OpenDILab RL Kubernetes Custom Resource and Operator Lib

Overview

DI Orchestrator

DI Orchestrator is designed to manage DI (Decision Intelligence) jobs using Kubernetes Custom Resource and Operator.

Prerequisites

  • A well-prepared kubernetes cluster. Follow the instructions to create a kubernetes cluster, or create a local kubernetes node referring to kind or minikube
  • Cert-manager. Installation on kubernetes please refer to cert-manager docs. Or you can install it by the following command.
kubectl create -f ./config/certmanager/cert-manager.yaml

Install DI Orchestrator

DI Orchestrator consists of two components: di-operator and di-server. Install di-operator and di-server with the following command.

kubectl create -f ./config/di-manager.yaml

di-operator and di-server will be installed in di-system namespace.

$ kubectl get pod -n di-system
NAME                               READY   STATUS    RESTARTS   AGE
di-operator-57cc65d5c9-5vnvn   1/1     Running   0          59s
di-server-7b86ff8df4-jfgmp     1/1     Running   0          59s

Install global components of DIJob defined in AggregatorConfig:

kubectl create -f config/samples/agconfig.yaml -n di-system

Submit DIJob

# submit DIJob
$ kubectl create -f config/samples/dijob-cartpole.yaml

# get pod and you will see coordinator is created by di-operator
# a few seconds later, you will see collectors and learners created by di-server
$ kubectl get pod

# get logs of coordinator
$ kubectl logs cartpole-dqn-coordinator

User Guide

Refers to user-guide. For Chinese version, please refer to 中文手册

Contributing

Refers to developer-guide.

Contact us throw [email protected]

Comments
  • 在 Pod 内增加集群信息

    在 Pod 内增加集群信息

    希望以 dijob replica 方式提交时,每个 pod 都能见到整个 replica 的 host 信息和自己的启动顺序,增加以下几个环境变量:

    1. replica 中所有 pod 的 FQDN,依据启动顺序排序
    2. 当前 pod 的 FQDN
    3. 当前 pod 的顺序编号

    DI-engine 中会根据这些变量实现对应的网络连接,attach-to 的生成逻辑可以从 di-orchestrator 中移除

    enhancement 
    opened by sailxjx 3
  • add tasks to dijob spec

    add tasks to dijob spec

    1. goal

    There is only one pod template defined in a dijob, which results in that we can not define different commands or resources for different componets of di-engine such as collector, learner and evaluator. So we are supposed to find a more general way to define a custom resource of dijob.

    2. design *

    Inspired by VolcanoJob, we define the spec.tasks to describe different componets of di-engine. spec.tasks is a list, which allows us to define multiple tasks. We can specify different task.type to label the task as one of collector, learner, evaluator and none. none means the task is a general task, which is the default value.

    After change, the dijob can be defined as follow:

    apiVersion: diengine.opendilab.org/v2alpha1
    kind: DIJob
    metadata:
      name: job-with-tasks
    spec:
      priority: "normal"  # job priority, which is a reserved field for allocator
      backoffLimit: 0  # restart count
      cleanPodPolicy: "Running"  # the policy to clean pods after job completion
      preemptible: false  # job is preemtible or not
      minReplicas: 2  
      maxReplicas: 5
      tasks:
      - replicas: 1
        name: "learner"
        type: learner
        template:
          metadata:
            name: di
          spec:
            containers:
            - image: registry.sensetime.com/xlab/ding:nightly
              imagePullPolicy: IfNotPresent
              name: pydi
              env:
              - name: NCCL_DEBUG
                value: "INFO"
              command: ["/bin/bash", "-c",]
              args: 
              - |
                ditask --label learner xxx
              resources:
                requests:
                  cpu: "1"
                  nvidia.com/gpu: 1
            restartPolicy: Never
      - replicas: 1
        name: "evaluator"
        type: evaluator
        template:
          metadata:
            name: di
          spec:
            containers:
            - image: registry.sensetime.com/xlab/ding:nightly
              imagePullPolicy: IfNotPresent
              name: pydi
              env:
              - name: NCCL_DEBUG
                value: "INFO"
              command: ["/bin/bash", "-c",]
              args: 
              - |
                ditask --label evaluator xxx
            restartPolicy: Never
      - replicas: 2
        name: "collector"
        type: collector
        template:
          metadata:
            name: di
          spec:
            containers:
            - image: registry.sensetime.com/xlab/ding:nightly
              imagePullPolicy: IfNotPresent
              name: pydi
              env:
              - name: NCCL_DEBUG
                value: "INFO"
              command: ["/bin/bash", "-c",]
              args: 
              - |
                ditask --label collector xxx
            restartPolicy: Never
    status:
      conditions:
      - lastTransitionTime: "2022-05-26T07:25:11Z"
        lastUpdateTime: "2022-05-26T07:25:11Z"
        message: job created.
        reason: JobPending
        status: "False"
        type: Pending
      - lastTransitionTime: "2022-05-26T07:25:11Z"
        lastUpdateTime: "2022-05-26T07:25:11Z"
        message: job is starting since all pods are created.
        reason: JobStarting
        status: "False"
        type: Starting
      phase: Starting
      profilings: {}
      readyReplicas: 0
      replicas: 4
      taskStatus:
        learner:
          Pending: 1
        evaluator:
          Pending: 1
        collector:
          Pending: 2
      reschedules: 0
      restarts: 0
    

