%global _empty_manifest_terminate_build 0
Name: python-labml
Version: 0.4.162
Release: 1
Summary: Organize Machine Learning Experiments
License: MIT License
URL: https://github.com/labml.ai/labml
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1f/1c/0749cce878d9ae6e976062d18aa139d2fbb8559eae1ad63cacff6bf98d31/labml-0.4.162.tar.gz
BuildArch: noarch
Requires: python3-gitpython
Requires: python3-pyyaml
Requires: python3-numpy
%description
Monitor deep learning model training and hardware usage from mobile.
[](https://badge.fury.io/py/labml)
[](https://pepy.tech/project/labml)
[](https://docs.labml.ai/)
[](https://twitter.com/labmlai?ref_src=twsrc%5Etfw)
### 🔥 Features
* Monitor running experiments from [mobile phone](https://github.com/labmlai/labml/tree/master/app) (or laptop)
[](https://app.labml.ai/run/9e7f39e047e811ebbaff2b26e3148b3d)
* Monitor [hardware usage on any computer](https://github.com/labmlai/labml/blob/master/guides/hardware_monitoring.md) with a single command
* Integrate with just 2 lines of code (see examples below)
* Keeps track of experiments including infomation like git commit, configurations and hyper-parameters
* Keep Tensorboard logs organized
* Save and load checkpoints
* API for custom visualizations
[](https://colab.research.google.com/github/labmlai/labml/blob/master/samples/stocks/analysis.ipynb)
[](https://colab.research.google.com/github/vpj/poker/blob/master/kuhn_cfr/kuhn_cfr.ipynb)
* Pretty logs of training progress
* [Change hyper-parameters while the model is training](https://github.com/labmlai/labml/blob/master/guides/dynamic_hyperparameters.md)
* Open source! we also have a small hosted server for the mobile web app
### Installation
You can install this package using PIP.
```bash
pip install labml
```
### PyTorch example
[](https://colab.research.google.com/drive/1Ldu5tr0oYN_XcYQORgOkIY_Ohsi152fz?usp=sharing) [](https://www.kaggle.com/hnipun/monitoring-ml-model-training-on-your-mobile-phone)
```python
from labml import tracker, experiment
with experiment.record(name='sample', exp_conf=conf):
for i in range(50):
loss, accuracy = train()
tracker.save(i, {'loss': loss, 'accuracy': accuracy})
```
### PyTorch Lightning example
[](https://colab.research.google.com/drive/15aSPDwbKihDu_c3aFHNPGG5POjVlM2KO?usp=sharing) [](https://www.kaggle.com/hnipun/pytorch-lightning)
```python
from labml import experiment
from labml.utils.lightening import LabMLLighteningLogger
trainer = pl.Trainer(gpus=1, max_epochs=5, progress_bar_refresh_rate=20, logger=LabMLLighteningLogger())
with experiment.record(name='sample', exp_conf=conf, disable_screen=True):
trainer.fit(model, data_loader)
```
### TensorFlow 2.X Keras example
[](https://colab.research.google.com/drive/1lx1dUG3MGaIDnq47HVFlzJ2lytjSa9Zy?usp=sharing) [](https://www.kaggle.com/hnipun/monitor-keras-model-training-on-your-mobile-phone)
```python
from labml import experiment
from labml.utils.keras import LabMLKerasCallback
with experiment.record(name='sample', exp_conf=conf):
for i in range(50):
model.fit(x_train, y_train, epochs=conf['epochs'], validation_data=(x_test, y_test),
callbacks=[LabMLKerasCallback()], verbose=None)
```
### 📚 Documentation
* [Python API Reference](https://docs.labml.ai)
* [Samples](https://github.com/labmlai/labml/tree/master/samples)
##### Guides
* [API to create experiments](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/experiment.ipynb)
* [Track training metrics](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/tracker.ipynb)
