Skip to main content

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

Project description

  • Improve and analyse 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, time measurements etc.)
  • Get information about your host operating system, torch.nn.Module device, CUDA capabilities etc.
Version Docs Tests Coverage Style PyPI Python PyTorch Docker Roadmap
Version Documentation Tests Coverage codebeat PyPI Python PyTorch Docker Roadmap

:bulb: Examples

Check documentation here: https://szymonmaszke.github.io/torchfunc

1. Getting performance tips

  • 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()))

2. Seeding, weight freezing and others

  • 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)}")

3. Record torch.nn.Module internal state

  • 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-1608597931.tar.gz (24.2 kB view details)

Uploaded Source

Built Distribution

torchfunc_nightly-1608597931-py3-none-any.whl (30.6 kB view details)

Uploaded Python 3

File details

Details for the file torchfunc-nightly-1608597931.tar.gz.

File metadata

  • Download URL: torchfunc-nightly-1608597931.tar.gz
  • Upload date:
  • Size: 24.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.6

File hashes

Hashes for torchfunc-nightly-1608597931.tar.gz
Algorithm Hash digest
SHA256 2fc4e5c97adbb6fc6ea88dce33450214b754c53e9bb08cbc825859d8a387b634
MD5 f03901a3cf1f2c1071a09181bd166e20
BLAKE2b-256 5bc737712e0d85eca9b1573d37bbeefd43881aa4f017ed6435f5974f32e4fca9

See more details on using hashes here.

File details

Details for the file torchfunc_nightly-1608597931-py3-none-any.whl.

File metadata

  • Download URL: torchfunc_nightly-1608597931-py3-none-any.whl
  • Upload date:
  • Size: 30.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.25.1 setuptools/51.0.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.8.6

File hashes

Hashes for torchfunc_nightly-1608597931-py3-none-any.whl
Algorithm Hash digest
SHA256 52c70110580028842fcc166ac6867134cbf28e20d8ef35fc8def915da6fe3afd
MD5 520a6099bd3f518b495c49a6ad379a63
BLAKE2b-256 52f199c2644dfc81c6d4569a816fa347844c02f9f9be9c8106a5e88eedf0cf92

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