From 87f8b9fba2149e1b63cc1489b0f4c508e2f6f1da Mon Sep 17 00:00:00 2001 From: leonwanghui Date: Sun, 12 Apr 2020 00:13:39 +0800 Subject: [PATCH] Update docker image section in README.md Signed-off-by: leonwanghui --- README.md | 66 ++++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 56 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index be8ca5189ac..8b6b363de3e 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ ![MindSpore Logo](docs/MindSpore-logo.png "MindSpore logo") ============================================================ -- [What is MindSpore?](#what-is-MindSpore) +- [What is MindSpore?](#what-is-mindspore) - [Automatic Differentiation](#automatic-differentiation) - [Automatic Parallel](#automatic-parallel) - [Installation](#installation) @@ -53,7 +53,7 @@ The goal of MindSpore automatic parallel is to build a training method that comb Automatic Parallel -At present, MindSpore uses a fine-grained parallel strategy of splitting operators, that is, each operator in the figure is splited into a cluster to complete parallel operations. The splitting strategy during this period may be very complicated, but as a developer advocating Pythonic, you don't need to care about the underlying implementation, as long as the top-level API compute is efficient. +At present, MindSpore uses a fine-grained parallel strategy of splitting operators, that is, each operator in the figure is splitted into a cluster to complete parallel operations. The splitting strategy during this period may be very complicated, but as a developer advocating Pythonic, you don't need to care about the underlying implementation, as long as the top-level API compute is efficient. ## Installation @@ -70,9 +70,9 @@ MindSpore offers build options across multiple backends: | GPU CUDA 10.1 | Ubuntu-x86 | ✔️ | | CPU | Ubuntu-x86 | ✔️ | -For installation using pip, take `Ubuntu-x86` and `CPU` build version as an example: +For installation using `pip`, take `Ubuntu-x86` and `CPU` build version as an example: -1. Download whl from [MindSpore website](https://www.mindspore.cn/), and install the package. +1. Download whl from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package. ``` pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/cpu/ubuntu-x86/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl @@ -96,14 +96,60 @@ currently the containerized build options are supported as follows: | Hardware Platform | Docker Image URL | | :---------------- | :--------------- | | CPU | `mindspore/mindspore-cpu:0.1.0-alpha` | -| GPU CUDA 9.2 | `mindspore/mindspore-cuda9.2:0.1.0-alpha` | -| GPU CUDA 10.1 | `mindspore/mindspore-cuda10.1:0.1.0-alpha` | +| GPU | `mindspore/mindspore-gpu:0.1.0-alpha` | | Ascend |
—
| -Take `CPU` for example, you can directly pull the image using the below command: -``` -docker pull mindspore/mindspore-cpu:0.1.0-alpha -``` +* CPU + + For `CPU` backend, you can directly pull and run the image using the below command: + ``` + docker pull mindspore/mindspore-cpu:0.1.0-alpha + docker run -it mindspore/mindspore-cpu:0.1.0-alpha python -c 'import mindspore' + ``` + +* GPU + + For `GPU` backend, please make sure the `nvidia-container-toolkit` has been installed in advance, here are some install guidelines for Ubuntu users: + ``` + DISTRIBUTION=$(. /etc/os-release; echo $ID$VERSION_ID) + curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add - + curl -s -L https://nvidia.github.io/nvidia-docker/$DISTRIBUTION/nvidia-docker.list | tee /etc/apt/sources.list.d/nvidia-docker.list + + sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit nvidia-docker2 + sudo systemctl restart docker + ``` + + Then you can pull and run the image using the below command: + ``` + docker pull mindspore/mindspore-gpu:0.1.0-alpha + docker run -it --runtime=nvidia --privileged=true mindspore/mindspore-gpu:0.1.0-alpha /bin/bash + ``` + + To test if the docker image works, please execute the python code below and check the output: + ```python + import numpy as np + from mindspore import Tensor + from mindspore.ops import functional as F + import mindspore.context as context + + context.set_context(device_target="GPU") + x = Tensor(np.ones([1,3,3,4]).astype(np.float32)) + y = Tensor(np.ones([1,3,3,4]).astype(np.float32)) + print(F.tensor_add(x, y)) + ``` + ``` + [[[ 2. 2. 2. 2.], + [ 2. 2. 2. 2.], + [ 2. 2. 2. 2.]], + + [[ 2. 2. 2. 2.], + [ 2. 2. 2. 2.], + [ 2. 2. 2. 2.]], + + [[ 2. 2. 2. 2.], + [ 2. 2. 2. 2.], + [ 2. 2. 2. 2.]]] + ``` If anyone wants to learn more about the build process of MindSpore docker images, please check out `docker` folder for the details.