* [Monitored training loop and other iterators](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/monitor.ipynb)
* [API for custom visualizations](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/analytics.ipynb)
* [Configurations management API](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/configs.ipynb)
* [Logger for stylized logging](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/logger.ipynb)
### 🖥 Screenshots
#### Formatted training loop output
#### Custom visualizations based on Tensorboard logs
## Tools
### [Hosting your own experiments server](https://docs.labml.ai/cli/labml.html#cmdoption-labml-arg-app-server)
```sh
# Install the package
pip install labml-app -U
# Start the server
labml app-server
```
### [Training models on cloud](https://github.com/labmlai/labml/tree/master/remote)
```bash
# Install the package
pip install labml_remote
# Initialize the project
labml_remote init
# Add cloud server(s) to .remote/configs.yaml
# Prepare the remote server(s)
labml_remote prepare
# Start a PyTorch distributed training job
labml_remote helper-torch-launch --cmd 'train.py' --nproc-per-node 2 --env GLOO_SOCKET_IFNAME enp1s0
```
### [Monitoring hardware usage](https://github.com/labmlai/labml/blob/master/guides/hardware_monitoring.md)
```sh
# Install packages and dependencies
pip install labml psutil py3nvml
# Start monitoring
labml monitor
```
## Other Guides
#### [Setting up a local Ubuntu workstation for deep learning](https://github.com/labmlai/labml/blob/master/guides/local-ubuntu.md)
#### [Setting up a cloud computer for deep learning](https://github.com/labmlai/labml/blob/master/guides/remote-python.md)
## Citing
If you use LabML for academic research, please cite the library using the following BibTeX entry.
```bibtext
@misc{labml,
author = {Varuna Jayasiri, Nipun Wijerathne},
title = {labml.ai: A library to organize machine learning experiments},
year = {2020},
url = {https://labml.ai/},
}
```
%package -n python3-labml
Summary: Organize Machine Learning Experiments
Provides: python-labml
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-labml
Monitor deep learning model training and hardware usage from mobile.
[](https://badge.fury.io/py/labml)
[](https://pepy.tech/project/labml)
[](https://docs.labml.ai/)
[](https://twitter.com/labmlai?ref_src=twsrc%5Etfw)
### 🔥 Features
* Monitor running experiments from [mobile phone](https://github.com/labmlai/labml/tree/master/app) (or laptop)
[](https://app.labml.ai/run/9e7f39e047e811ebbaff2b26e3148b3d)
* Monitor [hardware usage on any computer](https://github.com/labmlai/labml/blob/master/guides/hardware_monitoring.md) with a single command
* Integrate with just 2 lines of code (see examples below)
* Keeps track of experiments including infomation like git commit, configurations and hyper-parameters
* Keep Tensorboard logs organized
* Save and load checkpoints
* API for custom visualizations
[](https://colab.research.google.com/github/labmlai/labml/blob/master/samples/stocks/analysis.ipynb)
[](https://colab.research.google.com/github/vpj/poker/blob/master/kuhn_cfr/kuhn_cfr.ipynb)
* Pretty logs of training progress
* [Change hyper-parameters while the model is training](https://github.com/labmlai/labml/blob/master/guides/dynamic_hyperparameters.md)
* Open source! we also have a small hosted server for the mobile web app
### Installation
You can install this package using PIP.
