# Installation

## Prerequisites

In this section we demonstrate how to prepare an environment with PyTorch.

MMRazor works on Linux, Windows and macOS. It requires Python 3.6+, CUDA 9.2+ and PyTorch 1.8+.

**Note:**
If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](##installation). Otherwise, you can follow these steps for the preparation.

**Step 0.** Download and install Miniconda from the [official website](https://docs.conda.io/en/latest/miniconda.html).

**Step 1.** Create a conda environment and activate it.

```shell
conda create --name openmmlab python=3.8 -y
conda activate openmmlab
```

**Step 2.** Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.

On GPU platforms:

```shell
conda install pytorch torchvision -c pytorch
```

On CPU platforms:

```shell
conda install pytorch torchvision cpuonly -c pytorch
```

## Installation

We recommend that users follow our best practices to install MMRazor.

### Best Practices

**Step 0.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).

```shell
pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0"
```

**Step 1.** Install MMRazor.

Case a: If you develop and run mmrazor directly, install it from source:

```shell
git clone -b main https://github.com/open-mmlab/mmrazor.git
cd mmrazor
pip install -v -e .
# '-v' means verbose, or more output
# '-e' means installing a project in editable mode,
# thus any local modifications made to the code will take effect without reinstallation.
```

Case b: If you use mmrazor as a dependency or third-party package, install it with pip:

```shell
pip install "mmrazor>=1.0.0"
```