark metrics, the ground-truth dataset, and + what "passing" actually means before a model can be promoted. -- [`grafana/explanation.md`](grafana/explanation.md) — Grafana provisioning system, - datasource proxy model, editable vs managed resources, and how to build and - persist dashboards. +- [`../pipelines/retraining/explanation.md`](../pipelines/retraining/explanation.md) + — fine-tuning on manually-labelled data, full MLflow logging, and how it + hands off to the optimizer/evaluation pipelines unchanged. + +- [`../services/label-ui/explanation.md`](../services/label-ui/explanation.md) + — the manual-labelling web UI that feeds the retraining pipeline and + triggers it via Airflow's REST API. + +- [`../orchestration/explanation.md`](../orchestration/explanation.md) — how + Airflow DAGs get into the cluster, `retrain_dag.py`'s three-task promotion + flow, `drift_dag.py`'s hourly drift-check-and-auto-retrain loop, and the + CLI commands used to inspect DAGs day to day. --- @@ -53,25 +91,45 @@ Each sub-directory has its own detailed explanation file: ``` dev-start.sh → k3d cluster create/start sentinel + → docker build + k3d image import (classifier, stream-processor, drift, + mlflow, label-ui, retraining) → terraform apply (deploys everything below) K8s cluster (sentinel-data namespace) - PostgreSQL :5432 — classification results, model registry - MongoDB :27017 — flagged content for retraining - MinIO :9000 — ONNX model artifacts + PostgreSQL :5432 — classification results, model registry, drift stats, + Airflow's own metadata + MLflow's backend store + (each a separate database on the same instance) + MongoDB :27017 — flagged content for retraining, manual labels + MinIO :9000 — ONNX model artifacts + MLflow's artifact store + (models/, datasets/, mlflow/ buckets) Kafka :9092 — traces.raw topic (3 partitions) +K8s cluster (sentinel-app namespace) + classifier — FastAPI + ONNX inference, /v1/moderations primary endpoint + stream-processor — Kafka consumer → classify → PG + Mongo + label-ui — manual labelling UI for flagged_content, triggers retrain_dag + K8s cluster (sentinel-monitoring namespace) - Prometheus :9090 — scrapes classifier at host.k3d.internal:8000 + Prometheus :9090 — scrapes classifier via in-cluster Service DNS Grafana :3000 — queries Prometheus via in-cluster DNS Jaeger :16686 — receives OTLP traces from OTel Collector OTel Collector :4317/:4318 — receives spans, fans out to Kafka + Jaeger - -Host machine (started by dev-start.sh, not in K8s) - classifier :8000 — FastAPI + ONNX inference - stream-processor — Kafka consumer → classify → PG + Mongo + MLflow :5000 — experiment tracking for the retraining pipeline + +K8s cluster (sentinel-pipeline namespace) + spark-operator — manages the drift job's driver/executor pods + Airflow — scheduler + webserver (LocalExecutor), orchestrates + pipelines/ jobs. drift_dag.py runs hourly: submits + the drift Spark job, and if drift_flagged is true, + triggers retrain_dag.py, which fine-tunes (via a + KubernetesPodOperator pod), gates on quality, and + promotes + rolls out a new model. Both DAGs are also + reachable manually (services/label-ui's button, or + `airflow dags trigger`). ``` +Everything now runs in-cluster — there is no "host machine" component left. Port-forwards from the cluster to localhost are opened automatically by -`dev-start.sh`. Each can also be opened individually — see each service's -section in `docs/local-dev.md`. +`dev-start.sh` for local access (curl, browser UIs, psql, etc.); each can also +be opened individually via the `terraform output` commands in `outputs.tf`. +See each service's section in `docs/local-dev.md` for URLs and credentials. diff --git a/infra/mlflow/Dockerfile b/infra/mlflow/Dockerfile new file mode 100644 index 0000000..389314e --- /dev/null +++ b/infra/mlflow/Dockerfile @@ -0,0 +1,8 @@ +# The official image doesn't bundle a Postgres driver or S3 client — the base +# install only supports SQLite/MySQL backends and local-disk artifact storage. +# psycopg2-binary (backend-store-uri postgresql://...) and boto3 +# (default-artifact-root s3://... against MinIO) are both required for the +# `mlflow server` command in mlflow.tf to start at all. +FROM ghcr.io/mlflow/mlflow:v3.13.0 + +RUN pip install --no-cache-dir psycopg2-binary boto3 diff --git a/infra/mlflow/explanation.md b/infra/mlflow/explanation.md new file mode 100644 index 0000000..ff69622 --- /dev/null +++ b/infra/mlflow/explanation.md @@ -0,0 +1,73 @@ +# MLflow image — Explanation + +This directory holds exactly one file: a `Dockerfile`. There's no application +code here — MLflow's tracking server is a pip-installable CLI (`mlflow +server ...`), not something this repo builds. The Dockerfile exists to +extend the official image with the two extra clients it needs to talk to +this project's backing stores. + +--- + +## Why a custom image at all + +```dockerfile +FROM ghcr.io/mlflow/mlflow:v3.13.0 +RUN pip install --no-cache-dir psycopg2-binary boto3 +``` + +The official `ghcr.io/mlflow/mlflow` image only supports SQLite/MySQL +backend stores and local-disk artifact storage out of the box — it doesn't +bundle a PostgreSQL driver or an S3 client. This project's MLflow deployment +(`infra/terraform/local/mlflow.tf`) needs both: + +- `--backend-store-uri postgresql://...` (experiments/runs/params/metrics) + → needs `psycopg2-binary` +- `--default-artifact-root s3://mlflow/` (against MinIO) → needs `boto3` + +Without them, `mlflow server` fails immediately at startup trying to +construct the SQLAlchemy engine or the S3 client. This is a +well-documented, common gap — most MLflow-on-Postgres-and-S3 deployment +guides extend the base image the same way. + +**Version pinned to an exact tag** (`v3.13.0`, not `latest`), matching this +repo's general "never `:latest`" rule. Confirmed to exist in the `ghcr.io/ +mlflow/mlflow` registry before pinning to it, rather than guessed. + +--- + +## Built and imported like every other local image + +```bash +docker build -t sentinel-mlflow:local infra/mlflow/ +k3d image import sentinel-mlflow:local -c sentinel +``` + +Same pattern as classifier/stream-processor/drift/label-ui/retraining — +`dev-start.sh` does both steps automatically. `imagePullPolicy: Never` in +`mlflow.tf`'s Deployment spec tells Kubernetes to use only the local image +store, never attempt a registry pull. + +--- + +## Tips and tricks + +**Checking what's actually installed in the image:** +```bash +docker run --rm sentinel-mlflow:local pip list | grep -iE "mlflow|psycopg2|boto3" +``` + +**`mlflow server --help` is the fastest way to check a flag's exact +semantics** rather than guessing from memory — this is how the +`--allowed-hosts` and `--workers` behavior (see +[`../terraform/local/explanation.md`](../terraform/local/explanation.md)'s +MLflow section for what those two flags fixed) was actually confirmed, +not assumed: +```bash +docker run --rm sentinel-mlflow:local mlflow server --help +``` + +**Bumping the MLflow version later**: change the `FROM` tag, confirm the +new tag exists in the registry first (`ghcr.io/mlflow/mlflow`'s package +page, or just let `docker build` fail fast if it doesn't), rebuild, and +re-run `dev-start.sh` — no other file needs to change unless the new +version's CLI flags differ (check `--help` again). diff --git a/infra/terraform/local/airflow.tf b/infra/terraform/local/airflow.tf new file mode 100644 index 0000000..b15afa3 --- /dev/null +++ b/infra/terraform/local/airflow.tf @@ -0,0 +1,355 @@ +# ── Airflow (Phase 7: orchestration) ────────────────────────────────────────── +# LocalExecutor — tasks run as subprocesses of the scheduler pod. No Celery/ +# Redis/Flower needed at this scale, matching the project's "don't add +# infrastructure beyond what's needed" principle (same reasoning as pinning +# spark-operator's job namespace instead of a cluster-wide install). +# +# Reuses the existing PostgreSQL instance (separate "airflow" database, see +# main.tf's postgres_init 02_airflow_db.sql) instead of the chart's bundled +# Postgres subchart — one fewer stateful thing to operate locally. +# +# DAGs are mounted from a ConfigMap built from orchestration/*.py rather than +# git-sync or a custom image — same pattern main.tf already uses for +# Prometheus/Grafana config and the Postgres init scripts (Terraform reads +# the repo file, embeds it, mounts it). Simplest option that doesn't require +# rebuilding an image or recreating the k3d cluster with a host volume mount +# every time a DAG file changes. + +locals { + dag_files = fileset("${path.module}/../../../orchestration", "*.py") + + # Mounted per-file via subPath rather than mounting the ConfigMap as a + # whole directory. Kubernetes mounts ConfigMap volumes through a + # "..data -> .." symlink indirection to update them atomically + # — Airflow's DAG-directory walker (find_path_from_directory) doesn't + # handle that structure and aborts with "Detected recursive loop when + # walking DAG directory" (reproduced live). subPath mounts bypass that + # indirection entirely and appear as plain files, at the cost of one + # volumeMount per DAG file instead of one mount for the whole folder. + dag_volume_mounts = [ + for f in local.dag_files : { + name = "dags" + mountPath = "/opt/airflow/dags/${f}" + subPath = f + readOnly = true + } + ] +} + +resource "kubernetes_config_map" "airflow_dags" { + metadata { + name = "airflow-dags" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } + + data = { + for f in local.dag_files : + f => file("${path.module}/../../../orchestration/${f}") + } +} + +# Static Flask secret key for the webserver (signs session cookies). Without +# this, the chart auto-generates a fresh random key on every deploy — every +# `terraform apply` that touches the release invalidates all sessions and +# the UI shows a persistent "dynamic webserver secret key" warning banner. +# Key name inside the secret (webserver-secret-key) is fixed by the chart — +# see templates/_helpers.yaml's webserver_secret_key_secret definition. +resource "kubernetes_secret" "airflow_webserver_secret_key" { + metadata { + # Not "airflow-webserver-secret-key" — the chart's own auto-generated + # secret (created back when webserverSecretKeySecretName was unset) is + # already sitting at that exact name (its naming convention is + # {{ airflow.fullname }}-webserver-secret-key). A different name avoids + # the collision; the chart stops rendering its own version of this + # resource once webserverSecretKeySecretName is set below, and Helm + # prunes the now-orphaned one on this upgrade. + name = "airflow-webserver-secret-key-static" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } + data = { + webserver-secret-key = var.airflow_webserver_secret_key + } +} + +# Scheduler's identity — LocalExecutor runs tasks as scheduler subprocesses, +# so this is the identity that needs permission to trigger a rollout restart +# (added once the retrain DAG's promotion task lands in Phase 7.3; granted +# now so the ServiceAccount doesn't need to be re-plumbed through the chart +# values later). +resource "kubernetes_service_account" "airflow" { + metadata { + name = "airflow" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } +} + +# Cross-namespace binding: the Role lives where the permission applies +# (sentinel-app), the ServiceAccount lives where Airflow actually runs +# (sentinel-pipeline) — RoleBinding subjects support a different namespace +# than the Role itself. +resource "kubernetes_role" "airflow_rollout" { + metadata { + name = "airflow-rollout" + namespace = kubernetes_namespace.sentinel["sentinel-app"].metadata[0].name + } + rule { + api_groups = ["apps"] + resources = ["deployments"] + verbs = ["get", "list", "patch"] + } +} + +resource "kubernetes_role_binding" "airflow_rollout" { + metadata { + name = "airflow-rollout" + namespace = kubernetes_namespace.sentinel["sentinel-app"].metadata[0].name + } + role_ref { + api_group = "rbac.authorization.k8s.io" + kind = "Role" + name = kubernetes_role.airflow_rollout.metadata[0].name + } + subject { + kind = "ServiceAccount" + name = kubernetes_service_account.airflow.metadata[0].name + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } +} + +# Phase 7.3: retrain_dag.py's run_retraining task uses KubernetesPodOperator +# to launch the sentinel-retraining pod — needs permission to create/watch/ +# delete pods (+ read their logs) in its own namespace. Same shape as +# main.tf's spark_driver role, just for the airflow SA instead of spark. +resource "kubernetes_role" "airflow_pod_launcher" { + metadata { + name = "airflow-pod-launcher" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } + rule { + api_groups = [""] + resources = ["pods"] + verbs = ["create", "get", "list", "watch", "delete"] + } + rule { + api_groups = [""] + resources = ["pods/log"] + verbs = ["get", "list"] + } +} + +resource "kubernetes_role_binding" "airflow_pod_launcher" { + metadata { + name = "airflow-pod-launcher" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } + role_ref { + api_group = "rbac.authorization.k8s.io" + kind = "Role" + name = kubernetes_role.airflow_pod_launcher.metadata[0].name + } + subject { + kind = "ServiceAccount" + name = kubernetes_service_account.airflow.metadata[0].name + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } +} + +# Phase 7.4: drift_dag.py submits/polls/deletes the sentinel-drift- +# SparkApplication via plain kubernetes.client.CustomObjectsApi calls — a +# different API group (sparkoperator.k8s.io, the custom resource itself) +# from airflow_pod_launcher above (core API pods, for the retraining +# KubernetesPodOperator). The spark ServiceAccount's own Role (main.tf's +# spark_driver) covers what the DRIVER pod needs to manage its executors — +# this is a separate concern: what the AIRFLOW SA needs to create/watch/ +# delete the CR that spark-operator's controller then acts on. +resource "kubernetes_role" "airflow_spark_application" { + metadata { + name = "airflow-spark-application" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } + rule { + api_groups = ["sparkoperator.k8s.io"] + resources = ["sparkapplications", "sparkapplications/status"] + verbs = ["create", "get", "list", "watch", "delete"] + } +} + +resource "kubernetes_role_binding" "airflow_spark_application" { + metadata { + name = "airflow-spark-application" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } + role_ref { + api_group = "rbac.authorization.k8s.io" + kind = "Role" + name = kubernetes_role.airflow_spark_application.metadata[0].name + } + subject { + kind = "ServiceAccount" + name = kubernetes_service_account.airflow.metadata[0].name + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } +} + +# Mirrors Mongo/MinIO credentials into sentinel-pipeline for +# run_retraining's pod env (K8s secrets are namespace-scoped — same +# reasoning as main.tf's app_mongodb/app_minio mirrors into sentinel-app). +resource "kubernetes_secret" "retraining_mongo" { + metadata { + name = "retraining-mongo" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } + data = { + mongo-uri = "mongodb://sentinel:${var.mongodb_password}@mongodb.sentinel-data.svc.cluster.local:27017/sentinel" + } +} + +resource "kubernetes_secret" "retraining_minio" { + metadata { + name = "retraining-minio" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + } + data = { + root-user = var.minio_root_user + root-password = var.minio_root_password + } +} + +resource "helm_release" "airflow" { + name = "airflow" + repository = "https://airflow.apache.org" + chart = "airflow" + # Pinned to an exact version, not a "~>" constraint — the Helm provider + # resolves constraints to a concrete version during apply, which produced + # a "Provider produced inconsistent final plan" error (a known provider + # quirk) when combined with importing an existing release. Exact version + # sidesteps it and matches CLAUDE.md's "never :latest, always pinned" + # spirit anyway. + version = "1.15.0" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + create_namespace = false + wait = true + timeout = 600 # DB migration Job + scheduler + webserver take a while on first install + + values = [yamlencode({ + executor = "LocalExecutor" + + webserverSecretKeySecretName = kubernetes_secret.airflow_webserver_secret_key.metadata[0].name + + # Explicit, not trusting the chart/image default — services/label-ui + # authenticates to the REST API (POST .../dagRuns) with the same + # admin/ credentials the webserver UI login already uses. + # Without this, the stable API's default auth backend rejects Basic + # auth even though the webserver's own login page accepts it. + config = { + api = { + auth_backends = "airflow.api.auth.backend.basic_auth" + } + } + + # No Celery in this phase — LocalExecutor doesn't use them. + redis = { enabled = false } + flower = { enabled = false } + statsd = { enabled = false } + # No deferrable operators used yet — skip the extra pod. + triggerer = { enabled = false } + + # Explicit — the scheduler/webserver's wait-for-airflow-migrations init + # container crash-loops forever if this Job never gets created. Live- + # verified this doesn't happen reliably on its own with an external + # (non-subchart) postgresql, so it's set explicitly rather than trusting + # the chart default. + migrateDatabaseJob = { + enabled = true + useHelmHooks = false + } + + postgresql = { enabled = false } + data = { + metadataConnection = { + user = "sentinel" + pass = var.postgres_password + protocol = "postgresql" + host = "postgresql.sentinel-data.svc.cluster.local" + port = 5432 + db = "airflow" + sslmode = "disable" + } + } + + dags = { + gitSync = { enabled = false } + persistence = { enabled = false } + } + + # extraVolumes/extraVolumeMounts are per-component in this chart (under + # scheduler/webserver/workers/triggerer), not a top-level key — an + # earlier version of this config set them at the top level, which the + # chart silently ignored (no error, just an empty /opt/airflow/dags on + # every pod). Mounted on both scheduler (parses + executes DAGs) and + # webserver (renders the DAG list/graph in the UI), one subPath mount + # per file (local.dag_volume_mounts) — see that local's comment for why. + scheduler = { + serviceAccount = { + create = false + name = kubernetes_service_account.airflow.metadata[0].name + } + extraVolumes = [{ + name = "dags" + configMap = { + name = kubernetes_config_map.airflow_dags.metadata[0].name + } + }] + extraVolumeMounts = local.dag_volume_mounts + # retrain_dag.py's decide_promotion and drift_dag.py's check_drift + # both read this directly (os.environ["DATABASE_URL"]) to talk to the + # "sentinel" database (model_registry, drift_stats) — NOT + # data.metadataConnection above, which points at Airflow's own + # separate "airflow" database. Reuses the same drift-postgres secret + # already mirrored into this namespace for the drift SparkApplication's + # driver pod. Scheduler only — DAG *parsing* never touches Postgres, + # only task *execution* (a scheduler subprocess under LocalExecutor) + # does. (drift_dag.py's Kubernetes access goes through plain + # kubernetes.config.load_incluster_config(), the same as + # retrain_dag.py's rollout_restart — no Airflow Connection object + # needed for that, unlike an earlier version of this DAG that used + # apache-airflow-providers-cncf-kubernetes' SparkKubernetesOperator/ + # Sensor and their KubernetesHook-based "kubernetes_default" + # connection; see drift_dag.py's module docstring for why that was + # abandoned.) + env = [{ + name = "DATABASE_URL" + valueFrom = { + secretKeyRef = { + name = kubernetes_secret.drift_postgres.metadata[0].name + key = "database-url" + } + } + }] + } + + webserver = { + defaultUser = { + enabled = true + username = "admin" + password = var.airflow_admin_password + role = "Admin" + email = "admin@sentinel.local" + firstName = "Sentinel" + lastName = "Admin" + } + extraVolumes = [{ + name = "dags" + configMap = { + name = kubernetes_config_map.airflow_dags.metadata[0].name + } + }] + extraVolumeMounts = local.dag_volume_mounts + } + })] + + depends_on = [ + kubernetes_config_map.airflow_dags, + kubernetes_secret.airflow_webserver_secret_key, + ] +} diff --git a/infra/terraform/local/explanation.md b/infra/terraform/local/explanation.md index da4073c..3cafdaf 100644 --- a/infra/terraform/local/explanation.md +++ b/infra/terraform/local/explanation.md @@ -77,6 +77,8 @@ no production credentials are hardcoded. | `prometheus_storage_size` | `5Gi` | PVC size for Prometheus TSDB | | `grafana_admin_password` | `admin` | Grafana admin login | | `kafka_storage_size` | `2Gi` | PVC size for Kafka topic data | +| `airflow_admin_password` | `sentinel` | Airflow webserver admin login | +| `airflow_webserver_secret_key` | (dev-only static string) | Flask session-signing key — see the Airflow section's gotcha #7 for why this must not be left unset | **To override without editing the file**, use `-var`: ```bash @@ -386,9 +388,16 @@ Acceptable for local dev on a private network; never disable in production. ### Kafka +Uses the **official `apache/kafka` image**, not Bitnami — so env vars are the +bare `KAFKA_*` names (`KAFKA_PROCESS_ROLES`, `KAFKA_LISTENERS`, etc.), *not* +Bitnami's `KAFKA_CFG_*`-prefixed convention. Easy to mix up if you've worked +with the Bitnami chart before; the two images silently ignore each other's env +var names instead of erroring, so a `KAFKA_CFG_X` var on this image just does +nothing. + ```hcl -env { name = "KAFKA_CFG_PROCESS_ROLES"; value = "broker,controller" } -env { name = "KAFKA_CFG_CONTROLLER_QUORUM_VOTERS"; value = "1@localhost:9093" } +env { name = "KAFKA_PROCESS_ROLES"; value = "broker,controller" } +env { name = "KAFKA_CONTROLLER_QUORUM_VOTERS"; value = "1@localhost:9093" } ``` **KRaft mode** (no ZooKeeper) — Kafka 3.3+ includes KRaft as production-stable. @@ -400,9 +409,9 @@ voter because the controller and broker are in the same pod. **Two listeners:** ```hcl -env { name = "KAFKA_CFG_LISTENERS"; +env { name = "KAFKA_LISTENERS"; value = "PLAINTEXT://:9092,CONTROLLER://:9093,EXTERNAL://:9094" } -env { name = "KAFKA_CFG_ADVERTISED_LISTENERS"; +env { name = "KAFKA_ADVERTISED_LISTENERS"; value = "PLAINTEXT://kafka.sentinel-data.svc.cluster.local:9092,EXTERNAL://localhost:9094" } ``` @@ -421,7 +430,7 @@ env { name = "KAFKA_CFG_ADVERTISED_LISTENERS"; what Kafka tells clients to use after the initial metadata fetch. If this is wrong, clients can connect initially but fail when they try to produce/consume. -**`KAFKA_CFG_OFFSETS_TOPIC_REPLICATION_FACTOR=1`** — the `__consumer_offsets` +**`KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR=1`** — the `__consumer_offsets` internal topic defaults to a replication factor of 3. With only one broker, 3 replicas are impossible and Kafka would never create the topic, leaving consumers unable to commit offsets. Setting it to 1 makes single-broker operation possible. @@ -453,6 +462,58 @@ kubectl exec -n sentinel-data statefulset/kafka -- \ --- +### Gotcha: PVC mounted, but Kafka wasn't writing to it + +The `data` volume has always been mounted at `/bitnami/kafka` (a path name +left over from when Bitnami's image was being evaluated), but for a long time +nothing told Kafka to actually put its KRaft log segments there. The official +`apache/kafka` image's default `log.dirs` is `/tmp/kraft-combined-logs` — a +path that has nothing to do with the mounted PVC. Every pod restart was +silently wiping all topic data, because the "persistent" volume was never +where Kafka actually wrote. + +**Fix:** set `KAFKA_LOG_DIRS` explicitly to a path under the mount: + +```hcl +env { + name = "KAFKA_LOG_DIRS" + value = "/bitnami/kafka/data" +} +``` + +**How this was caught:** not by reading the image's docs — by restarting the +`kafka-0` pod live and comparing `kafka-consumer-groups.sh --describe` output +(topic offsets, consumer group lag) before and after. Identical output after +a restart is the actual proof persistence works; a green `kubectl get pods` +proves nothing about whether the *data* survived. + +### Gotcha: pinning the image version can be a silent downgrade + +Following the general "never `:latest`, always pin" rule, `apache/kafka:latest` +got pinned to `apache/kafka:3.9.0` — a specific, well-tested version. This +immediately crash-looped every broker start with: + +``` +java.lang.IllegalArgumentException: No MetadataVersion with feature level 30 +``` + +Root cause: `:latest` had already drifted to Kafka 4.3.1 by the time this PVC +was first formatted (confirmed by inspecting the cached image's jar filename: +`kafka_2.13-4.3.1.jar`), and KRaft's on-disk metadata format is +**forward-compatible only** — an older broker cannot read metadata a newer +one wrote. `3.9.0` is *older* than whatever had been running, so pinning to it +was a downgrade, not a stability improvement. + +**The fix isn't "always pin to the newest stable release"** — it's "pin to +whatever version is actually compatible with data already on disk, or wipe +the PVC first." For a fresh cluster with no existing data, pinning to 3.9.0 +would have been fine. For this one, the right pin was 4.3.1 (matching what +had already formatted the volume). **If you ever bump this version going +forward, wipe the PVC first** (`kubectl delete pvc data-kafka-0 -n sentinel-data`) +unless you've confirmed the new version can read the old metadata format. + +--- + ### Jaeger ```hcl @@ -582,6 +643,388 @@ every span as it arrives. Remove `debug` before committing. --- +### spark-operator (sentinel-pipeline) + +```hcl +resource "helm_release" "spark_operator" { + repository = "https://kubeflow.github.io/spark-operator" + chart = "spark-operator" + version = "~2.1" + namespace = kubernetes_namespace.sentinel["sentinel-pipeline"].metadata[0].name + values = [yamlencode({ + controller = { workers = 1 } + webhook = { enable = true } + spark = { jobNamespaces = ["sentinel-pipeline"] } + })] +} +``` + +The operator itself doesn't run Spark jobs — it watches for `SparkApplication` +custom resources (defined in `pipelines/drift/spark-application.yaml`) and +translates each one into a driver pod + N executor pods, using its own +`spark` ServiceAccount to create/delete them. `spark.jobNamespaces` scopes +which namespaces it's allowed to watch — restricting it to `sentinel-pipeline` +means a `SparkApplication` submitted anywhere else is simply ignored, not an +error. + +**RBAC**: the driver pod runs as its own `kubernetes_service_account.spark`, +bound to a `kubernetes_role.spark_driver` with `create/get/list/watch/delete/ +deletecollection/update/patch` on `pods/services/configmaps/ +persistentvolumeclaims`. The driver needs to create and later clean up its +own executor pods — `deletecollection` specifically is required for the +operator's cleanup step; without it, completed/failed SparkApplications leave +orphaned executor pods behind (`Forbidden` errors in the operator's logs are +the tell). + +See [`../../pipelines/drift/explanation.md`](../../pipelines/drift/explanation.md) +for what actually runs inside the driver/executor pods. + +--- + +### Airflow (sentinel-pipeline, `airflow.tf`) + +Deployed via the official Apache Airflow Helm chart with `LocalExecutor` — +tasks run as subprocesses of the scheduler pod, so there's no Celery/Redis/ +Flower to operate at this scale. Reuses the existing PostgreSQL instance (a +separate `airflow` database) instead of the chart's bundled Postgres subchart. + +```hcl +resource "helm_release" "airflow" { + chart = "airflow" + version = "1.15.0" # exact version, not "~> 1.15" — see gotcha below + values = [yamlencode({ + executor = "LocalExecutor" + redis = { enabled = false } + flower = { enabled = false } + statsd = { enabled = false } + triggerer = { enabled = false } + postgresql = { enabled = false } + migrateDatabaseJob = { enabled = true, useHelmHooks = false } + data = { metadataConnection = { host = "postgresql.sentinel-data.svc.cluster.local", db = "airflow", ... } } + scheduler = { extraVolumes = [...], extraVolumeMounts = local.dag_volume_mounts } + webserver = { extraVolumes = [...], extraVolumeMounts = local.dag_volume_mounts } + })] +} +``` + +DAGs are mounted from a `kubernetes_config_map` built by reading every file +in `orchestration/*.py` (same `file()`-and-embed pattern used for Prometheus +config and the Postgres init scripts above) — no git-sync sidecar, no custom +image to rebuild every time a DAG changes. + +This deployment surfaced more live-only gotchas than anything else in this +repo. In the order they were actually hit: + +**1. `airflow` database has to exist before the chart's migration Job can +run.** The Postgres init SQL that creates it (`CREATE DATABASE airflow OWNER +sentinel;`, in `main.tf`'s `postgres_init` ConfigMap) only executes on a +*fresh* Postgres data directory — it does nothing on a cluster whose Postgres +already has data. On an existing cluster, create it manually once: +```bash +echo "CREATE DATABASE airflow OWNER sentinel;" | \ + kubectl exec -i -n sentinel-data postgresql-0 -- psql -U sentinel -d sentinel +``` + +**2. `migrateDatabaseJob.enabled` needs to be explicit.** Without it, the +scheduler/webserver's `wait-for-airflow-migrations` init container +crash-loops forever — no migration Job ever gets created to satisfy it. The +chart's default didn't reliably create the Job when using an external +(non-subchart) Postgres. Set it explicitly: +```hcl +migrateDatabaseJob = { enabled = true, useHelmHooks = false } +``` + +**3. `extraVolumes`/`extraVolumeMounts` are per-component, not top-level.** +Setting them at the top level of the values object is accepted by YAML/Helm +with *no error at all* — it's just silently ignored, because this chart +scopes those keys under `scheduler.*`, `webserver.*`, `workers.*`, etc., not +globally. The tell: `/opt/airflow/dags` exists but is empty on every pod, no +matter how correct the ConfigMap itself looks. Nest under the specific +component: +```hcl +scheduler = { extraVolumes = [...], extraVolumeMounts = [...] } +webserver = { extraVolumes = [...], extraVolumeMounts = [...] } +``` + +**4. A ConfigMap mounted as a directory breaks Airflow's DAG file walker.** +Even correctly mounted, `airflow dags list` failed with: +``` +RuntimeError: Detected recursive loop when walking DAG directory /opt/airflow/dags: +/opt/airflow/dags/..2026_07_03_05_03_34.641811799 has appeared more than once. +``` +Kubernetes mounts ConfigMap volumes through a `..data -> ..` +symlink indirection so updates are atomic. Airflow's DAG-directory walker +(`find_path_from_directory`) doesn't understand that structure and aborts. +**Fix:** mount each DAG file individually via `subPath` instead of mounting +the ConfigMap as a whole directory — `subPath` mounts bypass the symlink +indirection entirely and appear as plain files: +```hcl +locals { + dag_volume_mounts = [ + for f in fileset("${path.module}/../../../orchestration", "*.py") : { + name = "dags", mountPath = "/opt/airflow/dags/${f}", subPath = f, readOnly = true + } + ] +} +``` +One `volumeMount` per DAG file — more entries as `orchestration/` grows, but +generated dynamically from the same `fileset()` the ConfigMap's `data` uses, +so it never needs manual updates. + +**5. `"~> 1.15"` version constraints can break `helm_release` imports.** Using +a loose constraint produced `Error: Provider produced inconsistent final +plan... was cty.StringVal("~> 1.15"), but now cty.StringVal("1.15.0")` — a +known Helm-provider quirk where the constraint resolves to a concrete version +mid-apply. Pin the exact version instead; it also matches the "never +`:latest`" spirit anyway. + +**6. An interrupted `terraform apply` can leave a healthy release stuck in +`pending-install`.** If the apply is killed after Helm's `helm install` has +already started server-side, the underlying pods can come up completely +healthy while Helm's own bookkeeping (a `sh.helm.release.v1..v1` +Secret) never gets marked `deployed`. Symptom: `helm list` shows +`pending-install` and any new `helm upgrade`/`terraform apply` fails with +`cannot re-use a name that is still in use`. **Don't reflexively +uninstall+reinstall** — that destroys working pods for no reason. Instead, +patch the release Secret's stored status directly: +```bash +# decode .data.release (double base64 + gzip), fix info.status to "deployed", +# re-encode, kubectl patch the secret, then: +terraform import helm_release.airflow "sentinel-pipeline/airflow" +``` + +**7. `webserverSecretKeySecretName` needs to be set explicitly, or you get a +warning banner and unstable sessions.** Without either `webserverSecretKey` +or `webserverSecretKeySecretName`, the chart auto-generates a random Flask +secret key **on every deploy** — every `terraform apply` that touches the +release invalidates all webserver sessions, and Airflow shows a persistent +"Usage of a dynamic webserver secret key detected" dashboard warning. Fix: +create your own Secret and point the chart at it: +```hcl +resource "kubernetes_secret" "airflow_webserver_secret_key" { + metadata { name = "airflow-webserver-secret-key-static" } # NOT the chart's + # own default name + # (already taken + # by its auto- + # generated one) + data = { webserver-secret-key = var.airflow_webserver_secret_key } +} +# then: webserverSecretKeySecretName = kubernetes_secret.airflow_webserver_secret_key.metadata[0].name +``` +Verify it's actually static by deleting the webserver pod and diffing the +Secret's value before/after — it should be byte-for-byte identical. + +**8. Port 8080 is not actually free in a k3d cluster.