```bash
pip install labml
```
### PyTorch example
[](https://colab.research.google.com/drive/1Ldu5tr0oYN_XcYQORgOkIY_Ohsi152fz?usp=sharing) [](https://www.kaggle.com/hnipun/monitoring-ml-model-training-on-your-mobile-phone)
```python
from labml import tracker, experiment
with experiment.record(name='sample', exp_conf=conf):
for i in range(50):
loss, accuracy = train()
tracker.save(i, {'loss': loss, 'accuracy': accuracy})
```
### PyTorch Lightning example
[](https://colab.research.google.com/drive/15aSPDwbKihDu_c3aFHNPGG5POjVlM2KO?usp=sharing) [](https://www.kaggle.com/hnipun/pytorch-lightning)
```python
from labml import experiment
from labml.utils.lightening import LabMLLighteningLogger
trainer = pl.Trainer(gpus=1, max_epochs=5, progress_bar_refresh_rate=20, logger=LabMLLighteningLogger())
with experiment.record(name='sample', exp_conf=conf, disable_screen=True):
trainer.fit(model, data_loader)
```
### TensorFlow 2.X Keras example
[](https://colab.research.google.com/drive/1lx1dUG3MGaIDnq47HVFlzJ2lytjSa9Zy?usp=sharing) [](https://www.kaggle.com/hnipun/monitor-keras-model-training-on-your-mobile-phone)
```python
from labml import experiment
from labml.utils.keras import LabMLKerasCallback
with experiment.record(name='sample', exp_conf=conf):
for i in range(50):
model.fit(x_train, y_train, epochs=conf['epochs'], validation_data=(x_test, y_test),
callbacks=[LabMLKerasCallback()], verbose=None)
```
### 📚 Documentation
* [Python API Reference](https://docs.labml.ai)
* [Samples](https://github.com/labmlai/labml/tree/master/samples)
##### Guides
* [API to create experiments](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/experiment.ipynb)
* [Track training metrics](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/tracker.ipynb)
* [Monitored training loop and other iterators](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/monitor.ipynb)
* [API for custom visualizations](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/analytics.ipynb)
* [Configurations management API](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/configs.ipynb)
* [Logger for stylized logging](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/logger.ipynb)
### 🖥 Screenshots
#### Formatted training loop output
#### Custom visualizations based on Tensorboard logs
## Tools
### [Hosting your own experiments server](https://docs.labml.ai/cli/labml.html#cmdoption-labml-arg-app-server)
```sh
# Install the package
pip install labml-app -U
# Start the server
labml app-server
```
### [Training models on cloud](https://github.com/labmlai/labml/tree/master/remote)
```bash
# Install the package
pip install labml_remote
# Initialize the project
labml_remote init
# Add cloud server(s) to .remote/configs.yaml
# Prepare the remote server(s)
labml_remote prepare
# Start a PyTorch distributed training job
labml_remote helper-torch-launch --cmd 'train.py' --nproc-per-node 2 --env GLOO_SOCKET_IFNAME enp1s0
```
### [Monitoring hardware usage](https://github.com/labmlai/labml/blob/master/guides/hardware_monitoring.md)
```sh
# Install packages and dependencies
pip install labml psutil py3nvml
# Start monitoring
labml monitor
```
## Other Guides
#### [Setting up a local Ubuntu workstation for deep learning](https://github.com/labmlai/labml/blob/master/guides/local-ubuntu.md)
#### [Setting up a cloud computer for deep learning](https://github.com/labmlai/labml/blob/master/guides/remote-python.md)
## Citing
If you use LabML for academic research, please cite the library using the following BibTeX entry.
```bibtext
@misc{labml,
author = {Varuna Jayasiri, Nipun Wijerathne},
title = {labml.ai: A library to organize machine learning experiments},
year = {2020},
url = {https://labml.ai/},
}
```
%package help
Summary: Development documents and examples for labml
Provides: python3-labml-doc
%description help
Monitor deep learning model training and hardware usage from mobile.
[](https://badge.fury.io/py/labml)
[](https://pepy.tech/project/labml)
[](https://docs.labml.ai/)
[](https://twitter.com/labmlai?ref_src=twsrc%5Etfw)
### 🔥 Features
* Monitor running experiments from [mobile phone](https://github.com/labmlai/labml/tree/master/app) (or laptop)
[](https://app.labml.ai/run/9e7f39e047e811ebbaff2b26e3148b3d)
* Monitor [hardware usage on any computer](https://github.com/labmlai/labml/blob/master/guides/hardware_monitoring.md) with a single command
* Integrate with just 2 lines of code (see examples below)
* Keeps track of experiments including infomation like git commit, configurations and hyper-parameters
* Keep Tensorboard logs organized
* Save and load checkpoints
* API for custom visualizations
[](https://colab.research.google.com/github/labmlai/labml/blob/master/samples/stocks/analysis.ipynb)
[](https://colab.research.google.com/github/vpj/poker/blob/master/kuhn_cfr/kuhn_cfr.ipynb)
* Pretty logs of training progress
* [Change hyper-parameters while the model is training](https://github.com/labmlai/labml/blob/master/guides/dynamic_hyperparameters.md)
* Open source! we also have a small hosted server for the mobile web app
### Installation
You can install this package using PIP.
