Skip to content
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -23,8 +23,8 @@ In addition to the features offered by traditional HPO and NAS libraries, NePS s
NePS provides capabilities for optimizing DL architectures in an expressive and natural fashion.
- [Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars (NeurIPS 2023)](https://arxiv.org/abs/2211.01842) <br /> <br />
1. **Zero-effort Parallelization and an Experience Tailored to DL:** <br />
NePS simplifies the process of parallelizing optimization tasks both on individual computers and in distributed
computing environments. As NePS is made for deep learners, all technical choices are made with DL in mind and common
NePS simplifies the process of parallelizing optimization tasks both on individual computers, where multiple workers coordinate
through the shared results directory, and in distributed computing environments, naturally supporting PyTorch [DDP](https://docs.pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html) and [FSDP](https://docs.pytorch.org/tutorials/intermediate/FSDP1_tutorial.html). As NePS is made for deep learners, all technical choices are made with DL in mind and common
DL tools such as Tensorboard are [embraced](https://automl.github.io/neps/latest/reference/analyse/#visualizing-results).

## Installation
Expand Down
Loading