** The Airflow webserver +listens on 8080 inside the pod, so the obvious port-forward is +`8080:8080` — but k3d's own `-serverlb` container already publishes +host port 8080 for its own ingress (`0.0.0.0:8080->80/tcp`), invisible to +`lsof`/`fuser` because it's a Docker-managed listener, not a process in this +shell's namespace. `kubectl port-forward` silently loses that race. Forward +to a different **local** port instead — the remote/pod side stays 8080: +```bash +kubectl port-forward -n sentinel-pipeline svc/airflow-webserver 8090:8080 +``` + +**Tip — the fastest way to check "did my DAG actually load":** +```bash +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags list-import-errors # "No data found" == clean +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags trigger healthcheck && sleep 15 && \ +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags list-runs -d healthcheck +``` + +**9. Editing a DAG file and re-applying Terraform updates the ConfigMap, but +not the mounted file — a direct consequence of gotcha #4's own fix.** +Mounting each DAG via `subPath` (to dodge the DAG-walker's recursive-loop +bug) means bypassing the `..data -> ..` symlink Kubernetes +normally uses to make ConfigMap updates appear live with no pod restart. +Trading away the symlink to fix the walker also trades away the live-update +behavior — confirmed live by editing `orchestration/retrain_dag.py`, +running `terraform apply`, and `grep`-ing the file's content inside the +scheduler pod to find it unchanged. **Any DAG file edit needs an explicit +restart of both pods that mount it:** +```bash +kubectl rollout restart statefulset/airflow-scheduler -n sentinel-pipeline +kubectl rollout restart deployment/airflow-webserver -n sentinel-pipeline +``` + +**10. The stable REST API needs its own auth backend enabled — the +webserver UI login working doesn't imply the API accepts the same +credentials.** `services/label-ui` triggers `retrain_dag` via `POST +/api/v1/dags/retrain_dag/dagRuns` using HTTP Basic auth with the same +admin/`` the UI login form uses — but the REST API validates +requests against `[api] auth_backends`, a separate config surface from the +webserver's own session-based login. Without setting it explicitly, that +first API call failed even though the UI login worked fine. Fixed with an +explicit `config` block in the Helm values (matching this file's established +"don't trust chart defaults silently" pattern — see gotcha #2): +```hcl +config = { + api = { auth_backends = "airflow.api.auth.backend.basic_auth" } +} +``` + +**11. `KubernetesPodOperator` needs its own RBAC — separate from the +rollout-restart Role.** `retrain_dag.py`'s `run_retraining` task launches a +pod (`sentinel-retraining:local`) to do the actual fine-tuning — this needs +permission to `create`/`get`/`list`/`watch`/`delete` `pods` (+ `get`/`list` +on `pods/log`) in `sentinel-pipeline`, which is a *different* permission +from `kubernetes_role.airflow_rollout`'s `apps/deployments` patch access in +`sentinel-app`. Added as a second Role/RoleBinding pair +(`airflow_pod_launcher`) bound to the same `airflow` ServiceAccount — same +shape as `spark_driver`'s Role, just for a different SA: +```hcl +resource "kubernetes_role" "airflow_pod_launcher" { + rule { api_groups = [""]; resources = ["pods"]; verbs = ["create","get","list","watch","delete"] } + rule { api_groups = [""]; resources = ["pods/log"]; verbs = ["get","list"] } +} +``` + +**12. A third RBAC surface: custom resources are their own API group, +separate from core-API pods.** `drift_dag.py` (Phase 7.4) submits/polls/ +deletes a `SparkApplication` — a custom resource in the +`sparkoperator.k8s.io` group, not the core `""` group `airflow_pod_launcher` +above covers. Needed its own Role: +```hcl +resource "kubernetes_role" "airflow_spark_application" { + rule { + api_groups = ["sparkoperator.k8s.io"] + resources = ["sparkapplications", "sparkapplications/status"] + verbs = ["create", "get", "list", "watch", "delete"] + } +} +``` +This is unrelated to `spark_driver`'s Role (`main.tf`) — that one lets the +**driver pod** manage its own executor pods once spark-operator's +controller has already created it from the CR; this one lets the +**Airflow SA** create/watch/delete the CR in the first place. + +**13. `scheduler.env` needs `DATABASE_URL` explicitly — the scheduler pod +has no way to reach the "sentinel" database otherwise.** `data. +metadataConnection` above configures Airflow's connection to *its own* +metadata database (named `airflow`) — a completely separate database on +the same Postgres instance from `model_registry`/`drift_stats`/ +`classifications` (the `sentinel` database). Both `retrain_dag.py`'s +`decide_promotion` and `drift_dag.py`'s `check_drift` read +`os.environ["DATABASE_URL"]` directly to reach the latter. This was a +**latent bug for a while**: `decide_promotion` was written and deployed +without this env var ever being set, and it went unnoticed because the +first test run raised its own `ValueError` (quality gate failed) *before* +ever reaching the `psycopg2.connect(os.environ["DATABASE_URL"])` line — +the missing env var only became visible once a run actually needed to read +it. A reminder that a code path "working" in one test doesn't mean every +line in it executed. Fixed by adding it to the scheduler's `env`, reusing +the same `drift-postgres` secret already mirrored into this namespace for +the drift job's driver pod — one secret, three consumers (the driver pod, +`decide_promotion`, `check_drift`), no duplication. + +**14. A Connection was added, then removed, once the approach it supported +was abandoned.** An earlier version of `drift_dag.py` used +`apache-airflow-providers-cncf-kubernetes`'s `SparkKubernetesOperator`/ +`SparkKubernetesSensor`, which go through Airflow's own `KubernetesHook` +and need a real `Connection` object (not just "happens to be running +in-cluster") — added via `AIRFLOW_CONN_KUBERNETES_DEFAULT` as a JSON env +var (`jsonencode({conn_type = "kubernetes", extra = {in_cluster = true}})` +— JSON has worked directly in `AIRFLOW_CONN_*` since Airflow 2.3, sidestepping +the fragile provider-specific URI-extra-field encoding). That whole +approach was later abandoned (see +[`../../orchestration/explanation.md`](../../orchestration/explanation.md)'s +`drift_dag.py` section for why) in favor of plain +`kubernetes.config.load_incluster_config()` calls, which need no Airflow +Connection at all — so this env var was removed again once nothing +referenced it. Worth knowing if you ever see a stray `AIRFLOW_CONN_*` var +that looks orphaned: check whether the code that needed it is still there +before assuming it's still load-bearing. + +--- + +### MLflow (sentinel-monitoring, `mlflow.tf`) + +A `kubernetes_deployment`/`kubernetes_service` pair, not a Helm chart — no +official chart exists for MLflow the way one does for Airflow. Deployment +(not StatefulSet), same reasoning as Grafana: all real state lives +elsewhere (Postgres backend store, MinIO artifact store), so the pod itself +is stateless and freely reschedulable. + +```hcl +args = [ + "server", + "--backend-store-uri", "postgresql://sentinel:$(PG_PASSWORD)@postgresql.../mlflow", + "--default-artifact-root", "s3://mlflow/", + "--serve-artifacts", + "--workers", "2", + "--allowed-hosts", join(",", ["localhost", "localhost:5000", "mlflow", ...]), +] +``` + +**Custom image, not the official one directly** (`infra/mlflow/Dockerfile`, +own explanation.md) — `ghcr.io/mlflow/mlflow` doesn't bundle a Postgres +driver or S3 client, so `--backend-store-uri postgresql://...` and +`--default-artifact-root s3://...` both fail to start against the base +image. + +**Reuses existing infra rather than standing up new stateful services** — +same philosophy as Airflow's separate `airflow` database on the existing +Postgres instance: a `03_mlflow_db.sql` init script adds an `mlflow` +database, and the MinIO bucket-init Job gets an `mlflow` bucket alongside +`models`/`datasets`. + +**Three live-only bugs, all found by actually deploying it, not by reading +docs:** + +1. **The `03_mlflow_db.sql` init script never ran** — same class of gotcha + as Airflow's #1: Postgres init scripts only execute against a *fresh* + data directory, and this cluster's Postgres already had data from + earlier phases. Fixed the same way: create the database manually once + against the live instance (`CREATE DATABASE mlflow OWNER sentinel;`); + the SQL script stays in `main.tf` so a from-scratch cluster still gets + it automatically. +2. **OOMKilled at both 512Mi and 1Gi memory limits.** MLflow's FastAPI/ + uvicorn tracking server defaults to 4 worker processes — heavier than + the single-process Grafana/Jaeger images this resource block was + originally copied from. Fixed with `--workers 2` and a `2Gi`/`768Mi` + limit/request — the node had ~10Gi free the whole time, this was purely + a cgroup limit being too tight, not real memory pressure. +3. **403 "Invalid Host header — possible DNS rebinding attack detected"** + when the retraining pod (a different namespace) connected. MLflow 3.5+ + ships a security middleware that only allows `localhost` + private IPs + by default — an in-cluster DNS name like + `mlflow.sentinel-monitoring.svc.cluster.local` isn't recognized. + `--allowed-hosts` **replaces** the default rather than extending it, so + `localhost` had to be re-added explicitly too, or the port-forwarded + UI/browser access would have broken instead. Matching is against the + full `Host:port` header, not just the hostname — bare `mlflow` 403'd + just like the DNS name did until the `:5000`-suffixed forms were added + too. + +--- + +### Label UI (sentinel-app, `label-ui.tf`) + +Plain `kubernetes_deployment`/`kubernetes_service`, same shape as +classifier/stream-processor — no new pattern introduced. The only thing +worth noting here is credential mirroring: this service needs both the +`sentinel-app`-local `app_mongodb` secret (already mirrored there for other +app services) *and* `airflow_admin_password`, which otherwise only exists +in `sentinel-pipeline` — K8s Secrets are namespace-scoped, so a new +`app_airflow` secret mirrors that one value into `sentinel-app` too, same +pattern as `app_mongodb`/`app_minio`. + +```hcl +resource "kubernetes_secret" "app_airflow" { + metadata { name = "airflow-credentials"; namespace = "sentinel-app" } + data = { admin-password = var.airflow_admin_password } +} +``` + +See [`../../services/label-ui/explanation.md`](../../services/label-ui/explanation.md) +for what the service actually does with these credentials (triggering +`retrain_dag` via Airflow's REST API). + +--- + ## outputs.tf All outputs are port-forward commands. Running `terraform output` after `apply` @@ -599,6 +1042,18 @@ $(terraform output -raw jaeger_port_forward) & $(terraform output -raw otel_collector_grpc_port_forward) & ``` +**`airflow_webserver_port_forward` forwards local `8090` to the pod's `8080`** +— the only asymmetric one. `kubectl port-forward :` supports +different port numbers on each side; only the local side needed to move here +(k3d's own load balancer already owns host port 8080), the Service and pod +both still listen on 8080 internally. + +**`mlflow_port_forward` / label-ui's port** — MLflow's UI is at +`http://localhost:5000` after `kubectl port-forward -n sentinel-monitoring +svc/mlflow 5000:5000`; the labelling UI is at `http://localhost:8001` via +`kubectl port-forward -n sentinel-app svc/label-ui 8001:8001`. Both are +symmetric (same port on both sides) — 5000 and 8001 were simply free. + --- ## State files diff --git a/infra/terraform/local/label-ui.tf b/infra/terraform/local/label-ui.tf new file mode 100644 index 0000000..d0022c0 --- /dev/null +++ b/infra/terraform/local/label-ui.tf @@ -0,0 +1,126 @@ +# ── Label UI (Phase 7: manual labelling for retraining) ────────────────────── +# Deployment in sentinel-app, same shape as classifier/stream-processor. +# Reads/writes MongoDB flagged_content directly (reuses the app_mongodb +# secret already mirrored into this namespace) and triggers orchestration/ +# retrain_dag.py via Airflow's REST API (needs its own admin-password +# secret mirrored here — airflow_admin_password isn't otherwise available +# outside sentinel-pipeline, K8s secrets being namespace-scoped). + +resource "kubernetes_secret" "app_airflow" { + metadata { + name = "airflow-credentials" + namespace = kubernetes_namespace.sentinel["sentinel-app"].metadata[0].name + } + data = { + admin-password = var.airflow_admin_password + } +} + +resource "kubernetes_deployment" "label_ui" { + metadata { + name = "label-ui" + namespace = kubernetes_namespace.sentinel["sentinel-app"].metadata[0].name + labels = { app = "label-ui" } + } + + spec { + replicas = 1 + + selector { match_labels = { app = "label-ui" } } + + template { + metadata { labels = { app = "label-ui" } } + + spec { + container { + name = "label-ui" + image = "sentinel-label-ui:local" + image_pull_policy = "Never" + + env { + name = "MONGO_PASSWORD" + value_from { + secret_key_ref { + name = kubernetes_secret.app_mongodb.metadata[0].name + key = "sentinel-password" + } + } + } + env { + name = "MONGO_URI" + value = "mongodb://sentinel:$(MONGO_PASSWORD)@mongodb.sentinel-data.svc.cluster.local:27017/sentinel" + } + env { + name = "AIRFLOW_BASE_URL" + value = "http://airflow-webserver.sentinel-pipeline.svc.cluster.local:8080" + } + env { + name = "AIRFLOW_ADMIN_USER" + value = "admin" + } + env { + name = "AIRFLOW_ADMIN_PASSWORD" + value_from { + secret_key_ref { + name = kubernetes_secret.app_airflow.metadata[0].name + key = "admin-password" + } + } + } + + port { container_port = 8001 } + + liveness_probe { + http_get { + path = "/health" + port = 8001 + } + initial_delay_seconds = 5 + period_seconds = 10 + failure_threshold = 3 + } + + readiness_probe { + http_get { + path = "/health" + port = 8001 + } + initial_delay_seconds = 5 + period_seconds = 5 + failure_threshold = 6 + } + + resources { + requests = { cpu = "50m", memory = "128Mi" } + limits = { cpu = "200m", memory = "256Mi" } + } + } + } + } + } + + # Same reasoning as classifier/stream-processor: on the very first apply + # the local image may not exist yet if this is applied outside dev-start.sh. + wait_for_rollout = false + + timeouts { + create = "3m" + update = "3m" + } +} + +resource "kubernetes_service" "label_ui" { + metadata { + name = "label-ui" + namespace = kubernetes_namespace.sentinel["sentinel-app"].metadata[0].name + } + + spec { + selector = { app = "label-ui" } + port { + port = 8001 + target_port = 8001 + } + type = "ClusterIP" + } +} diff --git a/infra/terraform/local/main.tf b/infra/terraform/local/main.tf index 05b1b59..3a9ff6f 100644 --- a/infra/terraform/local/main.tf +++ b/infra/terraform/local/main.tf @@ -105,6 +105,25 @@ resource "kubernetes_config_map" "postgres_init" { CREATE INDEX IF NOT EXISTS drift_stats_model_version_idx ON drift_stats (model_version, computed_at DESC); SQL + + # Airflow's own metadata store — a separate database on the same + # instance rather than a second PostgreSQL deployment (Phase 7 reuses + # existing infra where it can, per the project's "don't add + # infrastructure beyond what's needed" principle). CREATE DATABASE + # can't run inside the same transaction/file as CREATE TABLE reliably + # across all psql invocations, so it's a separate init file — this + # image's entrypoint runs *.sql files individually, one psql + # invocation per file, so a top-level CREATE DATABASE here is safe. + "02_airflow_db.sql" = <<-SQL + CREATE DATABASE airflow OWNER sentinel; + SQL + + # MLflow's own backend store (experiments, runs, params, metrics) — a + # separate database on the same instance, same reasoning as Airflow's: + # reuse existing infra rather than standing up a second PostgreSQL. + "03_mlflow_db.sql" = <<-SQL + CREATE DATABASE mlflow OWNER sentinel; + SQL } } @@ -273,6 +292,10 @@ resource "kubernetes_config_map" "mongodb_init" { db.flagged_content.createIndex({ label: 1, ts: -1 }); db.flagged_content.createIndex({ model_version: 1, ts: -1 }); + // services/label-ui's queue query: docs still awaiting a manual + // labelling decision, oldest first. + db.flagged_content.createIndex({ training_decision: 1, ts: 1 }); + // Mirrors the partial unique index on classifications (span_id, text_type) // in Postgres — enforces idempotency at the DB level so Kafka redelivery // can't duplicate a flagged_content doc, even if application code regresses. @@ -583,6 +606,7 @@ resource "kubernetes_service" "minio" { # Buckets created: # models — ONNX artifacts uploaded by the optimizer pipeline # datasets — training data archives used by the retrain pipeline +# mlflow — MLflow's artifact store (default_artifact_root, s3://mlflow/) resource "kubernetes_job_v1" "minio_init" { metadata { @@ -610,6 +634,7 @@ resource "kubernetes_job_v1" "minio_init" { done mc mb --ignore-existing minio/models mc mb --ignore-existing minio/datasets + mc mb --ignore-existing minio/mlflow echo "Buckets ready." SCRIPT ] @@ -1108,7 +1133,7 @@ resource "kubernetes_stateful_set" "kafka" { spec { container { - name = "kafka" + name = "kafka" # Pinned to match whatever :latest had already drifted to and # formatted the PVC's KRaft metadata with (verified: the cached # apache/kafka:latest image is kafka_2.13-4.3.1.jar). KRaft's @@ -1267,7 +1292,7 @@ resource "kubernetes_job_v1" "kafka_topic_init" { spec { restart_policy = "OnFailure" container { - name = "kafka-topic-init" + name = "kafka-topic-init" # Matches the broker's pinned version above — this only runs # kafka-topics.sh as a client, but keeping both pins identical # avoids a second version to track. @@ -1286,7 +1311,7 @@ resource "kubernetes_job_v1" "kafka_topic_init" { backoff_limit = 10 } - depends_on = [kubernetes_stateful_set.kafka, kubernetes_service.kafka] + depends_on = [kubernetes_stateful_set.kafka, kubernetes_service.kafka] wait_for_completion = true timeouts { create = "5m" } } diff --git a/infra/terraform/local/mlflow.tf b/infra/terraform/local/mlflow.tf new file mode 100644 index 0000000..4955200 --- /dev/null +++ b/infra/terraform/local/mlflow.tf @@ -0,0 +1,190 @@ +# ── MLflow (Phase 7: experiment tracking) ───────────────────────────────────── +# Deployment, not StatefulSet — all state lives in Postgres (backend store: +# experiments/runs/params/metrics) and MinIO (artifact store: model files), +# same reasoning as Grafana. No PVC needed. +# +# Custom image (infra/mlflow/Dockerfile) — the official ghcr.io/mlflow/mlflow +# image doesn't bundle a Postgres driver or S3 client, so `mlflow server +# --backend-store-uri postgresql://...` and `--default-artifact-root s3://...` +# both fail to start against the base image. Built and imported into k3d by +# dev-start.sh, same as classifier/stream-processor/drift. +# +# Backend DB and MinIO bucket: see main.tf's postgres_init (03_mlflow_db.sql) +# and minio_init Job (mc mb minio/mlflow) — reuses existing infra rather than +# standing up a second Postgres/object store instance. + +resource "kubernetes_secret" "monitoring_postgres" { + metadata { + name = "postgresql-credentials" + namespace = kubernetes_namespace.sentinel["sentinel-monitoring"].metadata[0].name + } + data = { + password = var.postgres_password + } +} + +resource "kubernetes_secret" "monitoring_minio" { + metadata { + name = "minio-credentials" + namespace = kubernetes_namespace.sentinel["sentinel-monitoring"].metadata[0].name + } + data = { + root-user = var.minio_root_user + root-password = var.minio_root_password + } +} + +resource "kubernetes_deployment" "mlflow" { + metadata { + name = "mlflow" + namespace = kubernetes_namespace.sentinel["sentinel-monitoring"].metadata[0].name + labels = { app = "mlflow" } + } + + spec { + replicas = 1 + + selector { match_labels = { app = "mlflow" } } + + template { + metadata { labels = { app = "mlflow" } } + + spec { + container { + name = "mlflow" + image = "sentinel-mlflow:local" + image_pull_policy = "Never" + + env { + name = "PG_PASSWORD" + value_from { + secret_key_ref { + name = kubernetes_secret.monitoring_postgres.metadata[0].name + key = "password" + } + } + } + # --serve-artifacts: proxies artifact reads/writes through the + # tracking server itself rather than handing clients direct MinIO + # credentials — keeps AWS_ACCESS_KEY_ID/SECRET scoped to this pod. + command = ["mlflow"] + args = [ + "server", + "--backend-store-uri", "postgresql://sentinel:$(PG_PASSWORD)@postgresql.sentinel-data.svc.cluster.local:5432/mlflow", + "--default-artifact-root", "s3://mlflow/", + "--serve-artifacts", + "--host", "0.0.0.0", + "--port", "5000", + # Default is 4 workers, but each one is heavy enough that 4 of + # them starting concurrently OOM-killed the pod even at a 1Gi + # limit (live-reproduced) — no concurrent load in local dev to + # justify the default worker count anyway. + "--workers", "2", + # Default allowed-hosts is localhost + private IPs only — the + # retraining pod connects via the in-cluster DNS name, not a raw + # IP, and got rejected with a 403 "possible DNS rebinding + # attack" (live-reproduced) since that name isn't in the + # default list. --allowed-hosts replaces the default rather + # than extending it, so localhost has to be re-added explicitly + # too, or the port-forwarded UI/browser access breaks instead. + # Matching is against the full Host header including port + # (bare hostnames alone still 403'd, live-reproduced) — both + # forms are listed since kubectl port-forward's client sends + # "localhost:5000" while some tools may send bare "localhost". + "--allowed-hosts", join(",", [ + "localhost", "localhost:5000", + "mlflow", "mlflow:5000", + "mlflow.sentinel-monitoring.svc.cluster.local", "mlflow.sentinel-monitoring.svc.cluster.local:5000", + "mlflow.sentinel-monitoring.svc", "mlflow.sentinel-monitoring.svc:5000", + "mlflow.sentinel-monitoring", "mlflow.sentinel-monitoring:5000", + ]), + ] + + env { + name = "MLFLOW_S3_ENDPOINT_URL" + value = "http://minio.sentinel-data.svc.cluster.local:9000" + } + env { + name = "AWS_ACCESS_KEY_ID" + value_from { + secret_key_ref { + name = kubernetes_secret.monitoring_minio.metadata[0].name + key = "root-user" + } + } + } + env { + name = "AWS_SECRET_ACCESS_KEY" + value_from { + secret_key_ref { + name = kubernetes_secret.monitoring_minio.metadata[0].name + key = "root-password" + } + } + } + + port { container_port = 5000 } + + # 512Mi and then 1Gi both OOM-killed the pod during startup + # (live-reproduced twice) — the FastAPI/uvicorn tracking server's + # default 4 worker processes (capped to 2 above) are heavier than + # the single-process Grafana/Jaeger images this block was + # originally copied from. Node has ample headroom (~10Gi free) — + # this is purely a cgroup limit, not real resource contention. + resources { + requests = { cpu = "200m", memory = "768Mi" } + limits = { cpu = "1000m", memory = "2Gi" } + } + + readiness_probe { + http_get { + path = "/health" + port = 5000 + } + initial_delay_seconds = 10 + period_seconds = 5 + failure_threshold = 6 + } + + liveness_probe { + http_get { + path = "/health" + port = 5000 + } + initial_delay_seconds = 30 + period_seconds = 15 + failure_threshold = 3 + } + } + } + } + } + + # Same reasoning as the classifier: on the very first apply, the local + # image may not exist yet (dev-start.sh builds it before this resource is + # created, but a bare `terraform apply` without dev-start.sh would not). + wait_for_rollout = false + + timeouts { + create = "5m" + update = "5m" + } +} + +resource "kubernetes_service" "mlflow" { + metadata { + name = "mlflow" + namespace = kubernetes_namespace.sentinel["sentinel-monitoring"].metadata[0].name + } + + spec { + selector = { app = "mlflow" } + + port { + port = 5000 + target_port = 5000 + } + + type = "ClusterIP" + } +} diff --git a/infra/terraform/local/outputs.tf b/infra/terraform/local/outputs.tf index 252dc85..f4de380 100644 --- a/infra/terraform/local/outputs.tf +++ b/infra/terraform/local/outputs.tf @@ -106,3 +106,13 @@ output "otel_collector_http_port_forward" { description = "Command to send OTLP HTTP traces to the collector from your local machine (:4318)" value = "kubectl port-forward -n sentinel-monitoring svc/otel-collector 4318:4318" } + +output "airflow_webserver_port_forward" { + description = "Command to open the Airflow UI from your local machine (http://localhost:8090, admin/). Local port 8090, not 8080 — k3d's serverlb container already publishes host port 8080 for its own ingress." + value = "kubectl port-forward -n sentinel-pipeline svc/airflow-webserver 8090:8080" +} + +output "mlflow_port_forward" { + description = "Command to open the MLflow UI from your local machine (http://localhost:5000)" + value = "kubectl port-forward -n sentinel-monitoring svc/mlflow 5000:5000" +} diff --git a/infra/terraform/local/variables.tf b/infra/terraform/local/variables.tf index b34fe5a..71de2f3 100644 --- a/infra/terraform/local/variables.tf +++ b/infra/terraform/local/variables.tf @@ -74,3 +74,17 @@ variable "kafka_storage_size" { type = string default = "2Gi" } + +variable "airflow_webserver_secret_key" { + description = "Static Flask secret key for the Airflow webserver — signs session cookies. Without one, the chart auto-generates a fresh key on every deploy, invalidating every session and showing a dashboard warning." + type = string + sensitive = true + default = "sentinel-dev-only-static-webserver-secret-key" +} + +variable "airflow_admin_password" { + description = "Airflow webserver admin user password" + type = string + sensitive = true + default = "sentinel" +} diff --git a/orchestration/drift_dag.py b/orchestration/drift_dag.py new file mode 100644 index 0000000..fa56f12 --- /dev/null +++ b/orchestration/drift_dag.py @@ -0,0 +1,369 @@ +"""Drift detection → conditional retrain trigger. + +Runs on a schedule (unlike healthcheck/retrain_dag, both schedule=None): +submits the same SparkApplication CRD pipelines/drift/spark-application.yaml +defines, waits for it to reach a terminal state, then reads the freshly- +written drift_stats row directly from Postgres and triggers retrain_dag if +drift_flagged is true. + +This is the piece CLAUDE.md's data flow describes as "Airflow DAG: triggers +retrain when PSI > 0.2" — previously only reachable by a human clicking +"Trigger Retraining" in services/label-ui. That manual path still exists +and is unaffected; this DAG is a second, automatic caller of the exact same +retrain_dag, via Airflow's own cross-DAG trigger mechanism rather than an +HTTP call (label-ui has to use HTTP + Basic auth because it's a separate +process outside Airflow; from inside Airflow, TriggerDagRunOperator is the +native way to do the same thing). + +Submits/polls/deletes the SparkApplication via the plain Kubernetes Python +client (same pattern as retrain_dag.py's rollout_restart) rather than +apache-airflow-providers-cncf-kubernetes' SparkKubernetesOperator/ +SparkKubernetesSensor, despite both being available in this image. Tried +those first; abandoned after they turned up three separate undocumented +quirks in a single debugging session: (1) the manifest must carry +spec.driver.labels/spec.executor.labels or the operator's own post- +submission bookkeeping raises a bare KeyError, unrelated to whether the +actual Spark job runs fine; (2) SparkKubernetesOperator's execute() is +inherited from KubernetesPodOperator, which generates its own pod/resource +name from task_id + a random suffix regardless of the name= constructor +arg or the YAML's own metadata.name — so a downstream sensor built to poll +a predictable, static name never finds the actual submitted resource; +(3) delete_on_termination=True deletes the resource the instant the +*submitting* operator's own execute() returns (right after the driver pod +is confirmed started), not after the job actually finishes — deleting it +out from under any downstream consumer. Each was individually fixable, but +three independent surprises from one library in one sitting is a signal to +use a smaller, fully-understood tool instead: three plain functions using +the same kubernetes.client the rest of this repo's Kubernetes-facing DAG +code already uses. + +Why the branch reads drift_stats directly instead of trusting the +SparkApplication's own pass/fail status: pipelines/drift/drift_job.py +deliberately exits with code 2 when drift IS detected (not a crash — see +that file's docstring) and only 0 when everything ran cleanly with no +drift. spark-operator's CRD status reports both as "FAILED" (2) vs +"COMPLETED" (0) — a real crash (config/DB error, exit 1) also shows FAILED, +indistinguishable from "drift detected" at that layer. drift_job.py always +calls write_drift_stats() BEFORE it ever calls sys.exit() (0 and 2 alike), +so the Postgres row is the actual source of truth regardless of how the +K8s layer classified the pod — check_drift runs with trigger_rule="all_done" +specifically so it executes no matter what state the job ended up in, and +does its own freshness check to tell "job genuinely didn't run" (a real +crash, or drift_job.py's MIN_REFERENCE_SIZE/empty-window skip) apart from +"job ran and found no drift." +""" + +from __future__ import annotations + +import logging +import os +import time +import uuid +from datetime import datetime, timedelta, timezone + +import yaml +from airflow import DAG +from airflow.operators.empty import EmptyOperator +from airflow.operators.python import BranchPythonOperator, PythonOperator +from airflow.operators.trigger_dagrun import TriggerDagRunOperator + +logger = logging.getLogger(__name__) + +NAMESPACE = "sentinel-pipeline" +API_GROUP = "sparkoperator.k8s.io" +API_VERSION = "v1beta2" +PLURAL = "sparkapplications" + +# Mirrors pipelines/drift/spark-application.yaml — kept as a second copy +# rather than a shared file because orchestration/'s ConfigMap mount +# (infra/terraform/local/airflow.tf) only picks up orchestration/*.py, and +# the YAML file isn't mounted into the Airflow pods. If the manifest +# changes, update both. metadata.name is overwritten at submission time +# with a per-run unique name (see _submit_drift_job) — the value here is a +# placeholder. +SPARK_APPLICATION_YAML = """ +apiVersion: sparkoperator.k8s.io/v1beta2 +kind: SparkApplication +metadata: + name: sentinel-drift + namespace: sentinel-pipeline +spec: + type: Python + pythonVersion: "3" + mode: cluster + image: sentinel-drift:local + imagePullPolicy: Never + mainApplicationFile: local:///opt/spark/work-dir/drift_job.py + arguments: + - "--hours" + - "24" + - "--reference-size" + - "1000" + sparkVersion: "3.5.3" + restartPolicy: + type: Never + driver: + cores: 1 + coreLimit: "1200m" + memory: "512m" + serviceAccount: spark + env: + - name: DATABASE_URL + valueFrom: + secretKeyRef: + name: drift-postgres + key: database-url + - name: PYSPARK_PYTHON + value: /usr/bin/python3 + - name: PYSPARK_DRIVER_PYTHON + value: /usr/bin/python3 + executor: + cores: 1 + instances: 2 + memory: "512m" + env: + - name: PYSPARK_PYTHON + value: /usr/bin/python3 +""" + +# Fallback only — see _check_drift. Primary correlation is against this +# run's own submission timestamp (pushed by _submit_drift_job), not a +# wall-clock guess; this window only applies if that XCom is unexpectedly +# missing. The drift Spark job (small dataset, 2 executors) typically +# finishes in well under this window. +FRESHNESS_WINDOW = timedelta(minutes=30) + +# Absorbs clock skew between the scheduler pod (where submitted_at is +# recorded) and the Spark driver pod (which writes drift_stats.computed_at) +# — real K8s nodes are NTP-synced to well under this, it's a generous +# margin, not a measured value. +CLOCK_SKEW_GRACE = timedelta(seconds=30) + +# Bounds how long wait_for_drift_job polls before giving up. The one +# pre-existing successful manual run (before this DAG existed) took ~42s +# end to end for 1 driver + 2 executors — generous multiple of that. +POLL_TIMEOUT = timedelta(minutes=10) +POLL_INTERVAL_S = 15 + + +def _submit_drift_job(**context) -> str: + from kubernetes import client, config + + config.load_incluster_config() + api = client.CustomObjectsApi() + + manifest = yaml.safe_load(SPARK_APPLICATION_YAML) + # A fresh name every run, not the static name from the YAML — avoids + # both known failure modes of a fixed name: colliding with a leftover + # from a previous run that didn't clean up in time, and (as this repo + # briefly used) an operator's reattach-by-name logic silently skipping + # a genuinely new submission because something with that name already + # existed and looked "done." + name = f"sentinel-drift-{uuid.uuid4().hex[:8]}" + manifest["metadata"]["name"] = name + + # Recorded before submission so _check_drift can correlate a + # drift_stats