```bash
pip install labml
```
### PyTorch example
[](https://colab.research.google.com/drive/1Ldu5tr0oYN_XcYQORgOkIY_Ohsi152fz?usp=sharing) [](https://www.kaggle.com/hnipun/monitoring-ml-model-training-on-your-mobile-phone)
```python
from labml import tracker, experiment
with experiment.record(name='sample', exp_conf=conf):
for i in range(50):
loss, accuracy = train()
tracker.save(i, {'loss': loss, 'accuracy': accuracy})
```
### PyTorch Lightning example
[](https://colab.research.google.com/drive/15aSPDwbKihDu_c3aFHNPGG5POjVlM2KO?usp=sharing) [](https://www.kaggle.com/hnipun/pytorch-lightning)
```python
from labml import experiment
from labml.utils.lightening import LabMLLighteningLogger
trainer = pl.Trainer(gpus=1, max_epochs=5, progress_bar_refresh_rate=20, logger=LabMLLighteningLogger())
with experiment.record(name='sample', exp_conf=conf, disable_screen=True):
trainer.fit(model, data_loader)
```
### TensorFlow 2.X Keras example
[](https://colab.research.google.com/drive/1lx1dUG3MGaIDnq47HVFlzJ2lytjSa9Zy?usp=sharing) [](https://www.kaggle.com/hnipun/monitor-keras-model-training-on-your-mobile-phone)
```python
from labml import experiment
from labml.utils.keras import LabMLKerasCallback
with experiment.record(name='sample', exp_conf=conf):
for i in range(50):
model.fit(x_train, y_train, epochs=conf['epochs'], validation_data=(x_test, y_test),
callbacks=[LabMLKerasCallback()], verbose=None)
```
### 📚 Documentation
* [Python API Reference](https://docs.labml.ai)
* [Samples](https://github.com/labmlai/labml/tree/master/samples)
##### Guides
* [API to create experiments](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/experiment.ipynb)
* [Track training metrics](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/tracker.ipynb)
* [Monitored training loop and other iterators](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/monitor.ipynb)
* [API for custom visualizations](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/analytics.ipynb)
* [Configurations management API](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/configs.ipynb)
* [Logger for stylized logging](https://colab.research.google.com/github/labmlai/labml/blob/master/guides/logger.ipynb)
### 🖥 Screenshots
#### Formatted training loop output
#### Custom visualizations based on Tensorboard logs
## Tools
### [Hosting your own experiments server](https://docs.labml.ai/cli/labml.html#cmdoption-labml-arg-app-server)
```sh
# Install the package
pip install labml-app -U
# Start the server
labml app-server
```
### [Training models on cloud](https://github.com/labmlai/labml/tree/master/remote)
```bash
# Install the package
pip install labml_remote
# Initialize the project
labml_remote init
# Add cloud server(s) to .remote/configs.yaml
# Prepare the remote server(s)
labml_remote prepare
# Start a PyTorch distributed training job
labml_remote helper-torch-launch --cmd 'train.py' --nproc-per-node 2 --env GLOO_SOCKET_IFNAME enp1s0
```
### [Monitoring hardware usage](https://github.com/labmlai/labml/blob/master/guides/hardware_monitoring.md)
```sh
# Install packages and dependencies
pip install labml psutil py3nvml
# Start monitoring
labml monitor
```
## Other Guides
#### [Setting up a local Ubuntu workstation for deep learning](https://github.com/labmlai/labml/blob/master/guides/local-ubuntu.md)
#### [Setting up a cloud computer for deep learning](https://github.com/labmlai/labml/blob/master/guides/remote-python.md)
## Citing
If you use LabML for academic research, please cite the library using the following BibTeX entry.
```bibtext
@misc{labml,
author = {Varuna Jayasiri, Nipun Wijerathne},
title = {labml.ai: A library to organize machine learning experiments},
year = {2020},
url = {https://labml.ai/},
}
```
%prep
%autosetup -n labml-0.4.162
%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-labml -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 0.4.162-1
- Package Spec generated
