refactor files

docs/multi_objective_scores.md

@@ -106,7 +106,7 @@ config = ObjectiveConfig(
 ### Step 3 — Pass it to the trainer
 
 ```python
-from opto.trainer.algorithms.basic_algorithms import BasicSearchAlgorithm
+from examples.trainers.basic_algorithms import BasicSearchAlgorithm
 
 trainer = BasicSearchAlgorithm(agent, optimizer)
 trainer.train(

docs/tutorials/minibatch.ipynb

@@ -550,7 +550,19 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "from opto import trace\nfrom opto.utils.llm import LLM\nfrom opto.optimizers import OptoPrime\nfrom opto.trainer.algorithms.basic_algorithms import MinibatchAlgorithm\nfrom opto.trainer.loggers import TensorboardLogger\nfrom opto.trainer.guide import LLMJudge\nfrom opto.features.predefined_agents import BasicLearner\nfrom typing import Any\n\n# Use the predefined BasicLearner instead of defining our own\nLearner = BasicLearner"
+   "source": [
+    "from opto import trace\n",
+    "from opto.utils.llm import LLM\n",
+    "from opto.optimizers import OptoPrime\n",
+    "from examples.trainers.basic_algorithms import MinibatchAlgorithm\n",
+    "from opto.trainer.loggers import TensorboardLogger\n",
+    "from opto.trainer.guide import LLMJudge\n",
+    "from opto.features.predefined_agents import BasicLearner\n",
+    "from typing import Any\n",
+    "\n",
+    "# Use the predefined BasicLearner instead of defining our own\n",
+    "Learner = BasicLearner"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -799,15 +811,45 @@
   },
   {
    "cell_type": "markdown",
-   "source": "## Simplified Training with `trainer.train()`\n\nInstead of manually setting up the algorithm, optimizer, guide, and logger, you can use the simplified `trainer.train()` function that handles all the setup for you. This is the recommended approach for most use cases.",
-   "metadata": {}
+   "metadata": {},
+   "source": [
+    "## Simplified Training with `trainer.train()`\n",
+    "\n",
+    "Instead of manually setting up the algorithm, optimizer, guide, and logger, you can use the simplified `trainer.train()` function that handles all the setup for you. This is the recommended approach for most use cases."
+   ]
   },
   {
    "cell_type": "code",
-   "source": "# Using the simplified trainer.train approach\nfrom opto import trainer\n\n# Create a fresh agent for simplified training\nsimple_agent = Learner(\n    system_prompt=\"You're a helpful agent answering math problems.\",\n    llm=LLM()\n)\n\nprint(\"STARTING SIMPLIFIED TRAINING\")\nmetrics, final_score = trainer.train(\n    model=simple_agent,\n    train_dataset=train_dataset,\n    algorithm='MinibatchAlgorithm',\n    guide=LLMJudge(llm=LLM()),\n    # trainer kwargs\n    num_epochs=num_epochs,\n    batch_size=batch_size,\n    eval_frequency=eval_frequency,\n    test_dataset=test_dataset,\n    num_threads=num_threads,\n    verbose='output',\n)\nprint(\"FINISHED SIMPLIFIED TRAINING\")\nprint(f\"Final score: {final_score}\")",
-   "metadata": {},
    "execution_count": null,
-   "outputs": []
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Using the simplified trainer.train approach\n",
+    "from opto import trainer\n",
+    "\n",
+    "# Create a fresh agent for simplified training\n",
+    "simple_agent = Learner(\n",
+    "    system_prompt=\"You're a helpful agent answering math problems.\",\n",
+    "    llm=LLM()\n",
+    ")\n",
+    "\n",
+    "print(\"STARTING SIMPLIFIED TRAINING\")\n",
+    "metrics, final_score = trainer.train(\n",
+    "    model=simple_agent,\n",
+    "    train_dataset=train_dataset,\n",
+    "    algorithm='MinibatchAlgorithm',\n",
+    "    guide=LLMJudge(llm=LLM()),\n",
+    "    # trainer kwargs\n",
+    "    num_epochs=num_epochs,\n",
+    "    batch_size=batch_size,\n",
+    "    eval_frequency=eval_frequency,\n",
+    "    test_dataset=test_dataset,\n",
+    "    num_threads=num_threads,\n",
+    "    verbose='output',\n",
+    ")\n",
+    "print(\"FINISHED SIMPLIFIED TRAINING\")\n",
+    "print(f\"Final score: {final_score}\")"
+   ]
   }
  ],
  "metadata": {
@@ -831,4 +873,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/tutorials/trainers.ipynb

@@ -323,9 +323,9 @@
     "from opto import trace, trainer\n",
     "from opto.optimizers import OptoPrime\n",
     "from opto.optimizers.utils import print_color\n",
-    "from opto.trainer.algorithms.basic_algorithms import MinibatchAlgorithm, BasicSearchAlgorithm\n",
-    "from opto.trainer.algorithms.beamsearch_algorithm import BeamsearchAlgorithm, BeamsearchHistoryAlgorithm\n",
-    "from opto.trainer.algorithms.UCBsearch import UCBSearchAlgorithm\n",
+    "from examples.trainers.basic_algorithms import MinibatchAlgorithm, BasicSearchAlgorithm\n",
+    "from examples.trainers.beamsearch_algorithm import BeamsearchAlgorithm, BeamsearchHistoryAlgorithm\n",
+    "from examples.trainers.UCBsearch import UCBSearchAlgorithm\n",
     "from opto.features.predefined_agents import BasicLearner\n",
     "\n",
     "# Create alias for backward compatibility in this tutorial\n",

examples/bbeh/bbeh_trace.py

@@ -19,7 +19,7 @@
 import opto.trace.operators as trace_ops
 import numpy as np
 from tqdm import tqdm
-from opto.trainer.algorithms.basic_algorithms import MinibatchAlgorithm, evaluate
+from examples.trainers.basic_algorithms import MinibatchAlgorithm, evaluate
 from opto.trainer.guide import Guide
 
 

examples/minibatch_bbh_aynsc/run_bigbench_trace_async.py

@@ -10,7 +10,7 @@
 import autogen
 import pickle
 import os
-from opto.trainer.algorithms.basic_algorithms import MinibatchAlgorithm, evaluate
+from examples.trainers.basic_algorithms import MinibatchAlgorithm, evaluate
 from opto.trainer.guide import Guide
 
 

examples/multi_objective_convex_fn.py

@@ -572,7 +572,7 @@ def _init_certificate(self) -> None:
 from opto.trainer.loggers import TensorboardLogger
 from opto import trainer
 from opto.trainer.objectives import ObjectiveConfig
-from opto.trainer.algorithms.basic_algorithms import BasicSearchAlgorithm as SearchAlgorithm
+from examples.trainers.basic_algorithms import BasicSearchAlgorithm as SearchAlgorithm
 from typing import Tuple
 from copy import copy