Skip to main content

torch compatibility layer

Project description

Current PyPi Version Supported Python Versions codecov docs tests style

pip install torchcompat

Features

  • Provide a super set implementation of pytorch device interface to enable code to run seamlessly between different accelerators.

  • Identify uniquely devices

import torchcompat.core as accelerator

# on  cuda accelerator == torch.cuda
# on  rocm accelerator == torch.cuda
# on   xpu accelerator == torch.xpu
# on gaudi accelerator == ...

assert accelerator.is_available() == true
assert accelerator.device_name in ('xpu', 'cuda', "hpu")           # rocm is seen as cuda by pytorch
assert accelerator.device_string(0) == "cuda:0" or "xpu:0" or "hpu:0"
assert accelerator.fetch_device(0) == torch.device("cuda:0")


accelerator.set_enable_tf32(true) # toggle the right flags for each backend

Example

example here

Project details


Download files

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

Source Distribution

torchcompat-1.1.4.tar.gz (5.9 kB view details)

Uploaded Source

Built Distribution

torchcompat-1.1.4-py3-none-any.whl (9.0 kB view details)

Uploaded Python 3

File details

Details for the file torchcompat-1.1.4.tar.gz.

File metadata

  • Download URL: torchcompat-1.1.4.tar.gz
  • Upload date:
  • Size: 5.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.19

File hashes

Hashes for torchcompat-1.1.4.tar.gz
Algorithm Hash digest
SHA256 fabd54bea79624f2e7058842f38536b3d80b2a586c1ac88303d42b8e06ae8c84
MD5 2a0b3cb74f9a4f4a8de7d29797755a1c
BLAKE2b-256 2cdeac95694f30034e716e83427a1e9e84ea368735b566300f696178e9e910be

See more details on using hashes here.

File details

Details for the file torchcompat-1.1.4-py3-none-any.whl.

File metadata

  • Download URL: torchcompat-1.1.4-py3-none-any.whl
  • Upload date:
  • Size: 9.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.19

File hashes

Hashes for torchcompat-1.1.4-py3-none-any.whl
Algorithm Hash digest
SHA256 2e3b3c56717590450588a4d431be387355d16864fdd74edb22fb189b9feca47b
MD5 9ce230c5d8993854bc52f95edc03fd3a
BLAKE2b-256 94b29b5d6f9aa9ffc7d35a42ff8a0e2fcf2e9458be5f0bb6077ad98925f8c043

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