|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|D2l En||18,967||21 days ago||2||November 13, 2022||95||other||Python|
|Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.|
|Deeplearningexamples||11,463||6 days ago||279||Jupyter Notebook|
|State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.|
|Catalyst||3,151||19||13||2 months ago||108||April 29, 2022||6||apache-2.0||Python|
|Accelerated deep learning R&D|
|Recbole||2,834||2||a day ago||9||February 25, 2022||160||mit||Python|
|A unified, comprehensive and efficient recommendation library|
|Spotlight||2,809||9 months ago||72||mit||Python|
|Deep recommender models using PyTorch.|
|Torchrec||1,531||1||14 hours ago||352||August 20, 2023||131||bsd-3-clause||Python|
|Pytorch domain library for recommendation systems|
|Transformers4rec||900||17 days ago||9||June 16, 2022||72||apache-2.0||Python|
|Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.|
|Fuxictr||571||2 months ago||8||July 03, 2022||3||apache-2.0||Python|
|A configurable, tunable, and reproducible library for CTR prediction https://fuxictr.github.io/tutorials|
|Daisyrec||496||6 months ago||15||August 14, 2022||mit||Python|
|This is the repository of our article published in RecSys 2020 "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" and of several follow-up studies.|
|Crslab||397||4 months ago||2||March 28, 2021||6||mit||Python|
|CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).|
TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs.
Torchrec requires Python >= 3.7 and CUDA >= 11.0 (CUDA is highly recommended for performance but not required). The example below shows how to install with CUDA 11.8. This setup assumes you have conda installed.
Experimental binary on Linux for Python 3.7, 3.8 and 3.9 can be installed via pip wheels
TO use the library without cuda, use the *-cpu fbgemm installations. However, this will be much slower than the CUDA variant. Nightly conda install pytorch pytorch-cuda=11.8 -c pytorch-nightly -c nvidia pip install torchrec_nightly Stable conda install pytorch pytorch-cuda=11.8 -c pytorch -c nvidia pip install torchrec If you have no CUDA device: Nightly pip uninstall fbgemm-gpu-nightly -y pip install fbgemm-gpu-nightly-cpu Stable pip uninstall fbgemm-gpu -y pip install fbgemm-gpu-cpu
We are currently iterating on the setup experience. For now, we provide manual instructions on how to build from source. The example below shows how to install with CUDA 11.3. This setup assumes you have conda installed.
Install pytorch. See pytorch documentation
conda install pytorch pytorch-cuda=11.8 -c pytorch-nightly -c nvidia
pip install -r requirements.txt
Download and install TorchRec.
git clone --recursive https://github.com/pytorch/torchrec cd torchrec python setup.py install develop
Test the installation.
GPU mode torchx run -s local_cwd dist.ddp -j 1x2 --gpu 2 --script test_installation.py CPU Mode torchx run -s local_cwd dist.ddp -j 1x2 --script test_installation.py -- --cpu_only
See TorchX for more information on launching distributed and remote jobs.
If you want to run a more complex example, please take a look at the torchrec DLRM example.
Before landing, please make sure that pyre and linting look okay. To run our linters, you will need to
pip install pre-commit
, and run it.
For Pyre, you will need to
cat .pyre_configuration pip install pyre-check-nightly==<VERSION FROM CONFIG> pyre check
We will also check for these issues in our GitHub actions.
TorchRec is BSD licensed, as found in the LICENSE file.