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+%global _empty_manifest_terminate_build 0
+Name:		python-torchfunc-nightly
+Version:	1663034600
+Release:	1
+Summary:	PyTorch functions to improve performance, analyse models and make your life easier.
+License:	MIT
+URL:		https://github.com/szymonmaszke/torchfunc
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d1/4f/ab187f42f60ca8a06bf1e22832fd52e9990326698bd4834baa833829f217/torchfunc-nightly-1663034600.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-torch
+
+%description
+<img align="left" width="256" height="256" src="https://github.com/szymonmaszke/torchfunc/blob/master/assets/logos/medium.png">
+
+* 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](https://img.shields.io/static/v1?label=&message=0.2.0&color=377EF0&style=for-the-badge)](https://github.com/szymonmaszke/torchfunc/releases) | [![Documentation](https://img.shields.io/static/v1?label=&message=docs&color=EE4C2C&style=for-the-badge)](https://szymonmaszke.github.io/torchfunc/)  | ![Tests](https://github.com/szymonmaszke/torchfunc/workflows/test/badge.svg) | ![Coverage](https://img.shields.io/codecov/c/github/szymonmaszke/torchfunc?label=%20&logo=codecov&style=for-the-badge) | [![codebeat](https://img.shields.io/static/v1?label=&message=CB&color=27A8E0&style=for-the-badge)](https://codebeat.co/projects/github-com-szymonmaszke-torchfunc-master) | [![PyPI](https://img.shields.io/static/v1?label=&message=PyPI&color=377EF0&style=for-the-badge)](https://pypi.org/project/torchfunc/) | [![Python](https://img.shields.io/static/v1?label=&message=3.6&color=377EF0&style=for-the-badge&logo=python&logoColor=F8C63D)](https://www.python.org/) | [![PyTorch](https://img.shields.io/static/v1?label=&message=>=1.2.0&color=EE4C2C&style=for-the-badge)](https://pytorch.org/) | [![Docker](https://img.shields.io/static/v1?label=&message=docker&color=309cef&style=for-the-badge)](https://hub.docker.com/r/szymonmaszke/torchfunc) | [![Roadmap](https://img.shields.io/static/v1?label=&message=roadmap&color=009688&style=for-the-badge)](https://github.com/szymonmaszke/torchfunc/blob/master/ROADMAP.md) |
+
+# :bulb: Examples
+
+__Check documentation here:__ [https://szymonmaszke.github.io/torchfunc](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`:__
+
+```python
+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:__
+
+```python
+# 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:__
+
+```python
+# 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](https://szymonmaszke.github.io/torchfunc/)
+
+# :wrench: Installation
+
+## :snake: [pip](<https://pypi.org/project/torchfunc/>)
+
+### Latest release:
+
+```shell
+pip install --user torchfunc
+```
+
+### Nightly:
+
+```shell
+pip install --user torchfunc-nightly
+```
+
+## :whale2: [Docker](https://hub.docker.com/r/szymonmaszke/torchfunc)
+
+__CPU standalone__ and various versions of __GPU enabled__ images are available
+at [dockerhub](https://hub.docker.com/r/szymonmaszke/torchfunc/tags).
+
+For CPU quickstart, issue:
+
+```shell
+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](https://github.com/NVIDIA/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](https://help.github.com/en/articles/creating-an-issue) or [create Pull Request](https://help.github.com/en/articles/creating-a-pull-request-from-a-fork).
+
+To get an overview of things one can do to help this project, see [Roadmap](https://github.com/szymonmaszke/torchfunc/blob/master/ROADMAP.md).
+
+
+
+
+%package -n python3-torchfunc-nightly
+Summary:	PyTorch functions to improve performance, analyse models and make your life easier.
+Provides:	python-torchfunc-nightly
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-torchfunc-nightly
+<img align="left" width="256" height="256" src="https://github.com/szymonmaszke/torchfunc/blob/master/assets/logos/medium.png">
+
+* 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](https://img.shields.io/static/v1?label=&message=0.2.0&color=377EF0&style=for-the-badge)](https://github.com/szymonmaszke/torchfunc/releases) | [![Documentation](https://img.shields.io/static/v1?label=&message=docs&color=EE4C2C&style=for-the-badge)](https://szymonmaszke.github.io/torchfunc/)  | ![Tests](https://github.com/szymonmaszke/torchfunc/workflows/test/badge.svg) | ![Coverage](https://img.shields.io/codecov/c/github/szymonmaszke/torchfunc?label=%20&logo=codecov&style=for-the-badge) | [![codebeat](https://img.shields.io/static/v1?label=&message=CB&color=27A8E0&style=for-the-badge)](https://codebeat.co/projects/github-com-szymonmaszke-torchfunc-master) | [![PyPI](https://img.shields.io/static/v1?label=&message=PyPI&color=377EF0&style=for-the-badge)](https://pypi.org/project/torchfunc/) | [![Python](https://img.shields.io/static/v1?label=&message=3.6&color=377EF0&style=for-the-badge&logo=python&logoColor=F8C63D)](https://www.python.org/) | [![PyTorch](https://img.shields.io/static/v1?label=&message=>=1.2.0&color=EE4C2C&style=for-the-badge)](https://pytorch.org/) | [![Docker](https://img.shields.io/static/v1?label=&message=docker&color=309cef&style=for-the-badge)](https://hub.docker.com/r/szymonmaszke/torchfunc) | [![Roadmap](https://img.shields.io/static/v1?label=&message=roadmap&color=009688&style=for-the-badge)](https://github.com/szymonmaszke/torchfunc/blob/master/ROADMAP.md) |
+
+# :bulb: Examples
+
+__Check documentation here:__ [https://szymonmaszke.github.io/torchfunc](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`:__
+
+```python
+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:__
+
+```python
+# 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:__
+
+```python
+# 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](https://szymonmaszke.github.io/torchfunc/)
+
+# :wrench: Installation
+
+## :snake: [pip](<https://pypi.org/project/torchfunc/>)
+
+### Latest release:
+
+```shell
+pip install --user torchfunc
+```
+
+### Nightly:
+
+```shell
+pip install --user torchfunc-nightly
+```
+
+## :whale2: [Docker](https://hub.docker.com/r/szymonmaszke/torchfunc)
+
+__CPU standalone__ and various versions of __GPU enabled__ images are available
+at [dockerhub](https://hub.docker.com/r/szymonmaszke/torchfunc/tags).
