- Improve and analyse performance of your neural network (e.g. Tensor Cores compatibility)
- Record/analyse internal state of
torch.nn.Moduleas data passes through it - Do the above based on external conditions (using single
Callableto specify it) - Day-to-day neural network related duties (model size, seeding, time measurements etc.)
- Get information about your host operating system,
torch.nn.Moduledevice, CUDA capabilities etc.
| Version | Docs | Tests | Coverage | Style | PyPI | Python | PyTorch | Docker | Roadmap |
|---|---|---|---|---|---|---|---|---|---|
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
💡 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
numpyandcuda), 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
🔧 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.
1 maintainer
Metadata
1 maintainer
Release files for torchfunc-nightly 1641603358
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| torchfunc-nightly-1641603358.tar.gz | 24.5 kB | Without provenance | Details for torchfunc-nightly-1641603358.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| torchfunc_nightly-1641603358-py3-none-any.whl | Python 3 | none | any | Details for torchfunc_nightly-1641603358-py3-none-any.whl |
Total release size: 55.5 kB
Release files / torchfunc-nightly-1641603358.tar.gz
| Download URL | torchfunc-nightly-1641603358.tar.gz |
|---|---|
| Size | 24.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
5f39aba0acdc9e81b600e399b2d3a8f03073f230cf29e8d5d0c3f1d37310c049
|
|
BLAKE2b-256 checksum How to use checksums |
c890a0e538465867a6e840af8bfb9cb1b3d10267a0d9890c15d17db0db730e4d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9
|
Release files / torchfunc_nightly-1641603358-py3-none-any.whl
| Download URL | torchfunc_nightly-1641603358-py3-none-any.whl |
|---|---|
| Size | 31.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
87d2d5cd1ae3ce4509e3633104894cebffa04f01eed35bfec7450412894d28be
|
|
BLAKE2b-256 checksum How to use checksums |
05828dd21551695587caf14d7eb5c15486ecb59c5839e13a8727d0e6cc5acb28
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.27.1 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9
|
Security for torchfunc-nightly 1641603358
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
