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Merge pull request #10 from idrl-lab/dev 

update README
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README.md
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[![License](https://img.shields.io/github/license/analysiscenter/pydens.svg)](https://www.apache.org/licenses/LICENSE-2.0)
[![Python](https://img.shields.io/badge/python-3.8-blue.svg)](https://python.org)
[![Documentation Status](https://readthedocs.org/projects/idrlnet/badge/?version=latest)](https://idrlnet.readthedocs.io/en/latest/?badge=latest)
# IDRLnet
[![License](https://img.shields.io/github/license/analysiscenter/pydens.svg)](https://www.apache.org/licenses/LICENSE-2.0)
[![Python](https://img.shields.io/badge/python-3.7/3.8/3.9-blue.svg)](https://python.org)
[![Documentation Status](https://readthedocs.org/projects/idrlnet/badge/?version=latest)](https://idrlnet.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/idrlnet.svg)](https://badge.fury.io/py/idrlnet)
[![DockerHub](https://img.shields.io/docker/pulls/idrl/idrlnet.svg)](https://hub.docker.com/r/idrl/idrlnet)
[![CodeFactor](https://www.codefactor.io/repository/github/idrl-lab/idrlnet/badge/master)](https://www.codefactor.io/repository/github/idrl-lab/idrlnet/overview/master)
**IDRLnet** is a machine learning library on top of [PyTorch](https://pytorch.org/). Use IDRLnet if you need a machine
learning library that solves both forward and inverse differential equations via physics-informed neural
networks (PINN). IDRLnet is a flexible framework inspired by [Nvidia Simnet](https://developer.nvidia.com/simnet>).
## Docs
- [Full docs](https://idrlnet.readthedocs.io/en/latest/)
- [Tutorial](https://idrlnet.readthedocs.io/en/latest/user/get_started/tutorial.html)
## Installation
Choose one of the following installation methods.
### PyPI
Simple installation from PyPI
Simple installation from PyPI.
```bash
pip install -U idrlnet
@ -25,13 +33,23 @@ Note: To avoid version conflicts, please use some tools to create a virtual envi
### Docker
Pull latest docker image from Dockerhub.
```bash
docker pull idrl/idrlnet:latest
docker run -it idrl/idrlnet:latest bash
```
Note: Available tags can be found in [Dockerhub](https://hub.docker.com/repository/docker/idrl/idrlnet).
### Anaconda
```bash
conda create -n idrlnet_dev python=3.8 -y
conda activate idrlnet_dev
pip install idrlnet
```
### From Source

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docs/index.rst
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@ -12,8 +12,8 @@ Welcome to idrlnet's documentation!
Features
--------
IDRLnet is a machine learning library on top of `Pytorch <https://www.tensorflow.org/>`_. Use IDRLnet if you need a machine
learning library that solves both forward and inverse partial differential equations (PDEs) via physics-informed neural
IDRLnet is a machine learning library on top of `PyTorch <https://pytorch.org/>`_. Use IDRLnet if you need a machine
learning library that solves both forward and inverse differential equations via physics-informed neural
networks (PINN). IDRLnet is a flexible framework inspired by `Nvidia Simnet <https://developer.nvidia.com/simnet>`_.
IDRLnet supports

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docs/user/installation.md
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We recommend using conda to manage the environment.
Other methods may also work well such like using docker or virtual env.
Choose one of the following installation methods.
## PyPI
Simple installation from PyPI
```bash
pip install -U idrlnet
```
Note: To avoid version conflicts, please use some tools to create a virtual environment first.
## Docker
Pull latest docker image from Dockerhub.
```bash
docker pull idrl/idrlnet:latest
docker run -it idrl/idrlnet:latest bash
```
Note: Available tags can be found in [Dockerhub](https://hub.docker.com/repository/docker/idrl/idrlnet).
## Anaconda
```bash
git clone https://git.idrl.site/pengwei/idrlnet
cd idrlnet
conda create -n idrlnet_dev python=3.8 -y
conda activate idrlnet_dev
pip install -r requirements.txt
pip install idrlnet
```
## From Source
```
git clone https://github.com/idrl-lab/idrlnet
cd idrlnet
pip install -e .
```

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requirements.txt
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@ -17,5 +17,5 @@ sympy==1.5.1
pyevtk==1.1.1
flask==1.1.2
requests==2.25.0
torch==1.7.1
torch>=1.7.1
networkx==2.5.1

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setup.py
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@ -31,8 +31,14 @@ setuptools.setup(
packages=setuptools.find_packages(),
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Environment :: Console",
"Intended Audience :: Developers",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
python_requires=">=3.6",
install_requires=load_requirements(),