automatic import of python-trainableopeneuler20.03

1 files changed, 404 insertions, 0 deletions

diff --git a/python-trainable.spec b/python-trainable.spec
new file mode 100644
index 0000000..f13eed1
--- /dev/null
+++ b/python-trainable.spec
@@ -0,0 +1,404 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-trainable
+Version:	0.1.3.post9
+Release:	1
+Summary:	The flexible training toolbox
+License:	MIT License
+URL:		https://pypi.org/project/trainable/
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/cc/70/a754512b9ac8b974c7371b313fb6dcc02827c989aa7f08d0bd2860d79cd3/trainable-0.1.3.post9.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-torch
+Requires:	python3-torchvision
+Requires:	python3-tqdm
+Requires:	python3-matplotlib
+Requires:	python3-numpy
+
+%description
+# Trainable: The Flexible PyTorch Training Toolbox
+
+If you're sick of dealing with all of the boilerplate code involved in training, evaluation, visualization, and
+preserving your models, then you're in luck. Trainable offers a simple, yet extensible framework to make understanding
+the latest papers the *only* headache of Neural Network training.
+
+## Installation
+```bash
+pip install trainable
+```
+
+## Usage
+The typical workflow for trainable involves defining a callable Algorithm to describe how to train 
+your network on a batch, and how you'd like to label your losses:
+
+```python
+class MSEAlgorithm(Algorithm):
+    def __init__(self, eval=False, **args):
+        super().__init__(eval)
+        self.mse = nn.MSELoss()
+
+    def __call__(self, model, batch, device):
+        x, target = batch
+        x, target = x.to(device), target.to(device)
+        y = model(x)
+
+        loss = self.mse(y, target)
+        loss.backward()
+
+        metrics = { self.key("MSE Loss"):loss.item() }
+        return metrics
+```
+
+Then you simply instantiate your model, dataset, and optimizer...
+
+```python
+device = torch.device('cuda')
+
+model = MyModel().to(device)
+optim = FancyPantsOptimizer(model.parameters(), lr=1e-4)
+
+train_data = DataLoader(SomeTrainingDataset('path/to/your/data'), batch_size=32)
+test_data = DataLoader(SomeTestingDataset('path/to/your/data'), batch_size=32)
+```
+
+...and let trainable take care of the rest!
+```python
+trainer = Trainer(
+  visualizer=MyVisualizer(),  # Typically Plotter() or Saver()
+  train_alg=MyFancyAlgorithm(),
+  test_alg=MyFancyAlgorithm(eval=True)
+  display_freq=1,
+  visualize_freq=10,
+  validate_freq=10,
+  autosave_freq=10,
+  device=device
+)
+
+save_path = "desired/save/path/for/your/session.sesh"
+trainer.start_session(model, optim, path)
+
+trainer.name_session('Name')
+
+trainer.describe_session("""
+A beautiful detailed description of what the heck 
+you were trying to accomplish with this training.
+""")
+
+metrics = trainer.train(train_data, test_data, epochs=200)
+```
+
+Plotting your data is simple as well:
+```python
+import matplotlib.pyplot as plt
+
+for key in metrics:
+    plt.plot(metrics[key])
+    plt.show()
+```
+
+## Tunable Options
+The Trainer interface gives you a nice handful of options to configure your training experience.
+They include:
+* **Display Frequency:** How often (in batches) information such as your training loss is updated in your progress bar.
+* **Visualization Frequency:** How often (in batches) the training produces a visualization of your model's outputs. 
+* **Validation Frequency:** How often (in epochs) the trainer performs validation with your test data.
+* **Autosave Frequency:** How often your session is saved out to disk. 
+* **Device:** On which hardware your training should occur.
+
+## Customization
+Do you want a little more granularity in how you visualize your data? Or perhaps
+running an epoch with your model is a little more involved than just training
+on each batch of data? Wondering why the heck pytorch doesn't have a built-in dataset for unsupervised images?
+Maybe your training algorithm involves VGG? Got you covered. Check out the source for the various submodules:
+* [trainable.visualize](https://github.com/hiltonjp/trainable/blob/master/trainable/visualize.py) -- for customizing visualization.
+* [trainable.epoch](https://github.com/hiltonjp/trainable/blob/master/trainable/epoch.py) -- for customizing epochs.
+* [trainable.data](https://github.com/hiltonjp/trainable/tree/master/trainable/data) -- for common datasets and transforms
+    not found in pytorch's modules.
