MindSpore Operator
PyTorch Operator and TF Operator. Right now MindSpore supports running LeNet with MNIST dataset on a single node, distributed training examples are expected in the near future.
Experimental notice: This project is still experimental and only serves as a proof of concept for running MindSpore on Kubernetes. The current version of ms-operator is based on an early version ofIntroduction of MindSpore and ms-operator
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.
This project contains the specification and implementation of MSJob custom resource definition. We will demonstrate running a walkthrough of creating 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 features will be developed in the coming days.
This project defines the following:
- The ms-operator
- A way to deploy the operator
- MindSpore LeNet MNIST training example
- Future goal: distributed MindSpore training example
MindSpore Docker Image
MindSpore docker image is hosted on Docker Hub, currently both CPU
and GPU
are supported as follows:
- CPU:
mindspore/mindspore-cpu:0.1.0-alpha
- 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
The yaml file we used to create our MNIST training job is defined as follows:
apiVersion: v1
kind: Pod
metadata:
name: msjob-mnist
spec:
containers:
- image: mindspore/mindspore-cpu:0.1.0-alpha
imagePullPolicy: IfNotPresent
name: msjob-mnist
command: ["/bin/bash", "-c", "python /tmp/test/MNIST/lenet.py"]
volumeMounts:
- name: training-result
mountPath: /tmp/result
- name: ms-mnist
mountPath: /tmp/test
restartPolicy: OnFailure
volumes:
- name: training-result
emptyDir: {}
- name: ms-mnist
hostPath:
path: /root/gopath/src/gitee.com/mindspore/ms-operator/examples/
Overview of MindSpore in Kubeflow Ecosystem
The high-level view of how MindSpore fits in the ecosystem of Kubeflow and its components.
Getting Started
Prerequisites
-
Helm and Tiller:
v2.9.0
-
go:
go1.12.1
-
docker:
v18.06.1-ce
-
Kubernetes:
v1.14.0
Steps of running the example
First, pull the ms-operator image from Docker Hub:
docker pull mindspore/ms-operator:latest
Or you build the ms-operator image on local machine:
docker build . -t mindspore/ms-operator
After the installation, check the image status using docker images
command:
REPOSITORY TAG IMAGE ID CREATED SIZE
mindspore/ms-operator latest 729960ae415e 28 hours ago 175MB
The MindSpore image we download from docker hub is 0.1.0-alpha
version:
REPOSITORY TAG IMAGE ID CREATED SIZE
mindspore/mindspore-cpu 0.1.0-alpha 9a124f33ed27 2 hours ago 1.19GB
MindSpore supports heterogeneous computing including multiple hardware and
backends (CPU
, GPU
, Ascend
), the device_target of MindSpore is
Ascend
by default but we will use the CPU version here.
Install the msjob crd, ms-operator deployment and pod:
RBAC=true # set false if you do not have an RBAC cluster
helm install ms-operator-chart/ -n ms-operator --set rbac.install=${RBAC} --wait --replace
Using helm status ms-operator
command to check generated resources:
LAST DEPLOYED: Tue Mar 24 11:36:51 2020
NAMESPACE: default
STATUS: DEPLOYED
RESOURCES:
==> v1beta1/CustomResourceDefinition
NAME AGE
msjobs.kubeflow.org 1d
==> v1beta1/Deployment
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
ms-operator 1 1 1 1 1d
==> v1/Pod(related)
NAME READY STATUS RESTARTS AGE
ms-operator-7b5b457d69-dpd2b 1/1 Running 0 1d
We will do a MNIST training to check the eligibility of MindSpore running on Kubernetes:
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.
kubectl get pod msjob-mnist && kubectl logs msjob-mnist
NAME READY STATUS RESTARTS AGE
msjob-mnist 0/1 Completed 0 3h53m
============== Starting Training ==============
epoch: 1 step: 1, loss is 2.3005836
epoch: 1 step: 2, loss is 2.2978227
epoch: 1 step: 3, loss is 2.3004227
epoch: 1 step: 4, loss is 2.3054247
epoch: 1 step: 5, loss is 2.3068798
epoch: 1 step: 6, loss is 2.298408
epoch: 1 step: 7, loss is 2.3055573
epoch: 1 step: 8, loss is 2.2998955
epoch: 1 step: 9, loss is 2.3028255
epoch: 1 step: 10, loss is 2.2972553
Since MindSpore is in the early stage of open source, the whole community is
still working on implementing distributed training of LeNet with MNIST dataset
on Kubernetes, together with the distributed training on different backends
(GPU || Ascend
) are also expected in the near future.
