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Improve document #126

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3566b90
improve document
tocean Nov 7, 2023
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fix comments
tocean Nov 7, 2023
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recommend using pytorch container when installing from source
tocean Nov 7, 2023
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25 changes: 20 additions & 5 deletions docs/getting-started/installation.mdx
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Expand Up @@ -18,14 +18,29 @@ Here're the system requirements for MS-AMP.
* CUDA version 11 or later (which can be checked by running `nvcc --version`).
* PyTorch version 1.14 or later (which can be checked by running `python -c "import torch; print(torch.__version__)"`).

We strongly recommend using [PyTorch NGC Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch). For example, to start PyTorch 2.1 container, run the following command:
You can try MS-AMP in two ways: Using Docker or installing from source:

* Using Docker is a convenient way to get started with MS-AMP. You can use the pre-built Docker image to quickly set up an environment for running MS-AMP.
* On the other hand, installing from source gives you more control over the installation process and allows you to customize the installation to your needs.

## Use Docker

You can try the latest MS-AMP Docker container with the following commands:

```bash
sudo docker run -it -d --name=msampcu121 --privileged --net=host --ipc=host --gpus=all -v /:/hostroot ghcr.io/azure/msamp:main-cuda12.1 bash
sudo docker exec -it msampcu121 bash
```

MS-AMP is pre-installed in Docker container and you can verify it by running:

```bash
sudo docker run -it -d --name=msamp --privileged --net=host --ipc=host --gpus=all nvcr.io/nvidia/pytorch:23.04-py3 bash
sudo docker exec -it msamp bash
python -c 'import msamp;print(msamp.__version__)'
```

## Install MS-AMP
We also provide stable Docker images [here](../user-tutorial/container-images.mdx).

## Install from source
You can clone the source from GitHub.

```bash
Expand All @@ -34,7 +49,7 @@ cd MS-AMP
git submodule update --init --recursive
```

If you want to train model with multiple GPU, you need to install MSCCL to support FP8.
If you want to train model with multiple GPU, you need to install MSCCL to support FP8. Please note that the compilation of MSCCL may take ~40 minutes on A100 nodes and ~7 minutes on H100 node.

```bash
cd third_party/msccl
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4 changes: 2 additions & 2 deletions docs/getting-started/run-msamp.md
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Expand Up @@ -14,10 +14,10 @@ After installing MS-AMP, you can run several simple examples using MS-AMP. Pleas
python mnist.py --enable-msamp --opt-level=O2
```

### 2. Run mnist using multi GPUS in single node
### 2. Run mnist using multi GPUs in single node

```bash
torchrun --nproc_per_node=$GPUS mnist_ddp.py --enable-msamp --opt-level=O2
torchrun --nproc_per_node=8 mnist_ddp.py --enable-msamp --opt-level=O2
```

## CIFAR10
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