Skip to main content

PyTorch functions to improve performance, analyse models and make your life easier.

Project description

torchfunc Logo


Version Docs Tests Coverage Style PyPI Python PyTorch Docker Roadmap
Version Documentation Tests Coverage codebeat PyPI Python PyTorch Docker Roadmap

torchfunc is library revolving around PyTorch with a goal to help you with:

  • Improving and analysing performance of your neural network (e.g. Tensor Cores compatibility)
  • Record/analyse internal state of torch.nn.Module as data passes through it
  • Do the above based on external conditions (using single Callable to specify it)
  • Day-to-day neural network related duties (model size, seeding, performance measurements etc.)
  • Get information about your host operating system, CUDA devices and others

:bulb: Examples

  • Get instant performance tips about your module. All problems described by comments will be shown by torchfunc.performance.tips:
class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.convolution = torch.nn.Sequential(
            torch.nn.Conv2d(1, 32, 3),
            torch.nn.ReLU(inplace=True),  # Inplace may harm kernel fusion
            torch.nn.Conv2d(32, 128, 3, groups=32),  # Depthwise is slower in PyTorch
            torch.nn.ReLU(inplace=True),  # Same as before
            torch.nn.Conv2d(128, 250, 3),  # Wrong output size for TensorCores
        )

        self.classifier = torch.nn.Sequential(
            torch.nn.Linear(250, 64),  # Wrong input size for TensorCores
            torch.nn.ReLU(),  # Fine, no info about this layer
            torch.nn.Linear(64, 10),  # Wrong output size for TensorCores
        )

    def forward(self, inputs):
        convolved = torch.nn.AdaptiveAvgPool2d(1)(self.convolution(inputs)).flatten()
        return self.classifier(convolved)

# All you have to do
print(torchfunc.performance.tips(Model()))
  • Seed globaly (including numpy and cuda), freeze weights, check inference time and model size:
# Inb4 MNIST, you can use any module with those functions
model = torch.nn.Linear(784, 10)
torchfunc.seed(0)
frozen = torchfunc.module.freeze(model, bias=False)

with torchfunc.Timer() as timer:
  frozen(torch.randn(32, 784)
  print(timer.checkpoint()) # Time since the beginning
  frozen(torch.randn(128, 784)
  print(timer.checkpoint()) # Since last checkpoint

print(f"Overall time {timer}; Model size: {torchfunc.sizeof(frozen)}")
  • Record and sum per-layer activation statistics as data passes through network:
# Still MNIST but any module can be put in it's place
model = torch.nn.Sequential(
    torch.nn.Linear(784, 100),
    torch.nn.ReLU(),
    torch.nn.Linear(100, 50),
    torch.nn.ReLU(),
    torch.nn.Linear(50, 10),
)
# Recorder which sums all inputs to layers
recorder = torchfunc.hooks.recorders.ForwardPre(reduction=lambda x, y: x+y)
# Record only for torch.nn.Linear
recorder.children(model, types=(torch.nn.Linear,))
# Train your network normally (or pass data through it)
...
# Activations of all neurons of first layer! 
print(recorder[1]) # You can also post-process this data easily with apply

For other examples (and how to use condition), see documentation

:wrench: Installation

:snake: pip

Latest release:

pip install --user torchfunc

Nightly:

pip install --user torchfunc-nightly

:whale2: Docker

CPU standalone and various versions of GPU enabled images are available at dockerhub.

For CPU quickstart, issue:

docker pull szymonmaszke/torchfunc:18.04

Nightly builds are also available, just prefix tag with nightly_. If you are going for GPU image make sure you have nvidia/docker installed and it's runtime set.

:question: Contributing

If you find any issue or you think some functionality may be useful to others and fits this library, please open new Issue or create Pull Request.

To get an overview of things one can do to help this project, see Roadmap.

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 Distribution

torchfunc-nightly-1581750264.tar.gz (22.7 kB view details)

Uploaded Source

Built Distribution

torchfunc_nightly-1581750264-py3-none-any.whl (29.1 kB view details)

Uploaded Python 3

File details

Details for the file torchfunc-nightly-1581750264.tar.gz.

File metadata

  • Download URL: torchfunc-nightly-1581750264.tar.gz
  • Upload date:
  • Size: 22.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/45.1.0 requests-toolbelt/0.9.1 tqdm/4.42.1 CPython/3.7.6

File hashes

Hashes for torchfunc-nightly-1581750264.tar.gz
Algorithm Hash digest
SHA256 5996f6e4b82127aee102f45530681318bb69eef98748baf86591293927b2f9b3
MD5 df272a146de57d6ba9c79f13e109c5b6
BLAKE2b-256 3e413fbc3c45707caad3759ecd806a525d990cbe54fb91de659e31f002f1b5d6

See more details on using hashes here.

File details

Details for the file torchfunc_nightly-1581750264-py3-none-any.whl.

File metadata

  • Download URL: torchfunc_nightly-1581750264-py3-none-any.whl
  • Upload date:
  • Size: 29.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/45.1.0 requests-toolbelt/0.9.1 tqdm/4.42.1 CPython/3.7.6

File hashes

Hashes for torchfunc_nightly-1581750264-py3-none-any.whl
Algorithm Hash digest
SHA256 253f817f20e255427269861710c22b2769324283984fe4a39c608dc2b1cbf21b
MD5 315638f76ed41c69d85ff1c5d7f1b19e
BLAKE2b-256 8448d84f23fa717f8fe95a47b4fb83987acb9f437a8531fc54b13e3662dc8842

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