    task definition:

    type Task struct {
    	Name string `json:"name,omitempty"`
    
    	Type TaskType `json:"type,omitempty"`
    
    	Replicas int32 `json:"replicas,omitempty"`
    
    	Template corev1.PodTemplateSpec `json:"template,omitempty"`
    }
    
    type TaskType string
    
    const (
    	TaskTypeLearner TaskType = "learner"
    
    	TaskTypeCollector TaskType = "collector"
    
    	TaskTypeEvaluator TaskType = "evaluator"
    
    	TaskTypeNone TaskType = "none"
    )
    
    

    status.taskStatus definition:

    type DIJobStatus struct {
      // Phase defines the observed phase of the job
      // +kubebuilder:default=Pending
      Phase Phase `json:"phase,omitempty"`
    
      // ...
      
      // map for different task statuses. key: task.name, value: TaskStatus
      TaskStatus map[string]TaskStatus
    
      // ...
    }
    
    // count of different pod phases
    type TaskStatus map[corev1.PodPhase]int32
    
    enhancement 
    opened by konnase 1
  • new version for di-engine new architecture

    new version for di-engine new architecture

    release notes

    features

    • v1.0.0 for DI-engine new architecture
    • remove webhook
    • manage commands with cobra
    • refactor orchestrator architecture inspired from adaptdl
    • use gin to rewrite di-server
    • update di-server http interface
    enhancement 
    opened by konnase 1
  • v0.2.0

    v0.2.0

    • [x] split webhook and operator
    • [x] add dockerfile.dev
    • [x] update CleanPolicyALL to CleanPolicyAll
    • [x] remove k8s service related operations from server, and operator is responsible for managing services
    • [x] add e2e test
    enhancement 
    opened by konnase 1
  • refactor job spec

    refactor job spec

    • refactor job spec definition and add spec.tasks to support multi tasks #20
    • add DI_RANK to pod env and remove engineFields in job.spec #16
    • add e2e test
    • add validator to validate the correctness of dijob spec
    • change job.phase to Pending when job replicas scaled to 0
    • implement a processor to process di-server requests
    • refactor project structure
    enhancement 
    opened by konnase 0
  • Release/v1.0

    Release/v1.0

    release notes

    features

    • v1.0.0 for DI-engine new architecture
    • remove webhook
    • manage commands with cobra
    • refactor orchestrator architecture inspired from adaptdl
    • use gin to rewrite di-server
    • update di-server http interface
    enhancement 
    opened by konnase 0
  • fix: job failed submit when collector/learner missed

    fix: job failed submit when collector/learner missed

    job failed submit when collector/learner missed because webhook create an empty dijob, and golang builder add some default value to some feilds of collector/learner, which result in invalid type error. solved by make coordinator/collector/learner as pointers.

