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%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 <Python_Bot@openeuler.org> - 0.1.5-1
- Package Spec generated
|
