%global _empty_manifest_terminate_build 0 Name: python-hideandseek Version: 0.1.5 Release: 1 Summary: library for deep learning and privacy preserving deep learning License: MIT License URL: https://github.com/jsyoo61/hideandseek Source0: https://mirrors.nju.edu.cn/pypi/web/packages/95/0a/554f1ae7e4f35482523a3494164c40048beb6476ec72fa7550a51df3f4a0/hideandseek-0.1.5.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-pandas Requires: python3-matplotlib Requires: python3-hydra-core Requires: python3-tools-jsyoo61 %description # hideandseek Highly modularized deep learning training library. Why use `hideandseek`? - Easy training & saving deep learning models along with other modules (ex: preprocessing modules) required in inference - Run multiple deep learning experiments in parallel on multiples GPUs (powered by [hydra](https://hydra.cc/docs/intro/), and python multiprocessing) - Design and analyze experiments scientifically by modifying variables (powered by [hydra](https://hydra.cc/docs/intro/)) - Modularized machine learning pipeline allows using the same script for all types of experiments - The same training code can be run in privacy preserving setting by minimal modifications Currently prettifying codes. (30.10.2022.) import torch import torch.nn as nn # Generate data x = torch.rand(200,1) y = 5*x+2 model = nn.Linear(1,1) dataset = torch.utils.data.TensorDataset(x, y) criterion = nn.MSELoss() cfg = { 'lr': 1e-2, 'batch_size': 32, 'epoch': 10 # optional } # Training configuration. All you need to train a neural network kwargs = { 'model':model, 'dataset':dataset, 'cfg_train':cfg, 'criterion':criterion, 'name': 'Test' # optional } trainer = hs.N.Node(**kwargs) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") trainer.model.to(device) # Train for predefined number of epochs trainer.train() # Train for predefined number of epochs trainer.train(5) # Train for specified number of epochs trainer.train(epoch=5) # Same thing with trainer.train(5) trainer.train(step=500) # Train for specified number of updates node.model.cpu() and simply run multiple batch of experiments with a single line command such as: python train.py -m lr=1e-3,1e-2 batch_size=32,64 "random_seed=range(0,5)" \ hydra/launcher=joblib hydra.launcher.n_jobs=8 # Runs total of 2*2*5=40 batch of experiments, with 8 processes at a time. Experiment results are stored in hydra.sweep.dir which can be overridden. To do - [ ] Draw figures to explain hideandseek - [ ] GUI for generating experiment scripts when conducting variable sweeps %package -n python3-hideandseek Summary: library for deep learning and privacy preserving deep learning Provides: python-hideandseek BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-hideandseek # hideandseek Highly modularized deep learning training library. Why use `hideandseek`? - Easy training & saving deep learning models along with other modules (ex: preprocessing modules) required in inference - Run multiple deep learning experiments in parallel on multiples GPUs (powered by [hydra](https://hydra.cc/docs/intro/), and python multiprocessing) - Design and analyze experiments scientifically by modifying variables (powered by [hydra](https://hydra.cc/docs/intro/)) - Modularized machine learning pipeline allows using the same script for all types of experiments - The same training code can be run in privacy preserving setting by minimal modifications Currently prettifying codes. (30.10.2022.) import torch import torch.nn as nn # Generate data x = torch.rand(200,1) y = 5*x+2 model = nn.Linear(1,1) dataset = torch.utils.data.TensorDataset(x, y) criterion = nn.MSELoss() cfg = { 'lr': 1e-2, 'batch_size': 32, 'epoch': 10 # optional } # Training configuration. All you need to train a neural network kwargs = { 'model':model, 'dataset':dataset, 'cfg_train':cfg, 'criterion':criterion, 'name': 'Test' # optional } trainer = hs.N.Node(**kwargs) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") trainer.model.to(device) # Train for predefined number of epochs trainer.train() # Train for predefined number of epochs trainer.train(5) # Train for specified number of epochs trainer.train(epoch=5) # Same thing with trainer.train(5) trainer.train(step=500) # Train for specified number of updates node.model.cpu() and simply run multiple batch of experiments with a single line command such as: python train.py -m lr=1e-3,1e-2 batch_size=32,64 "random_seed=range(0,5)" \ hydra/launcher=joblib hydra.launcher.n_jobs=8 # Runs total of 2*2*5=40 batch of experiments, with 8 processes at a time. Experiment results are stored in hydra.sweep.dir which can be overridden. To do - [ ] Draw figures to explain hideandseek - [ ] GUI for generating experiment scripts when conducting variable sweeps %package help Summary: Development documents and examples for hideandseek Provides: python3-hideandseek-doc %description help # hideandseek Highly modularized deep learning training library. Why use `hideandseek`? - Easy training & saving deep learning models along with other modules (ex: preprocessing modules) required in inference - Run multiple deep learning experiments in parallel on multiples GPUs (powered by [hydra](https://hydra.cc/docs/intro/), and python multiprocessing) - Design and analyze experiments scientifically by modifying variables (powered by [hydra](https://hydra.cc/docs/intro/)) - Modularized machine learning pipeline allows using the same script for all types of experiments - The same training code can be run in privacy preserving setting by minimal modifications Currently prettifying codes. (30.10.2022.) import torch import torch.nn as nn # Generate data x = torch.rand(200,1) y = 5*x+2 model = nn.Linear(1,1) dataset = torch.utils.data.TensorDataset(x, y) criterion = nn.MSELoss() cfg = { 'lr': 1e-2, 'batch_size': 32, 'epoch': 10 # optional } # Training configuration. All you need to train a neural network kwargs = { 'model':model, 'dataset':dataset, 'cfg_train':cfg, 'criterion':criterion, 'name': 'Test' # optional } trainer = hs.N.Node(**kwargs) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") trainer.model.to(device) # Train for predefined number of epochs trainer.train() # Train for predefined number of epochs trainer.train(5) # Train for specified number of epochs trainer.train(epoch=5) # Same thing with trainer.train(5) trainer.train(step=500) # Train for specified number of updates node.model.cpu() and simply run multiple batch of experiments with a single line command such as: python train.py -m lr=1e-3,1e-2 batch_size=32,64 "random_seed=range(0,5)" \ hydra/launcher=joblib hydra.launcher.n_jobs=8 # Runs total of 2*2*5=40 batch of experiments, with 8 processes at a time. Experiment results are stored in hydra.sweep.dir which can be overridden. To do - [ ] Draw figures to explain hideandseek - [ ] GUI for generating experiment scripts when conducting variable sweeps %prep %autosetup -n hideandseek-0.1.5 %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-hideandseek -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon May 29 2023 Python_Bot - 0.1.5-1 - Package Spec generated