    bug 
    opened by konnase 0
  • Feat/job create event

    Feat/job create event

    • add event handler for dijob, and mark job as Created when job submitted
    • mark collector and learner as optional, only coordinator is required(https://github.com/opendilab/DI-orchestrator/pull/13/commits/653e64af01ec7752b08d4bf8381738d566fca224)
    • mark job Failed when the submitted job is incorrect(https://github.com/opendilab/DI-orchestrator/pull/13/commits/bea840a5eee3508be18b53b325168a5647daff94), but it's hard to test since client-go reflector decodes DIJob strictly, we have no chance to handle DIJob add event when incorrect job submitted
    • version -> v0.2.1
    enhancement 
    opened by konnase 0
  • allocate的一些问题

    allocate的一些问题

    1.目前的allocator的逻辑,对于不可被抢占的job的初始分配,仅利用minreplicas修改replicas属性,那job的pods部署到哪个节点是完全由K8S决定吗?而且Release1.13代码的allocator.go中对不可被抢占job的初始分配部分貌似还没有写。 2.job是否可以被抢占的含义具体是什么?和是否能被调度是不是等价的? 3.调度策略的FitPolicy的Allocate和Optimize方法也没有进行实现,这部分内容什么时候可以补充? 4.文档中存在许多与最新代码不符合的地方,比如DIJob.Spec.Group属性在代码中已经被移除,文档中提到的job.spec.minreplicas属性代码中也没有,而是在JobInfo中。可以更新一下文档吗? 感谢!

    opened by RZ-Q 3
Releases(v1.1.3)
  • v1.1.3(Aug 22, 2022)

  • v1.1.2(Jul 21, 2022)

    bugs fix

    • global cmd flag error(https://github.com/opendilab/DI-orchestrator/pull/23)
    • wrong pod subdomain(https://github.com/opendilab/DI-orchestrator/pull/24)
    • incorrect to get global rank(https://github.com/opendilab/DI-orchestrator/pull/25)
    Source code(tar.gz)
    Source code(zip)
    di-manager.yaml(445.36 KB)
  • v1.1.1(Jul 4, 2022)

  • v1.1.0(Jun 30, 2022)

    • refactor job spec definition and add spec.tasks to support multi tasks #20
    • add DI_RANK to pod env and remove engineFields in job.spec #16
    • add e2e test
    • add validator to validate the correctness of dijob spec
    • change job.phase to Pending when job replicas scaled to 0
    • implement a processor to process di-server requests
    • refactor project structure

    see details in https://github.com/opendilab/DI-orchestrator/pull/21

    Source code(tar.gz)
    Source code(zip)
    di-manager.yaml(374.01 KB)
  • v1.0.0(Mar 23, 2022)

  • v0.2.2(Dec 15, 2021)

  • v0.2.1(Oct 12, 2021)

    feature

    • add event handler for dijob, and mark job as Created when job submitted(https://github.com/opendilab/DI-orchestrator/pull/13)
    • mark collector and learner as optional, only coordinator is required(https://github.com/opendilab/DI-orchestrator/pull/13/commits/653e64af01ec7752b08d4bf8381738d566fca224)
    • mark job Failed when the submitted job is incorrect(https://github.com/opendilab/DI-orchestrator/pull/13/commits/bea840a5eee3508be18b53b325168a5647daff94), but it's hard to test since client-go reflector decodes DIJob strictly, we have no chance to handle DIJob add event when incorrect job submitted
    Source code(tar.gz)
    Source code(zip)
    di-manager.yaml(1.38 MB)
  • v0.2.0(Sep 28, 2021)

  • v0.2.0-rc.0(Sep 6, 2021)

    • split webhook and operator
    • add dockerfile.dev
    • update CleanPolicyALL to CleanPolicyAll
    • remove k8s service related operations from server, and operator is responsible for managing services
    • add e2e test
    Source code(tar.gz)
    Source code(zip)
  • v0.1.0(Jul 8, 2021)

    Features

    • Define DIJob CRD to support DI jobs' submission
    • Define AggregatorConfig CRD to support aggregator definition
    • Add webhook to validate DIJob submission
    • Provide http service for DI jobs to request for DI modules
    • Docs to introduce DI-orchestrator architecture
    Source code(tar.gz)
    Source code(zip)
Owner
OpenDILab
Open sourced Decision Intelligence (DI)
OpenDILab
Code for the Image similarity challenge.