row against THIS run precisely, rather than guessing + # from wall-clock age alone (a row written before this run even + # started can't be this run's result, no matter how "fresh" it looks + # by age). + submitted_at = datetime.now(timezone.utc) + api.create_namespaced_custom_object( + group=API_GROUP, version=API_VERSION, namespace=NAMESPACE, plural=PLURAL, body=manifest + ) + logger.info("Submitted SparkApplication %s", name) + context["ti"].xcom_push(key="submitted_at", value=submitted_at.isoformat()) + return name # auto-XComed under the default "return_value" key + + +def _wait_for_drift_job(**context) -> None: + from kubernetes import client, config + + name = context["ti"].xcom_pull(task_ids="submit_drift_job") + if not name: + # trigger_rule="all_done" means this runs even if submit_drift_job + # failed before its `return name` line — nothing to poll for. + # Passing name=None to the K8s API below raises + # kubernetes.client.exceptions.ApiValueError, which does NOT + # inherit from ApiException (confirmed live against the actual + # installed client) — the except clause further down wouldn't + # catch it, and this task would crash with a confusing "missing + # required parameter" error instead of the intended "run no matter + # what happened upstream" behavior. cleanup_drift_job already + # guards this same case. + logger.warning("No SparkApplication name from submit_drift_job — nothing to wait for") + return + + config.load_incluster_config() + api = client.CustomObjectsApi() + + deadline = time.monotonic() + POLL_TIMEOUT.total_seconds() + while time.monotonic() < deadline: + try: + obj = api.get_namespaced_custom_object_status( + group=API_GROUP, version=API_VERSION, namespace=NAMESPACE, plural=PLURAL, name=name + ) + except client.exceptions.ApiException as exc: + if exc.status == 404: + logger.warning("SparkApplication %s not found yet — waiting", name) + time.sleep(POLL_INTERVAL_S) + continue + raise + + state = obj.get("status", {}).get("applicationState", {}).get("state") + if state in ("COMPLETED", "FAILED"): + # FAILED is not necessarily an error — see module docstring. + # This task never raises on it; check_drift reads the actual + # truth from Postgres regardless of which terminal state this + # was. + logger.info("SparkApplication %s reached terminal state: %s", name, state) + return + logger.info("SparkApplication %s still running (state=%s)", name, state or "SUBMITTED") + time.sleep(POLL_INTERVAL_S) + + logger.warning("Timed out after %s waiting for SparkApplication %s", POLL_TIMEOUT, name) + + +def _cleanup_drift_job(**context) -> None: + from kubernetes import client, config + + name = context["ti"].xcom_pull(task_ids="submit_drift_job") + if not name: + return + + config.load_incluster_config() + api = client.CustomObjectsApi() + try: + api.delete_namespaced_custom_object( + group=API_GROUP, version=API_VERSION, namespace=NAMESPACE, plural=PLURAL, name=name + ) + logger.info("Deleted SparkApplication %s", name) + except client.exceptions.ApiException as exc: + if exc.status != 404: + raise + logger.info("SparkApplication %s already gone", name) + + +def _check_drift(**context) -> str: + """Reads drift_stats directly for the branch decision — see the module + docstring for why the SparkApplication's own pass/fail status isn't a + reliable signal here. + + Known, accepted limitation: this function has no way to distinguish + "the drift job hit a real error" from "it ran fine and had nothing new + to report" — both produce a stale-or-missing drift_stats row relative + to this run's submission. Both correctly fall back to + no_drift_detected below (never retrain on an ambiguous signal), but + that also means a genuine outage in the drift pipeline currently + produces no distinct alerting signal, only a silent no-op. A real fix + belongs in pipelines/drift/drift_job.py's exit-code/status semantics + (e.g. an explicit status column in drift_stats distinguishing + ok/skipped/error, rather than overloading "no fresh row" to mean all + three) — deliberately not attempted here, since it touches a + separately-tested pipeline this PR didn't otherwise modify, and the + current fallback behavior is safe, just not observable. + """ + # psycopg2, not psycopg (v3, used everywhere else in this repo, + # including pipelines/drift/db.py's own drift_stats queries) — + # deliberate: confirmed live that this Airflow image only bundles + # psycopg2 (a transitive dependency of apache-airflow-providers- + # postgres), not psycopg. See orchestration/retrain_dag.py's + # _decide_promotion for the same note. + import psycopg2 + + submitted_at_str = context["ti"].xcom_pull(task_ids="submit_drift_job", key="submitted_at") + submitted_at = datetime.fromisoformat(submitted_at_str) if submitted_at_str else None + + conn = psycopg2.connect(os.environ["DATABASE_URL"]) + try: + with conn.cursor() as cur: + cur.execute( + "SELECT drift_flagged, computed_at, psi FROM drift_stats " + "ORDER BY computed_at DESC LIMIT 1" + ) + row = cur.fetchone() + finally: + conn.close() + + if row is None: + logger.warning("No drift_stats rows exist yet — treating as no drift") + return "no_drift_detected" + + drift_flagged, computed_at, psi = row + + if submitted_at is not None: + # Precise correlation to THIS run's own submission, not a + # wall-clock guess — a row written before this run even started + # can't be this run's result no matter how "fresh" it looks by age + # alone (e.g. a previous run's row that's only 10 minutes old would + # pass a naive age check but still be the wrong run's data). + if computed_at < submitted_at - CLOCK_SKEW_GRACE: + logger.warning( + "Latest drift_stats row (computed_at=%s) predates this run's " + "submission (submitted_at=%s) — drift job likely didn't write " + "new data this run; not triggering retrain", + computed_at, + submitted_at, + ) + return "no_drift_detected" + else: + # Defensive fallback only — submit_drift_job's XCom should always + # be present by the time this task runs (trigger_rule=all_done + # means it still runs after a submit failure, but that failure + # would leave `row` reflecting an OLDER run anyway, which the + # wall-clock window below still catches in the common case). + age = datetime.now(timezone.utc) - computed_at + if age > FRESHNESS_WINDOW: + logger.warning( + "Latest drift_stats row is stale (age=%s > %s) and no submission " + "timestamp was available to correlate precisely — not triggering retrain", + age, + FRESHNESS_WINDOW, + ) + return "no_drift_detected" + + logger.info("Latest drift_stats | psi=%.4f | drift_flagged=%s", psi, drift_flagged) + return "trigger_retrain" if drift_flagged else "no_drift_detected" + + +with DAG( + dag_id="drift_dag", + description="Periodically checks for input distribution drift and triggers retrain_dag if found", + schedule=timedelta(hours=1), + start_date=datetime(2025, 1, 1), + catchup=False, + tags=["sentinel", "drift"], +) as dag: + submit_drift_job = PythonOperator( + task_id="submit_drift_job", + python_callable=_submit_drift_job, + ) + + wait_for_drift_job = PythonOperator( + task_id="wait_for_drift_job", + python_callable=_wait_for_drift_job, + trigger_rule="all_done", + ) + + cleanup_drift_job = PythonOperator( + task_id="cleanup_drift_job", + python_callable=_cleanup_drift_job, + trigger_rule="all_done", # always clean up, whatever happened above + ) + + check_drift = BranchPythonOperator( + task_id="check_drift", + python_callable=_check_drift, + trigger_rule="all_done", + ) + + trigger_retrain = TriggerDagRunOperator( + task_id="trigger_retrain", + trigger_dag_id="retrain_dag", + ) + + no_drift_detected = EmptyOperator(task_id="no_drift_detected") + + submit_drift_job >> wait_for_drift_job + wait_for_drift_job >> cleanup_drift_job + wait_for_drift_job >> check_drift >> [trigger_retrain, no_drift_detected] diff --git a/orchestration/explanation.md b/orchestration/explanation.md new file mode 100644 index 0000000..b9ad68b --- /dev/null +++ b/orchestration/explanation.md @@ -0,0 +1,479 @@ +# Orchestration (Airflow DAGs) — Explanation + +This directory holds Airflow DAG definitions. There is no Python package +structure, no `__init__.py`, no local dependency management — every `.py` +file here is read directly by `infra/terraform/local/airflow.tf`, embedded +into a `kubernetes_config_map`, and mounted into the scheduler/webserver +pods. See [`../infra/terraform/local/explanation.md`](../infra/terraform/local/explanation.md)'s +Airflow section for exactly how that mount works and the gotchas involved in +getting it right — this file focuses on writing and operating DAGs, not on +the deployment mechanism. + +--- + +## Why `orchestration/`, not `dags/` + +Airflow's own convention calls this a "dags folder," and most tutorials name +the directory `dags/`. This repo uses `orchestration/` instead — it reads +more clearly as "the thing that orchestrates `pipelines/`" alongside +`services/` and `pipelines/` at the repo root, and avoids the slightly +confusing stutter of `orchestration/dags/dags_folder.py`. Functionally +identical; Airflow doesn't care what the directory is called, only what's +mounted at `/opt/airflow/dags` inside the pods. + +--- + +## `healthcheck_dag.py` + +```python +with DAG( + dag_id="healthcheck", + schedule=None, # manual trigger only + start_date=datetime(2025, 1, 1), + catchup=False, + tags=["sentinel", "smoke-test"], +) as dag: + PythonOperator(task_id="print_ready", python_callable=_print_ready) +``` + +Exists purely to prove the deployment mechanism works — that a `.py` file +placed here actually gets mounted, parsed with zero import errors, and can +run to completion — before any real pipeline logic (the eventual +`retrain_dag.py`) depends on the same mechanism. `schedule=None` means it +never runs on its own; it only executes when triggered manually or via CI. + +**`catchup=False`** matters even for a manually-triggered DAG: without it, +Airflow would try to backfill every scheduled interval between `start_date` +and now the first time the DAG is unpaused — for a `schedule=None` DAG this +is a no-op, but it's worth knowing for whatever DAG replaces this one on an +actual schedule (`PostgresSensor` polling on a `timedelta`, per the retrain +DAG design). + +--- + +## Operating DAGs from the CLI + +`dev-start.sh` verifies DAGs load correctly on every run, but here's what it +does, spelled out, for when something needs debugging manually: + +```bash +# List every DAG Airflow has found. fileloc shows the exact mounted path — +# useful for confirming a new file actually landed where expected. +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags list + +# The single most useful command when a DAG "isn't showing up": confirms +# whether it parsed at all, and if not, why. "No data found" == clean. +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags list-import-errors + +# DAGs start paused by default — unpause before a scheduled run will ever fire. +# Not needed for a manual trigger (see below), only for schedule!=None DAGs. +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags unpause + +# Manually trigger a run right now, regardless of schedule. +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags trigger + +# Check run history / final state (success, failed, running). +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags list-runs -d + +# Per-task state within a specific run — the run_id comes from list-runs above. +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow tasks states-for-dag-run "" +``` + +**Why `-c scheduler`?** The `airflow-scheduler-0` pod runs two containers: +`scheduler` (the actual scheduler process, where the CLI and DAG parsing +live) and `scheduler-log-groomer` (a sidecar that periodically deletes old +log files). Every `airflow` CLI command needs to target the `scheduler` +container explicitly — `kubectl exec` picks the first container by default, +which may not be the right one. + +**Faster than the UI for scripting/CI** — every command above is exactly +what a shell script (or `dev-start.sh`) can poll and assert on, without +needing to drive a browser. The UI (`http://localhost:8090`, see the infra +explanation.md's port-forward gotcha for why it's 8090 not 8080) is better +for visually inspecting the DAG graph, Gantt charts, and log output. + +--- + +## LocalExecutor semantics, in case you're used to Celery/Kubernetes executors + +Every task in every DAG runs as a **subprocess of the scheduler pod itself** +— there's no separate worker pod, no task queue, no message broker. This +means: + +- **A task's resource usage counts against the scheduler pod's limits.** A + memory-hungry task (e.g., the eventual retrain DAG's model-loading step) + competes with the scheduler process's own memory, not an isolated worker. + For heavier pipeline steps, prefer `KubernetesPodOperator` (runs the task + in its own pod, using the existing `sentinel-drift:local` / + `sentinel-optimizer` images) over a plain `PythonOperator` that imports and + runs pipeline code in-process. +- **Parallelism is bounded by the scheduler pod's CPU**, not by the number of + workers you can scale out. Fine for this project's current scale (one + pipeline run at a time); would need `KubernetesExecutor` or + `CeleryExecutor` to genuinely parallelize across nodes. +- **No task queue to inspect** — if a task should be running and isn't, + the answer is always "check the scheduler pod's logs and process table," + not "check a Redis/RabbitMQ queue depth." + +--- + +## `retrain_dag.py` — the first DAG with real logic + +Three tasks, linear dependency chain: + +```python +run_retraining >> decide_promotion >> rollout_restart +``` + +`schedule=None` — manually triggered only, same as `healthcheck`. Today the +trigger source is `services/label-ui`'s "Trigger Retraining" button (`POST +/api/v1/dags/retrain_dag/dagRuns` against Airflow's REST API); later, drift's +PSI signal (once wired in) can trigger the same DAG the same way. The DAG +itself doesn't know or care who called the API — this is why `schedule=None` +plus an external trigger, rather than a `PostgresSensor` polling loop, ended +up being the right shape here: the trigger is an *event* (an operator decided +enough data is labelled), not a *condition* to poll for. + +### 1. `run_retraining` — `KubernetesPodOperator` + +```python +run_retraining = KubernetesPodOperator( + task_id="run_retraining", + namespace="sentinel-pipeline", + image="sentinel-retraining:local", + cmds=["python", "-m", "pipelines.retraining"], + service_account_name="airflow", + env_vars=[...], # DATABASE_URL, MONGO_URI, MINIO_*, MLFLOW_TRACKING_URI + do_xcom_push=True, + get_logs=True, + is_delete_operator_pod=True, + container_resources=k8s.V1ResourceRequirements( + requests={"cpu": "2", "memory": "4Gi"}, + limits={"cpu": "8", "memory": "8Gi"}, + ), +) +``` + +Launches a **fresh pod** rather than running the retraining pipeline +in-process as a `PythonOperator` — this is the LocalExecutor caveat from +above in practice: `pipelines.retraining` needs torch/transformers/mlflow, +none of which belong in the Airflow image, and the pod isolates its (large, +variable) resource footprint from the scheduler's own. + +`do_xcom_push=True` reads whatever the pod wrote to +`/airflow/xcom/return.json` — `pipelines/retraining/pipeline.py`'s `run()` +writes its final report there via a plain `Path("/airflow/xcom").exists()` +check, so the pipeline package itself has zero Airflow-specific imports and +stays runnable standalone (`python -m pipelines.retraining` from a shell, +no DAG required). See +[`../pipelines/retraining/explanation.md`](../pipelines/retraining/explanation.md) +for what's actually in that report. + +**`container_resources` went through three rounds of live tuning**, all +found by actually triggering runs and watching them fail, not by guessing: + +- **2Gi/4Gi → OOMKilled** (`exit_code: 137`) around the point fine-tuning + started. No traceback ever appeared in any log — `SIGKILL` gives a + process zero chance to flush stdout, so the pod's logs just went silent. + The real reason was only visible in Airflow's own **persisted** task log + (`/opt/airflow/logs/dag_id=retrain_dag/run_id=.../task_id=run_retraining/ + attempt=1.log`), which records the pod's final K8s status including + `reason: OOMKilled` — `kubectl logs`, live or `-f`, showed nothing useful + for a SIGKILLed process, because there was nothing left to stream. +- **4Gi → 8Gi limit, still OOMKilled.** The actual root cause was in + `pipelines/retraining/train.py`: every training example was tokenized with + `padding="max_length"` (a fixed 512 tokens), regardless of its real + length — wasting enormous memory via attention's O(seq_len²) scaling for + what are mostly short chat spans. Throwing more memory at it would likely + have worked eventually, but the actual fix was efficiency + (`DataCollatorWithPadding`, dynamic per-batch padding), not a bigger pod. +- **CPU limit of 2 cores, benchmark stage pathologically slow (19+ minutes, + still running).** `pipelines/evaluation/benchmark.py` opens its ONNX + Runtime session with no `SessionOptions` (see that package's + explanation.md), so ORT auto-detects thread count from the **host's** + real CPU count (16, here) rather than the pod's cgroup limit — those + threads then thrash against a 2-core quota instead of running in + parallel. Not fixed in `benchmark.py` itself (shared, unmodified code — + this feature's whole design is "reuse `pipelines/optimizer` and + `pipelines/evaluation` unchanged"); fixed here instead, by raising the + pod's CPU limit to 8 so ORT's auto-detected thread count and the cgroup + limit stop fighting each other. + +### 2. `decide_promotion` — `PythonOperator` + +```python +def _decide_promotion(**context) -> None: + report = context["ti"].xcom_pull(task_ids="run_retraining") + if not report.get("gate_passed"): + raise ValueError(f"Quality gate failed: {report.get('gate_reasons')}") + + conn = psycopg2.connect(os.environ["DATABASE_URL"]) + cur.execute("UPDATE model_registry SET status = 'retired' WHERE status = 'active'") + cur.execute("UPDATE model_registry SET status = 'active', promoted_at = NOW() WHERE model_version = %s", (model_version,)) +``` + +Runs as a scheduler subprocess (LocalExecutor — see above), talking to the +`sentinel` Postgres database directly via `psycopg2` (already bundled in +the official Airflow image, since it's also what the scheduler uses to talk +to its *own* metadata DB — no extra dependency needed). This is the only +place in the entire codebase that writes `model_registry.status = 'active'`, +matching the repo's "Model Registry Source of Truth" rule: pipelines only +ever register as `'staging'`. + +**Fails loudly, on purpose, when the quality gate fails.** Raising +`ValueError` marks this task `failed`, which gives `rollout_restart` +`upstream_failed` via Airflow's default `trigger_rule` — it never runs. This +was live-verified as a success case, not a bug: a run fine-tuned on only 40 +labelled examples scored 50% accuracy (coin-flip) on the 3780-row held-out +set, `pipelines/evaluation/validate.py` correctly flagged it +(`accuracy 0.5000 below minimum 0.8500`), and this task correctly refused to +touch `model_registry` or restart anything. The system did exactly what +it's for: reject a bad model instead of promoting it. + +### 3. `rollout_restart` — `PythonOperator` + +```python +def _rollout_restart(**context) -> None: + from kubernetes import client, config + config.load_incluster_config() + apps_v1 = client.AppsV1Api() + for deployment in ("classifier", "stream-processor"): + apps_v1.patch_namespaced_deployment( + name=deployment, namespace="sentinel-app", + body={"spec": {"template": {"metadata": {"annotations": {"sentinel/restartedAt": ...}}}}}, + ) +``` + +Uses the **Kubernetes Python client** (`kubernetes` package, bundled with +the `apache-airflow-providers-cncf-kubernetes` provider — confirmed present +in the image via `pip list` before writing this, rather than assumed), not +a shelled-out `kubectl rollout restart` — **there is no `kubectl` binary in +the Airflow image** (`which kubectl` exits 1). Patching the pod template's +annotations is exactly what `kubectl rollout restart` does under the hood; +the Python client just does it directly via the API. + +This is the payoff for RBAC provisioned a full phase earlier: +`kubernetes_role.airflow_rollout` (`infra/terraform/local/airflow.tf`) grants +the `airflow` ServiceAccount `get/list/patch` on `apps/deployments` in +`sentinel-app` — added back in Phase 7.1 specifically for this step, per +that file's own comment ("granted now so the ServiceAccount doesn't need to +be re-plumbed through the chart values later"). It sat unused until this DAG +finally called it. + +### Debugging gotcha: `subPath` ConfigMap mounts don't live-update + +Editing `retrain_dag.py` and re-running `terraform apply` updates the +`airflow-dags` ConfigMap correctly, but **the mounted file inside the +scheduler/webserver pods does not change** — confirmed live by `grep`-ing +the file's content inside the pod after an apply that should have changed +it, and seeing the old content. This is the flip side of the +`subPath`-mount fix documented in +[`../infra/terraform/local/explanation.md`](../infra/terraform/local/explanation.md)'s +Airflow gotcha #4: `subPath` mounts deliberately bypass the `..data -> +..` symlink indirection (that's *why* they fix Airflow's DAG +walker), but that same indirection is exactly the mechanism a plain +ConfigMap volume mount uses to pick up changes without a pod restart. +Trading away the symlink to fix the walker bug means trading away +live-updates too. **After any `retrain_dag.py` edit, both the scheduler and +webserver need an explicit restart:** + +```bash +kubectl rollout restart statefulset/airflow-scheduler -n sentinel-pipeline +kubectl rollout restart deployment/airflow-webserver -n sentinel-pipeline +``` + +### Debugging gotcha: reading a live pod's logs vs. Airflow's persisted log + +`kubectl logs -f ` looked like it "froze" mid-training on more than one +run — new lines just stopped appearing, no error, connection still open. +Two different real causes produced the same symptom here, which is the +actual lesson: **a frozen-looking log stream is not itself a diagnosis.** + +1. Early on, `tqdm`'s default progress bar (used internally by + `transformers.Trainer`) redraws a single line via `\r` rather than + emitting newlines — plausible as a cause of confused line-based log + streaming in a headless pod, so it was disabled + (`disable_tqdm=True` in `pipelines/retraining/train.py`) as a + reasonable fix on its own merits. +2. That turned out not to be the actual cause of the "freezing" — the real + cause (both before and after disabling tqdm) was the OOM kill described + above. A `SIGKILL`ed process cannot flush a traceback, so of course the + log stream "freezes" with no error: there is no more output, ever, for + that container. + +The reliable diagnostic, once this was understood, was to stop trusting +live `kubectl logs` output entirely and read Airflow's own **persisted** +task log file directly from inside the scheduler pod — it records the +pod's final Kubernetes status (`exit_code`, `reason: OOMKilled`, etc.) +regardless of whether the pod itself has already been deleted +(`is_delete_operator_pod=True` deletes it quickly after the task instance +finishes): + +```bash +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + cat "/opt/airflow/logs/dag_id=retrain_dag/run_id=/task_id=run_retraining/attempt=1.log" +``` + +--- + +## `drift_dag.py` — the first DAG that actually runs on a schedule + +```python +schedule=timedelta(hours=1) +``` + +Every other DAG in this repo is `schedule=None` (manually triggered only). +This is the piece CLAUDE.md's data flow describes as "Airflow DAG: triggers +retrain when PSI > 0.2" — it periodically submits the drift Spark job, +reads the result, and calls `retrain_dag` automatically when drift is +found. The manual trigger path (`services/label-ui`'s button) is untouched +by this — `retrain_dag` doesn't know or care which caller hit it. + +Six tasks: + +```python +submit_drift_job >> wait_for_drift_job +wait_for_drift_job >> cleanup_drift_job +wait_for_drift_job >> check_drift >> [trigger_retrain, no_drift_detected] +``` + +### Why plain Kubernetes API calls, not `SparkKubernetesOperator`/`SparkKubernetesSensor` + +Both classes ship in this image's `apache-airflow-providers-cncf-kubernetes` +install and looked like the obvious choice — submit/watch a +`SparkApplication` CRD Airflow-natively, matching how `run_retraining` uses +`KubernetesPodOperator` for a plain pod. They were tried first, and +abandoned after turning up **three separate undocumented behaviors in one +debugging session**, each independently fixable but adding up to more +friction than the abstraction was worth: + +1. **The manifest needs `spec.driver.labels`/`spec.executor.labels` + present (even `{}`) or the operator's own post-submission bookkeeping + raises a bare `KeyError`.** Traced to the exact line in + `custom_object_launcher.py` + (`labels=self.spark_obj_spec["spec"]["driver"]["labels"]`) by reading + the installed package's source directly — a first guess at + `metadata.labels: {}` (the more obvious location) produced the identical + crash, unchanged. +2. **The operator generates its own resource name from `task_id` + a + random suffix, ignoring both the constructor's `name=` argument and the + YAML's own `metadata.name`.** `SparkKubernetesOperator` inherits from + `KubernetesPodOperator` + (`SparkKubernetesOperator.__mro__` confirms this), which has its own + independent pod-naming logic. A sensor built to poll a name assumed to + be static and known in advance was therefore always polling a resource + that had never existed — it failed (or, with `soft_fail=True`, silently + skipped) on its very first poke every single time, and this was easy to + misread as "the sensor doesn't work" rather than "the sensor is looking + in the wrong place." +3. **`delete_on_termination=True` deletes the `SparkApplication` the + instant the *submitting* operator's own `execute()` returns** — which + is as soon as the driver pod is confirmed *started* + (`custom_object_launcher.py`'s `spark_job_not_running()` poll loop + waits for startup, not completion) — not once the actual PySpark job + finishes. A downstream sensor meant to watch for real completion was + deleting-out-from-under-itself before it ever got a meaningful chance + to poll. + +After the third fix, the pattern was clear enough to stop debugging the +library and just replace it: `submit_drift_job`, `wait_for_drift_job`, and +`cleanup_drift_job` below are three plain functions using +`kubernetes.client.CustomObjectsApi` directly — the exact same tool +`retrain_dag.py`'s `rollout_restart` already uses successfully for a +different K8s resource. Fewer moving parts, and every line is something +this codebase already understands rather than an opaque provider +abstraction. + +```python +def _submit_drift_job(**context) -> str: + name = f"sentinel-drift-{uuid.uuid4().hex[:8]}" # unique per run, not static + manifest = yaml.safe_load(SPARK_APPLICATION_YAML) + manifest["metadata"]["name"] = name + api.create_namespaced_custom_object(group="sparkoperator.k8s.io", ..., body=manifest) + return name # auto-XComed to the tasks below +``` + +A **fresh, unique name every run** (not the fixed `sentinel-drift` from +`pipelines/drift/spark-application.yaml`) is deliberate, and fixes a +second, independent bug found along the way: a stale `SparkApplication` +resource left over from earlier manual testing (predating this DAG +entirely) shared that exact static name, and the operator's +`reattach_on_restart` default silently treated it as *this run's* result — +reporting instant "success" without ever submitting a new job. A +UUID-suffixed name makes that class of collision structurally impossible. + +```python +def _wait_for_drift_job(**context) -> None: + name = context["ti"].xcom_pull(task_ids="submit_drift_job") + while time.monotonic() < deadline: + state = ...["status"]["applicationState"]["state"] + if state in ("COMPLETED", "FAILED"): + return # never raises — check_drift reads the real truth + time.sleep(POLL_INTERVAL_S) +``` + +Never raises on `FAILED` — see the next section for why `FAILED` is +ambiguous at this layer and shouldn't gate anything on its own. +`cleanup_drift_job` (`trigger_rule="all_done"`) always runs afterward +regardless of how this task ends, deleting the named resource so repeated +hourly runs never accumulate leftovers. + +### `check_drift` — reads Postgres directly, not the SparkApplication's own status + +```python +def _check_drift(**context) -> str: + cur.execute("SELECT drift_flagged, computed_at, psi FROM drift_stats ORDER BY computed_at DESC LIMIT 1") + ... + if age > FRESHNESS_WINDOW: + return "no_drift_detected" # "we don't know" must never trigger a retrain + return "trigger_retrain" if drift_flagged else "no_drift_detected" +``` + +`pipelines/drift/drift_job.py` deliberately exits with code `2` when drift +**is** detected (not a crash — see that file's own docstring and +explanation.md) and `0` when it ran cleanly and found none. At the +Kubernetes/spark-operator layer, both a genuine crash (exit `1`) and the +intentional exit-`2` "drift found" case surface identically as +`applicationState.state == "FAILED"` — there's no way to tell them apart +from that field alone. `drift_job.py` always calls `write_drift_stats()` +**before** it ever calls `sys.exit()` (in the `0` and `2` cases), so the +Postgres row is the actual source of truth regardless of what the K8s +layer reported. `check_drift` runs with `trigger_rule="all_done"` +specifically so it executes no matter what `wait_for_drift_job` saw, and +does its own freshness check (`FRESHNESS_WINDOW = timedelta(minutes=30)`) +to distinguish "the job ran and found nothing" from "the job never wrote +anything this run" (a real crash, or `drift_job.py`'s own +`MIN_REFERENCE_SIZE`/empty-current-window skip) — either of the latter must +default to *not* retraining, since triggering an unattended fine-tune run +on missing or stale data would be worse than doing nothing. + +**Live-verified this exact "job ran, wrote nothing" case, and it wasn't a +bug**: a real end-to-end test run's driver pod completed cleanly in ~8 +seconds (checked its actual application logs, not just Airflow's task +log, before `cleanup_drift_job` could delete it) because +`model_registry`'s current `active` row had **zero** matching rows in +`classifications` — `drift_job.py`'s `MIN_REFERENCE_SIZE` guard correctly +refused to compute PSI against an empty baseline and exited `0` without +writing anything. `check_drift` correctly saw a stale `drift_stats` row and +chose `no_drift_detected`. The whole chain worked exactly as designed; the +"no drift" outcome just reflected that the currently-tracked active model +has no real traffic yet, not a flaw in the DAG. + +### `trigger_retrain` — `TriggerDagRunOperator`, not an HTTP call + +```python +trigger_retrain = TriggerDagRunOperator(task_id="trigger_retrain", trigger_dag_id="retrain_dag") +``` + +`services/label-ui` has to call Airflow's REST API with Basic auth because +it's a separate process outside Airflow entirely. From *inside* a DAG, +`TriggerDagRunOperator` is the native, in-process way to start another DAG +— no HTTP round-trip, no credentials to manage. Both callers land on the +exact same `retrain_dag`, which has no way to tell them apart and doesn't +need to. diff --git a/orchestration/healthcheck_dag.py b/orchestration/healthcheck_dag.py new file mode 100644 index 0000000..868724f --- /dev/null +++ b/orchestration/healthcheck_dag.py @@ -0,0 +1,29 @@ +"""Smoke-test DAG for the Phase 7 Airflow deployment. + +Proves the orchestration/ ConfigMap mount and LocalExecutor actually work +before any real pipeline logic depends on them. Superseded by retrain_dag.py +once Phase 7.3 lands — kept as a minimal, fast-running sanity check. +""" + +from datetime import datetime + +from airflow import DAG +from airflow.operators.python import PythonOperator + + +def _print_ready() -> None: + print("Airflow scheduler + LocalExecutor + DAG mount are all working.") + + +with DAG( + dag_id="healthcheck", + description="Confirms the Airflow deployment can load and run a DAG", + schedule=None, # manual trigger only — this isn't a real pipeline + start_date=datetime(2025, 1, 1), + catchup=False, + tags=["sentinel", "smoke-test"], +) as dag: + PythonOperator( + task_id="print_ready", + python_callable=_print_ready, + ) diff --git a/orchestration/retrain_dag.py b/orchestration/retrain_dag.py new file mode 100644 index 0000000..ce44df8 --- /dev/null +++ b/orchestration/retrain_dag.py @@ -0,0 +1,225 @@ +"""Retraining DAG — triggered by services/label-ui's "Trigger Retraining" +button (POST /api/v1/dags/retrain_dag/dagRuns) today, and by drift's future +PSI signal later; this DAG doesn't care who calls the API (schedule=None). + +Three tasks: + 1. run_retraining — KubernetesPodOperator launches sentinel-retraining:local + (pipelines/retraining), which fine-tunes, logs to + MLflow, and reuses pipelines/optimizer + evaluation + unchanged to register a new 'staging' model_registry + row. Its report.json comes back via XCom (the pod + writes it to /airflow/xcom/return.json, the sidecar + this operator provisions tails that path). + 2. decide_promotion — reads the XCom report; on gate_passed, promotes the + new model_version to 'active' (retiring the old + one) directly via psycopg2 — model_registry is the + single source of truth per CLAUDE.md, and only + Airflow writes to it. On failure this task raises, + so rollout_restart's default trigger_rule skips it. + 3. rollout_restart — patches classifier + stream-processor Deployments' + pod-template annotations via the Kubernetes Python + client (no kubectl binary in this image — verified + live) — same effect as `kubectl rollout restart`. + Uses kubernetes_role.airflow_rollout, RBAC that was + provisioned back in Phase 7.1 specifically for this + step (see infra/terraform/local/airflow.tf). +""" + +from __future__ import annotations + +import json +import logging +import os +from datetime import datetime, timezone + +from airflow import DAG +from airflow.operators.python import PythonOperator +from airflow.providers.cncf.kubernetes.operators.pod import KubernetesPodOperator +from kubernetes.client import models as k8s + +logger = logging.getLogger(__name__) + +PIPELINE_NAMESPACE = "sentinel-pipeline" +APP_NAMESPACE = "sentinel-app" + + +def _decide_promotion(**context) -> None: + # psycopg2, not psycopg (v3, used everywhere else in this repo — + # pipelines/optimizer/registry.py, pipelines/drift/db.py) — + # deliberate, not an inconsistency to clean up: confirmed live that + # this Airflow image only bundles psycopg2 (a transitive dependency of + # apache-airflow-providers-postgres, used for Airflow's own metadata + # DB), not psycopg. Switching this import to match the rest of the + # repo would break with ModuleNotFoundError in this specific runtime. + import psycopg2 + + report = context["ti"].xcom_pull(task_ids="run_retraining") + if not report: + raise RuntimeError("run_retraining produced no XCom report") + if isinstance(report, str): + report = json.loads(report) + + if not report.get("gate_passed"): + raise ValueError(f"Quality gate failed: {report.get('gate_reasons')}") + + model_version = report["model_version"] + model_path = report.get("model_path", "") + if model_path.startswith("/"): + # MinIO was unreachable when this model was optimized (see + # pipelines/optimizer/pipeline.py's local-fallback branch) — that + # path only ever existed inside run_retraining's own now-deleted + # pod (is_delete_operator_pod=True), unreachable from classifier/ + # stream-processor pods after a rollout restart. Promoting it would + # leave "active" pointing at a model nothing can actually load — + # download.py would silently fall back to whatever model those + # pods already had, while model_registry claims the new one is live. + raise ValueError( + f"Refusing to promote model_version={model_version}: model_path " + f"{model_path!r} is a local fallback path, not a MinIO artifact " + "(MinIO was unreachable when this model was optimized)" + ) + + conn = psycopg2.connect(os.environ["DATABASE_URL"]) + try: + with conn.cursor() as cur: + cur.execute("UPDATE model_registry SET status = 'retired' WHERE status = 'active'") + cur.execute( + "UPDATE model_registry SET status = 'active', promoted_at = NOW() " + "WHERE model_version = %s", + (model_version,), + ) + if cur.rowcount == 0: + # No row matched model_version — e.g. this pod's own + # register_model() write somehow never committed. Without + # this check, the retire-UPDATE above would still commit, + # leaving model_registry with zero active rows. Roll back + # both statements together so the previously active model + # stays active instead. + conn.rollback() + raise RuntimeError( + f"No model_registry row matched model_version={model_version!r} — " + "not promoting; the previously active model remains active" + ) + conn.commit() + finally: + conn.close() + + logger.info("Promoted model_version=%s to active", model_version) + context["ti"].xcom_push(key="promoted_version", value=model_version) + + +def _rollout_restart(**context) -> None: + from kubernetes import client, config + + config.load_incluster_config() + apps_v1 = client.AppsV1Api() + restarted_at = datetime.now(timezone.utc).isoformat() + + for deployment in ("classifier", "stream-processor"): + patch = { + "spec": { + "template": {"metadata": {"annotations": {"sentinel/restartedAt": restarted_at}}} + } + } + apps_v1.patch_namespaced_deployment(name=deployment, namespace=APP_NAMESPACE, body=patch) + logger.info("Restarted deployment/%s", deployment) + + +with DAG( + dag_id="retrain_dag", + description="Fine-tune, evaluate, and (if it passes the quality gate) promote a new model", + schedule=None, # manually triggered — by services/label-ui today, drift's PSI signal later + start_date=datetime(2025, 1, 1), + catchup=False, + tags=["sentinel", "retraining"], +) as dag: + run_retraining = KubernetesPodOperator( + task_id="run_retraining", + name="sentinel-retraining", + namespace=PIPELINE_NAMESPACE, + image="sentinel-retraining:local", + image_pull_policy="Never", + cmds=["python", "-m", "pipelines.retraining"], + arguments=["--output-dir", "/tmp/artifacts", "--log-dir", "/tmp/logs"], + service_account_name="airflow", + env_vars=[ + k8s.V1EnvVar( + name="DATABASE_URL", + value_from=k8s.V1EnvVarSource( + secret_key_ref=k8s.V1SecretKeySelector( + name="drift-postgres", key="database-url" + ) + ), + ), + k8s.V1EnvVar( + name="MONGO_URI", + value_from=k8s.V1EnvVarSource( + secret_key_ref=k8s.V1SecretKeySelector(name="retraining-mongo", key="mongo-uri") + ), + ), + k8s.V1EnvVar( + name="MINIO_ENDPOINT", + value="http://minio.sentinel-data.svc.cluster.local:9000", + ), + k8s.V1EnvVar( + name="MINIO_ACCESS_KEY", + value_from=k8s.V1EnvVarSource( + secret_key_ref=k8s.V1SecretKeySelector(name="retraining-minio", key="root-user") + ), + ), + k8s.V1EnvVar( + name="MINIO_SECRET_KEY", + value_from=k8s.V1EnvVarSource( + secret_key_ref=k8s.V1SecretKeySelector( + name="retraining-minio", key="root-password" + ) + ), + ), + k8s.V1EnvVar( + name="MLFLOW_TRACKING_URI", + value="http://mlflow.sentinel-monitoring.svc.cluster.local:5000", + ), + ], + do_xcom_push=True, + get_logs=True, + is_delete_operator_pod=True, + in_cluster=True, + startup_timeout_seconds=600, + # 2Gi/4Gi OOM-killed the pod within ~2 minutes of the training loop + # starting (live-reproduced repeatedly — same signature as mlflow's + # earlier OOM: crashes silently right as the heavy step begins, no + # traceback since SIGKILL gives the process no chance to flush + # stdout). Node has ample headroom (~10Gi free) — this is a cgroup + # limit problem, not real contention. RoBERTa-base fine-tuning + # (weights + AdamW optimizer state + gradients + activations for + # batch_size=8 x seq_len=512, all fp32 on CPU) plus a second full + # train-set forward pass per epoch (_TrainMetricsCallback) needs + # more headroom than a lean inference-only pod like the classifier. + # cpu limit=2 made the benchmark stage pathologically slow (still + # running after 19+ minutes, live-reproduced) — pipelines/evaluation/ + # benchmark.py opens its ONNX Runtime session with no SessionOptions, + # so ORT auto-detects thread count from the host's real CPU count + # (16 here), not the pod's cgroup limit. Those threads then thrash + # against a 2-core quota instead of running in parallel. Not fixed + # in benchmark.py itself (shared, unmodified pipeline code per this + # feature's design — "rest of the pipeline remains the same"); + # fixed here instead by giving the pod enough real cores that ORT's + # thread count and the cgroup limit stop fighting each other. Host + # has 16 cores with room to spare. + container_resources=k8s.V1ResourceRequirements( + requests={"cpu": "2", "memory": "4Gi"}, + limits={"cpu": "8", "memory": "8Gi"}, + ), + ) + + decide_promotion = PythonOperator( + task_id="decide_promotion", + python_callable=_decide_promotion, + ) + + rollout_restart = PythonOperator( + task_id="rollout_restart", + python_callable=_rollout_restart, + ) + + run_retraining >> decide_promotion >> rollout_restart diff --git a/pipelines/drift/explanation.md b/pipelines/drift/explanation.md new file mode 100644 index 0000000..7bbe6f4 --- /dev/null +++ b/pipelines/drift/explanation.md @@ -0,0 +1,282 @@ +# Drift Detection Pipeline — Explanation + +A one-shot PySpark job (`drift_job.py`) that compares the classifier's recent +score distribution against a reference baseline, computes PSI and JSD, writes +the result to `drift_stats`, and exits with a status code Airflow can branch +on. Runs to completion and exits — this is a K8s `Job` (via the +`SparkApplication` CRD), not a `Deployment`, matching the repo's +service-vs-job split described in the root `CLAUDE.md`. + +**Phase 7.4 update**: this job now runs automatically, hourly, via +[`../../orchestration/drift_dag.py`](../../orchestration/explanation.md) — +`spark-application.yaml` in this directory is still the reference manifest +(and still useful for a manual `kubectl apply -f` test independent of +Airflow), but the actual submitted resource each hour is a copy of it with +a unique generated name, created directly via the Kubernetes API rather +than this file. See that DAG's explanation for the full story, including +why the higher-level `SparkKubernetesOperator`/`SparkKubernetesSensor` +Airflow operators were tried first and abandoned. + +--- + +## Why PySpark for what's currently a few thousand floats + +At today's data volume this could be a 20-line `numpy` script. It's built on +Spark on purpose — it's the deliberate "learn distributed data processing" +phase of this project (see root `CLAUDE.md`'s phase table), and writing the +binning/aggregation as genuine Spark DataFrame operations (not +`.toPandas()` immediately) is what makes `.explain()` on the physical plan +meaningful practice. The design constraint that keeps it honest: **only the +final 10-bin aggregate (10 rows) is ever `.collect()`-ed to the driver** — +see `metrics.py`'s Step 8. Everything upstream of that (binning, grouping, +joining, proportion math) stays as unevaluated Spark transformations, so the +same code would scale to a real production score table without a rewrite. + +--- + +## Data flow + +``` +PostgreSQL classifications table + → db.read_reference_scores() — earliest 1000 rows for the active model_version + → db.read_current_scores() — last `hours` (default 24) rows, capped at 100k + → spark.createDataFrame(...) — Python list → Spark DataFrame (driver-side) + → metrics.compute_drift() — binning, PSI, JSD (all Spark ops) + → db.write_drift_stats() — one row into drift_stats + → sys.exit(0 | 1 | 2) — Airflow's KubernetesPodOperator branches on this +``` + +Exit codes (`drift_job.py`'s module docstring): +- `0` — ran successfully, no drift +- `1` — configuration or DB error (e.g. `DATABASE_URL` unset, no classifications at all) +- `2` — ran successfully, **drift detected** (PSI > 0.2) — the signal the + eventual `retrain_dag.py` branches on + +--- + +## PSI and JSD, and why both + +**PSI (Population Stability Index)** — the industry-standard metric for "has +this distribution shifted enough to worry about." Sum over bins of +`(p - q) × ln(p / q)`, where `p` is the current proportion and `q` is the +reference proportion in that bin. Thresholds used here (from `metrics.py`'s +docstring, standard in the field): + +| PSI | Meaning | +|---|---| +| < 0.10 | No significant change | +| 0.10 – 0.20 | Moderate shift, monitor | +| > 0.20 | Significant drift — triggers retrain | + +**JSD (Jensen-Shannon Divergence)** — computed alongside PSI but not +currently used for the go/no-go decision. It's symmetric and bounded +`[0, ln(2)]` even when a bin is empty in one distribution (unlike KL +divergence, which blows up to infinity on a zero-probability bin) — recorded +for visibility into *how* the distribution moved, and as a second signal to +eyeball before trusting a borderline PSI. Both use the same epsilon-smoothed +`p`/`q` (`_EPSILON = 1e-6`) so neither ever divides by zero or takes `ln(0)`. + +### The bin-clamping gotcha (`_bin_scores`) + +```python +F.greatest(F.least(F.floor(F.col("score") * n_bins).cast("int"), F.lit(n_bins - 1)), F.lit(0)) +``` + +`floor(score * 10)` puts a score of exactly `1.0` in bin `10`, which doesn't +exist (bins are `0`–`9`) — `F.least(..., 9)` clamps the high end. The +**low-end clamp** (`F.greatest(..., 0)`) was added after review: nothing +upstream enforces scores are in `[0, 1]` (no DB `CHECK` constraint on +`classifications.score`), so a floating-point rounding artifact or a future +differently-calibrated model producing a small negative value would silently +fall out of the join in `compute_drift()` instead of landing in bin 0. Clamp +symmetrically rather than trust the input range. + +--- + +## The `get_active_model_version` gotcha (`db.py`) + +This is the single most surprising thing in this pipeline, and it went +through two different (both live-tested) implementations before landing on +the current one. + +**The trap:** `model_registry` holds rows written by two different +processes that don't share a `model_version` *value* even when they refer to +the same underlying deployment: + +1. `services/classifier/db.py`'s `get_active_model()` reads `model_registry` + to decide *which MinIO artifact to download* — it prefers a row with + `status='active'`, falling back to `'staging'`. +2. Once a classifier pod has loaded a model, `services/classifier/model.py` + **self-registers a new row** under its own freshly-derived + `model_version` string (`sentinel-roberta-{deployed_at}-{quant_tag}`) — + and *that* string, not the one it downloaded from, is what actually gets + written into every `classifications.model_version` value going forward. + +The first fix mirrored `get_active_model()`'s exact `'active'`-preferred +ordering, on the reasoning that "the drift job should look at whatever's +canonically active." Live-tested against a real cluster, this was wrong: the +`'active'`-status row was a stale promotion pointing at a `model_version` +string from a previous deploy, with **zero** matching rows in +`classifications` — the drift job silently found nothing to compare and +exited before even reaching Spark. + +The fix that's actually in the code now drops the status preference +entirely: + +```python +SELECT model_version FROM model_registry +WHERE status IN ('active', 'staging') +ORDER BY created_at DESC +LIMIT 1 +``` + +"Whichever pod self-registered most recently" is what's actually running +and writing classifications right now — at the time this was fixed, +nothing reliably kept `status='active'` pointed at the right namespace. +This was a known, accepted gap: once `retrain_dag.py`'s promotion step +landed and started flipping `status` deliberately (rather than every pod +self-registering as `'staging'` on boot), this query should probably go +back to preferring `'active'`. Still subject to the same rolling-restart +race noted in the code comment — old- and new-version pods can both +self-register within moments of each other — just no longer compounded by +a status filter pointing at the wrong `model_version` namespace +altogether. + +**Update, now that `retrain_dag.py`'s promotion step exists (Phase 7.3):** +the gap hasn't fully closed on its own. Live-tested during `drift_dag.py`'s +(Phase 7.4) end-to-end verification: `model_registry`'s `active` row +pointed at a `model_version` with **zero** rows in `classifications` — +promoted during retraining-pipeline testing, but never actually served +real traffic, because no classifier pod had been rolled out against it +with real requests flowing yet. `get_active_model_version()`'s +`created_at DESC` (ignoring status) correctly fell back to the most +recently *self-registered* version instead, which is exactly the row with +real data — so the existing fix continues to be the right one even with +promotion logic now in place. The underlying lesson holds either way: +`model_registry.status='active'` answers "which model *should* be +serving," not "which `model_version` string is actually showing up in +`classifications` right now" — and this pipeline specifically needs the +second answer, not the first. + +**Lesson for future "obvious" fixes in this codebase:** when a query joins +two tables/processes that evolved independently, verify the *values* +actually line up in a live cluster before trusting that mirroring another +query's logic is correct — matching *shape* isn't the same as matching +*semantics*. + +--- + +## Guardrails added after live testing + +- **`MIN_REFERENCE_SIZE = 10`** (`drift_job.py`) — below this many reference + rows, epsilon-smoothing (`_EPSILON = 1e-6`) dominates the reference + histogram and PSI/JSD against it aren't statistically meaningful. The job + now `sys.exit(0)`s (not just logs a warning) rather than risk writing a + false `drift_flagged=True` — important once Phase 7 wires this exit code + directly into an unattended retrain trigger; a spurious retrain from an + unreliable baseline would be expensive and pointless. +- **`MAX_CURRENT_ROWS = 100_000`** (`db.py`) — bounds how much data gets + pulled into the driver process's memory twice: once via `psycopg`'s + `fetchall()`, then again via `spark.createDataFrame([(s,) for s in + scores], ...)`. The query itself is structured as a subquery — `ORDER BY + ts DESC LIMIT max_rows`, then re-sorted `ASC` in the outer query — so the + cap keeps the *most recent* rows in the window, not an arbitrary subset + from the start of it. + +--- + +## Running it locally vs. in-cluster + +```bash +# Local, all cores, explicit DB URL +python drift_job.py --hours 24 --database-url postgresql://sentinel:sentinel@localhost:5432/sentinel + +# Local, explicit Spark master +python drift_job.py --master local[4] + +# In-cluster: spark-on-k8s-operator sets spark.master itself — passing +# --master here would override what the operator configured and break it. +# The SparkApplication CRD (spark-application.yaml) omits --master for +# exactly this reason. +``` + +`drift_job.py` only calls `.master()` on the `SparkSession.builder` **if** +`--master` was explicitly passed — see the `if args.master:` guard right +before `spark = builder.getOrCreate()`. This is what lets the identical +script run correctly both ways. + +--- + +## `spark-application.yaml` (SparkApplication CRD) + +```yaml +image: sentinel-drift:local +imagePullPolicy: Never +mainApplicationFile: local:///opt/spark/work-dir/drift_job.py +sparkVersion: "3.5.3" +driver: + memory: "512m" + serviceAccount: spark +executor: + instances: 2 + memory: "512m" +``` + +- **`imagePullPolicy: Never`** + `local:///` file scheme — the job's own + Docker image (built and `k3d image import`-ed by `dev-start.sh`, same + pattern as classifier/stream-processor) already contains `drift_job.py`, + `db.py`, `metrics.py`; nothing is pulled from a registry, and + `mainApplicationFile` points at a path *inside that image*, not a remote + URL. +- **`serviceAccount: spark`** on the driver — the operator needs this + identity to create/manage the executor pods it spins up on the driver's + behalf (RBAC granted by spark-operator's own Helm chart, see + `infra/terraform/local/explanation.md`'s spark-operator section). + `restartPolicy: Never` — matches the "one-shot job" semantics; a failed + drift run should surface as a failure for Airflow to see, not silently + retry and mask a real problem. +- **`PYSPARK_PYTHON=/usr/bin/python3`** on both driver and executor — the + `apache/spark-py` base image needs this set explicitly or PySpark can't + find the interpreter to run `db.py`/`metrics.py` inside the executor + processes. +- **`arguments: ["--hours", "24", "--reference-size", "1000"]`** — the + operator passes these straight through as CLI args to + `mainApplicationFile`, same as running `drift_job.py --hours 24 + --reference-size 1000` locally. + +--- + +## Using `.explain()` to verify the physical plan + +`metrics.py`'s `compute_drift()` calls `combined.explain()` right before the +one `.collect()` call. This is the standard way to confirm Spark isn't doing +more work than it needs to — worth running manually when touching this file: + +```python +>>> combined.explain() +== Physical Plan == +*(5) Project [...] ++- *(5) BroadcastHashJoin ... + :- *(3) HashAggregate(keys=[bin], functions=[count(1)]) ... # ref_counts + +- *(4) HashAggregate(keys=[bin], functions=[count(1)]) ... # cur_counts +``` + +Two separate `HashAggregate` stages (one per DataFrame) confirms the +reference and current binning genuinely run as independent Spark jobs +rather than accidentally sharing/recomputing state — the thing to check for +whenever this function is refactored is a *redundant full scan* appearing +twice for what should be one source DataFrame, which is the classic +`.explain()`-catchable mistake this project's "Common Interview Points" +section calls out. + +--- + +## Tests + +`pipelines/drift/tests/` — run with `pytest pipelines/drift/tests/` from +the `pipelines/drift/` directory (it has its own `pyproject.toml`, separate +from the rest of the repo, since PySpark manages its own environment via +`spark-submit --py-files`, per the root `CLAUDE.md`'s target folder +structure notes). Uses a local `SparkSession` (`local[1]` or similar) to run +`compute_drift()` against small hand-built DataFrames — no live PostgreSQL +or cluster needed for the metrics math itself. diff --git a/pipelines/evaluation/explanation.md b/pipelines/evaluation/explanation.md new file mode 100644 index 0000000..4344503 --- /dev/null +++ b/pipelines/evaluation/explanation.md @@ -0,0 +1,195 @@ +# Evaluation Pipeline — Explanation + +The model quality gate: `benchmark.py` scores a candidate ONNX model against +a held-out ground-truth dataset and writes accuracy/precision/recall/F1/ +AUC-ROC + latency/memory numbers to a JSON report; `validate.py` reads that +report (optionally alongside the currently-active model's report) and +decides pass/fail via a non-zero exit code. Both are one-shot CLI scripts — +eventually a K8s `Job` step in the retrain DAG, run locally today. + +Neither script talks to `model_registry` or flips any status. Per +`CLAUDE.md`'s "Model Registry Source of Truth," promotion is exclusively +Airflow's job (Phase 7, not yet built) — this pipeline only produces the +signal that a human operator, or later `retrain_dag.py`, acts on. + +--- + +## Data flow + +``` +pipelines/optimizer output (a quantized ONNX model dir) + → benchmark.py --model-dir logs/optimizer//int8 + → datasets.eval_holdout.load_holdout() — 3780 labeled examples + → ONNX Runtime inference, batched + → benchmark_report.json (accuracy, F1, AUC-ROC, latency, memory) + → validate.py --candidate [--baseline ] + → exit 0 (PASS) or exit 1 (FAIL), reasons logged +``` + +Run both from the repo root with `uv run --package sentinel-evaluation` +(see `pyproject.toml` in this directory) — a separate package from the rest +of `pipelines/`, same reasoning as `pipelines/drift/`'s own `pyproject.toml`: +each pipeline step is meant to become an independently-buildable container. + +--- + +## The held-out dataset (`datasets/eval_holdout.py`) + +`datasets/test_dataset.csv` — 3780 rows, sourced from +`github.com/VjayRam/Content-Identifier`, balanced across 9 risk categories +(VC, DEF, ESP, PII, SHS, IP, CBRN, CSAE, SCAM) at 210 harmful + 210 safe +examples each. `label=1` means the row's text matches its risk category +(harmful); `label=0` is a safe or counterfactual example for the same +category. This is strictly an **evaluation** set — never used for training, +which is what makes it valid as a promotion gate (a model that was fine-tuned +on data leaking into this set would look artificially good here). + +`csv.field_size_limit(10_000_000)` exists because several rows are +multi-turn conversations stored as one multiline CSV field — comfortably +past Python's 128KB default field-size limit, which raises +`_csv.Error: field larger than field limit` on the first oversized row +without it. `load_holdout(sample_size=...)` draws a plain random sample +(no explicit stratification) for fast local iteration; since the source set +is already exactly balanced 50/50, an unstratified sample stays balanced +in expectation without needing separate per-class sampling logic. + +--- + +## `benchmark.py` + +### Scoring mirrors the classifier, deliberately not by importing it + +`_score_batch()`'s sigmoid-for-single-logit / softmax-last-class branching +is a copy of `services/classifier/model.py`'s `Classifier.predict()` scoring +logic — **kept standalone rather than imported**. `pipelines/` and +`services/` are separate deployable packages by design (see `CLAUDE.md`'s +target production folder structure — each becomes its own container with +its own `Dockerfile`), so sharing runtime code between them would mean +either a shared internal package (not worth it yet at this scale) or an ugly +cross-package import reaching into another service's source tree. Small +enough duplication (a dozen lines) that keeping it standalone is the +pragmatic call — but it does mean: **if the classifier's scoring logic ever +changes (e.g. a different activation, a different logit convention), this +function has to be updated to match by hand.** Nothing enforces the two +stay in sync. + +### AUC-ROC without scikit-learn + +```python +def _auc_roc(scores, labels) -> float | None: + combined = np.concatenate([pos, neg]) + order = np.argsort(combined) + ranks = np.empty_like(order, dtype=float) + ranks[order] = np.arange(1, len(combined) + 1) + pos_rank_sum = ranks[: len(pos)].sum() + auc = (pos_rank_sum - len(pos) * (len(pos) + 1) / 2) / (len(pos) * len(neg)) +``` + +This is the Mann-Whitney U statistic formulation of AUC-ROC: rank every +score across both classes, sum the ranks belonging to the positive class, +subtract the minimum possible sum, normalize by the number of +positive/negative pairs. Avoids pulling in scikit-learn as a dependency for +one metric. The `argsort`-of-`argsort` trick (`order = argsort(combined)`, +then scatter `1..N` back into rank-order via `ranks[order] = ...`) gives +exact ranks for distinct scores; under ties it approximates (ties should get +the *average* rank, this gives each tied element its own consecutive rank +instead) — acceptable for a promotion gate where "is this candidate roughly +as good," not a research benchmark needing exact tie handling. Returns +`None` (not `0.0` or an exception) when a class is entirely absent from the +sample — `validate.py` never looks at `auc_roc` for pass/fail today, so this +mainly guards against a `ZeroDivisionError` on a pathological +`--sample-size` draw. + +### `peak_memory_mb` via `resource.getrusage` + +```python +peak_memory_mb = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024 +``` + +`ru_maxrss` is the process's peak resident-set size **since the process +started**, not a windowed measurement around just the inference loop — it +includes interpreter startup, `onnxruntime`/`transformers` import overhead, +and model loading. Good enough for the gate's actual question ("did this +candidate get dramatically heavier than the baseline"), not a substitute for +a real profiler if you need to know where memory goes. Units: `ru_maxrss` is +**kilobytes on Linux** (the only platform this runs on) — macOS reports +bytes instead, which would need `/1024/1024` rather than `/1024`; the `/ +1024` here would silently be wrong by 1000x if this ever ran on a Mac. + +### Batching + +`BATCH_SIZE = 32`, plain Python list slicing (`texts[i:i+BATCH_SIZE]`) — +one ONNX Runtime `session.run()` call per batch, results concatenated with +`np.concatenate`. No async, no `DynamicBatcher` (that's the classifier +service's concern for handling concurrent live requests) — this is a +sequential offline batch job with no concurrency to coalesce. + +--- + +## `validate.py` + +```python +MIN_ACCURACY = 0.85 +MAX_ACCURACY_DROP = 0.01 # vs baseline, if a baseline report is given +``` + +Two independent gates, both must pass: + +1. **Absolute floor** — candidate accuracy must be ≥ 0.85, regardless of + what came before. Catches a candidate that's just bad in isolation (e.g. + a broken quantization pass, a corrupted checkpoint). +2. **Regression guard** — *only checked if `--baseline` is passed* — the + candidate can't be more than 1 percentage point worse than the currently + active model. Catches a candidate that clears the absolute floor but is + still a meaningful step backward (e.g. retraining on a skewed new data + slice that improves one risk category at the expense of others). + +`--baseline` is optional by design: the very first model promoted ever has +no baseline to compare against, and the absolute floor alone is the gate in +that case. `validate()` (the pure function, no I/O) returns `(passed: bool, +reasons: list[str])` rather than just a bool — `reasons` is what gets logged +on failure and is exactly what a human (or eventually a Slack/GitHub-issue +notification from the retrain DAG) needs to see to understand *why* a +promotion was blocked, not just that it was. + +Exit codes mirror `pipelines/drift/drift_job.py`'s convention deliberately: +`0` = proceed, non-zero = don't. This is what lets `retrain_dag.py` +branch on both pipeline steps the same way (`BranchPythonOperator` / +`ShortCircuitOperator` checking a `KubernetesPodOperator`'s exit code) +without needing per-step-specific logic. + +--- + +## Running it locally + +```bash +# 1. Benchmark a candidate produced by the optimizer pipeline +uv run --package sentinel-evaluation python -m pipelines.evaluation.benchmark \ + --model-dir logs/optimizer//int8 \ + --output logs/evaluation//benchmark_report.json + +# 2. Gate it against the floor only +uv run --package sentinel-evaluation python -m pipelines.evaluation.validate \ + --candidate logs/evaluation//benchmark_report.json + +# 2b. Or gate it against the currently-active model's own report too +uv run --package sentinel-evaluation python -m pipelines.evaluation.validate \ + --candidate logs/evaluation//benchmark_report.json \ + --baseline logs/evaluation//benchmark_report.json + +echo $? # 0 = PASS, 1 = FAIL +``` + +`--sample-size` on `benchmark.py` is useful for a fast sanity check during +iteration (a few hundred examples instead of all 3780) — don't use a +sampled run's report as the actual promotion-gate input; the full 3780-row +set is what the 0.85 floor and 1%-drop tolerance were calibrated against. + +--- + +## What's next (Phase 7.3) + +Once `retrain_dag.py` exists, this becomes two `KubernetesPodOperator` tasks +(benchmark, then validate) sandwiched between the optimizer pipeline and the +promotion step — see [`../../orchestration/explanation.md`](../../orchestration/explanation.md)'s +"What's next" section for the full intended DAG shape. diff --git a/pipelines/optimizer/explanation.md b/pipelines/optimizer/explanation.md index f77c5c0..02a0488 100644 --- a/pipelines/optimizer/explanation.md +++ b/pipelines/optimizer/explanation.md @@ -7,7 +7,10 @@ pipelines/optimizer/ export.py — Stage 1: HuggingFace Hub → ONNX FP32 optimize.py — Stage 2: ONNX FP32 → ONNX O2 (graph optimization) quantize.py — Stage 3: ONNX O2 → ONNX INT8 (dynamic quantization) - pipeline.py — Orchestrator: runs all 3 stages, writes report.json + upload.py — MinIO upload for each stage's artifacts + the final report + registry.py — model_registry INSERT (status='staging') + pipeline.py — Orchestrator: runs all 3 stages, uploads, registers, writes report.json + __main__.py — CLI entry point (`python -m pipelines.optimizer`) ``` Each stage is a separate file because each produces a checkpoint artifact on disk. If quantization fails with a bad config, you re-run from Stage 3 without re-downloading and re-exporting the model. In Airflow (Phase 7), each stage becomes a separate task so the DAG can retry exactly the step that failed. @@ -138,6 +141,113 @@ O2 produces no size reduction (same math, different graph structure). INT8 gives --- +## upload.py + +### Every stage uploads to MinIO immediately after it completes + +`pipeline.py`'s stage loop calls `upload_stage(run_id, dir_name, ...)` right +after each of `export`/`optimize`/`quantize` finishes, not once at the end — +so `models//fp32/`, `.../o2/`, `.../int8/` land in MinIO as the +pipeline progresses, mirroring the local `models//` tree exactly. If +the pipeline crashes partway through (e.g. quantization OOMs), whatever +stages already completed are still durably in MinIO, not lost with the pod. + +### `_s3_client()` is `@lru_cache`d + +```python +@lru_cache(maxsize=1) +def _s3_client(): + return boto3.client("s3", ...) +``` + +A fresh `boto3.client("s3", ...)