semvec/pypi-packages huggingface dataset:
back 0.1.4
Backbone for PyTorch training loop
License: MIT
Requires Python: >=3.7.0
Keywords: pytorch,deep-learning,training-loop,backbone

# Backbone for PyTorch training loop
Will try to keep it minimalistic.

`pip install back`
```python
from back import Bone
```

## Features
- Progress bar
- Checkpoints saving/loading
- Schedulers
- Tensorboard logging
- Early stopping
- Multiple GPUs

## TODO
- [ ] Learning Rate Finder
- [ ] Notebook example
- [ ] Docs/tests

## Docker
`docker build -t backbone_pytorch .`

or

`sudo docker pull digitman/backbone_pytorch:latest`

## Examples
### SRM 
https://github.com/EvgenyKashin/SRMnet
Personne 1.0.0
Tester les Personnes
License: MIT

# Introduction 
TODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project. 
[![Build Status](https://dev.azure.com/AsItSoft/Test%20Nassim/_apis/build/status/Python-repo?branchName=master)](https://dev.azure.com/AsItSoft/Test%20Nassim/_build/latest?definitionId=20&branchName=master)
# Getting Started
TODO: Guide users through getting your code up and running on their own system. In this section you can talk about:
1.	Installation process
2.	Software dependencies
3.	Latest releases
4.	API references

# Build and Test
TODO: Describe and show how to build your code and run the tests. 

# Contribute
TODO: Explain how other users and developers can contribute to make your code better. 

If you want to learn more about creating good readme files then refer the following [guidelines](https://docs.microsoft.com/en-us/azure/devops/repos/git/create-a-readme?view=azure-devops). You can also seek inspiration from the below readme files:
- [ASP.NET Core](https://github.com/aspnet/Home)
- [Visual Studio Code](https://github.com/Microsoft/vscode)
- [Chakra Core](https://github.com/Microsoft/ChakraCore)
s5a 0.4
Sentinel-5 Algorithms
License: MIT

Sentinel-5 Algorithms
=====================

.. image:: https://travis-ci.com/emissions-api/sentinel5algorithms.svg?branch=master
    :target: https://travis-ci.com/emissions-api/sentinel5algorithms
    :alt: Travis CI build status
.. image:: https://img.shields.io/github/issues-raw/emissions-api/sentinel5algorithms?color=blue
    :target: https://github.com/emissions-api/sentinel5algorithms/issues
    :alt: GitHub issues
.. image:: https://img.shields.io/github/license/emissions-api/sentinel5algorithms
    :target: https://github.com/emissions-api/sentinel5algorithms/blob/master/LICENSE
    :alt: MIT license

This library provides easy access to different algorithms which intend to make working with data from the European Space Agency's Sentinel 5 data easier.