提交 f4a92171 编写于 作者: M mindspore-ci-bot 提交者: Gitee

!5 Update some introduction sections in README.md

Merge pull request !5 from leonwanghui/doc-update
...@@ -4,14 +4,17 @@ ...@@ -4,14 +4,17 @@
- [MindSpore Operator](#mindspore-operator) - [MindSpore Operator](#mindspore-operator)
- [Introduction of MindSpore and ms-operator](#introduction-of-mindspore-and-ms-operator) - [Introduction of MindSpore and ms-operator](#introduction-of-mindspore-and-ms-operator)
- [MindSpore Docker Image](#mindspore-docker-image) - [MindSpore docker image](#mindspore-docker-image)
- [Design](#Design) - [Design](#Design)
- [Overview of MindSpore in Kubeflow Ecosystem](#overview-of-mindspore-in-kubeflow-ecosystem) - [Overview of MindSpore in Kubeflow ecosystem](#overview-of-mindspore-in-kubeflow-ecosystem)
- [Getting Started](#getting-started) - [Getting Started](#getting-started)
- [Prerequisites](#prerequisites) - [Prerequisites](#prerequisites)
- [Steps of running the example](#steps-of-running-the-example) - [Steps of running the example](#steps-of-running-the-example)
- [Future Work](#future-work) - [Future Work](#future-work)
- [Appendix: Example yaml file](#appendix:-example-yaml-file) - [Appendix: Example yaml file](#appendix:-example-yaml-file)
- [Community](#community)
- [Contributing](#contributing)
- [License](#license)
## Introduction of MindSpore and ms-operator ## Introduction of MindSpore and ms-operator
...@@ -24,7 +27,7 @@ processor, and software hardware co-optimization. ...@@ -24,7 +27,7 @@ processor, and software hardware co-optimization.
This project contains the specification and implementation of MSJob custom This project contains the specification and implementation of MSJob custom
resource definition. We will demonstrate running a walkthrough of creating resource definition. We will demonstrate running a walkthrough of creating
ms-operator, as well as MNIST training job on Kubernetes with MindSpore ms-operator, as well as MNIST training job on Kubernetes with MindSpore
0.1.0-alpha image (x86 CPU build version) on a single node. More completed `0.1.0-alpha` image (x86 CPU build version) on a single node. More completed
features will be developed in the coming days. features will be developed in the coming days.
This project defines the following: This project defines the following:
...@@ -33,17 +36,10 @@ This project defines the following: ...@@ -33,17 +36,10 @@ This project defines the following:
- MindSpore LeNet MNIST training example - MindSpore LeNet MNIST training example
- Future goal: distributed MindSpore training example - Future goal: distributed MindSpore training example
### MindSpore Docker Image ### MindSpore docker image
MindSpore docker image is hosted on [Docker Hub](https://hub.docker.com/r/mindspore), currently both `CPU` and `GPU` are supported as follows: Please refer to MindSpore [docker image introduction](https://gitee.com/mindspore/mindspore/blob/master/README.md#docker-image)
- CPU: `mindspore/mindspore-cpu:0.1.0-alpha` for details.