` per call means a new TLS handshake and +credential resolution every time — wasteful across the 4 upload calls one +pipeline run makes (3 stages + report). Cached so the whole run reuses one +client. Not literally shared with `services/classifier/download.py`'s +near-identical factory function — separately deployed packages by design +(see that file's own comment) — so this is a deliberate small duplication, +not an oversight. + +`connect_timeout=5` + `retries={"max_attempts": 2}` on the client's `Config` +— the boto3 default connect timeout is 60 seconds; without shortening it, a +MinIO outage would hang the whole pipeline for a minute per upload attempt +instead of failing fast into the local-fallback path below. + +### Parallel uploads within a stage + +```python +with ThreadPoolExecutor(max_workers=min(8, len(files))) as pool: + list(pool.map(_upload_one, files)) +``` + +The FP32 stage alone uploads the full model file plus several tokenizer +files — uploading them one at a time sequentially left the pipeline waiting +on network I/O for no reason, since the files are fully independent. +`boto3` clients are thread-safe for concurrent calls, so a `ThreadPoolExecutor` +is sufficient (no need for `asyncio`/`aioboto3`). `list(pool.map(...))` +forces the map to fully evaluate and **re-raises the first exception** it +hits — this preserves the old sequential loop's behavior of failing the +whole stage on any single file's upload error, now just running the happy +path in parallel. + +### `upload_stage` and `upload_report` never raise — they return `None` + +Both catch `(BotoCoreError, ClientError, OSError)` internally and log a +warning instead of propagating. This is deliberate: a MinIO outage +shouldn't take down the whole optimization run — the pipeline still +produces valid local artifacts and a local `report.json`; only the "survive +pod termination" property is lost for that run. `pipeline.py` checks the +return value (`None` = failure) to decide whether to register the MinIO +path or fall back to the local path — see `pipeline.py`'s `minio_ok` section +below. + +--- + +## registry.py + +### `register_model()` takes a connection, not a DSN + +```python +def register_model(conn: psycopg.Connection, run_id: str, model_path: str, threshold: float = 0.5) -> None: +``` + +Earlier this opened its own `psycopg.connect()` per call. `pipeline.py` +only calls it once per run today, but the signature was changed to accept +an already-open `conn` because a pipeline run legitimately might need to +register more than once (e.g. a retry after a partial failure, or future +per-stage registration) — every `psycopg.connect()` costs a TCP handshake +plus auth round-trip, so the caller now opens **one** connection for the +whole run (`with psycopg.connect(DSN) as conn:` in `pipeline.py`) and passes +it down, rather than each callee opening its own. `DSN` was renamed from a +private `_DSN` to a public export specifically so `pipeline.py` could import +and use it for that one connection. + +`ON CONFLICT (model_version) DO NOTHING` — makes registration idempotent if +the pipeline (or a retried Airflow task) calls it twice for the same +`run_id`. Status is unconditionally `'staging'`; nothing in this pipeline +ever writes `'active'` — that transition is exclusively Airflow's job per +`CLAUDE.md`'s Model Registry Source of Truth section. + +--- + +## `__main__.py` + +```bash +python -m pipelines.optimizer --model-id VijayRam1812/content-classifier-roberta --output-dir models/ +``` + +A dedicated `__main__.py` rather than the `if __name__ == "__main__":` block +living in `pipeline.py` itself — lets the module be run as +`python -m pipelines.optimizer` (the package) instead of +`python -m pipelines.optimizer.pipeline` (a specific file inside it). This +is the more standard invocation for a package meant to be run as a unit — +matches how `python -m pytest`, `python -m http.server`, etc. work — and +keeps `pipeline.py` purely a library module (importable by Airflow as a +plain `run()` function call, with argument parsing living only at the CLI +boundary). + +--- + ## pipeline.py ### Run ID @@ -208,21 +318,71 @@ Logging is configured here and nowhere else. The stage modules use `logging.getL ```python stages = [ - ("export", lambda: export(model_id, run_artifacts / "fp32", opset=opset)), - ("optimize", lambda: optimize(run_artifacts / "fp32", run_artifacts / "o2")), - ("quantize", lambda: quantize(run_artifacts / "o2", run_artifacts / "int8")), + ("export", lambda: export(model_id, run_artifacts / "fp32", opset=opset), "fp32"), + ("optimize", lambda: optimize(run_artifacts / "fp32", run_artifacts / "o2"), "o2"), + ("quantize", lambda: quantize(run_artifacts / "o2", run_artifacts / "int8"), "int8"), ] ``` -A list of `(name, callable)` pairs instead of three separate blocks. Adding a new stage is one line. In Phase 7 when this becomes an Airflow DAG, each tuple maps directly to one `PythonOperator` and the stage name becomes the task ID. +A list of `(name, callable, dir_name)` triples instead of three separate +blocks — the third element (`dir_name`) doubles as both the local +subdirectory the stage writes to and the MinIO key prefix +(`upload_stage(run_id, dir_name, ...)`), so the bucket layout mirrors the +local `models//` tree exactly without a second naming scheme to keep +in sync. Adding a new stage is one line. In Phase 7 when this becomes an +Airflow DAG, each triple maps directly to one `PythonOperator`/ +`KubernetesPodOperator` and the stage name becomes the task ID. -### `report.json` +### MinIO upload + registration, and the local-fallback path + +After each stage's callable runs, `upload_stage()` is called immediately +(see `upload.py` above) and its return value is folded into +`report["stages"][name]["minio_path"]`. A module-level `minio_ok` flag +starts `True` and flips to `False` the first time any stage's upload +returns `None` (MinIO unreachable). -Records per-stage duration and output path for every run. This file is the contract between the optimizer and the evaluate pipeline — `benchmark.py` and `validate.py` read it to find the INT8 checkpoint without hardcoded paths. In Phase 7 it becomes the schema for what gets logged to MLflow. +After all three stages complete, `model_path` — the value that ends up in +`model_registry.model_path`, and what the classifier's `download.py` +actually fetches on pod startup — is derived like this: -### `if __name__ == "__main__"` +```python +if minio_ok: + model_path = f"{report['stages']['quantize']['minio_path']}/model_quantized.onnx" +else: + model_path = str(run_artifacts / "int8") # local fallback +``` + +Deliberately built from the quantize stage's **actual** `upload_stage()` +return value rather than re-deriving `f"models/{run_id}/int8"` by hand — an +earlier version hardcoded the `"models"` bucket-name prefix here a second +time, which risked silently diverging from `upload.py`'s real +`MINIO_BUCKET` env var if that were ever set to something other than +`"models"` (bug tracked as #35). Reading it back out of the report the +upload step already wrote means there's exactly one place the bucket name +is resolved. + +The local-fallback branch means a MinIO outage doesn't stop `register_model` +from recording that *a* run happened — the registry entry just won't be +resolvable by any other pod (only the machine that ran the pipeline has that +local path), so it's logged with a warning, not silently treated as normal. + +### `report.json` -Makes the script both runnable directly and importable. Airflow can call `run()` as a Python function without spawning a subprocess. Running directly: +Records per-stage duration, output path, and MinIO path for every run, +plus a top-level `model_path` mirroring whatever was actually registered. +This file is the contract between the optimizer and the evaluation +pipeline — `benchmark.py`/`validate.py` read it (indirectly, via +`--model-dir`) to find the INT8 checkpoint without hardcoded paths. It's +also uploaded to MinIO itself (`upload_report()`) after being written +locally, so it survives pod termination the same way the model artifacts +do. In Phase 7 it becomes the schema for what gets logged to MLflow. + +### Kept importable, not just runnable + +`pipeline.py` itself has no `if __name__ == "__main__":` block anymore — CLI +parsing lives entirely in `__main__.py` (see above). `run()` stays a plain +function so Airflow can call it directly as a Python callable without +spawning a subprocess. Running directly: ```bash uv run python -m pipelines.optimizer \ diff --git a/pipelines/optimizer/upload.py b/pipelines/optimizer/upload.py index 4911c6d..386562f 100644 --- a/pipelines/optimizer/upload.py +++ b/pipelines/optimizer/upload.py @@ -1,3 +1,4 @@ +import json import logging import os from concurrent.futures import ThreadPoolExecutor @@ -53,6 +54,52 @@ def upload_report(run_id: str, report_path: Path) -> str | None: return None +def upload_benchmark_report(run_id: str, report_path: Path) -> str | None: + """Upload a benchmark report JSON to MinIO at models//benchmark_report.json. + + A separate key from upload_report's report.json (the optimizer's own + stage-timing report) — this one lets a *later* retrain's quality gate + look up a specific model_version's accuracy/f1/etc. as a regression + baseline (see pipelines/retraining/pipeline.py's _get_baseline_report) + without re-running inference against it. Same non-fatal-on-failure + contract as upload_report. + """ + try: + s3 = _s3_client() + key = f"{run_id}/benchmark_report.json" + logger.info("Uploading benchmark report → s3://%s/%s", _BUCKET, key) + s3.upload_file(str(report_path), _BUCKET, key) + return f"{_BUCKET}/{key}" + except (BotoCoreError, ClientError, OSError) as exc: + logger.warning( + "MinIO benchmark report upload failed — report kept locally only. Reason: %s", exc + ) + return None + + +def download_report(run_id: str, filename: str) -> dict | None: + """Download and parse a JSON report from MinIO at models//. + + Returns None whenever the report isn't available for any reason — + MinIO unreachable, the key doesn't exist (e.g. a run that predates + benchmark-report uploads), or the body isn't valid JSON. Callers should + treat None as "no data to use," not as an error to propagate; the two + failure modes aren't distinguished on purpose, since every caller's + correct response to either is the same (fall back to not having this + report). + """ + try: + s3 = _s3_client() + key = f"{run_id}/{filename}" + obj = s3.get_object(Bucket=_BUCKET, Key=key) + return json.loads(obj["Body"].read()) + except (BotoCoreError, ClientError, OSError, json.JSONDecodeError) as exc: + logger.warning( + "Could not download/parse s3://%s/%s: %s", _BUCKET, f"{run_id}/{filename}", exc + ) + return None + + def upload_stage(run_id: str, stage: str, stage_dir: Path) -> str | None: """Upload all files in stage_dir to MinIO under models///. diff --git a/pipelines/retraining/Dockerfile b/pipelines/retraining/Dockerfile new file mode 100644 index 0000000..7c80821 --- /dev/null +++ b/pipelines/retraining/Dockerfile @@ -0,0 +1,42 @@ +# Build context MUST be the repo root, not this directory — unlike drift/'s +# self-contained Dockerfile, pipeline.py imports pipelines.optimizer, +# pipelines.evaluation, and datasets.eval_holdout (transitively, via +# benchmark.py) directly, so all three trees need to be in the build context: +# docker build -f pipelines/retraining/Dockerfile -t sentinel-retraining:local . +FROM python:3.12-slim + +WORKDIR /app + +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +# CPU-only torch, explicitly — this pod runs inside k3d, which has no GPU +# passthrough configured (adding it was out of scope for this phase; see +# pipelines/retraining/explanation.md). Installing the default (CUDA) wheel +# here would pull in ~2GB of CUDA runtime this pod can never use. +RUN uv venv /app/.venv && \ + uv pip install --python /app/.venv/bin/python \ + --index-url https://download.pytorch.org/whl/cpu \ + "torch>=2.2" && \ + uv pip install --python /app/.venv/bin/python \ + "transformers>=4.48" \ + "accelerate>=0.26.0" \ + "mlflow>=2.19" \ + "pymongo>=4.9" \ + "numpy>=2.0" \ + "psycopg[binary]>=3.2" \ + "optimum[onnxruntime]>=2.1.0" \ + "onnxruntime>=1.27.0" \ + "onnx>=1.17" \ + "boto3>=1.35" + +ENV PATH="/app/.venv/bin:$PATH" + +# pipelines/retraining imports pipelines.optimizer and pipelines.evaluation +# directly (pipeline.py) — copy the whole pipelines/ and datasets/ tree, not +# just this package, so those imports resolve without a workspace install. +COPY pipelines /app/pipelines +COPY datasets /app/datasets + +WORKDIR /app + +CMD ["python", "-m", "pipelines.retraining"] diff --git a/pipelines/retraining/__init__.py b/pipelines/retraining/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/pipelines/retraining/__main__.py b/pipelines/retraining/__main__.py new file mode 100644 index 0000000..96d9932 --- /dev/null +++ b/pipelines/retraining/__main__.py @@ -0,0 +1,52 @@ +"""CLI entry point — run via `python -m pipelines.retraining`.""" + +import argparse +import logging +import os +import sys + +from pipelines.retraining.pipeline import run + +logger = logging.getLogger(__name__) + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Retraining pipeline") + parser.add_argument( + "--mongo-uri", + default=os.environ.get("MONGO_URI", "mongodb://sentinel:sentinel@localhost:27017/sentinel"), + ) + parser.add_argument( + "--base-model-id", + default=os.environ.get("BASE_MODEL_ID", "VijayRam1812/content-classifier-roberta"), + ) + parser.add_argument("--output-dir", required=True) + parser.add_argument("--log-dir", default="logs") + parser.add_argument( + "--initial-dataset-path", + default=os.environ.get("INITIAL_DATASET_PATH"), + help="Optional CSV (raw_text,label columns) to sample alongside accepted flagged_content", + ) + parser.add_argument("--sample-size", type=int, default=500) + parser.add_argument("--epochs", type=int, default=3) + args = parser.parse_args() + + # Explicit try/except + logger.exception rather than letting the + # default excepthook print the traceback — guarantees a clearly + # greppable "Retraining pipeline failed" banner around it, independent + # of any stdout buffering quirks (see disable_tqdm in train.py, a + # similar issue: unbuffered but line-oriented log capture upstream — + # kubectl logs -f, Airflow's pod_manager — behaves better with plain + # logging calls than mixed-mode stdout). + try: + run( + mongo_uri=args.mongo_uri, + base_model_id=args.base_model_id, + output_dir=args.output_dir, + log_dir=args.log_dir, + initial_dataset_path=args.initial_dataset_path, + sample_size=args.sample_size, + epochs=args.epochs, + ) + except Exception: + logger.exception("Retraining pipeline failed") + sys.exit(1) diff --git a/pipelines/retraining/dataset.py b/pipelines/retraining/dataset.py new file mode 100644 index 0000000..80045a3 --- /dev/null +++ b/pipelines/retraining/dataset.py @@ -0,0 +1,100 @@ +"""Builds the fine-tuning dataset from manually-accepted flagged_content, +plus an optional sample of an external labelled CSV. + +No initial dataset file ships with this repo — datasets/test_dataset.csv is +the evaluation pipeline's held-out set and must never be sampled from here +(that would contaminate the accuracy numbers pipelines/evaluation gates +promotion on). initial_dataset_path is therefore optional and unset by +default; when provided, it must be a CSV with the same raw_text,label shape +datasets/eval_holdout.py already parses, so whatever gets dropped in later +works with no format guessing. +""" + +import csv +import logging +import random +from pathlib import Path + +import pymongo + +logger = logging.getLogger(__name__) + +# Multi-turn conversations can be one multi-line CSV field — well past csv's +# 128KB default field size limit. Mirrors datasets/eval_holdout.py. +csv.field_size_limit(10_000_000) + +_LABEL_MAP = {"1": "harm", "0": "safe"} +VAL_FRACTION = 0.15 + + +def _load_accepted(db: pymongo.database.Database) -> list[tuple[str, str]]: + cursor = db.flagged_content.find( + {"training_decision": "accepted"}, + {"input_text": 1, "manual_label": 1}, + ) + return [(d["input_text"], d["manual_label"]) for d in cursor if d.get("manual_label")] + + +def _load_initial_sample(path: str, sample_size: int, seed: int) -> list[tuple[str, str]]: + with Path(path).open(newline="", encoding="utf-8") as f: + rows = list(csv.DictReader(f)) + pairs = [(r["raw_text"], _LABEL_MAP[r["label"]]) for r in rows] + if sample_size < len(pairs): + pairs = random.Random(seed).sample(pairs, sample_size) + return pairs + + +def build_dataset( + db: pymongo.database.Database, + initial_dataset_path: str | None = None, + sample_size: int = 500, + seed: int = 0, +) -> dict: + """Returns {"train": [(text,label),...], "val": [...], "sources": {...}}. + + Plain shuffle + split, not stratified — same reasoning as + eval_holdout.py's sampling: with class balance already reasonable in + expectation (flagged_content's SAFE_SAMPLE_RATE keeps it from skewing + all-harm), a random split stays balanced in expectation without needing + explicit stratification logic for a dataset this small. + """ + accepted = _load_accepted(db) + + initial: list[tuple[str, str]] = [] + if initial_dataset_path and Path(initial_dataset_path).exists(): + initial = _load_initial_sample(initial_dataset_path, sample_size, seed) + elif initial_dataset_path: + logger.warning("initial_dataset_path=%s does not exist — skipping", initial_dataset_path) + + combined = accepted + initial + # Need at least 2 — one for training, one for validation. With exactly + # 1, n_val's max(1, ...) below would take that single example for val + # and leave train empty, silently constructing a Trainer with a + # zero-length train_dataset instead of failing loudly. + if len(combined) < 2: + raise ValueError( + f"Only {len(combined)} labelled example(s) available — need at least 2 " + "(one for training, one for validation). Accept more flagged_content in " + "the labelling UI first, or pass --initial-dataset-path." + ) + + rng = random.Random(seed) + rng.shuffle(combined) + n_val = max(1, int(len(combined) * VAL_FRACTION)) + val, train = combined[:n_val], combined[n_val:] + + logger.info( + "Dataset built | accepted=%d initial_sample=%d train=%d val=%d", + len(accepted), + len(initial), + len(train), + len(val), + ) + return { + "train": train, + "val": val, + "sources": { + "flagged_content_accepted": len(accepted), + "initial_dataset_sample": len(initial), + }, + } diff --git a/pipelines/retraining/explanation.md b/pipelines/retraining/explanation.md new file mode 100644 index 0000000..e942678 --- /dev/null +++ b/pipelines/retraining/explanation.md @@ -0,0 +1,384 @@ +# Retraining Pipeline — Explanation + +Fine-tunes the content classifier on manually-labelled data, then hands off +to the *existing, unmodified* `pipelines/optimizer` (ONNX export/quantize/ +register-as-staging) and `pipelines/evaluation` (quality gate) pipelines — +this package's whole job is producing a good fine-tuned checkpoint and +proving it's good enough, not re-implementing anything downstream of that. +Triggered by `orchestration/retrain_dag.py`, which is triggered by +`services/label-ui`'s "Trigger Retraining" button. + +--- + +## Directory structure + +``` +pipelines/retraining/ + dataset.py — build_dataset(): accepted flagged_content + optional CSV sample + train.py — fine-tune with transformers.Trainer, log everything to MLflow + pipeline.py — orchestrates dataset → train → optimizer → evaluation → report + __main__.py — CLI entry point, `python -m pipelines.retraining` + Dockerfile — CPU-only torch, repo-root build context (see below) + pyproject.toml +``` + +Mirrors `pipelines/optimizer/`'s shape deliberately — `pipeline.py`'s `run()` +returns a `report_path`, same as the optimizer's `run()`; `__main__.py` +exists as a separate file from `pipeline.py` for the same reason the +optimizer split them (`python -m pipelines.retraining`, not +`python -m pipelines.retraining.pipeline`). + +--- + +## `dataset.py` — where the training data actually comes from + +```python +def build_dataset(db, initial_dataset_path=None, sample_size=500, seed=0) -> dict: + accepted = _load_accepted(db) # flagged_content WHERE training_decision = 'accepted' + initial = _load_initial_sample(...) # optional CSV, same shape as datasets/test_dataset.csv + combined = accepted + initial + ... + return {"train": train, "val": val, "sources": {...}} +``` + +**No initial dataset ships with this repo, and that's deliberate.** +`datasets/test_dataset.csv` is `pipelines/evaluation`'s held-out set — every +retraining run benchmarks the resulting model against it, so sampling from +it here would let the model see (a sample of) the exact data it's later +graded against, inflating the accuracy number the quality gate trusts. +`initial_dataset_path` is optional, `None` by default; when given, it must +be a CSV with the same `raw_text,label` columns `datasets/eval_holdout.py` +already parses — whatever gets dropped in later just works, no new format +to design. + +**Plain shuffle + 85/15 split, not stratified** — same reasoning +`datasets/eval_holdout.py` uses for its own sampling: with the input +already reasonably balanced (stream processor's `SAFE_SAMPLE_RATE` keeps it +from skewing all-harm), a random split stays balanced in expectation +without needing explicit stratification logic at this data scale. + +**`db.flagged_content.find({"training_decision": "accepted"}, {"input_text": 1, "manual_label": 1})`** +— reads `manual_label`, the human's decision, not `label` (the model's own +classification that flagged the document in the first place). Training on +the model's own predictions would just teach it to keep agreeing with +itself; the whole point of the manual-labelling step +(`services/label-ui`) is a human correcting or confirming that label before +it's trusted as ground truth. + +--- + +## `train.py` — fine-tuning + full MLflow logging + +### Always restarts from the base model, never from a previous fine-tune + +```python +model = AutoModelForSequenceClassification.from_pretrained( + base_model_id, num_labels=2, id2label=ID2LABEL, label2id=LABEL2ID, + ignore_mismatched_sizes=True, +) +``` + +`model_registry` only ever stores **ONNX artifacts** (for serving), never a +resumable HuggingFace checkpoint — there is nothing to fine-tune *from* +except the original base model. Every retrain therefore trains the base +model fresh on the **full accumulated** accepted-label set, not an +incremental fine-tune-of-a-fine-tune. + +**`num_labels=2` + `ignore_mismatched_sizes=True` is a deliberate, +documented simplification.** The base checkpoint's exact original +classification-head shape (a 1-logit sigmoid head vs. a 2-class softmax +head) isn't knowable without a live fetch from the HuggingFace Hub, so this +pins a standard 2-class head and lets HF silently reinitialize it if the +checkpoint's actual head doesn't match. Confirmed live — the console log +for `VijayRam1812/content-classifier-roberta` reads: +``` +Some weights of RobertaForSequenceClassification were not initialized from +the model checkpoint ... because the shapes did not match: +- classifier.out_proj.bias: found shape torch.Size([1]) ... torch.Size([2]) +``` +This means a from-scratch classification head needs enough data and epochs +to learn a good decision boundary — it isn't continuing the original +checkpoint's already-tuned boundary, it's learning a new one. With only a +handful of labelled examples (as in early testing — 40 accepted docs), this +produces a genuinely bad model (50% accuracy, i.e. a coin flip) — which is +exactly what the quality gate downstream is for. See +[`../../orchestration/explanation.md`](../../orchestration/explanation.md)'s +`decide_promotion` section for that gate actually catching this live. + +### Dynamic per-batch padding, not fixed-length — the actual OOM fix + +```python +class _TextDataset(Dataset): + def __getitem__(self, idx: int) -> dict: + enc = self.tokenizer(self.texts[idx], truncation=True, max_length=self.max_length) + enc["labels"] = self.labels[idx] + return enc + +... +trainer = Trainer( + ..., + data_collator=DataCollatorWithPadding(tokenizer), +) +``` + +The first version of this file padded every example individually to a +fixed `max_length=512` inside `__getitem__` (`padding="max_length"`). This +OOM-killed the retraining pod (`exit_code: 137, reason: OOMKilled`) at +*both* a 4Gi and an 8Gi memory limit, live-reproduced repeatedly. Root +cause: transformer attention cost scales `O(seq_len²)`, and +`flagged_content` spans (chat prompts/responses) are mostly far shorter +than 512 tokens — padding every one of them up to 512 anyway wastes +enormous memory for no accuracy benefit. This is the exact same class of +bug already found and fixed once in `services/classifier/model.py` (see +that file's explanation.md) — it's a natural mistake to reach for `padding= +"max_length"` because it's the simplest thing that works on any single +input, and the memory cost only shows up under batched training/inference. +**The fix is `DataCollatorWithPadding`**: `_TextDataset` returns +variable-length token sequences, and the collator pads each *batch* only to +that batch's own longest example — dramatically less wasted computation for +short-text data. + +**No traceback ever appeared for this bug in any log** — `SIGKILL` (what +the kernel OOM-killer sends) gives a process zero chance to flush stdout. +The only way this was actually diagnosed was reading Airflow's *persisted* +task log file, which records the pod's final Kubernetes container status +independent of whatever the container itself managed to print. See +[`../../orchestration/explanation.md`](../../orchestration/explanation.md)'s +debugging-gotcha section for the exact command. + +### Per-epoch train *and* eval metrics — HF Trainer only gives you eval for free + +```python +class _TrainMetricsCallback(TrainerCallback): + def on_epoch_end(self, args, state, control, **kwargs): + trainer = self._trainer_ref[0] + output = trainer.predict(self.train_dataset, metric_key_prefix="train") + mlflow.log_metrics({"train_loss": output.metrics["train_loss"], ...}, step=epoch) +``` + +`Trainer` with `eval_strategy="epoch"` automatically scores `eval_dataset` +every epoch and calls `compute_metrics` on the result — but it never does +this for the *training* set, since re-scoring your own training data isn't +normally something you need. This project explicitly wants both (the user +asked for training loss/accuracy/precision/recall/F1 *and* the same for +eval, at each epoch and at the end), so a `TrainerCallback.on_epoch_end` +runs one extra full forward pass over the train split each epoch — +`metric_key_prefix="train"` makes `predict()`'s own internal +`compute_metrics` call come back with keys already named `train_loss`, +`train_accuracy`, etc., instead of the default `test_*` prefix. + +**`trainer_ref` is a one-element list, not a direct reference** — the +callback object has to be constructed *before* passing it into +`Trainer(callbacks=[...])`, but it needs to call methods on that same +`Trainer` once training starts. A mutable one-element list is populated +with the real `Trainer` instance immediately after construction +(`trainer_ref[0] = trainer`), giving the callback a way to reach an object +that didn't exist yet when the callback itself was built. + +### No scikit-learn — confusion-matrix arithmetic instead + +```python +def _metrics_from_confusion(tp, tn, fp, fn) -> dict: + accuracy = (tp + tn) / max(tp + tn + fp + fn, 1) + precision = tp / max(tp + fp, 1) + recall = tp / max(tp + fn, 1) + f1 = 2 * precision * recall / max(precision + recall, 1e-9) +``` + +Matches `pipelines/evaluation/benchmark.py`'s existing no-sklearn +convention (that file computes AUC-ROC via a rank-based Mann-Whitney U +formula for the same reason) — a handful of confusion-matrix counts don't +justify pulling in a second, heavier metrics dependency alongside torch/ +transformers/mlflow, which this package already needs regardless. + +### `report_to=[]` + `disable_tqdm=True` — logging is manual, and quiet + +```python +args = TrainingArguments( + ..., + report_to=[], # avoid double-logging via Trainer's own MLflow auto-integration + disable_tqdm=True, +) +``` + +`report_to=[]` disables `Trainer`'s own built-in MLflow callback — without +it, `Trainer` would log its own version of these metrics to whatever MLflow +run happens to be active, duplicating (and potentially conflicting with) +the explicit `mlflow.log_metrics()` calls this file already makes. +`disable_tqdm=True` was added chasing what looked like a `kubectl logs -f` +streaming issue (tqdm's `\r`-based progress bar seemed like a plausible +cause of "new lines stop appearing"); it turned out the real cause both +before and after this change was the OOM kill above, not tqdm — but +disabling a meaningless progress bar in a headless pod's logs is a +reasonable thing to do regardless, so it stayed. + +### What doesn't get logged to MLflow, and why + +```python +checkpoint_dir = output_dir / "checkpoint" +trainer.save_model(str(checkpoint_dir)) +tokenizer.save_pretrained(str(checkpoint_dir)) +mlflow.set_tag("checkpoint_path", str(checkpoint_dir)) # not mlflow.log_artifact(...) +``` + +The fine-tuned checkpoint is saved to local disk and referenced by a plain +MLflow **tag** (a string), not uploaded as an MLflow **artifact**. MinIO +already becomes the artifact store of record once `pipelines/optimizer` +uploads the ONNX conversion under `model_registry.model_path` — logging the +same model's weights a second time (as a raw PyTorch checkpoint, in +MLflow's own S3 artifact store) would mean two systems of record for what +is conceptually one model version. + +--- + +## `pipeline.py` — orchestration, reusing everything downstream unchanged + +```python +mlflow.set_experiment("sentinel-retraining") +with mlflow.start_run(run_name=run_id) as mlflow_run: + train_result = train(dataset, base_model_id, run_artifacts / "finetuned", epochs=epochs) + checkpoint_dir = train_result.pop("checkpoint_dir") + +optimizer_report_path = run_optimizer(model_id=str(checkpoint_dir), output_dir=output_dir, log_dir=log_dir) +... +benchmark_report = run_benchmark(model_dir=str(int8_dir)) +gate_passed, reasons = validate(benchmark_report) +``` + +**The fine-tuned checkpoint plugs into `pipelines/optimizer` with zero +changes to that package.** `run_optimizer(model_id=...)` passes `model_id` +straight through to `optimum.exporters.onnx.main_export()`, which accepts a +local directory path exactly as readily as a HuggingFace Hub id — the +export/optimize/quantize/upload/register chain has no idea (and doesn't +need to know) whether `model_id` came from the Hub or from a fine-tuning +run five seconds ago in the same process. + +**Same pattern for `pipelines/evaluation`** — `run_benchmark(model_dir=...)` +and `validate(...)` are plain importable functions, called directly rather +than shelled out to a subprocess, exactly the way `pipelines/optimizer/ +pipeline.py` calls its own stage functions (`export()`, `optimize()`, +`quantize()`) as direct Python calls. + +**Registers as `'staging'`, never `'active'`** — `run_optimizer`'s own +`registry.py` always inserts new rows as `'staging'` (see that package's +explanation.md); this pipeline doesn't touch that behavior. Promotion to +`'active'` happens exactly once, in `orchestration/retrain_dag.py`'s +`decide_promotion` task, and only if `gate_passed` is `True`. + +### The XCom handoff — no Airflow import in this package + +```python +xcom_dir = Path("/airflow/xcom") +if xcom_dir.exists(): + (xcom_dir / "return.json").write_text(json.dumps(report)) +``` + +`orchestration/retrain_dag.py`'s `KubernetesPodOperator` (`do_xcom_push=True`) +mounts a sidecar container at `/airflow/xcom` and tails whatever gets +written to `return.json` there, making it available to downstream tasks via +Airflow's normal XCom mechanism. This package doesn't import anything +Airflow-specific to participate in that — it just checks whether that path +exists and writes to it if so. Running `python -m pipelines.retraining` +directly from a shell (no Airflow involved at all) skips this block +entirely and works exactly the same otherwise; the DAG is a caller, not a +dependency. + +--- + +## `Dockerfile` — CPU-only torch, repo-root build context + +```dockerfile +# Build context MUST be the repo root, not this directory: +# docker build -f pipelines/retraining/Dockerfile -t sentinel-retraining:local . +FROM python:3.12-slim +... +RUN uv pip install --python /app/.venv/bin/python \ + --index-url https://download.pytorch.org/whl/cpu "torch>=2.2" && \ + uv pip install --python /app/.venv/bin/python \ + "transformers>=4.48" "accelerate>=0.26.0" "mlflow>=2.19" ... +COPY pipelines /app/pipelines +COPY datasets /app/datasets +``` + +**Repo-root build context, unlike every other Dockerfile in this repo.