These examples shows how to run MindSpore via Volcano. Since MindSpore itself is relatively new, these examples maybe oversimplified, but will evolve with both communities.
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios. MindSpore is designed to provide development experience with friendly design and efficient execution for the data scientists and algorithmic engineers, native support for Ascend AI processor, and software hardware co-optimization.
MindSpore is open sourced on both Github and Gitee.
These two examples are tested under below env:
- Ubuntu:
16.04.6 LTS - docker:
v18.06.1-ce - Kubernetes:
v1.16.6 - NVIDIA Docker:
2.3.0 - NVIDIA/k8s-device-plugin:
1.0.0-beta6 - NVIDIA drivers:
418.39 - CUDA:
10.1
Using a modified MindSpore CPU image as the container image which trains LeNet with MNIST dataset.
pull image: docker pull lyd911/mindspore-cpu-example:0.2.0
to run: kubectl apply -f mindspore-cpu.yaml
to check the result: kubectl logs mindspore-cpu-pod-0
Using a modified image which the openssh-server is installed from the official MindSpore GPU image. To check the eligibility of MindSpore GPU's ability to communicate with other processes, we leverage the mpimaster and mpiworker task spec of Volcano. In this example, we launch one mpimaster and two mpiworkers, the python script is taken from MindSpore Gitee README, which is also modified to be able to run parallelly.
pull image: docker pull lyd911/mindspore-gpu-example:0.2.0
to run: kubectl apply -f mindspore-gpu.yaml
to check result: kubectl logs mindspore-gpu-mpimster-0
The expected output should be (2*3) of multi-dimensional array.
An end to end example of training a network using MindSpore on distributed GPU via Volcano is expected in the future.