+* [trainable.features](https://github.com/hiltonjp/trainable/tree/master/trainable/features) -- for working with intermediate
+    activations and features, such as with VGG-based losses.
+
+## Contributing
+Find any other headaches in neural net training that you think you can simplify with Trainable? Feel free to make a
+pull request from my [github repo](https://github.com/hiltonjp/trainable). 
+
+## Contact
+Email me anytime at [jeffhilton.code@gmail.com.]()
+
+
+
+
+%package -n python3-trainable
+Summary:	The flexible training toolbox
+Provides:	python-trainable
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-trainable
+# Trainable: The Flexible PyTorch Training Toolbox
+
+If you're sick of dealing with all of the boilerplate code involved in training, evaluation, visualization, and
+preserving your models, then you're in luck. Trainable offers a simple, yet extensible framework to make understanding
+the latest papers the *only* headache of Neural Network training.
+
+## Installation
+```bash
+pip install trainable
+```
+
+## Usage
+The typical workflow for trainable involves defining a callable Algorithm to describe how to train 
+your network on a batch, and how you'd like to label your losses:
+
+```python
+class MSEAlgorithm(Algorithm):
+    def __init__(self, eval=False, **args):
+        super().__init__(eval)
+        self.mse = nn.MSELoss()
+
+    def __call__(self, model, batch, device):
+        x, target = batch
+        x, target = x.to(device), target.to(device)
+        y = model(x)
+
+        loss = self.mse(y, target)
+        loss.backward()
+
+        metrics = { self.key("MSE Loss"):loss.item() }
+        return metrics
+```
+
+Then you simply instantiate your model, dataset, and optimizer...
+
+```python
+device = torch.device('cuda')
+
+model = MyModel().to(device)
+optim = FancyPantsOptimizer(model.parameters(), lr=1e-4)
+
+train_data = DataLoader(SomeTrainingDataset('path/to/your/data'), batch_size=32)
+test_data = DataLoader(SomeTestingDataset('path/to/your/data'), batch_size=32)
+```
+
+...and let trainable take care of the rest!
+```python
+trainer = Trainer(
+  visualizer=MyVisualizer(),  # Typically Plotter() or Saver()
+  train_alg=MyFancyAlgorithm(),
+  test_alg=MyFancyAlgorithm(eval=True)
+  display_freq=1,
+  visualize_freq=10,
+  validate_freq=10,
+  autosave_freq=10,
+  device=device
+)
+
+save_path = "desired/save/path/for/your/session.sesh"
+trainer.start_session(model, optim, path)
+
+trainer.name_session('Name')
+
+trainer.describe_session("""
+A beautiful detailed description of what the heck 
+you were trying to accomplish with this training.
+""")
+
+metrics = trainer.train(train_data, test_data, epochs=200)
+```
+
+Plotting your data is simple as well:
+```python
+import matplotlib.pyplot as plt
+
+for key in metrics:
+    plt.plot(metrics[key])
+    plt.show()
+```
+
+## Tunable Options
+The Trainer interface gives you a nice handful of options to configure your training experience.
+They include:
+* **Display Frequency:** How often (in batches) information such as your training loss is updated in your progress bar.
+* **Visualization Frequency:** How often (in batches) the training produces a visualization of your model's outputs. 
+* **Validation Frequency:** How often (in epochs) the trainer performs validation with your test data.
+* **Autosave Frequency:** How often your session is saved out to disk. 
+* **Device:** On which hardware your training should occur.
+
+## Customization
+Do you want a little more granularity in how you visualize your data? Or perhaps
+running an epoch with your model is a little more involved than just training
+on each batch of data? Wondering why the heck pytorch doesn't have a built-in dataset for unsupervised images?
+Maybe your training algorithm involves VGG? Got you covered. Check out the source for the various submodules:
+* [trainable.visualize](https://github.com/hiltonjp/trainable/blob/master/trainable/visualize.py) -- for customizing visualization.
+* [trainable.epoch](https://github.com/hiltonjp/trainable/blob/master/trainable/epoch.py) -- for customizing epochs.
+* [trainable.data](https://github.com/hiltonjp/trainable/tree/master/trainable/data) -- for common datasets and transforms
+    not found in pytorch's modules.
+* [trainable.features](https://github.com/hiltonjp/trainable/tree/master/trainable/features) -- for working with intermediate
+    activations and features, such as with VGG-based losses.
+
+## Contributing
+Find any other headaches in neural net training that you think you can simplify with Trainable? Feel free to make a
+pull request from my [github repo](https://github.com/hiltonjp/trainable). 
