Skip to main content

Pytorch domain library for recommendation systems

Project description

TorchRec (Beta Release)

Docs

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 contains:

  • Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism.
  • The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel, table-wise, row-wise, table-wise-row-wise, and column-wise sharding.
  • The TorchRec planner can automatically generate optimized sharding plans for models.
  • Pipelined training overlaps dataloading device transfer (copy to GPU), inter-device communications (input_dist), and computation (forward, backward) for increased performance.
  • Optimized kernels for RecSys powered by FBGEMM.
  • Quantization support for reduced precision training and inference.
  • Common modules for RecSys.
  • Production-proven model architectures for RecSys.
  • RecSys datasets (criteo click logs and movielens)
  • Examples of end-to-end training such the dlrm event prediction model trained on criteo click logs dataset.

Installation

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 11.6. This setup assumes you have conda installed.

Binaries

Experimental binary on Linux for Python 3.7, 3.8 and 3.9 can be installed via pip wheels

Installations

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=11.7 -c pytorch-nightly -c nvidia
pip install torchrec_nightly

Stable

conda install pytorch pytorch-cuda=11.7 -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

Colab example: introduction + install

See our colab notebook for an introduction to torchrec which includes runnable installation. - Tutorial Source - Open in Google Colab

From Source

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.

  1. Install pytorch. See pytorch documentation

    conda install pytorch pytorch-cuda=11.7 -c pytorch-nightly -c nvidia
    
  2. Install Requirements

    pip install -r requirements.txt
    
  3. Download and install TorchRec.

    git clone --recursive https://github.com/pytorch/torchrec
    
    cd torchrec
    python setup.py install develop
    
  4. 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.

  5. If you want to run a more complex example, please take a look at the torchrec DLRM example.

License

TorchRec is BSD licensed, as found in the LICENSE file.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

torchrec_nightly-2022.11.30-py39-none-any.whl (342.0 kB view details)

Uploaded Python 3.9

torchrec_nightly-2022.11.30-py38-none-any.whl (342.0 kB view details)

Uploaded Python 3.8

torchrec_nightly-2022.11.30-py37-none-any.whl (342.0 kB view details)

Uploaded Python 3.7

File details

Details for the file torchrec_nightly-2022.11.30-py39-none-any.whl.

File metadata

File hashes

Hashes for torchrec_nightly-2022.11.30-py39-none-any.whl
Algorithm Hash digest
SHA256 6d45d24377ded67a95b4a358ac34a4178548bbb3a8a5314c17fffe5fec795f8b
MD5 d90dcd0a5b682c64976241bf5d682860
BLAKE2b-256 f4a3ecbb5b76b2131d5bd38c1768ae834076523d7386f621ff3f2becef6605cb

See more details on using hashes here.

File details

Details for the file torchrec_nightly-2022.11.30-py38-none-any.whl.

File metadata

File hashes

Hashes for torchrec_nightly-2022.11.30-py38-none-any.whl
Algorithm Hash digest
SHA256 9b53cd3db80f94ed424be34aaf028f1f5e2be801015919b125ec9b08246f139a
MD5 95cd122fb5bcd6a5d4d04397fcae16f3
BLAKE2b-256 6fd93582c674819114a4c3976cc99bb4c934f07917814783d124736deb773488

See more details on using hashes here.

File details

Details for the file torchrec_nightly-2022.11.30-py37-none-any.whl.

File metadata

File hashes

Hashes for torchrec_nightly-2022.11.30-py37-none-any.whl
Algorithm Hash digest
SHA256 d49fbe9c3d2c07ce9435df3089f25976d1c529d2b68fb33b7e3d6c9f725b2431
MD5 95751436bc72001fe89446e7ffba3f4c
BLAKE2b-256 68d16d60176f4ec9d87381e45da566972cdda2b0a6209216138f15bda566421c

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page