+
+For CPU quickstart, issue:
+
+```shell
+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](https://github.com/NVIDIA/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](https://help.github.com/en/articles/creating-an-issue) or [create Pull Request](https://help.github.com/en/articles/creating-a-pull-request-from-a-fork).
+
+To get an overview of things one can do to help this project, see [Roadmap](https://github.com/szymonmaszke/torchfunc/blob/master/ROADMAP.md).
+
+
+
+
+%package help
+Summary:	Development documents and examples for torchfunc-nightly
+Provides:	python3-torchfunc-nightly-doc
+%description help
+<img align="left" width="256" height="256" src="https://github.com/szymonmaszke/torchfunc/blob/master/assets/logos/medium.png">
+
+* 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](https://img.shields.io/static/v1?label=&message=0.2.0&color=377EF0&style=for-the-badge)](https://github.com/szymonmaszke/torchfunc/releases) | [![Documentation](https://img.shields.io/static/v1?label=&message=docs&color=EE4C2C&style=for-the-badge)](https://szymonmaszke.github.io/torchfunc/)  | ![Tests](https://github.com/szymonmaszke/torchfunc/workflows/test/badge.svg) | ![Coverage](https://img.shields.io/codecov/c/github/szymonmaszke/torchfunc?label=%20&logo=codecov&style=for-the-badge) | [![codebeat](https://img.shields.io/static/v1?label=&message=CB&color=27A8E0&style=for-the-badge)](https://codebeat.co/projects/github-com-szymonmaszke-torchfunc-master) | [![PyPI](https://img.shields.io/static/v1?label=&message=PyPI&color=377EF0&style=for-the-badge)](https://pypi.org/project/torchfunc/) | [![Python](https://img.shields.io/static/v1?label=&message=3.6&color=377EF0&style=for-the-badge&logo=python&logoColor=F8C63D)](https://www.python.org/) | [![PyTorch](https://img.shields.io/static/v1?label=&message=>=1.2.0&color=EE4C2C&style=for-the-badge)](https://pytorch.org/) | [![Docker](https://img.shields.io/static/v1?label=&message=docker&color=309cef&style=for-the-badge)](https://hub.docker.com/r/szymonmaszke/torchfunc) | [![Roadmap](https://img.shields.io/static/v1?label=&message=roadmap&color=009688&style=for-the-badge)](https://github.com/szymonmaszke/torchfunc/blob/master/ROADMAP.md) |
+
+# :bulb: Examples
+
+__Check documentation here:__ [https://szymonmaszke.github.io/torchfunc](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`:__
+
+```python
+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:__
+
+```python
+# 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:__
+
+```python
+# 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](https://szymonmaszke.github.io/torchfunc/)
+
+# :wrench: Installation
+
+## :snake: [pip](<https://pypi.org/project/torchfunc/>)
+
+### Latest release:
+
+```shell
+pip install --user torchfunc
+```
+
+### Nightly:
+
+```shell
+pip install --user torchfunc-nightly
+```
+
+## :whale2: [Docker](https://hub.docker.com/r/szymonmaszke/torchfunc)
+
+__CPU standalone__ and various versions of __GPU enabled__ images are available
+at [dockerhub](https://hub.docker.com/r/szymonmaszke/torchfunc/tags).
+
+For CPU quickstart, issue:
+
+```shell
+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](https://github.com/NVIDIA/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](https://help.github.com/en/articles/creating-an-issue) or [create Pull Request](https://help.github.com/en/articles/creating-a-pull-request-from-a-fork).
+
+To get an overview of things one can do to help this project, see [Roadmap](https://github.com/szymonmaszke/torchfunc/blob/master/ROADMAP.md).
+
+
+
+
+%prep
+%autosetup -n torchfunc-nightly-1663034600
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-torchfunc-nightly -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1663034600-1
+- Package Spec generated