- GPU (Cuda 10.1): `mindspore/mindspore-cuda10.1:0.1.0-alpha`
- GPU (Cuda 9.2): `mindspore/mindspore-cuda9.2:0.1.0-alpha`
Take CPU for example, you can directly pull the image using the below command:
```
docker pull mindspore/mindspore-cpu:0.1.0-alpha
```
### Design ### Design
...@@ -73,7 +69,7 @@ spec: ...@@ -73,7 +69,7 @@ spec:
path: /root/gopath/src/gitee.com/mindspore/ms-operator/examples/ path: /root/gopath/src/gitee.com/mindspore/ms-operator/examples/
``` ```
### Overview of MindSpore in Kubeflow Ecosystem ### Overview of MindSpore in Kubeflow ecosystem
<img src="./docs/pics/ms-operator-in-kubeflow.png" alt="ms-operator in Kubeflow" width=600/> <img src="./docs/pics/ms-operator-in-kubeflow.png" alt="ms-operator in Kubeflow" width=600/>
...@@ -96,7 +92,7 @@ First, pull the ms-operator image from [Docker Hub](https://hub.docker.com/r/min ...@@ -96,7 +92,7 @@ First, pull the ms-operator image from [Docker Hub](https://hub.docker.com/r/min
docker pull mindspore/ms-operator:latest docker pull mindspore/ms-operator:latest
``` ```
Or you build the ms-operator image on local machine: Or you can build the ms-operator image on local machine:
``` ```
docker build . -t mindspore/ms-operator docker build . -t mindspore/ms-operator
``` ```
...@@ -110,7 +106,7 @@ mindspore/ms-operator latest 729960ae415e 28 h ...@@ -110,7 +106,7 @@ mindspore/ms-operator latest 729960ae415e 28 h
The MindSpore image we download from docker hub is `0.1.0-alpha` version: The MindSpore image we download from docker hub is `0.1.0-alpha` version:
``` ```
REPOSITORY TAG IMAGE ID CREATED SIZE REPOSITORY TAG IMAGE ID CREATED SIZE
mindspore/mindspore-cpu 0.1.0-alpha 9a124f33ed27 2 hours ago 1.19GB mindspore/mindspore-cpu 0.1.0-alpha 9a124f33ed27 2 hours ago 1.16GB
``` ```
MindSpore supports heterogeneous computing including multiple hardware and MindSpore supports heterogeneous computing including multiple hardware and
...@@ -149,7 +145,7 @@ Kubernetes: ...@@ -149,7 +145,7 @@ Kubernetes:
cd examples/ && kubectl apply -f ms-mnist.yaml cd examples/ && kubectl apply -f ms-mnist.yaml
``` ```
The job is simply importing MindSpore packges, the dataset is already included in the `MNIST_Data` folder, executing only one epoch and printing result which should only consume little time. After the job completed, you should be able to check the job status and see the result logs. You can check the source training code in `examples/` folder. The job is simply importing MindSpore packages, the dataset is already included in the `MNIST_Data` folder, executing only one epoch and printing result which should only consume little time. After the job completed, you should be able to check the job status and see the result logs. You can check the source training code in `examples/` folder.
``` ```
kubectl get pod msjob-mnist && kubectl logs msjob-mnist kubectl get pod msjob-mnist && kubectl logs msjob-mnist
``` ```
...@@ -186,7 +182,7 @@ including: ...@@ -186,7 +182,7 @@ including:
- etc. - etc.
The MindSpore community is driving to collaborate with the Kubeflow community The MindSpore community is driving to collaborate with the Kubeflow community
as well as making the ms-operator more complex, well-orgnized and its as well as making the ms-operator more complex, well-organized and its
dependencies up-to-date. All these components make it easy for machine learning dependencies up-to-date. All these components make it easy for machine learning
engineers and data scientists to leverage cloud assets (public or on-premise) engineers and data scientists to leverage cloud assets (public or on-premise)
for machine learning workloads. for machine learning workloads.
...@@ -283,3 +279,15 @@ including multiple hardware and backends (`CPU`, `GPU`, `Ascend`), ...@@ -283,3 +279,15 @@ including multiple hardware and backends (`CPU`, `GPU`, `Ascend`),
the device_target of MindSpore is `Ascend` by default. the device_target of MindSpore is `Ascend` by default.
We define `masterPort` that groups will use to communicate with master service. We define `masterPort` that groups will use to communicate with master service.
## Community
- [MindSpore Slack](https://join.slack.com/t/mindspore/shared_invite/enQtOTcwMTIxMDI3NjM0LTNkMWM2MzI5NjIyZWU5ZWQ5M2EwMTQ5MWNiYzMxOGM4OWFhZjI4M2E5OGI2YTg3ODU1ODE2Njg1MThiNWI3YmQ) - Ask questions and find answers.
## Contributing
Welcome contributions. See our [Contributor Wiki](https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md) for more details.
## License
[Apache License 2.0](LICENSE)
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