** +`pipeline.py` imports `pipelines.optimizer`, `pipelines.evaluation`, and +(transitively, via `benchmark.py`) `datasets.eval_holdout` directly — all +three trees need to be present in the image, not just this package's own +files. `drift/`'s Dockerfile is self-contained by contrast because +`drift_job.py` doesn't import across package boundaries the same way. + +**CPU-only torch wheel, installed explicitly via `--index-url .../whl/cpu`** +— this pod runs inside k3d, which has no GPU passthrough configured (real +production systems run retraining on separately-provisioned, +training-suitable compute — a dedicated GPU node pool or a managed training +service — rather than sharing a serving cluster's resources; adding that +here would be new infra beyond this project's current phase). Installing +the default (CUDA) wheel would pull in several GB of CUDA runtime this pod +can never use. + +**`accelerate>=0.26.0` is a hard requirement, not optional** — +`transformers.Trainer` raises `ImportError: Using the Trainer with PyTorch +requires accelerate>=0.26.0` at the moment `Trainer(...)` is constructed if +it's missing. Easy to miss when writing the code locally against an +environment that already has it installed as some other package's +dependency; only surfaced once this ran inside the minimal container image. + +--- + +## Live-tuned pod resources (set in `orchestration/retrain_dag.py`, not here) + +This package has no opinion on how much CPU/memory it gets — that's the +DAG's `KubernetesPodOperator.container_resources`. Worth knowing when +debugging a failure in *this* code that's actually a resource-limit +problem one layer up: see +[`../../orchestration/explanation.md`](../../orchestration/explanation.md)'s +`run_retraining` section for the full three-round tuning story (memory +OOM → the padding fix above → a separate CPU-limit issue that made +`pipelines/evaluation/benchmark.py`'s ONNX Runtime session thrash against +too few cores). + +--- + +## Environment variables / CLI arguments + +| Variable / flag | Default | Effect | +|---|---|---| +| `MONGO_URI` / `--mongo-uri` | `mongodb://sentinel:sentinel@localhost:27017/sentinel` | Source of accepted `flagged_content` | +| `BASE_MODEL_ID` / `--base-model-id` | `VijayRam1812/content-classifier-roberta` | HF Hub id to fine-tune from | +| `--output-dir` | *(required)* | Where checkpoints + ONNX artifacts land | +| `--log-dir` | `logs` | Where `report.json` lands | +| `INITIAL_DATASET_PATH` / `--initial-dataset-path` | `None` | Optional CSV sample, see `dataset.py` above | +| `--sample-size` | `500` | Max rows drawn from the initial CSV, if given | +| `--epochs` | `3` | Fine-tuning epochs | +| `DATABASE_URL` | *(read by `pipelines.optimizer.registry`)* | Where the new `model_registry` row gets inserted | +| `MINIO_ENDPOINT`/`MINIO_ACCESS_KEY`/`MINIO_SECRET_KEY` | *(read by `pipelines.optimizer.upload`)* | Where the ONNX artifacts get uploaded | +| `MLFLOW_TRACKING_URI` | *(read by the `mlflow` client itself)* | Where the fine-tuning run gets logged | + +--- + +## Tips and tricks + +**Run the whole pipeline locally, outside Airflow entirely** (useful for +iterating on `train.py` without a K8s pod round-trip each time): +```bash +uv run --package sentinel-retraining python -m pipelines.retraining \ + --output-dir artifacts --log-dir logs --epochs 1 +``` + +**Check a run's full report without digging through MLflow's UI:** +```bash +cat logs/retraining//report.json | python3 -m json.tool +``` + +**Confirm the dynamic-padding fix is actually in effect** (i.e. you're not +looking at a stale image): a fixed-`max_length` tokenizer call pads every +batch to the same shape regardless of content, so a quick way to check +without reading source is to compare wall-clock time for a tiny 1-epoch run +before/after — dynamic padding should be visibly faster on short text. + +**Watch a live run's logs without the `kubectl logs -f` streaming issue** +described above (and in `orchestration/explanation.md`) — prefer polling +snapshots over `-f`: +```bash +watch -n 5 'kubectl logs -n sentinel-pipeline -c base --tail=20' +``` diff --git a/pipelines/retraining/pipeline.py b/pipelines/retraining/pipeline.py new file mode 100644 index 0000000..f02fb98 --- /dev/null +++ b/pipelines/retraining/pipeline.py @@ -0,0 +1,193 @@ +"""Orchestrates the retraining pipeline: build the fine-tuning dataset from +manually-labelled flagged_content (+ optional initial sample), fine-tune, +then hand off to the existing optimizer (ONNX export/quantize/register-as- +staging) and evaluation (quality gate) pipelines unchanged — same shape as +pipelines/optimizer/pipeline.py's run(). +""" + +import json +import logging +import uuid +from datetime import datetime, timezone +from pathlib import Path + +import mlflow +import psycopg +import pymongo + +from pipelines.evaluation.benchmark import run as run_benchmark +from pipelines.evaluation.validate import validate +from pipelines.optimizer.pipeline import run as run_optimizer +from pipelines.optimizer.registry import DSN +from pipelines.optimizer.upload import download_report, upload_benchmark_report +from pipelines.retraining.dataset import build_dataset +from pipelines.retraining.train import train + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s — %(message)s", +) +logger = logging.getLogger(__name__) + + +def _get_baseline_report() -> dict | None: + """Best-effort lookup of the currently active model's stored benchmark + report, to pass as validate()'s regression baseline. + + Every failure path here returns None rather than raising, and None is + exactly what validate() already treats as "skip the regression check, + apply only the absolute accuracy floor" — the safe, correct behavior + for the very first retrain ever (no active model to compare against + yet) is unchanged. What this adds is the regression check actually + running once there IS an active model with a stored report — without + it, an unattended drift-triggered retrain (orchestration/drift_dag.py) + could promote a model that regressed accuracy relative to the one + already serving traffic, since only the absolute floor gated it. + """ + try: + with psycopg.connect(DSN) as conn: + row = conn.execute( + "SELECT model_path FROM model_registry WHERE status = 'active' LIMIT 1" + ).fetchone() + except psycopg.Error: + logger.warning("Could not query model_registry for a baseline model", exc_info=True) + return None + + if row is None: + logger.info("No active model yet — nothing to regression-check against") + return None + + active_model_path = row[0] + if active_model_path.startswith("/"): + # Local-fallback path (MinIO was unreachable when that model was + # optimized) — no MinIO run_id to look up a stored report under. + logger.warning("Active model_path is a local fallback path — no stored baseline available") + return None + + # active_model_path is "models//int8/model_quantized.onnx". + run_id = active_model_path.split("/")[1] + baseline = download_report(run_id, "benchmark_report.json") + if baseline is None: + logger.warning("No stored benchmark_report.json for active model run_id=%s", run_id) + return baseline + + +def run( + mongo_uri: str, + base_model_id: str, + output_dir: str, + log_dir: str = "logs", + initial_dataset_path: str | None = None, + sample_size: int = 500, + epochs: int = 3, +) -> Path: + run_id = str(uuid.uuid4()) + run_artifacts = Path(output_dir) / run_id + run_artifacts.mkdir(parents=True, exist_ok=True) + run_log = Path(log_dir) / "retraining" / run_id + run_log.mkdir(parents=True, exist_ok=True) + + logger.info("Starting retraining pipeline | run_id=%s", run_id) + + mongo_db = pymongo.MongoClient(mongo_uri).get_default_database() + try: + dataset = build_dataset(mongo_db, initial_dataset_path, sample_size) + except ValueError as exc: + # No accepted training data yet — a real, expected condition on a + # fresh deployment or whenever orchestration/drift_dag.py's hourly + # schedule fires automatically before any operator has used + # services/label-ui. Reported as a clean gate failure instead of + # letting the exception crash the task: retrain_dag.py's + # decide_promotion already handles gate_passed=False correctly + # (raises its own clear error, no promotion, rollout_restart + # skipped) — this keeps that single, already-correct failure path + # instead of adding a second, differently-shaped one. + logger.warning("Cannot build training dataset — %s", exc) + report = { + "run_id": run_id, + "gate_passed": False, + "gate_reasons": [str(exc)], + "completed_at": datetime.now(timezone.utc).isoformat(), + } + report_path = run_log / "report.json" + report_path.write_text(json.dumps(report, indent=2)) + xcom_dir = Path("/airflow/xcom") + if xcom_dir.exists(): + (xcom_dir / "return.json").write_text(json.dumps(report)) + logger.info( + "Retraining pipeline stopped early | run_id=%s | reason=no training data", run_id + ) + return report_path + + # MLFLOW_TRACKING_URI is env-var driven (mlflow's client reads it + # automatically) rather than set here, matching how DATABASE_URL/ + # MONGO_URI are threaded through as plain args/env everywhere else in + # this repo rather than hardcoded. + mlflow.set_experiment("sentinel-retraining") + with mlflow.start_run(run_name=run_id) as mlflow_run: + mlflow_run_id = mlflow_run.info.run_id + train_result = train(dataset, base_model_id, run_artifacts / "finetuned", epochs=epochs) + checkpoint_dir = train_result.pop("checkpoint_dir") + + # Reuses pipelines/optimizer unchanged — the fine-tuned checkpoint plugs + # in as model_id since export() passes it straight to optimum's + # main_export(), which accepts a local directory just as readily as a + # HuggingFace hub id. Registers as 'staging' — promotion to 'active' is + # retrain_dag.py's job after the quality gate below passes. + optimizer_report_path = run_optimizer( + model_id=str(checkpoint_dir), output_dir=output_dir, log_dir=log_dir + ) + optimizer_report = json.loads(optimizer_report_path.read_text()) + model_path = optimizer_report["model_path"] + int8_dir = Path(optimizer_report["stages"]["quantize"]["output"]) + + benchmark_report = run_benchmark(model_dir=str(int8_dir)) + + # Uploaded under this run's own model_version so a LATER retrain can use + # it as a regression baseline (see _get_baseline_report) without ever + # needing to re-run inference against an old model. + benchmark_report_path = run_log / "benchmark_report.json" + benchmark_report_path.write_text(json.dumps(benchmark_report, indent=2)) + upload_benchmark_report(optimizer_report["run_id"], benchmark_report_path) + + baseline_report = _get_baseline_report() + gate_passed, reasons = validate(benchmark_report, baseline=baseline_report) + + report = { + "run_id": run_id, + "mlflow_run_id": mlflow_run_id, + "base_model_id": base_model_id, + "dataset_sources": dataset["sources"], + "train_size": len(dataset["train"]), + "val_size": len(dataset["val"]), + "final_train_eval_metrics": train_result, + "benchmark": benchmark_report, + "baseline_accuracy": baseline_report["accuracy"] if baseline_report else None, + "gate_passed": gate_passed, + "gate_reasons": reasons, + "model_version": optimizer_report["run_id"], + "model_path": model_path, + "completed_at": datetime.now(timezone.utc).isoformat(), + } + + report_path = run_log / "report.json" + report_path.write_text(json.dumps(report, indent=2)) + + # KubernetesPodOperator's XCom sidecar tails this exact path — a plain + # existence check, no Airflow import needed, so this pipeline stays a + # normal standalone script runnable outside Airflow too. + xcom_dir = Path("/airflow/xcom") + if xcom_dir.exists(): + (xcom_dir / "return.json").write_text(json.dumps(report)) + + logger.info( + "Retraining pipeline complete | run_id=%s | gate_passed=%s | model_version=%s", + run_id, + gate_passed, + report["model_version"], + ) + return report_path + + +# CLI entry point lives in pipelines/retraining/__main__.py — run via +# `python -m pipelines.retraining` rather than `python -m pipelines.retraining.pipeline`. diff --git a/pipelines/retraining/pyproject.toml b/pipelines/retraining/pyproject.toml new file mode 100644 index 0000000..bf6d4f2 --- /dev/null +++ b/pipelines/retraining/pyproject.toml @@ -0,0 +1,19 @@ +[project] +name = "sentinel-retraining" +version = "0.1.0" +requires-python = ">=3.12" +dependencies = [ + "torch>=2.2", + "transformers>=4.48", + "accelerate>=0.26.0", + "mlflow>=2.19", + "pymongo>=4.9", + "numpy>=2.0", + "psycopg[binary]>=3.2", + "sentinel-optimizer", + "sentinel-evaluation", +] + +[tool.uv.sources] +sentinel-optimizer = { workspace = true } +sentinel-evaluation = { workspace = true } diff --git a/pipelines/retraining/train.py b/pipelines/retraining/train.py new file mode 100644 index 0000000..323e67a --- /dev/null +++ b/pipelines/retraining/train.py @@ -0,0 +1,251 @@ +"""Fine-tunes the content classifier and logs the run to MLflow: dataset +sources/sizes, training config, and per-epoch + final train/eval loss, +accuracy, precision, recall, F1. + +Precision/recall/F1 are computed from confusion-matrix counts, not +scikit-learn — matches pipelines/evaluation/benchmark.py's existing +no-sklearn convention (this package doesn't want a second, heavier metrics +dependency when tp/fp/fn arithmetic is a few lines). +""" + +import logging +import random +import time +from pathlib import Path + +import mlflow +import numpy as np +from torch.utils.data import Dataset, Subset +from transformers import ( + AutoModelForSequenceClassification, + AutoTokenizer, + DataCollatorWithPadding, + Trainer, + TrainerCallback, + TrainingArguments, +) + +logger = logging.getLogger(__name__) + +ID2LABEL = {0: "safe", 1: "harm"} +LABEL2ID = {"safe": 0, "harm": 1} + +# _TrainMetricsCallback's per-epoch forward pass over the train split is +# pure logging overhead, not part of the actual training step — its cost +# must not scale unbounded with the training set size. This was part of +# the root cause of an earlier OOM (see orchestration/retrain_dag.py's +# container_resources comment); the real fix was dynamic per-batch padding +# (this file's DataCollatorWithPadding), but capping the sample size here +# bounds the remaining overhead regardless of how large the accepted-label +# set grows in the future. +MAX_TRAIN_METRICS_SAMPLES = 200 + + +class _TextDataset(Dataset): + def __init__(self, pairs: list[tuple[str, str]], tokenizer, max_length: int = 512): + self.texts = [t for t, _ in pairs] + self.labels = [LABEL2ID[label] for _, label in pairs] + self.tokenizer = tokenizer + self.max_length = max_length + + def __len__(self) -> int: + return len(self.texts) + + def __getitem__(self, idx: int) -> dict: + # No padding here — DataCollatorWithPadding pads per-batch to the + # longest example in that batch, not a fixed max_length. Padding + # every example to 512 tokens individually (the previous approach) + # OOM-killed the pod at both 4Gi and 8Gi limits (live-reproduced, + # exit_code 137/OOMKilled) — flagged_content spans are mostly much + # shorter than 512 tokens, so uniform max-length padding wastes + # enormous memory on attention's O(seq_len^2) scaling for no reason. + # Same class of bug already documented and fixed once in + # services/classifier/model.py — see that file's explanation.md. + enc = self.tokenizer(self.texts[idx], truncation=True, max_length=self.max_length) + enc["labels"] = self.labels[idx] + return enc + + +def _metrics_from_confusion(tp: int, tn: int, fp: int, fn: int) -> dict: + accuracy = (tp + tn) / max(tp + tn + fp + fn, 1) + precision = tp / max(tp + fp, 1) + recall = tp / max(tp + fn, 1) + f1 = 2 * precision * recall / max(precision + recall, 1e-9) + return {"accuracy": accuracy, "precision": precision, "recall": recall, "f1": f1} + + +def _compute_metrics(eval_pred) -> dict: + logits, labels = eval_pred + preds = np.argmax(logits, axis=-1) + labels = np.array(labels) + tp = int(((preds == 1) & (labels == 1)).sum()) + tn = int(((preds == 0) & (labels == 0)).sum()) + fp = int(((preds == 1) & (labels == 0)).sum()) + fn = int(((preds == 0) & (labels == 1)).sum()) + return _metrics_from_confusion(tp, tn, fp, fn) + + +class _TrainMetricsCallback(TrainerCallback): + """HF Trainer only auto-evaluates eval_dataset each epoch — this scores + the train split too (metric_key_prefix="train" so Trainer's own + compute_metrics call comes back pre-labelled train_accuracy/train_f1/...), + so both train_* and eval_* land in MLflow for every epoch. + + Scores a fixed subset of at most MAX_TRAIN_METRICS_SAMPLES, not the + full train split — this is a diagnostic forward pass with no bearing + on the actual training step, and its cost shouldn't grow unbounded + with the training set. The same fixed subset (seeded, chosen once at + construction) is reused every epoch so metric trends across epochs are + comparable against a consistent sample rather than a new random draw + each time. + + trainer_ref is a one-element list populated after Trainer construction — + the callback has to be built before the Trainer it references exists. + """ + + def __init__(self, trainer_ref: list, train_dataset): + self._trainer_ref = trainer_ref + if len(train_dataset) > MAX_TRAIN_METRICS_SAMPLES: + indices = random.Random(0).sample(range(len(train_dataset)), MAX_TRAIN_METRICS_SAMPLES) + self.train_dataset = Subset(train_dataset, indices) + else: + self.train_dataset = train_dataset + + def on_epoch_end(self, args, state, control, **kwargs): + trainer = self._trainer_ref[0] + output = trainer.predict(self.train_dataset, metric_key_prefix="train") + epoch = int(state.epoch) if state.epoch is not None else 0 + metrics = { + "train_loss": output.metrics["train_loss"], + "train_accuracy": output.metrics["train_accuracy"], + "train_precision": output.metrics["train_precision"], + "train_recall": output.metrics["train_recall"], + "train_f1": output.metrics["train_f1"], + } + mlflow.log_metrics(metrics, step=epoch) + logger.info("Epoch %d train metrics: %s", epoch, metrics) + + +def train( + dataset: dict, + base_model_id: str, + output_dir: Path, + epochs: int = 3, + batch_size: int = 8, + learning_rate: float = 2e-5, +) -> dict: + """Fine-tunes base_model_id on dataset['train'], evaluates on + dataset['val'] each epoch, logs everything to the caller's active MLflow + run (caller owns mlflow.start_run()), and saves the checkpoint to + output_dir. Returns final eval metrics plus checkpoint_dir. + + Always restarts from base_model_id rather than a previous fine-tune — + model_registry only stores ONNX artifacts (not resumable HF + checkpoints), so there is nothing to resume from; every retrain fine- + tunes the base model on the full accumulated accepted-label set instead. + num_labels=2 + ignore_mismatched_sizes=True: the base checkpoint's exact + original head shape (1-logit sigmoid vs 2-class softmax) isn't known + without a live hub fetch, so this pins a standard 2-class head and lets + HF reinitialize it if the base checkpoint's head doesn't already match — + a deliberate simplification for this phase, noted here since it means a + from-scratch head needs enough data/epochs to learn a good boundary + rather than continuing the original checkpoint's exact decision boundary. + """ + tokenizer = AutoTokenizer.from_pretrained(base_model_id) + model = AutoModelForSequenceClassification.from_pretrained( + base_model_id, + num_labels=2, + id2label=ID2LABEL, + label2id=LABEL2ID, + ignore_mismatched_sizes=True, + ) + + train_ds = _TextDataset(dataset["train"], tokenizer) + val_ds = _TextDataset(dataset["val"], tokenizer) + + mlflow.log_params( + { + "base_model_id": base_model_id, + "epochs": epochs, + "batch_size": batch_size, + "learning_rate": learning_rate, + "train_size": len(train_ds), + "val_size": len(val_ds), + **{f"source_{k}": v for k, v in dataset["sources"].items()}, + } + ) + + args = TrainingArguments( + output_dir=str(output_dir / "checkpoints"), + num_train_epochs=epochs, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + learning_rate=learning_rate, + eval_strategy="epoch", + logging_strategy="epoch", + save_strategy="no", # final checkpoint saved explicitly below + report_to=[], # logging to MLflow is manual — avoid double-logging via Trainer's own auto-integration + # tqdm's progress bar redraws a single line via \r rather than + # emitting newlines — in a headless pod, that confuses line-based + # log streaming (kubectl logs -f appeared to "freeze" mid-training + # with no new lines, live-reproduced) and is meaningless with no + # terminal to render it in anyway. + disable_tqdm=True, + ) + + trainer_ref: list = [None] + trainer = Trainer( + model=model, + args=args, + train_dataset=train_ds, + eval_dataset=val_ds, + # Pads each batch to its own longest example, not a fixed 512 — + # pairs with _TextDataset no longer padding in __getitem__. + data_collator=DataCollatorWithPadding(tokenizer), + compute_metrics=_compute_metrics, + callbacks=[_TrainMetricsCallback(trainer_ref, train_ds)], + ) + trainer_ref[0] = trainer + + t0 = time.perf_counter() + trainer.train() + training_time_s = time.perf_counter() - t0 + + # report_to=[] disabled Trainer's own MLflow auto-integration, so the + # per-epoch eval history (loss + compute_metrics output, "eval_" + # prefixed) is logged explicitly here from log_history instead. + eval_entries = [e for e in trainer.state.log_history if "eval_loss" in e] + for entry in eval_entries: + epoch = int(entry.get("epoch", 0)) + mlflow.log_metrics( + { + "eval_loss": entry["eval_loss"], + "eval_accuracy": entry.get("eval_accuracy", 0.0), + "eval_precision": entry.get("eval_precision", 0.0), + "eval_recall": entry.get("eval_recall", 0.0), + "eval_f1": entry.get("eval_f1", 0.0), + }, + step=epoch, + ) + + final_eval = eval_entries[-1] if eval_entries else {} + final_metrics = { + "eval_loss": final_eval.get("eval_loss", 0.0), + "eval_accuracy": final_eval.get("eval_accuracy", 0.0), + "eval_precision": final_eval.get("eval_precision", 0.0), + "eval_recall": final_eval.get("eval_recall", 0.0), + "eval_f1": final_eval.get("eval_f1", 0.0), + "training_time_s": training_time_s, + } + mlflow.log_metrics(final_metrics) # no step= — this is the run's summary metric set + + checkpoint_dir = output_dir / "checkpoint" + trainer.save_model(str(checkpoint_dir)) + tokenizer.save_pretrained(str(checkpoint_dir)) + # Not logged as an MLflow artifact — that would duplicate MinIO's role + # once the optimizer pipeline uploads the ONNX conversion. A tag keeps + # the run traceable back to the checkpoint without storing it twice. + mlflow.set_tag("checkpoint_path", str(checkpoint_dir)) + + logger.info("Training complete | %.1fs | final=%s", training_time_s, final_metrics) + return {"checkpoint_dir": checkpoint_dir, **final_metrics} diff --git a/pyproject.toml b/pyproject.toml index 650b38d..ec840fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ dependencies = [ ] [tool.uv.workspace] -members = ["services/*", "pipelines/optimizer", "pipelines/evaluation"] +members = ["services/*", "pipelines/optimizer", "pipelines/evaluation", "pipelines/retraining"] # CUDA 13.0 wheels — matches the RTX 4070 driver (CUDA Version: 13.0) [[tool.uv.index]] diff --git a/scripts/dev-start.sh b/scripts/dev-start.sh index 3c86302..8f4c198 100755 --- a/scripts/dev-start.sh +++ b/scripts/dev-start.sh @@ -4,12 +4,13 @@ # What this does: # 1. Ensures the k3d cluster is running (creates it if needed) # 2. Builds classifier + stream-processor Docker images and imports into k3d -# 3. Runs terraform apply (all infra + app deployments) -# 4. Waits for data-layer pods to pass readiness probes -# 5. Opens port-forwards for every service -# 6. Runs schema migration and model auto-bootstrap -# 7. Restarts app deployments so they pick up the bootstrapped model -# 8. Waits for classifier + stream-processor pods to become ready +# 3. Runs terraform apply (all infra + app deployments, incl. Airflow) +# 4. Waits for data-layer pods (+ Airflow scheduler/webserver) to pass readiness probes +# 5. Verifies Kafka topic exists and Airflow's DAGs load with no import errors +# 6. Opens port-forwards for every service +# 7. Runs schema migration and model auto-bootstrap +# 8. Restarts app deployments so they pick up the bootstrapped model +# 9. Waits for classifier + stream-processor pods to become ready # # Ctrl-C stops everything cleanly. # @@ -99,6 +100,25 @@ docker build -t sentinel-drift:local "$REPO_ROOT/pipelines/drift/" --quiet k3d image import sentinel-drift:local -c "$CLUSTER" 2>/dev/null info "Drift image imported" +info "Building mlflow image..." +docker build -t sentinel-mlflow:local "$REPO_ROOT/infra/mlflow/" --quiet +k3d image import sentinel-mlflow:local -c "$CLUSTER" 2>/dev/null +info "MLflow image imported" + +info "Building label-ui image..." +docker build -t sentinel-label-ui:local "$REPO_ROOT/services/label-ui/" --quiet +k3d image import sentinel-label-ui:local -c "$CLUSTER" 2>/dev/null +info "Label-UI image imported" + +# Build context is the repo root, not pipelines/retraining/ — pipeline.py +# imports pipelines.optimizer, pipelines.evaluation, and datasets.eval_holdout +# (transitively via benchmark.py) directly, so all three trees need to be in +# the build context. This one takes a while (torch + transformers). +info "Building retraining image (this can take a few minutes)..." +docker build -f "$REPO_ROOT/pipelines/retraining/Dockerfile" -t sentinel-retraining:local "$REPO_ROOT" --quiet +k3d image import sentinel-retraining:local -c "$CLUSTER" 2>/dev/null +info "Retraining image imported" + # ── terraform ───────────────────────────────────────────────────────────────── info "Applying Terraform..." cd "$TF_DIR" @@ -118,6 +138,10 @@ kubectl wait --for=condition=ready pod -l app=prometheus -n sentinel-monitori kubectl wait --for=condition=ready pod -l app=grafana -n sentinel-monitoring --timeout=120s kubectl wait --for=condition=ready pod -l app=jaeger -n sentinel-monitoring --timeout=60s kubectl wait --for=condition=ready pod -l app=otel-collector -n sentinel-monitoring --timeout=60s +kubectl wait --for=condition=ready pod -l release=airflow,component=scheduler -n sentinel-pipeline --timeout=180s +kubectl wait --for=condition=ready pod -l release=airflow,component=webserver -n sentinel-pipeline --timeout=180s +kubectl wait --for=condition=ready pod -l app=mlflow -n sentinel-monitoring --timeout=120s +kubectl wait --for=condition=ready pod -l app=label-ui -n sentinel-app --timeout=120s info "Data-layer pods ready" # ── ensure Kafka topic exists ───────────────────────────────────────────────── @@ -133,6 +157,45 @@ kubectl exec -n sentinel-data kafka-0 -- \ --replication-factor 1 >/dev/null 2>&1 && info "Kafka topic ready" \ || warn "Could not ensure Kafka topic — check kafka-0 pod" +# ── verify Airflow can actually load and run a DAG ──────────────────────────── +# healthcheck_dag.py (mounted from orchestration/ via ConfigMap) is a minimal +# smoke test: if it fails to parse or import here, retrain_dag.py (Phase 7.3) +# won't fare any better. Checks import errors rather than triggering a new +# run every dev-start.sh invocation, which would clutter run history for no +# benefit — the DAG's own logic is trivial enough that "it parses" is already +# a meaningful signal. +info "Verifying Airflow DAGs load correctly..." +_airflow_dag_ok=false +for _ in $(seq 1 20); do + _import_errors=$(kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags list-import-errors 2>/dev/null || true) + if echo "$_import_errors" | grep -q "No data found"; then + _airflow_dag_ok=true + break + fi + sleep 3 +done +if [[ "$_airflow_dag_ok" == true ]]; then + info "Airflow DAGs loaded with no import errors" +else + warn "Airflow DAG import errors detected (or scheduler not ready in time):" + warn "$_import_errors" +fi + +# New DAGs start paused by default — a paused DAG's triggered runs don't +# actually execute tasks, whether triggered manually, via the REST API +# (services/label-ui's "Trigger Retraining" button), or on drift_dag's own +# schedule. Live-verified: retrain_dag sat paused after its first deploy +# this session and silently never ran until unpaused. healthcheck stays +# untouched — it's a one-off smoke test, not meant to run unattended. +info "Unpausing retrain_dag and drift_dag..." +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags unpause retrain_dag >/dev/null 2>&1 \ + && info "retrain_dag unpaused" || warn "Could not unpause retrain_dag" +kubectl exec -n sentinel-pipeline airflow-scheduler-0 -c scheduler -- \ + airflow dags unpause drift_dag >/dev/null 2>&1 \ + && info "drift_dag unpaused (runs hourly)" || warn "Could not unpause drift_dag" + # ── sync PostgreSQL password ─────────────────────────────────────────────────── info "Syncing PostgreSQL password from secret..." PG_PASSWORD=$(kubectl get secret postgresql-credentials -n sentinel-data \ @@ -190,6 +253,20 @@ kubectl port-forward --address=0.0.0.0 -n sentinel-monitoring svc/jaeger 16686:1 kubectl port-forward --address=0.0.0.0 -n sentinel-monitoring svc/otel-collector 4317:4317 4318:4318 \ &>"$PF_DIR/otel-collector.log" & echo $! >"$PF_DIR/otel-collector.pid" +# Local port 8090, not 8080 — k3d's own serverlb container publishes host +# port 8080 -> its internal ingress (0.0.0.0:8080->80/tcp) by default, +# unrelated to anything in this repo. Binding 8080 here silently loses the +# race against it (or fails outright), so the webserver's remote port +# (8080, inside the pod/Service) stays the same; only the local side moves. +kubectl port-forward --address=0.0.0.0 -n sentinel-pipeline svc/airflow-webserver 8090:8080 \ + &>"$PF_DIR/airflow.log" & echo $! >"$PF_DIR/airflow.pid" + +kubectl port-forward --address=0.0.0.0 -n sentinel-monitoring svc/mlflow 5000:5000 \ + &>"$PF_DIR/mlflow.log" & echo $! >"$PF_DIR/mlflow.pid" + +kubectl port-forward --address=0.0.0.0 -n sentinel-app svc/label-ui 8001:8001 \ + &>"$PF_DIR/label-ui.log" & echo $! >"$PF_DIR/label-ui.pid" + # Classifier port-forward — allows local curl/tests against the in-cluster pod. kubectl port-forward --address=0.0.0.0 -n sentinel-app svc/classifier 8000:8000 \ &>"$PF_DIR/classifier.log" & echo $! >"$PF_DIR/classifier.pid" @@ -202,6 +279,9 @@ wait_for_port "Prometheus" 9090 prometheus || true wait_for_port "Grafana" 3000 grafana || true wait_for_port "Jaeger" 16686 jaeger || true wait_for_port "OTel Collector" 4317 otel-collector || true +wait_for_port "Airflow" 8090 airflow || true +wait_for_port "MLflow" 5000 mlflow || true +wait_for_port "Label UI" 8001 label-ui || true # ── schema ───────────────────────────────────────────────────────────────────── # Schema (model_registry, classifications, drift_stats, and all indexes) is @@ -326,6 +406,12 @@ echo " mongo-express → http://localhost:8081" echo "" echo " OTel Collector → grpc://localhost:4317 http://localhost:4318" echo "" +echo " Airflow UI → http://localhost:8090 (admin / sentinel)" +echo " drift_dag runs hourly; retrain_dag triggers from" +echo " drift_dag automatically or from Label UI manually" +echo " MLflow UI → http://localhost:5000" +echo " Label UI → http://localhost:8001" +echo "" echo " PostgreSQL → localhost:5432 (sentinel / sentinel)" echo " MongoDB → localhost:27017 (sentinel / sentinel)" echo "" diff --git a/services/classifier/explanation.md b/services/classifier/explanation.md index 504b908..cdad64d 100644 --- a/services/classifier/explanation.md +++ b/services/classifier/explanation.md @@ -18,8 +18,19 @@ services/classifier/ download.py — MinIO model download with local cache metrics.py — Prometheus metrics and log-to-metric bridge schemas.py — Pydantic request/response models + config.py — pydantic-settings Settings — every tunable env var, one place ``` +All the env-var-driven knobs described throughout this file (`CLASSIFY_THRESHOLD`, +`ORT_INTRA_THREADS`, `MAX_BATCH_SIZE`, `MAX_WAIT_MS`, `MAX_QUEUE_DEPTH`, +`MINIO_*`, `MODEL_PATH`, `DATABASE_URL`) are defined once in `config.py`'s +`Settings` class (pydantic-settings, reads `.env` + real env vars, validates +ranges via `Field(ge=..., le=...)