ISC 2021 This repository contains code for the Image Similarity Challenge 2021. Getting started The docs subdirectory has step-by-step instructions on

Facebook Research 173 Dec 12, 2022
Code repository for our paper "Learning to Generate Scene Graph from Natural Language Supervision" in ICCV 2021

Scene Graph Generation from Natural Language Supervision This repository includes the Pytorch code for our paper "Learning to Generate Scene Graph fro

Yiwu Zhong 64 Dec 24, 2022
Differentiable Factor Graph Optimization for Learning Smoothers @ IROS 2021

Differentiable Factor Graph Optimization for Learning Smoothers Overview Status Setup Datasets Training Evaluation Acknowledgements Overview Code rele

Brent Yi 60 Nov 14, 2022
Sparse Physics-based and Interpretable Neural Networks

Sparse Physics-based and Interpretable Neural Networks for PDEs This repository contains the code and manuscript for research done on Sparse Physics-b

28 Jan 03, 2023
SpecAugmentPyTorch - A Pytorch (support batch and channel) implementation of GoogleBrain's SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

SpecAugment An implementation of SpecAugment for Pytorch How to use Install pytorch, version=1.9.0 (new feature (torch.Tensor.take_along_dim) is used

IMLHF 3 Oct 11, 2022
KGDet: Keypoint-Guided Fashion Detection (AAAI 2021)

KGDet: Keypoint-Guided Fashion Detection (AAAI 2021) This is an official implementation of the AAAI-2021 paper "KGDet: Keypoint-Guided Fashion Detecti

Qian Shenhan 35 Dec 29, 2022
QMagFace: Simple and Accurate Quality-Aware Face Recognition

Quality-Aware Face Recognition 26.11.2021 start readme QMagFace: Simple and Accurate Quality-Aware Face Recognition Research Paper Implementation - To

Philipp Terhörst 59 Jan 04, 2023
GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]

GANimation: Anatomically-aware Facial Animation from a Single Image [Project] [Paper] Official implementation of GANimation. In this work we introduce

Albert Pumarola 1.8k Dec 28, 2022
ROS Basics and TurtleSim

Waypoint Follower Anna Garverick This package draws given waypoints, then waits for a service call with a start position to send the turtle to each wa

Anna Garverick 1 Dec 13, 2021
PyTorch implementation of Pointnet2/Pointnet++

Pointnet2/Pointnet++ PyTorch Project Status: Unmaintained. Due to finite time, I have no plans to update this code and I will not be responding to iss

Erik Wijmans 1.2k Dec 29, 2022
LIAO Shuiying 6 Dec 01, 2022
DL & CV-based indicator toolset for the vehicle drivers via live dash-cam footage.

Vehicle Indicator Toolset Deep Learning and Computer Vision based indicator toolset for vehicle drivers using live dash-cam footages. Tracking of vehi

Alex Xu 12 Dec 28, 2021
TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL, and utterance id

TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL

3 Dec 26, 2022
[ICME 2021 Oral] CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning

CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning This repository is the official PyTorch implementation of CORE-Text, a

Jingyang Lin 18 Aug 11, 2022
Official Implementation of CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback

CoSMo.pytorch Official Implementation of CoSMo: Content-Style Modulation for Image Retrieval with Text Feedback, Seungmin Lee*, Dongwan Kim*, Bohyung

Seung Min Lee 54 Dec 08, 2022
Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)

Change is Everywhere Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery by Zhuo Zheng, Ailong Ma, Liangpei Zhang and Yanfei

Zhuo Zheng 125 Dec 13, 2022
MT-GAN-PyTorch - PyTorch Implementation of Learning to Transfer: Unsupervised Domain Translation via Meta-Learning

MT-GAN-PyTorch PyTorch Implementation of AAAI-2020 Paper "Learning to Transfer: Unsupervised Domain Translation via Meta-Learning" Dependency: Python

29 Oct 19, 2022
Pytorch implementation for the paper: Contrastive Learning for Cold-start Recommendation

Contrastive Learning for Cold-start Recommendation This is our Pytorch implementation for the paper: Yinwei Wei, Xiang Wang, Qi Li, Liqiang Nie, Yan L

45 Dec 13, 2022
Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

Fully Convolutional Networks for Semantic Segmentation This is the reference implementation of the models and code for the fully convolutional network

Evan Shelhamer 3.2k Jan 08, 2023
Code and data accompanying our SVRHM'21 paper.

Code and data accompanying our SVRHM'21 paper. Requires tensorflow 1.13, python 3.7, scikit-learn, and pytorch 1.6.0 to be installed. Python scripts i

5 Nov 17, 2021