+
+## Contact
+Email me anytime at [jeffhilton.code@gmail.com.]()
+
+
+
+
+%package help
+Summary:	Development documents and examples for trainable
+Provides:	python3-trainable-doc
+%description help
+# Trainable: The Flexible PyTorch Training Toolbox
+
+If you're sick of dealing with all of the boilerplate code involved in training, evaluation, visualization, and
+preserving your models, then you're in luck. Trainable offers a simple, yet extensible framework to make understanding
+the latest papers the *only* headache of Neural Network training.
+
+## Installation
+```bash
+pip install trainable
+```
+
+## Usage
+The typical workflow for trainable involves defining a callable Algorithm to describe how to train 
+your network on a batch, and how you'd like to label your losses:
+
+```python
+class MSEAlgorithm(Algorithm):
+    def __init__(self, eval=False, **args):
+        super().__init__(eval)
+        self.mse = nn.MSELoss()
+
+    def __call__(self, model, batch, device):
+        x, target = batch
+        x, target = x.to(device), target.to(device)
+        y = model(x)
+
+        loss = self.mse(y, target)
+        loss.backward()
+
+        metrics = { self.key("MSE Loss"):loss.item() }
+        return metrics
+```
+
+Then you simply instantiate your model, dataset, and optimizer...
+
+```python
+device = torch.device('cuda')
+
+model = MyModel().to(device)
+optim = FancyPantsOptimizer(model.parameters(), lr=1e-4)
+
+train_data = DataLoader(SomeTrainingDataset('path/to/your/data'), batch_size=32)
+test_data = DataLoader(SomeTestingDataset('path/to/your/data'), batch_size=32)
+```
+
+...and let trainable take care of the rest!
+```python
+trainer = Trainer(
+  visualizer=MyVisualizer(),  # Typically Plotter() or Saver()
+  train_alg=MyFancyAlgorithm(),
+  test_alg=MyFancyAlgorithm(eval=True)
+  display_freq=1,
+  visualize_freq=10,
+  validate_freq=10,
+  autosave_freq=10,
+  device=device
+)
+
+save_path = "desired/save/path/for/your/session.sesh"
+trainer.start_session(model, optim, path)
+
+trainer.name_session('Name')
+
+trainer.describe_session("""
+A beautiful detailed description of what the heck 
+you were trying to accomplish with this training.
+""")
+
+metrics = trainer.train(train_data, test_data, epochs=200)
+```
+
+Plotting your data is simple as well:
+```python
+import matplotlib.pyplot as plt
+
+for key in metrics:
+    plt.plot(metrics[key])
+    plt.show()
+```
+
+## Tunable Options
+The Trainer interface gives you a nice handful of options to configure your training experience.
+They include:
+* **Display Frequency:** How often (in batches) information such as your training loss is updated in your progress bar.
+* **Visualization Frequency:** How often (in batches) the training produces a visualization of your model's outputs. 
+* **Validation Frequency:** How often (in epochs) the trainer performs validation with your test data.
+* **Autosave Frequency:** How often your session is saved out to disk. 
+* **Device:** On which hardware your training should occur.
+
+## Customization
+Do you want a little more granularity in how you visualize your data? Or perhaps
+running an epoch with your model is a little more involved than just training
+on each batch of data? Wondering why the heck pytorch doesn't have a built-in dataset for unsupervised images?
+Maybe your training algorithm involves VGG? Got you covered. Check out the source for the various submodules:
+* [trainable.visualize](https://github.com/hiltonjp/trainable/blob/master/trainable/visualize.py) -- for customizing visualization.
+* [trainable.epoch](https://github.com/hiltonjp/trainable/blob/master/trainable/epoch.py) -- for customizing epochs.
+* [trainable.data](https://github.com/hiltonjp/trainable/tree/master/trainable/data) -- for common datasets and transforms
+    not found in pytorch's modules.
+* [trainable.features](https://github.com/hiltonjp/trainable/tree/master/trainable/features) -- for working with intermediate
+    activations and features, such as with VGG-based losses.
+
+## Contributing
+Find any other headaches in neural net training that you think you can simplify with Trainable? Feel free to make a
+pull request from my [github repo](https://github.com/hiltonjp/trainable). 
+
+## Contact
+Email me anytime at [jeffhilton.code@gmail.com.]()
+
+
+
+
+%prep
+%autosetup -n trainable-0.1.3.post9
+
+%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-trainable -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.3.post9-1
+- Package Spec generated