` at process startup instead of failing +deep inside `batcher.py` or `model.py` on first use) and imported as the +single `settings` object everywhere else — `from config import settings`. +`schemas.py`'s `MAX_BATCH_SIZE` constant and `batcher.py`'s batching limit +both read `settings.max_batch_size`, so the two can never drift apart. + --- ## How to run @@ -44,12 +55,20 @@ Defines the wire format for all API endpoints using Pydantic v2. Pydantic validates and coerces incoming JSON before any route handler runs — invalid requests return 422 with field-level error messages, not Python tracebacks. -### `MAX_BATCH_SIZE = 64` +### `MAX_BATCH_SIZE = settings.max_batch_size` + +```python +MAX_BATCH_SIZE = settings.max_batch_size +``` -A module-level constant imported by both the schema (to constrain the request -field) and `batcher.py` (to cap the internal batch size). Defined once so the -two layers can never drift out of sync — if you raise the limit, both enforce -the new value automatically. +Originally a hardcoded `64` in this file. Now reads from `config.py`'s +`Settings` (env var `MAX_BATCH_SIZE`, default `64`) — the same object +`batcher.py`'s `DynamicBatcher` reads for its actual batching limit. Before +this unification, the request-validation cap here and the batcher's runtime +cap were two separate constants that happened to agree; raising one without +the other would have silently let requests larger than the batcher was +tuned for pass validation, or rejected requests the batcher could have +handled fine. One `settings.max_batch_size` value now drives both. ### `ClassifyResult` @@ -74,26 +93,63 @@ single-text response is exactly a result plus per-request metadata. ```python class BatchClassifyRequest(BaseModel): texts: list[str] = Field(min_length=1, max_length=MAX_BATCH_SIZE) - persist: bool = True ``` In Pydantic v2, `min_length`/`max_length` on a `list` field constrain the number -of items, not string length. Sending an empty list or more than 64 texts returns -a 422 before any inference runs. +of items, not string length. Sending an empty list or more than `MAX_BATCH_SIZE` +texts returns a 422 before any inference runs. -**`persist: bool = True`** — added in Phase 5. When the stream processor calls -the batch endpoint, it sets `persist=False` so the classifier skips its own -async PostgreSQL write. The stream processor then writes directly to PG with -`span_id` for idempotency. Any other caller (curl, tests, other services) leaves -this at the default `True` and the classifier persists as it always has. +`/classify/batch` still exists as an internal/testing endpoint, but it is +**no longer the endpoint the stream processor calls** — see the +`/v1/moderations` section below for why, and for how skip-persist is +signaled now that this schema has no `persist` field at all (moved to an +HTTP header specifically so it wouldn't need to live in a +request body schema meant to stay OpenAI-shaped). ### `BatchClassifyResponse` Returns `results` (one per input text, in the same order as the request), `latency_ms` (total wall time for the entire batch), `batch_size` (echo of the input count — lets callers verify they got a result for every input without -counting the array), and `model_version` (which the stream processor extracts -to write to PG). +counting the array), and `model_version`. + +--- + +## OpenAI Moderation API-compatible types (`/v1/moderations`) + +```python +class ModerationRequest(BaseModel): + input: str | Annotated[list[str], Field(min_length=1, max_length=MAX_BATCH_SIZE)] + +class ModerationCategories(BaseModel): + harm: bool + +class ModerationCategoryScores(BaseModel): + harm: float + +class ModerationResult(BaseModel): + flagged: bool + categories: ModerationCategories + category_scores: ModerationCategoryScores + +class ModerationResponse(BaseModel): + id: str # "modr-" + model: str # model version string + results: list[ModerationResult] +``` + +Shaped to match `openai.moderations.create()`'s request/response contract +exactly — `input` accepts either a single string or a list (mirroring the +real API), and the response nests `categories`/`category_scores` per result +the same way OpenAI's does, just with one category (`harm`) instead of +OpenAI's fixed taxonomy. This is the endpoint every caller — internal +(stream processor) and external — is meant to use going forward; +`/classify` and `/classify/batch` remain for direct testing and backwards +compatibility, but are not where new integration work should point. See +`main.py`'s `moderate()` route below for why this schema deliberately has +**no** Sentinel-internal fields (like the old `persist` flag) — a clean +OpenAI-compatible surface with zero fields an external caller would need to +know or care about. --- @@ -102,11 +158,13 @@ to write to PG). Owns the ORT inference session and the tokenizer. Everything outside this file calls `Classifier.predict()` — no other module imports ORT or transformers. -### `_THRESHOLD` and `_INTRA_THREADS` +### Threshold and thread count come from `config.settings`, not raw env vars -Read from environment variables at module import time, before any class is -instantiated. This means they are fixed for the lifetime of the process. To -change them, restart the service. +Read via `config.py`'s `Settings` object at import time, before any class is +instantiated — not `os.environ.get(...)` scattered through this file. This +means they are fixed for the lifetime of the process (to change them, +restart the service), and validated once at startup (`Field(ge=..., le=...)`) +rather than potentially failing deep inside a request. - **`CLASSIFY_THRESHOLD`** (default `0.5`): sigmoid/softmax cutoff. Score ≥ this → `harm`. Raising it makes the classifier more conservative (fewer false @@ -138,19 +196,42 @@ regardless of what directory `uvicorn` is started from. Anchoring paths to same whether you run from `services/classifier/`, `~/projects/sentinel/`, or as a K8s container where the working directory is arbitrary. +### `_deployed_at_from_dir(model_dir)` + +```python +def _deployed_at_from_dir(model_dir: Path) -> str: + onnx_file = next(model_dir.glob("*.onnx"), None) + mtime = onnx_file.stat().st_mtime if onnx_file else None + ts = datetime.fromtimestamp(mtime, tz=timezone.utc) if mtime else datetime.now(timezone.utc) + return ts.strftime("%Y%m%dT%H%M%SZ") +``` + +Extracted as its own helper because both construction paths need "a +`deployed_at` timestamp derived from this directory's `.onnx` file mtime" — +before this existed, that logic was duplicated inline in both +`_resolve_model_dir()`'s `MODEL_PATH` branch and `Classifier.__init__()`'s +"caller already resolved the directory" branch, with the risk of the two +copies drifting (e.g. one handling the "no `.onnx` file yet" case and the +other not). One function, two call sites. + ### `_resolve_model_dir()` Two-path local resolution for when the DB registry is not available: 1. **`MODEL_PATH` env var** — explicit override. Returns the path, derives - `deployed_at` from the ONNX file's mtime, and passes `source_model_id=None` + `deployed_at` via `_deployed_at_from_dir()`, and passes `source_model_id=None` (the HuggingFace ID is not known from just a local path). 2. **Auto-detect from `logs/optimizer/`** — scans for `report.json` files, - sorts by `completed_at` (ISO 8601 strings sort lexicographically so no - parsing is needed), picks the most recent, and reads the quantize stage's - output directory and the original `model_id`. This is the developer - convenience path so you don't need to copy-paste a run ID after every - optimizer run. + parses each **exactly once** into `(report_dict, path)` pairs and sorts + that list by `completed_at` (ISO 8601 strings sort lexicographically so + no date parsing is needed) — an earlier version sorted by a lambda key + that re-opened and re-parsed every file's JSON a second time just to read + the winner back out; parsing once and keeping the parsed dict alongside + its path avoids the redundant I/O and JSON parse for every file in + `logs/optimizer/` on every classifier cold start. Picks the most recent, + and reads the quantize stage's output directory and the original + `model_id`. This is the developer convenience path so you don't need to + copy-paste a run ID after every optimizer run. In normal operation (with `DATABASE_URL` set), the lifespan calls `db.get_active_model()` and passes `model_dir` to the constructor directly — `_resolve_model_dir` is @@ -259,15 +340,35 @@ Subtracting `max(x)` before exp prevents overflow for large logits (e.g., if a logit is 500, `exp(500)` would be inf without the shift). **Tokenizer parameters in `predict()`:** -- `padding=True` — pads all inputs to the longest sequence in the batch. - Without padding, sequences of different lengths cannot form a rectangular - input tensor. +- `padding=True` — pads all inputs to the **longest sequence in the batch**, + not to a fixed length. Without padding, sequences of different lengths + can't form a rectangular input tensor. - `truncation=True, max_length=512` — silently truncates at 512 tokens. RoBERTa's positional embedding table has exactly 512 entries; longer sequences would index out of bounds. - `return_tensors="np"` — returns NumPy arrays, avoiding a PyTorch allocation that ORT would have to convert. +### The `padding="max_length"` experiment, and why it was reverted + +At one point this was changed to `padding="max_length"` — the reasoning was +that padding every batch to a fixed 512 tokens makes inference latency +independent of input length (useful for predictable p99 latency, and a +common recommendation for production ORT serving). **This was tried, and +reverted, after live testing.** With `padding="max_length"`, every batch — +including a batch of short one-sentence inputs — allocates and runs +inference on the full `(batch_size, 512)` tensor shape. Under a sustained +load test (300-trace burst through the real stream processor → classifier +path), the pod hit its 1Gi memory limit and was OOM-killed **twice**, +reproduced live, not a theoretical concern. The code now stays on +`padding=True` (pad to the batch's actual longest sequence) with a comment +explaining the revert. If fixed-shape padding is revisited later, it needs +its own verification pass — e.g. bucketed padding to a few fixed lengths +(64/128/256/512) to get *some* shape predictability without paying full 512 +tokens for short inputs, or raising the pod's memory limit and re-running +the same sustained-load test to confirm it's actually stable — not a +same-session swap back without re-testing under load. + ### `warmup()` Calls `predict(["warmup"])` once during lifespan startup. ORT JIT-compiles the @@ -300,25 +401,36 @@ automatically, with no changes required to how clients call the API. @dataclass class _Pending: text: str - future: asyncio.Future = field(default_factory=asyncio.Future) + future: asyncio.Future ``` Pairs each input text with the `asyncio.Future` that will carry its result back to the waiting route handler. Using `asyncio.Future` (not a queue or event) lets the route handler `await` exactly its own result — no polling, no shared state. -`field(default_factory=asyncio.Future)` — the factory is called per-instance at -construction time. If written as `future: asyncio.Future = asyncio.Future()`, -the `Future()` call would execute at class definition time and every `_Pending` -would share the same Future object (a classic Python mutable-default-argument bug -applied to dataclasses). +No dataclass default here — `submit()` always constructs the `Future` itself +via `loop.create_future()` (needs the *running* event loop, which isn't +available at class-definition time anyway) and passes it in explicitly: +`_Pending(text=text, future=loop.create_future())`. Requiring the caller to +supply it avoids the classic mutable-default-argument trap a +`field(default_factory=asyncio.Future)` default would risk — `asyncio.Future()` +constructed without a running loop binds to whatever loop happens to be +current at import/definition time, not necessarily the one actually serving +requests. -### `asyncio.Queue` +### `asyncio.Queue` — bounded, not unbounded + +```python +self._queue: asyncio.Queue[_Pending] = asyncio.Queue(maxsize=settings.max_queue_depth) +``` -Thread-safe within a single event loop. Route handlers (`submit()`) put items in; -the `_loop` coroutine drains them. The queue is unbounded — at extremely high -load, unprocessed requests accumulate in memory. A production version would cap -the queue and return 503 when the backlog exceeds a threshold. +Thread-safe within a single event loop. Route handlers (`submit()`) put items in +via `put_nowait()`; the `_loop` coroutine drains them. The queue is **bounded** +(`MAX_QUEUE_DEPTH`, default `1000`, from `config.settings`) — `put_nowait()` +raises `asyncio.QueueFull` once it's full, which `main.py`'s `/classify` route +catches and turns into an HTTP 503 ("classifier queue full — retry later") +instead of accepting unbounded work and running the pod out of memory under +sustained overload. ### `_loop()` — the batching algorithm @@ -336,12 +448,12 @@ arrived while the previous batch was executing in the thread pool are immediatel collected into the next batch. Under sustained load, the queue is never empty and batches stay at or near `MAX_BATCH_SIZE`. -### `MAX_WAIT_MS = 10` +### `MAX_WAIT_MS` (default `10`, `settings.max_wait_ms`) The window for batch collection. If only one request arrives and no more come -within 10ms, the batcher flushes a batch of 1 rather than waiting indefinitely. +within the window, the batcher flushes a batch of 1 rather than waiting indefinitely. This caps the extra latency batching adds to single requests. Under high load, -the queue fills faster than 10ms and the deadline is never reached — `MAX_WAIT_MS` +the queue fills faster than the window and the deadline is never reached — `MAX_WAIT_MS` only matters at low traffic where every millisecond of wait would be wasted. **Tip:** tune `MAX_WAIT_MS` based on your traffic pattern: @@ -373,19 +485,76 @@ responses. Without this handler, a single ORT failure would leave every request in the batch suspended forever — the route handler would never unblock and connections would time out. -### `/classify` is `async def` — both routes are +`_fail_all()` is a `@staticmethod` used from two places: the `except Exception` +branch above, and the `except asyncio.CancelledError` branch below (shutdown). +Factored out because both need identical "set this exception on every +not-yet-done Future in the batch" logic. + +### `stop()` — async, and it drains the queue + +```python +async def stop(self) -> None: + if self._task: + self._task.cancel() + try: + await self._task + except asyncio.CancelledError: + pass + self._task = None + + while not self._queue.empty(): + pending = self._queue.get_nowait() + if not pending.future.done(): + pending.future.set_exception(RuntimeError("DynamicBatcher is shutting down")) +``` -Both `/classify` and `/classify/batch` are declared `async def`. The distinction -is what they do inside: +Two separate failure surfaces this has to cover, both found by live-testing +pod shutdown rather than by inspection: + +1. **A batch already pulled off the queue and mid-flight in `_loop()`** when + `.cancel()` fires — `_loop()`'s `except asyncio.CancelledError:` branch + calls `self._fail_all(batch, RuntimeError(...))` before re-raising, so + every request already committed to that in-flight batch gets a clean + exception instead of hanging. +2. **Requests still sitting in the queue that the loop never even picked + up** — cancelling `self._task` doesn't touch the queue at all. Without + the `while not self._queue.empty()` drain loop here, any request that + arrived in the window between the last batch starting and shutdown + beginning would sit on a `Future` that nothing will ever resolve — the + route handler's `await pending.future` in `submit()` would hang forever, + and (depending on how the ASGI server handles in-flight requests during + shutdown) that could block the pod from terminating cleanly. + +`stop()` had to become `async` (it wasn't originally) specifically to +`await self._task` after cancelling it — without that await, `stop()` +would return before `_loop()` actually finished unwinding, and the queue +drain below could race with `_loop()` still touching the same queue. +`main.py`'s `lifespan` shutdown sequence does `await _batcher.stop()` +before closing the DB pool for exactly this reason — see `main.py`'s +lifespan section below. + +### All three routes are `async def` — including `/v1/moderations` + +`/classify`, `/classify/batch`, and `/v1/moderations` are all declared +`async def`. This might look like it contradicts the root `CLAUDE.md`'s +"classifier design rules" note about sync routes for blocking calls — it +doesn't, because none of these routes call `session.run()` directly inline. +The distinction is what each does inside: - `/classify` awaits `_batcher.submit()` — a coroutine that puts one item in the - queue and waits for its Future. No blocking I/O directly. -- `/classify/batch` calls `loop.run_in_executor(None, _classifier.predict, ...)` — - offloads the blocking ORT call to a thread and awaits the result. + queue and waits for its Future. No blocking I/O directly; the actual ORT + call happens inside `batcher.py`'s `_loop()`, itself offloaded via + `run_in_executor`. +- `/classify/batch` and `/v1/moderations` both go through the shared + `_classify_and_persist()` helper (see `main.py` below), which calls + `loop.run_in_executor(None, _classifier.predict, texts)` — offloads the + blocking ORT call to a thread and awaits the result. Both approaches keep the event loop unblocked during inference. The key rule: **never call a blocking C function directly in an `async def` function without -`run_in_executor`**. +`run_in_executor`** — an `async def` route is safe as long as every blocking +call inside it is wrapped this way; it's not the `async def` itself that +would be wrong, it's calling `session.run()` unwrapped inside one. --- @@ -440,18 +609,34 @@ promoted — they have `promoted_at = NULL`, so fall back to `created_at`. ```sql INSERT INTO model_registry (model_version, model_path, threshold, status) -VALUES ($1, $2, $3, 'active') +VALUES ($1, $2, $3, 'staging') ON CONFLICT (model_version) DO NOTHING ``` -Called during lifespan startup, after the model is loaded. The `ON CONFLICT DO -NOTHING` makes it safe to call on every pod startup — in a multi-replica -deployment, the first pod registers the version, subsequent pods skip it silently. - -Note: inserts as `'active'` — this is intentional for the local dev flow where -Airflow is not present. In production with Airflow managing promotions, this -insert would conflict with the Airflow-managed row and do nothing (the Airflow -row is already `active`). +Called during lifespan startup, after the model is loaded — but **not +unconditionally**; see `main.py`'s lifespan section below for the +`model_dir is not None` gate that decides whether this gets called at all. +The `ON CONFLICT DO NOTHING` makes it safe to call on every pod startup — in +a multi-replica deployment, the first pod registers the version, subsequent +pods skip it silently. + +**Inserts as `'staging'`, not `'active'`.** An earlier version of this +inserted `'active'` directly, on the reasoning that local dev has no +Airflow to do real promotions, so the classifier might as well self-promote. +That's what `pipelines/drift/db.py`'s `get_active_model_version()` gotcha +(see [`../../pipelines/drift/explanation.md`](../../pipelines/drift/explanation.md)) +documents running into: every classifier pod self-registering as `'active'` +on every startup meant `model_registry` accumulated multiple `'active'` rows +across restarts/deploys, with no single one reliably being "the one that's +actually running." Inserting as `'staging'` here means promotion to +`'active'` is exclusively something else's job — today nothing promotes +anything (there's no automated flip yet), so `get_active_model()` above +falls back to "most recent staging row," which is honest about the current +state rather than pretending a promotion decision was made that wasn't. +Once Airflow's retrain DAG exists and does real promotions (Phase 7.3), this +staying `'staging'` is what makes that promotion step meaningful — if the +classifier kept self-promoting to `'active'`, Airflow's promotion would have +nothing distinctive to do. ### `write_classification` and `write_classifications_batch` @@ -459,15 +644,37 @@ Two write paths for single and batch requests respectively. `write_classifications_batch` uses `asyncpg.executemany()` — sends all rows in a single network round-trip to PostgreSQL rather than one INSERT per row. +Both now write `span_id` and `text_type` columns (always `NULL` on this path +— these are direct API calls, not Kafka-sourced, so there's no span to +attach) and use the same conflict target as +`services/stream-processor/writer.py`'s classification writer: + +```sql +ON CONFLICT (span_id, text_type) WHERE span_id IS NOT NULL DO NOTHING +``` + +Kept identical between the two write paths deliberately — before this, the +classifier's own writes and the stream processor's writes used slightly +different column sets, which meant neither one could be trusted to reflect +the actual schema constraint on its own. The partial index (`WHERE span_id +IS NOT NULL`) means this `ON CONFLICT` clause is a no-op for these +always-NULL-span_id rows — every direct-API classification is still +inserted every time — the idempotency guarantee only kicks in for the +stream processor's span-sourced rows. + Note: these writes are **fire-and-forget** from the route handler's perspective. The route creates an `asyncio.Task` for the write and returns the response immediately. If the write fails (PG down, FK constraint violation), the task logs the exception but the route already returned 200. This is an acceptable trade-off for low-latency inference — classification results are the primary product; -persistence is secondary. +persistence is secondary. `main.py`'s `lifespan` shutdown now tracks these +tasks in a module-level `_persist_tasks` set and `await`s them before closing +the pool — see the lifespan section below for why. -When `persist=False` is set (stream processor calls), these functions are not -called at all. The stream processor owns PG writes for those requests. +When the caller sets `X-Sentinel-Skip-Persist: true` on `/v1/moderations` +(the stream processor does), these functions are not called at all — the +stream processor owns PG writes for those requests, keyed by `span_id` for +idempotency on its own side. --- @@ -516,19 +723,32 @@ subsequent classifier restarts within the same pod lifetime are instant. Override with `MODEL_CACHE_DIR` env var if you want the cache in a mounted PVC. -### boto3 client setup +### `_s3_client()` — cached, and reads from `config.settings` ```python -boto3.client( - "s3", - endpoint_url=os.getenv("MINIO_ENDPOINT", "http://localhost:9000"), - aws_access_key_id=os.getenv("MINIO_ACCESS_KEY", "sentinel"), - aws_secret_access_key=os.getenv("MINIO_SECRET_KEY", "sentinel-minio"), - config=Config(signature_version="s3v4", connect_timeout=5, retries={"max_attempts": 2}), - region_name="us-east-1", -) +@lru_cache(maxsize=1) +def _s3_client(): + return boto3.client( + "s3", + endpoint_url=settings.minio_endpoint, + aws_access_key_id=settings.minio_access_key, + aws_secret_access_key=settings.minio_secret_key, + config=Config(signature_version="s3v4", connect_timeout=5, retries={"max_attempts": 2}), + region_name="us-east-1", + ) ``` +`@lru_cache(maxsize=1)` — a fresh `boto3.client("s3", ...)` per call means a +new TLS handshake and credential resolution every time; this function is +called on every `download_model()` invocation, so caching it means the +whole pod lifetime reuses one client after the first call. Deliberately +**not** shared as a common module with `pipelines/optimizer/upload.py`'s +near-identical factory function — `services/` and `pipelines/` are +separately deployed packages (different Dockerfiles, no shared workspace +member), so a real shared extraction would mean adding a new cross-package +dependency both would have to ship. Kept in sync by convention (same +`Config` args in both places) instead of by import. + `endpoint_url` overrides boto3's default AWS endpoint, pointing it at MinIO instead. `region_name="us-east-1"` is required by the S3v4 signature algorithm even though MinIO ignores the region — without it, the client raises a @@ -568,7 +788,7 @@ Counter("classifier_requests_total", ..., ["endpoint", "label"]) ``` Two label dimensions: -- `endpoint`: `"classify"` or `"classify_batch"` — which path received the request +- `endpoint`: `"classify"`, `"classify_batch"`, or `"moderations"` — which path received the request - `label`: `"safe"` or `"harm"` — classification outcome Useful PromQL: @@ -638,50 +858,99 @@ Adding the Prometheus handler after ensures it inherits the level filter. ```python @asynccontextmanager async def lifespan(app: FastAPI): - global _classifier, _batcher, _pool - - # 1. Open DB pool and query the model registry - dsn = os.environ.get("DATABASE_URL") - if dsn: - _pool = await _db.init_pool(dsn) - active = await _db.get_active_model(_pool) - if active: - # download_model is blocking boto3 I/O — run in thread - loop = asyncio.get_running_loop() - model_dir = await loop.run_in_executor(None, download_model, active["model_path"]) - - # 2. Load the model (falls back to local discovery if model_dir is None) + global _classifier, _batcher, _pool, _ready + + model_dir: Path | None = None + active: dict | None = None + + if settings.database_url: + try: + _pool = await _db.init_pool(settings.database_url) + active = await _db.get_active_model(_pool) + if active: + loop = asyncio.get_running_loop() + model_dir = await loop.run_in_executor(None, download_model, active["model_path"]) + except Exception: + logger.exception("DB init failed — running without persistence") + if _pool is not None: + await _pool.close() + _pool = None + else: + logger.warning("DATABASE_URL not set — classifications will not be persisted") + _classifier = Classifier(model_dir=model_dir) _classifier.warmup() - - # 3. Register this version in the registry (idempotent) - if _pool: - await _db.register_model(_pool, _classifier.model_version, ...) - - # 4. Start the dynamic batcher _batcher = DynamicBatcher(_classifier.predict) _batcher.start() - yield # — service handles requests here — + if _pool and model_dir is not None and active is not None: + try: + await _db.register_model(_pool, _classifier.model_version, active["model_path"], _classifier.threshold) + except Exception: + logger.exception("Failed to register model version in registry") + elif _pool: + logger.info("Skipping registry write — resolved via local fallback, not portable across pods") - _batcher.stop() + _ready = True + yield + + _ready = False + await _batcher.stop() if _pool: + if _persist_tasks: + await asyncio.gather(*_persist_tasks, return_exceptions=True) await _db.close_pool(_pool) + _classifier = None ``` `lifespan` replaced `@app.on_event("startup")` in FastAPI 0.93+. Code before -`yield` runs at startup; code after `yield` at shutdown. The `global` declarations -give route handlers (defined at module scope) access to the shared instances. +`yield` runs at startup; code after `yield` at shutdown. The `global` +declarations give route handlers (defined at module scope) access to the +shared instances. DSN and every other tunable now come from `settings` +(`config.py`), not raw `os.environ.get(...)` calls scattered through this +function. **`download_model` in `run_in_executor`** — boto3's `download_file` is blocking synchronous I/O. Calling it directly in `async def lifespan` would block the entire event loop during the download (~10–30s for the INT8 model). Running it in the executor offloads the I/O to a thread and keeps the event loop responsive. -**Why `register_model` inserts as `'active'`** — the local dev workflow has no -Airflow to promote models. The classifier registers itself as `active` on startup. -In production with Airflow, this `INSERT ... ON CONFLICT DO NOTHING` is a no-op -because the registry row already exists at `active` status from the promotion step. +**The `try`/`except` around DB init, and the pool-leak fix.** If +`init_pool()` succeeds but a later call in the same block raises (e.g. +`get_active_model()` fails, or `download_model` blows up in a way that +isn't caught inside it), the `except` branch now explicitly checks +`if _pool is not None: await _pool.close()` before setting `_pool = None`. +`asyncpg.create_pool(..., min_size=1)` opens a connection eagerly at +creation time — without closing it here, that connection leaked for the +rest of the pod's lifetime on any startup failure after a successful +`init_pool()`. This was a real bug (not hypothetical): a transient DB hiccup +between pool creation and the registry query would silently leave one +connection permanently checked out, and enough restarts under a flaky DB +could exhaust PostgreSQL's `max_connections`. + +**The `model_dir is not None and active is not None` registration gate — +and the bug it replaced.** This is the fix for a real production bug, found +by live-testing rather than by reading the code. The gate used to be +`_classifier.model_path.startswith("/")` (skip registration if the loaded +model's path "looks local"). That check was wrong in the *common* case, not +just an edge case: `download_model()` **always** caches the downloaded +model under a local path (`/tmp/sentinel-model-cache/...`), so +`_classifier.model_path` is *always* an absolute local path — even when the +model genuinely came from MinIO via the registry. The old check therefore +skipped registration for the normal, portable-across-pods case too. Live +symptom, confirmed via `kubectl logs`: a classifier pod loading a real +MinIO-backed model still logged "Skipping registry write," and every +subsequent `/v1/moderations` persist attempt then failed with +`psycopg.errors.ForeignKeyViolation` on `classifications_model_version_fkey` +— the model_version being written had never actually been inserted into +`model_registry`. The fix uses `model_dir is not None` instead — that +variable is only `None` when `Classifier()` fell all the way through to its +own `_resolve_model_dir()` local discovery (`MODEL_PATH` env var or +`logs/optimizer/`), which is the *actual* non-portable case. And when +registering, it passes `active["model_path"]` (the original MinIO key from +the registry row) rather than `_classifier.model_path` (the local cache +path) — the registry should record the portable MinIO reference, not a +path that only exists on this one pod's filesystem. ### `app.mount("/metrics", make_asgi_app())` @@ -693,24 +962,88 @@ Prometheus scrape config uses `metrics_path: /metrics/` (trailing slash) — Fas redirects `/metrics` → `/metrics/`. Specifying the final path avoids the redirect round-trip on every scrape. -### `_persist_single` and `_persist_batch` +### `_classify_and_persist()` — shared by `/classify/batch` and `/v1/moderations` -Both are called via `asyncio.create_task(...)` — fire-and-forget from the route -handler's perspective. The route returns the response immediately without waiting -for the DB write to complete. +```python +async def _classify_and_persist(texts, endpoint, persist) -> tuple[list[dict], float, datetime]: + t0 = time.perf_counter() + loop = asyncio.get_running_loop() + results = await loop.run_in_executor(None, _classifier.predict, texts) + latency_ms = round((time.perf_counter() - t0) * 1000, 2) + inference_at = datetime.now(timezone.utc) + + BATCH_SIZE.observe(len(texts)) + REQUEST_LATENCY.labels(endpoint=endpoint).observe(latency_ms / 1000) + for r in results: + REQUEST_COUNT.labels(endpoint=endpoint, label=r["label"]).inc() + + if _pool and persist: + records = [...] + task = asyncio.create_task(_persist_batch(records)) + _persist_tasks.add(task) + task.add_done_callback(_persist_tasks.discard) + + return results, latency_ms, inference_at +``` + +Extracted once both `/classify/batch` and `/v1/moderations` needed +"run inference off the event loop, record per-endpoint metrics, fire off +persistence" — before this existed the two routes duplicated that whole +sequence, and the metrics/persistence logic had already started drifting +slightly between the copies. `endpoint` is passed through so +`REQUEST_COUNT`/`REQUEST_LATENCY` still get the right label +(`"classify_batch"` vs `"moderations"`) even though the underlying work is +identical. + +**`_persist_tasks: set[asyncio.Task]`** — every fire-and-forget persistence +task (from `/classify`, `/classify/batch`, and `/v1/moderations` alike) is +added to this module-level set and removed via `task.add_done_callback(_persist_tasks.discard)` +once it finishes. This exists so `lifespan`'s shutdown sequence can +`await asyncio.gather(*_persist_tasks, return_exceptions=True)` before +closing the DB pool — without tracking these tasks, a classification +written in the last few requests before shutdown could still be +mid-flight when the pool closed underneath it, silently dropping that row. + +### `/v1/moderations` — the primary endpoint, and how it skips persistence ```python -if _pool and request.persist: - records = [...] - asyncio.create_task(_persist_batch(records)) +@app.post("/v1/moderations", response_model=ModerationResponse) +async def moderate( + request: ModerationRequest, + x_sentinel_skip_persist: bool = Header(False, alias="X-Sentinel-Skip-Persist"), +) -> ModerationResponse: + texts = [request.input] if isinstance(request.input, str) else list(request.input) + results, _, _ = await _classify_and_persist(texts, "moderations", persist=not x_sentinel_skip_persist) + return ModerationResponse(...) ``` -`request.persist` (default `True`) gates the write. When the stream processor -calls with `persist=False`, no task is created and the route has no DB side -effect — the stream processor is responsible for PG writes on those requests. +This is the endpoint the stream processor calls (not `/classify/batch`) — +dogfooding the same OpenAI-compatible, publicly-documented endpoint that +any external integration would use, rather than maintaining a +Sentinel-internal shape as the "real" one and an OpenAI-shaped one as a +facade. See `services/stream-processor/explanation.md` for the fuller story +of that decision and the accidental revert it survived mid-session. + +**Skip-persist via header, not a body field.