docs: update installation

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zweien 2021-07-16 11:35:43 +08:00
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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 # 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 **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 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>). 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 ## Installation
Choose one of the following installation methods. Choose one of the following installation methods.
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```bash ```bash
docker pull idrl/idrlnet:latest docker pull idrl/idrlnet:latest
docker run idrl/idrlnet:latest
``` ```
### Anaconda ### Anaconda
```bash
conda create -n idrlnet_dev python=3.8 -y
conda activate idrlnet_dev
pip install idrlnet
```
### From Source ### From Source

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Features Features
-------- --------
IDRLnet is a machine learning library on top of `Pytorch <https://www.tensorflow.org/>`_. Use IDRLnet if you need a machine 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 partial differential equations (PDEs) via physics-informed neural 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>`_. networks (PINN). IDRLnet is a flexible framework inspired by `Nvidia Simnet <https://developer.nvidia.com/simnet>`_.
IDRLnet supports IDRLnet supports

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We recommend using conda to manage the environment. We recommend using conda to manage the environment.
Other methods may also work well such like using docker or virtual env. 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
```bash
docker pull idrl/idrlnet:latest
docker run idrl/idrlnet:latest
```
## Anaconda ## Anaconda
```bash ```bash
git clone https://git.idrl.site/pengwei/idrlnet
cd idrlnet
conda create -n idrlnet_dev python=3.8 -y conda create -n idrlnet_dev python=3.8 -y
conda activate idrlnet_dev 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 . pip install -e .
``` ```

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pyevtk==1.1.1 pyevtk==1.1.1
flask==1.1.2 flask==1.1.2
requests==2.25.0 requests==2.25.0
torch==1.7.1 torch>=1.7.1
networkx==2.5.1 networkx==2.5.1