This is the Windows app named TorchRec whose latest release can be downloaded as v0.5.0.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named TorchRec with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS
Ad
TorchRec
DESCRIPTION
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. 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.
Features
- Built to provide common sparsity & parallelism primitives needed for large-scale recommender systems
- The TorchRec planner can automatically generate optimized sharding plans for models
- Torchrec requires Python >= 3.7 and CUDA >= 11.0
- Experimental binary on Linux for Python 3.7, 3.8 and 3.9 can be installed via pip wheels
- TorchRec is BSD licensed
- Quantization support for reduced precision training and inference
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/torchrec.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
