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@@ -0,0 +1 @@ +/torchility-0.7.2.tar.gz diff --git a/python-torchility.spec b/python-torchility.spec new file mode 100644 index 0000000..a650c5b --- /dev/null +++ b/python-torchility.spec @@ -0,0 +1,279 @@ +%global _empty_manifest_terminate_build 0 +Name: python-torchility +Version: 0.7.2 +Release: 1 +Summary: please add a summary manually as the author left a blank one +License: MIT +URL: https://github.com/hitlic/torchility +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f0/2d/6c3c41c629a165bab5142d301d3c8cd1896acd47107627cb5b09072fc17b/torchility-0.7.2.tar.gz +BuildArch: noarch + +Requires: python3-torch +Requires: python3-pytorch-lightning +Requires: python3-torchmetrics +Requires: python3-matplotlib +Requires: python3-pyyaml +Requires: python3-tensorboardX + +%description +# torchility + +A tool for training pytorch deep learning model more simply which is based on Pytorch-lightning. + +## Install + +- `pip install torchility` +### Dependency +- pytorch>=2.0 +- pytorch-lightning>=2.0,<2.1 +- torchmetrics>=0.11,<0.12 +- matplotlib>=3.3 +- pyyaml>=5.4 +- tensorboardX>=2.6 + +## Usage + +- MNIST + +```python +from torchility import Trainer +import torch +from torch import nn +from torch.nn import functional as F +from torchvision.datasets import MNIST +from torchvision import transforms +from torch.utils.data import DataLoader, random_split +import warnings +warnings.simplefilter("ignore") # ignore annoying warnings + +# datasets +data_dir = './datasets' +transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) +mnist_full = MNIST(data_dir, train=True, transform=transform, download=True) +train_ds, val_ds = random_split(mnist_full, [55000, 5000]) +test_ds = MNIST(data_dir, train=False, transform=transform, download=True) + +# dataloaders +train_dl = DataLoader(train_ds, batch_size=32) +val_dl = DataLoader(val_ds, batch_size=32) +test_dl = DataLoader(test_ds, batch_size=32) + +# pytorch model +channels, width, height = (1, 28, 28) +model = nn.Sequential( + nn.Flatten(), + nn.Linear(channels * width * height, 64), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(64, 64), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(64, 10) +) + +# optimizer +opt = torch.optim.Adam(model.parameters(), lr=2e-4) +# trainer +trainer = Trainer(model, F.cross_entropy, opt, epochs=2) +# train and validate +trainer.fit(train_dl, val_dl) +# test +trainer.test(test_dl) +``` + +- See the `examples` for more examples + + + +%package -n python3-torchility +Summary: please add a summary manually as the author left a blank one +Provides: python-torchility +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-torchility +# torchility + +A tool for training pytorch deep learning model more simply which is based on Pytorch-lightning. + +## Install + +- `pip install torchility` +### Dependency +- pytorch>=2.0 +- pytorch-lightning>=2.0,<2.1 +- torchmetrics>=0.11,<0.12 +- matplotlib>=3.3 +- pyyaml>=5.4 +- tensorboardX>=2.6 + +## Usage + +- MNIST + +```python +from torchility import Trainer +import torch +from torch import nn +from torch.nn import functional as F +from torchvision.datasets import MNIST +from torchvision import transforms +from torch.utils.data import DataLoader, random_split +import warnings +warnings.simplefilter("ignore") # ignore annoying warnings + +# datasets +data_dir = './datasets' +transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) +mnist_full = MNIST(data_dir, train=True, transform=transform, download=True) +train_ds, val_ds = random_split(mnist_full, [55000, 5000]) +test_ds = MNIST(data_dir, train=False, transform=transform, download=True) + +# dataloaders +train_dl = DataLoader(train_ds, batch_size=32) +val_dl = DataLoader(val_ds, batch_size=32) +test_dl = DataLoader(test_ds, batch_size=32) + +# pytorch model +channels, width, height = (1, 28, 28) +model = nn.Sequential( + nn.Flatten(), + nn.Linear(channels * width * height, 64), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(64, 64), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(64, 10) +) + +# optimizer +opt = torch.optim.Adam(model.parameters(), lr=2e-4) +# trainer +trainer = Trainer(model, F.cross_entropy, opt, epochs=2) +# train and validate +trainer.fit(train_dl, val_dl) +# test +trainer.test(test_dl) +``` + +- See the `examples` for more examples + + + +%package help +Summary: Development documents and examples for torchility +Provides: python3-torchility-doc +%description help +# torchility + +A tool for training pytorch deep learning model more simply which is based on Pytorch-lightning. + +## Install + +- `pip install torchility` +### Dependency +- pytorch>=2.0 +- pytorch-lightning>=2.0,<2.1 +- torchmetrics>=0.11,<0.12 +- matplotlib>=3.3 +- pyyaml>=5.4 +- tensorboardX>=2.6 + +## Usage + +- MNIST + +```python +from torchility import Trainer +import torch +from torch import nn +from torch.nn import functional as F +from torchvision.datasets import MNIST +from torchvision import transforms +from torch.utils.data import DataLoader, random_split +import warnings +warnings.simplefilter("ignore") # ignore annoying warnings + +# datasets +data_dir = './datasets' +transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) +mnist_full = MNIST(data_dir, train=True, transform=transform, download=True) +train_ds, val_ds = random_split(mnist_full, [55000, 5000]) +test_ds = MNIST(data_dir, train=False, transform=transform, download=True) + +# dataloaders +train_dl = DataLoader(train_ds, batch_size=32) +val_dl = DataLoader(val_ds, batch_size=32) +test_dl = DataLoader(test_ds, batch_size=32) + +# pytorch model +channels, width, height = (1, 28, 28) +model = nn.Sequential( + nn.Flatten(), + nn.Linear(channels * width * height, 64), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(64, 64), + nn.ReLU(), + nn.Dropout(0.1), + nn.Linear(64, 10) +) + +# optimizer +opt = torch.optim.Adam(model.parameters(), lr=2e-4) +# trainer +trainer = Trainer(model, F.cross_entropy, opt, epochs=2) +# train and validate +trainer.fit(train_dl, val_dl) +# test +trainer.test(test_dl) +``` + +- See the `examples` for more examples + + + +%prep +%autosetup -n torchility-0.7.2 + +%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-torchility -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.2-1 +- Package Spec generated @@ -0,0 +1 @@ +c809887d3047d70d37f6f783ae386011 torchility-0.7.2.tar.gz |