arena

README

Arena

Overview

Arena is a command-line interface for the data scientists to run and monitor the machine learning training jobs and check their results in an easy way. Currently it supports solo/distributed TensorFlow training. In the backend, it is based on Kubernetes, helm and Kubeflow. But the data scientists can have very little knowledge about kubernetes.

Meanwhile, the end users require GPU resource and node management. Arena also provides top command to check available GPU resources in the Kubernetes cluster.

In one word, Arena's goal is to make the data scientists feel like to work on a single machine but with the Power of GPU clusters indeed.

For the Chinese version, please refer to 中文文档

Setup

You can follow up the Installation guide

User Guide

Arena is a command-line interface to run and monitor the machine learning training jobs and check their results in an easy way. Currently it supports solo/distributed training.

• 1. Run a training Job with source code from git
• 2. Run a training Job with tensorboard
• 3. Run a distributed training Job
• 4. Run a distributed training Job with external data
• 5. Run a distributed training Job based on MPI
• 6. Run a distributed TensorFlow training job with gang scheduler
• 7. Run TensorFlow Serving
• 8. Run TensorFlow Estimator
• 9. Monitor GPUs of the training job
• 10. Run a distributed training job with RDMA
• 11. Run a distributed spark job
• 12. Run a Volcano job
• 13. Preempted mpi job
• 14. Submit jobs with node selectors
• 15. Submit jobs with tolerating taints
• 16. Run a custom serving job
• 17. Run a training Job with configuration files
• 18. Run a standalone Pytorch Job
• 19. Run a distributed Pytorch Job

Demo

Developing

Prerequisites:

• Go >= 1.8

mkdir -p $(go env GOPATH)/src/github.com/kubeflow
cd $(go env GOPATH)/src/github.com/kubeflow
git clone https://github.com/kubeflow/arena.git
cd arena
make

arena binary is located in directory arena/bin. You may want to add the directory to $PATH.

Then you can follow Installation guide for developer

CPU Profiling

# set profile rate (HZ)
export PROFILE_RATE=1000

# arena {command} --pprof
arena list --pprof
INFO[0000] Dump cpu profile file into /tmp/cpu_profile

Then you can analyze the profile by following Go CPU profiling: pprof and speedscope

FAQ

Please refer to FAQ

CLI Document

Please refer to arena.md

RoadMap

See RoadMap

Documentation

Overview

Copyright 2018 The Kubeflow Authors

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Index

• type ChartsInfo
• type Version
  • func GetVersion() Version
  • func (v Version) String() string

Types

type ChartsInfo

type ChartsInfo struct {
	ChartsVersion map[string]string
	ChartsHome    string
}

type Version

type Version struct {
	Version      string
	BuildDate    string
	GitCommit    string
	GitTag       string
	GitTreeState string
	GoVersion    string
	Compiler     string
	Platform     string
	ChartsInfo   ChartsInfo
}

Version contains Arena version information

func GetVersion

func GetVersion() Version

GetVersion returns the version information

func (Version) String

func (v Version) String() string