Future Work
Kubeflow just announced its first major 1.0 release recently with the graduation of a core set of stable applications including:
- Kubeflow's UI
- Jupyter notebook controller and web app
- Tensorflow Operator(TFJob), and PyTorch Operator for distributed training
- kfctl for deployment and upgrade
- etc.
The MindSpore community is driving to collaborate with the Kubeflow community as well as making the ms-operator more complex, well-orgnized and its 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) for machine learning workloads.
MindSpore is also looking forward to enable users to use Jupyter to develop models. Users in the future can use Kubeflow tools like fairing (Kubeflow’s python SDK) to build containers and create Kubernetes resources to train their MindSpore models.
Once training completed, users can use KFServing to create and deploy a server for inference thus completing the life cycle of machine learning.
Distributed training is another field MindSpore will be focusing on. There are
two major distributed training strategies nowadays: one based on parameter
servers and the other based on collective communication primitives such as
allreduce. MPI Operator is one of
the core components of Kubeflow which makes it easy to run synchronized,
allreduce-style distributed training on Kubernetes. MPI Operator provides a crd
for defining a training job on a single CPU/GPU, multiple CPU/GPUs, and multiple
nodes. It also implements a custom controller to manage the CRD, create
dependent resources, and reconcile the desired states. If MindSpore can leverage
MPI Operator together with the high performance Ascend
processor, it is
possible that MindSpore will bring distributed training to an even higher level.
Appendix: Example yaml file
The yaml file to create distributed training MSJob expected to be like this:
# WIP example for distributed training
apiVersion: "kubeflow.org/v1"
kind: "MSJob"
metadata:
name: "msjob-mnist"
spec:
backend: "tcp"
masterPort: "23456"
replicaSpecs:
- replicas: 1
replicaType: MASTER
template:
spec:
containers:
- image: mindspore/mindspore-cpu:0.1.0-alpha
imagePullPolicy: IfNotPresent
name: msjob-mnist
command: ["/bin/bash", "-c", "python /tmp/test/MNIST/lenet.py"]
volumeMounts:
- name: training-result
mountPath: /tmp/result
- name: ms-mnist-local-file
mountPath: /tmp/test
restartPolicy: OnFailure
volumes:
- name: training-result
emptyDir: {}
- name: entrypoint
configMap:
name: dist-train
defaultMode: 0755
restartPolicy: OnFailure
- replicas: 3
replicaType: WORKER
template:
spec:
containers:
- image: mindspore/mindspore-cpu:0.1.0-alpha
imagePullPolicy: IfNotPresent
name: msjob-mnist
command: ["/bin/bash", "-c", "python /tmp/test/MNIST/lenet.py"]
volumeMounts:
- name: training-result
mountPath: /tmp/result
- name: ms-mnist-local-file
hostPath:
path: /root/gopath/src/gitee.com/mindspore/ms-operator/examples
restartPolicy: OnFailure
volumes:
- name: training-result
emptyDir: {}
- name: entrypoint
configMap:
name: dist-train
defaultMode: 0755
restartPolicy: OnFailure
The MSJob currently is designed based on the TF Job and PyTorch Job, and is subject to change in future versions.
We define backend
protocol which the MS workers will use to communicate when
initializing the worker group. MindSpore supports heterogeneous computing
including multiple hardware and backends (CPU
, GPU
, Ascend
),
the device_target of MindSpore is Ascend
by default.
We define masterPort
that groups will use to communicate with master service.