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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

💡 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

🔧 Installation

🐍 pip

Latest release:

pip install --user torchfunc

Nightly:

pip install --user torchfunc-nightly

🐋 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.

❓ 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.

Metadata

Release files for torchfunc-nightly 1579590172

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for torchfunc-nightly 1579590172
File	Size	Uploaded
torchfunc-nightly-1579590172.tar.gz	22.7 kB	Jan 21, 2020	Details

Built distribution (wheel)

Table of built distributions (wheels) for torchfunc-nightly 1579590172
File	Interpreter	ABI	Platform	Reset
torchfunc_nightly-1579590172-py3-none-any.whl 29.1 kB Jan 21, 2020	Python 3	none	any	Details

Total release size: 51.8 kB

Release files / torchfunc-nightly-1579590172.tar.gz

Download URL	torchfunc-nightly-1579590172.tar.gz
Size	22.7 kB
Tags	Source
SHA-256 checksum How to use checksums	`a3e50e13d1e63a8191dc65eee4825962f058eb34af2edbcc6299dccebfc2ce10`
BLAKE2b-256 checksum How to use checksums	`637da301da01b17c68860789abb28908a030e6bbdb227a9117f5ce71f1c339bc`
Upload date	Jan 21, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/44.0.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.7.6`

Release files / torchfunc_nightly-1579590172-py3-none-any.whl

Download URL	torchfunc_nightly-1579590172-py3-none-any.whl
Size	29.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`5bbf5746e2b317814cc91ed55f5e1731ac8fcfb068eef0402ef5114194a20aab`
BLAKE2b-256 checksum How to use checksums	`65d20c92d3cb372a11b515c0838ebec8fde84ba53ae070baa03e3f9b0597a250`
Upload date	Jan 21, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/44.0.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.7.6`