** The classifier's own async +PostgreSQL write needs to be skippable when the stream processor calls this +endpoint — the stream processor writes to PG itself, keyed by `span_id` for +idempotency, and would double-write otherwise. Earlier this was a `persist: +bool` field on the request body (mirroring `/classify/batch`'s +`BatchClassifyRequest.persist`). That was deliberately removed: +`ModerationRequest` is meant to be a **clean OpenAI-compatible schema** — +zero Sentinel-internal fields visible to an external caller hitting this +endpoint directly (a real `openai.moderations.create()`-style client should +never need to know or care about Sentinel's internal persistence wiring). +`X-Sentinel-Skip-Persist` moves that internal signal to a header instead, +which keeps the body schema honestly OpenAI-shaped while still letting the +stream processor (an internal caller) suppress the classifier's write. ### Environment variables summary +All of these are `config.py` `Settings` fields (env var name matches the +field name upper-cased) — see the top of this document for why they're +centralized there rather than read ad hoc in each module. + | Variable | Default | Effect | |---|---|---| | `DATABASE_URL` | unset | asyncpg DSN; enables registry + persistence | @@ -723,3 +1056,4 @@ effect — the stream processor is responsible for PG writes on those requests. | `ORT_INTRA_THREADS` | `4` | CPU threads per ORT matrix op | | `MAX_BATCH_SIZE` | `64` | Max texts per dynamic batch | | `MAX_WAIT_MS` | `10` | Max ms to wait filling a batch | +| `MAX_QUEUE_DEPTH` | `1000` | Max pending requests before `/classify` returns 503 | diff --git a/services/label-ui/Dockerfile b/services/label-ui/Dockerfile new file mode 100644 index 0000000..e7c91e2 --- /dev/null +++ b/services/label-ui/Dockerfile @@ -0,0 +1,31 @@ +# ── Stage 1: install runtime dependencies ──────────────────────────────────── +FROM python:3.12-slim AS deps + +COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv + +WORKDIR /app + +RUN uv venv /app/.venv && \ + uv pip install --python /app/.venv/bin/python \ + "fastapi>=0.115" \ + "uvicorn[standard]>=0.34" \ + "pymongo>=4.9" \ + "httpx>=0.28" \ + "pydantic-settings>=2.3" + +# ── Stage 2: runtime image ─────────────────────────────────────────────────── +FROM python:3.12-slim + +WORKDIR /app + +COPY --from=deps /app/.venv /app/.venv + +# Source files + the static page — no pyproject.toml, no explanation.md. +COPY *.py ./ +COPY static ./static + +ENV PATH="/app/.venv/bin:$PATH" + +EXPOSE 8001 + +CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8001"] diff --git a/services/label-ui/config.py b/services/label-ui/config.py new file mode 100644 index 0000000..d10516a --- /dev/null +++ b/services/label-ui/config.py @@ -0,0 +1,14 @@ +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class Settings(BaseSettings): + mongo_uri: str = "mongodb://sentinel:sentinel@localhost:27017/sentinel" + + airflow_base_url: str = "http://localhost:8090" + airflow_admin_user: str = "admin" + airflow_admin_password: str = "sentinel" + + model_config = SettingsConfigDict(env_file=".env", extra="ignore") + + +settings = Settings() diff --git a/services/label-ui/explanation.md b/services/label-ui/explanation.md new file mode 100644 index 0000000..b0c7300 --- /dev/null +++ b/services/label-ui/explanation.md @@ -0,0 +1,210 @@ +# Label UI — Explanation + +The human-in-the-loop step between the stream processor flagging content +(`flagged_content` in MongoDB) and `pipelines/retraining` consuming it. An +operator reviews flagged spans, assigns a manual safe/harm label, decides +whether each one should feed the next fine-tuning run, and can kick off +that run directly from the same page. + +It's the smallest service in the repo on purpose — a FastAPI backend plus +one static HTML file with inline JS, no build step, no frontend framework. + +--- + +## Directory structure + +``` +services/label-ui/ + main.py — routes: queue, label, stats, trigger-retrain, health + config.py — pydantic-settings: mongo_uri, airflow_base_url, airflow creds + static/index.html — single-page table UI, inline JS, no build step + Dockerfile — 2-stage uv build, mirrors services/classifier/'s shape + pyproject.toml +``` + +--- + +## Why plain `def` routes, not `async def` + +```python +@app.get("/api/queue") +def get_queue(limit: int = 50, skip: int = 0) -> list[dict]: + ... +``` + +FastAPI dispatches sync (`def`) route handlers to its own threadpool +automatically — this is the standard idiom for wrapping blocking I/O +(`pymongo`'s sync client) in FastAPI without manually calling +`run_in_executor`. The classifier's rule ("never block the event loop with +a heavy call inside `async def`") doesn't transfer here as a literal +constraint to route around: that rule exists because ONNX inference under +*concurrent, batched* load would starve the loop. This service is a +single-operator internal tool doing occasional, fast Mongo queries — there's +no batching or throughput concern to design around, so the simplest correct +thing (plain `def`, let FastAPI's threadpool handle it) is also the right +thing, not a shortcut. + +--- + +## Routes + +### `GET /api/queue` — the labelling backlog + +```python +cursor = ( + _db.flagged_content.find({"training_decision": {"$in": [None, "pending"]}}) + .sort("ts", pymongo.ASCENDING) + .skip(skip) + .limit(limit) +) +``` + +**`$in: [None, "pending"]`, not just `{"training_decision": "pending"}`** — +documents written before this feature existed (by +`services/stream-processor/writer.py`, before `manual_label`/ +`training_decision` were added to its schema) have no `training_decision` +field at all. MongoDB's query semantics already treat "field missing" and +"field is null" as matching a `null`/`None` query value, so this one filter +covers both old and new documents without a migration or backfill script. + +**Sorted oldest-first** (`pymongo.ASCENDING` on `ts`) — matches the +`training_decision: 1, ts: 1` compound index added to `flagged_content` +(`infra/terraform/local/main.tf`), so this exact query pattern is indexed +rather than doing a collection scan. + +### `POST /api/label/{doc_id}` — record a decision + +```python +class LabelRequest(BaseModel): + manual_label: Literal["safe", "harm"] + training_decision: Literal["accepted", "rejected"] +``` + +`Literal["safe", "harm"]` and `Literal["accepted", "rejected"]` reject any +other value at the FastAPI/Pydantic validation layer (422) before the +handler body even runs — the same "let the type system enforce the +invariant" instinct as the `model_registry.status` CHECK constraint in +Postgres, just at the API boundary instead of the DB layer, since MongoDB +has no schema enforcement of its own to lean on here. + +A plain `update_one` with `$set` — this route is the *only* place these two +fields ever change after a document is first written, so there's no +redelivery/idempotency concern to design around here the way there is in +`writer.py`'s upsert (see that file's explanation.md for why `writer.py` +specifically needs `$setOnInsert` instead). + +### `GET /api/stats` — a cheap progress readout + +```python +pipeline = [{"$group": {"_id": {"$ifNull": ["$training_decision", "pending"]}, "count": {"$sum": 1}}}] +``` + +`$ifNull` folds the same "missing field counts as pending" logic from the +queue query into the aggregation, so old and new documents get counted +consistently here too. + +### `POST /api/trigger-retrain` — the button that starts everything downstream + +```python +resp = httpx.post( + f"{settings.airflow_base_url}/api/v1/dags/retrain_dag/dagRuns", + json={}, + auth=(settings.airflow_admin_user, settings.airflow_admin_password), +) +``` + +Calls Airflow's stable REST API directly with HTTP Basic auth — the same +admin credentials the Airflow UI login uses. This only works because +`infra/terraform/local/airflow.tf` explicitly sets +`config.api.auth_backends = "airflow.api.auth.backend.basic_auth"`; without +it, the REST API's default auth backend rejects Basic auth even when the +webserver's own login page accepts the same credentials fine — the UI +session login and the stable API are two separate auth surfaces. See +[`../../infra/terraform/local/explanation.md`](../../infra/terraform/local/explanation.md)'s +Airflow gotcha #10 for the fix. + +`json={}` — both `dag_run_id` and `logical_date` are optional in the +trigger-DAG-run API and auto-generate when omitted (`manual__`). +No need to construct either client-side. + +A `502`, not a `500`, on failure — this route is a proxy to a downstream +service (Airflow), so a failure here is "the upstream didn't respond +correctly," which is what 502 Bad Gateway means. Distinguishing this from +Sentinel's own bugs (which would be a 500) matters for whoever's debugging +a failed trigger: check Airflow first, not this service's own logic. + +--- + +## `static/index.html` — no build step, on purpose + +One file: inline ` + + + +

Sentinel — Manual Labelling Queue

+
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+ + + + + + + + + + + + + +
TextTypeModel label / scoreSessionYour labelDecision
+ + + + + + + diff --git a/services/stream-processor/explanation.md b/services/stream-processor/explanation.md index 5ae2b27..ef389b7 100644 --- a/services/stream-processor/explanation.md +++ b/services/stream-processor/explanation.md @@ -60,14 +60,20 @@ main.py — poll loop │ Parses OTLP JSON → list of span dicts │ One span → up to two entries (prompt + response) │ - ├─ POST /classify/batch (persist=False) - │ Classifier returns labels + scores - │ No classifier-side PG write (stream processor owns PG writes) + ├─ chunk texts to CLASSIFY_CHUNK_SIZE (default 64) + │ + ├─ POST /v1/moderations (X-Sentinel-Skip-Persist: true), one call per chunk + │ Classifier returns OpenAI-shaped moderation results + │ _moderation_results_to_label_score() maps them to {label, score} + │ Header (not a body field) suppresses the classifier's own PG write — + │ stream processor owns PG writes for this traffic │ ├─ writer.write_classifications() → PostgreSQL (all results) │ ON CONFLICT DO NOTHING on (span_id, text_type) │ ├─ writer.write_flagged_content() → MongoDB (harm + 10% safe) + │ bulk_write: UpdateOne upsert keyed on (span_id, text_type) when + │ span_id is present, InsertOne otherwise — idempotent on redelivery │ └─ consumer.commit() ← ONLY if all writes succeed ``` @@ -79,12 +85,12 @@ main.py — poll loop ### At-least-once delivery guarantee This is the core design principle. Kafka offset commits are manual and happen -only after both database writes succeed: +only after `_pg_write()` (which internally writes both PostgreSQL and +MongoDB) succeeds: ```python try: - write_classifications(...) - write_flagged_content(...) + pg_conn = _pg_write(pg_conn, spans, all_results, model_version, per_span_latency_ms, mongo_db) except Exception: logger.exception("DB write failed — not committing, Kafka will redeliver") continue # skip consumer.commit() @@ -97,13 +103,63 @@ the classifier returns a 5xx — the offset is not committed. On restart (or aft the session timeout expires), Kafka redelivers the same messages from the last committed offset. -**"At-least-once" means the same message can be processed more than once.** The -`ON CONFLICT DO NOTHING` on the partial unique index `(span_id, text_type) WHERE -span_id IS NOT NULL` in the `classifications` table prevents duplicate PostgreSQL -rows on redelivery. MongoDB writes are not deduplicated — a replayed batch may -produce duplicate `flagged_content` documents for the same span. This is -acceptable because the retraining pipeline deduplicates by `span_id` before -training. +**"At-least-once" means the same message can be processed more than once.** +Both persistence layers are now idempotent on redelivery: the `ON CONFLICT +DO NOTHING` on the partial unique index `(span_id, text_type) WHERE span_id +IS NOT NULL` in the `classifications` table prevents duplicate PostgreSQL +rows, and `writer.write_flagged_content()`'s `bulk_write` with `UpdateOne` +upserts (keyed on the same `(span_id, text_type)` pair) makes MongoDB +redelivery a no-op instead of a duplicate insert too — see `writer.py`'s +section below for why this changed from a plain `insert_many`. + +### `_pg_write()` — reconnect on drop, rollback on poisoned transaction + +```python +def _pg_write(pg_conn, spans, results, model_version, latency_ms, mongo_db) -> psycopg.Connection: + try: + write_classifications(pg_conn, spans, results, model_version, latency_ms) + write_flagged_content(mongo_db, spans, results, model_version, SAFE_SAMPLE_RATE) + return pg_conn + except psycopg.OperationalError: + # connection actually lost — close, reconnect, retry once + ... + except psycopg.Error: + # connection alive but transaction poisoned — roll back so the NEXT + # call can use it, then re-raise so THIS batch isn't committed + pg_conn.rollback() + raise +``` + +Two distinct PostgreSQL failure modes need different recovery, and +conflating them was a real bug found by live-testing, not a hypothetical: + +1. **`psycopg.OperationalError`** — the connection itself is gone (network + drop, PG pod restart). Recovery: close the dead connection, open a new + one, retry the write once on the fresh connection. If the retry also + fails, close that connection too before raising — otherwise a + double-failure would leak the second connection silently. +2. **Any other `psycopg.Error`** (e.g. a constraint violation) — the TCP + connection is still perfectly alive, but the **current transaction is + aborted**. Every subsequent command on that same connection then fails + with `psycopg.errors.InFailedSqlTransaction` until something calls + `.rollback()` — psycopg3 doesn't do this automatically. This was + reproduced live: a stale `model_version` (the classifier had + self-registered a model version the DB didn't actually have a matching + row for — see `services/classifier/explanation.md`'s model-registration + bug) triggered a `ForeignKeyViolation` on `classifications`. Before this + fix, that left the long-lived `pg_conn` permanently poisoned — **every + subsequent poll cycle's write failed the same way forever**, not just + the one batch that hit the actual FK violation, because nothing ever + rolled back the aborted transaction state. The fix calls + `pg_conn.rollback()` in this branch before re-raising: the current + batch still correctly fails (so its Kafka offset isn't committed and it + gets redelivered), but the connection is usable again for the *next* + poll cycle instead of being permanently wedged. + +`ON CONFLICT DO NOTHING` on `(span_id, text_type)` makes the retry-after- +reconnect path idempotent — if the first attempt's `write_classifications` +partially succeeded before the connection dropped, replaying it on the new +connection just no-ops on the rows that already landed. ### `consumer.poll()` vs iterating the consumer @@ -124,11 +180,12 @@ consumer.commit() # commits all fetched offsets at once ``` The stream processor uses `poll()` for batching. Instead of classifying one span -per HTTP request, it collects all spans from a full poll cycle and sends them in -a single `/classify/batch` call. This is significantly more efficient: -- One HTTP round-trip per poll cycle instead of one per message +per HTTP request, it collects all spans from a full poll cycle and sends them +in one or more `/v1/moderations` calls (chunked to `CLASSIFY_CHUNK_SIZE`, see +above). This is significantly more efficient: +- A small, bounded number of HTTP round-trips per poll cycle instead of one per message - One `executemany` INSERT instead of one per row -- One MongoDB `insert_many` instead of one per document +- One MongoDB `bulk_write` instead of one operation per document **`timeout_ms=1000`** — if no messages are available, `poll()` blocks for up to 1 second then returns an empty dict. The `while _running` loop immediately calls @@ -136,12 +193,13 @@ a single `/classify/batch` call. This is significantly more efficient: heartbeat thread time to fire (keeping the consumer group session alive). **`max_records=50`** — caps the batch size from Kafka per poll call. Without -this, a backlogged topic could deliver hundreds of messages in one poll, making -the subsequent `/classify/batch` call too large (exceeds `MAX_BATCH_SIZE=64`). -At 50 records, and assuming each OTLP message contains one LLM span with both -prompt and response, the worst case is 100 texts per classify call — the stream -processor uses the full `MAX_BATCH_SIZE=64` on the first call and leftovers on a -second if needed. In practice, the `MAX_POLL_RECORDS` env var lets you tune this. +this, a backlogged topic could deliver hundreds of messages in one poll, +producing an unbounded number of texts to classify at once. At 50 records, +and assuming each OTLP message contains one LLM span with both prompt and +response, the worst case is 100 texts per poll cycle — split into two +`CLASSIFY_CHUNK_SIZE=64`-sized chunks (64 + 36) rather than one oversized +call that would 422. In practice, the `MAX_POLL_RECORDS` env var lets you +tune this. ### KafkaConsumer configuration @@ -153,7 +211,7 @@ consumer = KafkaConsumer( enable_auto_commit=False, auto_offset_reset="earliest", value_deserializer=lambda v: json.loads(v.decode("utf-8")), - request_timeout_ms=30_000, + request_timeout_ms=40_000, session_timeout_ms=30_000, heartbeat_interval_ms=10_000, ) @@ -183,9 +241,12 @@ sends a heartbeat every 10 seconds — well within the 30-second timeout. A slow DB write (which blocks the poll loop) can cause missed heartbeats and unintentional rebalances. If DB writes consistently take > 20 seconds, increase `session_timeout_ms`. -**`request_timeout_ms=30_000`** — the maximum time to wait for a response to any -Kafka API request (fetch, produce, metadata). Keep this larger than -`session_timeout_ms`. +**`request_timeout_ms=40_000`** — the maximum time to wait for a response to any +Kafka API request (fetch, produce, metadata). Kept larger than +`session_timeout_ms` (30s) — a request timeout shorter than or equal to the +session timeout risks the client giving up on a slow-but-alive broker +response right around the same time a rebalance would otherwise trigger, +compounding the two failure modes instead of keeping them independent. ### Signal handling @@ -211,34 +272,98 @@ Without signal handling, Ctrl-C raises a `KeyboardInterrupt` that would skip the `consumer.close()` call. Kafka would wait 30 seconds (session timeout) before reassigning the partitions to another consumer — making restarts slow. -### `persist=False` on the classify call +### `/v1/moderations`, not `/classify/batch` — dogfooding the public endpoint ```python -resp = http.post("/classify/batch", json={"texts": texts, "persist": False}) +resp = http.post( + "/v1/moderations", + json={"input": chunk}, + headers={"X-Sentinel-Skip-Persist": "true"}, +) ``` -Without this flag, the classifier would also write to PostgreSQL asynchronously -(`asyncio.create_task`). The stream processor would still write its own rows, -producing duplicates. More critically, the classifier's async write is -fire-and-forget — it completes after the HTTP response, meaning the stream -processor cannot know if it succeeded before committing the Kafka offset. +The stream processor calls the classifier's **OpenAI-compatible** +`/v1/moderations` endpoint — the same one any external integration would +use — rather than a Sentinel-internal `/classify/batch` shape. This is a +deliberate architectural decision (see `CLAUDE.md`'s OTel GenAI semantic +conventions section): the project's own highest-volume internal traffic +exercises exactly the code path external callers get, instead of treating +`/classify/batch` as the "real" endpoint and `/v1/moderations` as a thin +facade nobody but external callers actually hits. If `/v1/moderations` ever +broke, the stream processor's own traffic would surface it immediately in +local dev, rather than only being caught when an external caller notices. + +**This decision was accidentally reverted once, mid-session, and caught by +the user asking "isnt the /classify/batch endpoint modified to be +/v1/moderations?"** — a fix for an unrelated issue moved this call back to +`/classify/batch`, undoing a deliberate choice from an earlier commit +without checking `git log`/`git blame` first. The lesson generalizes: before +"fixing" something that touches an existing, working call path, check +whether its current shape was a deliberate decision (commit message, code +comment, or explanation.md note) rather than an oversight — matching an +older pattern isn't automatically correct if the code moved past it on +purpose. + +**`_moderation_results_to_label_score()`** translates `/v1/moderations`' +OpenAI-shaped `{flagged, categories, category_scores}` results into this +service's internal `{label, score}` shape that `writer.py`'s functions +expect — named and factored out explicitly (not an inline dict comprehension +at the call site) so the translation reads as a deliberate boundary: calling +an OpenAI-compatible endpoint from internal code makes this remapping +inherent, not incidental, and worth a name. + +**Skip-persist via `X-Sentinel-Skip-Persist` header, not a `persist` body +field.** Without suppressing it, the classifier would also write to +PostgreSQL asynchronously from inside `/v1/moderations` — the stream +processor would still write its own rows too, producing duplicates. Earlier +this was a `persist: bool` field on the request body (mirroring +`/classify/batch`'s still-present `persist` field). It moved to a header +specifically so `ModerationRequest` — the schema an external +`openai.moderations.create()`-style caller sends — stays a clean, +zero-Sentinel-internals OpenAI-compatible shape; see +`services/classifier/explanation.md`'s `/v1/moderations` section for the +full reasoning from the classifier side. + +### Chunking to the classifier's batch limit -`persist=False` gives the stream processor full control over when PG writes -happen relative to the offset commit. The classifier becomes a pure inference -service for this call path. +```python +chunks = [texts[i : i + CLASSIFY_CHUNK_SIZE] for i in range(0, len(texts), CLASSIFY_CHUNK_SIZE)] +``` + +A single Kafka poll (`max_records=50`) can extract more spans than the +classifier accepts in one request — up to 100 texts if every message has +both a prompt and a response. `CLASSIFY_CHUNK_SIZE` (default `64`, env var) +splits the poll's texts into `/v1/moderations`-sized chunks, one HTTP call +per chunk, results concatenated back into `all_results`. + +**Not read from the same env var name as the classifier's own limit** — +`CLASSIFY_CHUNK_SIZE` here vs `MAX_BATCH_SIZE` in +`services/classifier/config.py` — because these are two separately deployed +services with independent configuration surfaces; sharing an env var name +across service boundaries would be an implicit, easy-to-break coupling. Both +default to `64` today, but if either is tuned away from that default, the +other has to be set explicitly too — nothing enforces they stay in sync +automatically. Exceeding the classifier's real limit gets a `422` from +Pydantic's `max_length` validation on `ModerationRequest.input`, which +`resp.raise_for_status()` below turns into a Kafka-redelivery retry rather +than a silent data loss. ### `per_span_latency_ms` ```python -per_span_latency_ms = body["latency_ms"] / max(len(texts), 1) +per_span_latency_ms = classify_ms_total / max(len(texts), 1) ``` -The batch endpoint returns a single `latency_ms` for the entire batch. There is -no per-span latency. Dividing by batch size is an approximation: it assumes the -batch processed all texts in parallel, which is true for the ORT matrix multiply -but not for tokenization or Python overhead. It gives a reasonable per-span -estimate to store in the `classifications` table's `latency_ms` column, which -is used for latency trend analysis in the drift detection phase. +`classify_ms_total` accumulates wall-clock time across every chunk's HTTP +call in this poll cycle (there can be more than one now, per the chunking +above) — not a single batch's `latency_ms` field. Dividing by the total +span count across all chunks gives a reasonable per-span estimate to store +in the `classifications` table's `latency_ms` column, which is used for +latency trend analysis in the drift detection phase. Same caveat as before: +this assumes even latency distribution across spans within and across +chunks, which is true for the ORT matrix multiply's parallelism but not +exactly for tokenization or per-request Python/HTTP overhead — a reasonable +approximation, not an exact per-span measurement. ### httpx `Client` (not `AsyncClient`) @@ -411,8 +536,20 @@ for span, result in zip(spans, results): if label == "harm" or random.random() < safe_sample_rate: docs.append({...}) -if docs: - db.flagged_content.insert_many(docs) +if not docs: + return + +operations = [ + pymongo.UpdateOne( + {"span_id": doc["span_id"], "text_type": doc["text_type"]}, + {"$set": doc}, + upsert=True, + ) + if doc["span_id"] + else pymongo.InsertOne(doc) + for doc in docs +] +result = db.flagged_content.bulk_write(operations, ordered=False) ``` **Why not write everything to MongoDB?** The `flagged_content` collection is the @@ -428,14 +565,45 @@ actual traffic distribution). The ratio is tunable via `SAFE_SAMPLE_RATE` env va For very low-traffic early deployments where harm examples are rare, consider `SAFE_SAMPLE_RATE=0.5` (50%) until you accumulate enough harm examples to balance. -**`insert_many` is not idempotent** — MongoDB's `insert_many` does not have a -built-in equivalent of `ON CONFLICT DO NOTHING`. On Kafka redelivery, the same -span may produce duplicate documents in `flagged_content`. This is acceptable: -- Duplicates are a small fraction of total documents (only during replay). -- The retrain pipeline deduplicates by `span_id` when building the training set. -- Adding a unique index on `(span_id, text_type)` in MongoDB would make - `insert_many` fail on conflict unless `ordered=False` is set. A future hardening - step could add this. +**`bulk_write` with per-document upsert, not `insert_many` — MongoDB writes +are now idempotent too.** This replaced a plain `insert_many(docs)` call. +The old version was explicitly documented as non-idempotent ("a replayed +batch may produce duplicate `flagged_content` documents") on the reasoning +that the retrain pipeline would dedupe by `span_id` later anyway — accepted +as a known gap rather than fixed. It was fixed: each document with a +non-null `span_id` becomes a `pymongo.UpdateOne` filtered on +`{span_id, text_type}` with `upsert=True` — the same natural key as +PostgreSQL's partial unique index — so redelivering the same span overwrites +the same document instead of inserting a second one. Documents with no +`span_id` (same gap as the PostgreSQL side: nothing to dedupe on) fall back +to a plain `pymongo.InsertOne`. + +**`$setOnInsert` for `manual_label`/`training_decision`, not `$set` — this +is the difference between preserving and clobbering a human's work.** These +two fields are owned by `services/label-ui` (see its explanation.md), not +by this writer — they hold the manual safe/harm label and accept/reject +decision an operator makes in the labelling UI, used later by +`pipelines/retraining` to build its fine-tuning dataset. The upsert's `$set` +clause only ever contains the ingestion fields (`ts`, `input_text`, `label`, +`score`, etc.) — never these two. If a Kafka redelivery re-runs this same +upsert after an operator has already labelled the document, `$set` would +silently overwrite `manual_label`/`training_decision` back to their +defaults, discarding completed manual work with no error and no trace. Live +verified: labelled a doc via the UI, simulated a redelivery by re-running +this exact upsert, and confirmed the label field was untouched while +`input_text`/`score`/`ts` correctly updated to the new values. Documents +with no `span_id` get the defaults set directly in the `InsertOne` payload +instead, since there's no upsert-vs-insert distinction to protect there. + +**`ordered=False` on `bulk_write`** — with the default `ordered=True`, +MongoDB stops processing the batch at the first failing operation, leaving +every operation after it un-run even if they'd have succeeded independently. +`ordered=False` lets every operation attempt independently, so one bad +document doesn't block the rest of an otherwise-healthy batch from +committing — a partial failure then only leaves the genuinely-failed +documents to be retried on redelivery, and every other document in the +batch is already a safe upsert that won't duplicate when that redelivery +happens. **Document shape:** @@ -444,16 +612,22 @@ span may produce duplicate documents in `flagged_content`. This is acceptable: "ts": "ISODate — when the classification ran", "input_text": "the text that was classified", "text_type": "prompt or response", - "label": "harm or safe", + "label": "harm or safe (the MODEL's own classification)", "score": 0.97, "model_version": "sentinel-roberta-20260627T003749Z-int8", "session_id": "conversation session from the chat app", "span_id": "OTLP hex span ID", "trace_id": "OTLP hex trace ID (links to Jaeger)", - "llm_model": "gpt-4o (which LLM the chat app called)" + "llm_model": "gpt-4o (which LLM the chat app called)", + "manual_label": "safe | harm | null — set by a human via label-ui, not this writer", + "training_decision": "pending | accepted | rejected — defaults to pending via $setOnInsert" } ``` +`manual_label`/`training_decision` start `null`/`"pending"` on every document +this writer creates and are never touched by it again — only +`services/label-ui`'s `POST /api/label/{doc_id}` route updates them. + The `trace_id` field is particularly useful: it links each classified span to the full distributed trace in Jaeger, letting you see the entire conversation context when investigating a flagged document. @@ -470,6 +644,7 @@ context when investigating a flagged document. | `MONGO_URI` | `mongodb://sentinel:sentinel@localhost:27017/sentinel` | MongoDB connection URI | | `SAFE_SAMPLE_RATE` | `0.1` | Fraction of safe spans stored in MongoDB | | `MAX_POLL_RECORDS` | `50` | Max messages per Kafka poll call | +| `CLASSIFY_CHUNK_SIZE` | `64` | Max texts per `/v1/moderations` call — must not exceed the classifier's own `MAX_BATCH_SIZE` (separate env var, separate service; keep both in sync manually if either is tuned) | --- @@ -504,6 +679,10 @@ kubectl exec -n sentinel-data statefulset/kafka -- \ **Tune throughput** — the main levers: - `MAX_POLL_RECORDS`: larger batches → fewer HTTP calls → higher throughput, but larger memory spikes and longer time between offset commits. +- `CLASSIFY_CHUNK_SIZE`: raising this reduces the number of `/v1/moderations` + calls per poll cycle, but must stay ≤ the classifier's `MAX_BATCH_SIZE` or + every oversized chunk 422s and the whole poll cycle fails (Kafka redelivers + forever until the mismatch is fixed). - `SAFE_SAMPLE_RATE`: lower value → fewer MongoDB writes → higher throughput. - `session_timeout_ms`: increase if slow DB writes cause rebalances. - Kafka partitions: currently 3. To scale beyond 3 stream processor replicas, diff --git a/services/stream-processor/writer.py b/services/stream-processor/writer.py index f6d046d..d487919 100644 --- a/services/stream-processor/writer.py +++ b/services/stream-processor/writer.py @@ -93,14 +93,25 @@ def write_flagged_content( # Spans without a span_id have no natural key to dedupe on (same gap that # already exists for classifications' partial unique index) — insert # those directly; upsert the rest keyed on (span_id, text_type). + # + # manual_label/training_decision (set by services/label-ui) go in + # $setOnInsert, not $set: a Kafka redelivery of an already-labelled span + # re-runs this upsert with the same ingestion fields, and $set would + # silently overwrite a completed manual label back to "pending" every + # time. $setOnInsert only applies the defaults the first time the + # document is created, so relabelling data can never be clobbered by + # redelivery. operations = [ pymongo.UpdateOne( {"span_id": doc["span_id"], "text_type": doc["text_type"]}, - {"$set": doc}, + { + "$set": doc, + "$setOnInsert": {"manual_label": None, "training_decision": "pending"}, + }, upsert=True, ) if doc["span_id"] - else pymongo.InsertOne(doc) + else pymongo.InsertOne({**doc, "manual_label": None, "training_decision": "pending"}) for doc in docs ] # ordered=False: one bad op doesn't block the rest from committing, so a diff --git a/uv.lock b/uv.lock index f6d39c1..e416c6f 100644 --- a/uv.lock +++ b/uv.lock @@ -11,10 +11,166 @@ members = [ "sentinel", "sentinel-classifier", "sentinel-evaluation", + "sentinel-label-ui", "sentinel-optimizer", + "sentinel-retraining", "sentinel-stream-processor", ] +[[package]] +name = "accelerate" +version = "1.14.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "huggingface-hub" }, + { name = "numpy" }, + { name = "packaging" }, + { name = "psutil" }, + { name = "pyyaml" }, + { name = "safetensors" }, + { name = "torch" }, +] +sdist = { 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