FrameworkController is built to orchestrate all kinds of applications on Kubernetes by a single controller.

These kinds of applications include but not limited to:

• Stateless and Stateful Service (Nginx, TensorFlow Serving, HBase, Kafka, etc)
• Stateless and Stateful Batch (KD-Tree Building, Batch Data Processing, etc)
• Any combination of above applications (Distributed TensorFlow Training, Stream Data Processing, etc)

In the open source community, there are so many specialized Kubernetes Pod controllers which are built for a specific kind of application, such as Kubernetes StatefulSet Controller, Kubernetes Job Controller, KubeFlow TFJob Operator. However, no one is built for all kinds of applications and combination of the existing ones still cannot support some kinds of applications. So, we have to learn, use, develop, deploy and maintain so many Pod controllers.

Build a General-Purpose Kubernetes Pod Controller: FrameworkController.

And then we can get below benefits from it:

• Support Kubernetes official unsupported applications

  Such as the Stateful Batch with Service application, like Distributed TensorFlow Training.

• Only need to learn, use, develop, deploy and maintain a single controller

• All kinds of applications can be orchestrated through the same interface with a unified experience

• If really required, only need to build specialized controllers on top of it, instead of building from scratch

  The similar practice is also adopted by Kubernetes official controllers, such as the Kubernetes Deployment Controller is built on top of the Kubernetes ReplicaSet Controller.

A Framework represents an application with a set of Tasks:

1. Executed by Kubernetes Pod
2. Partitioned to different heterogeneous TaskRoles which share the same lifecycle
3. Ordered in the same homogeneous TaskRole by TaskIndex
4. With consistent identity {FrameworkName}-{TaskRoleName}-{TaskIndex} as PodName
5. With fine grained RetryPolicy for each Task and the whole Framework
6. With fine grained FrameworkAttemptCompletionPolicy for each TaskRole
7. Guarantees at most one instance of a specific Task is running at any point in time
8. Guarantees at most one instance of a specific Framework is running at any point in time

1. Highly generalized as it is built for all kinds of applications
2. Light-weight as it is only responsible for Pod orchestration
3. Well-defined Framework consistency, state machine and failure model
4. Tolerate Pod/ConfigMap unexpected deletion, Node/Network/FrameworkController/Kubernetes failure
5. Support to specify how to classify and summarize Pod failures
6. Support to expose Framework and Pod history snapshots to external systems
7. Easy to leverage FrameworkBarrier to achieve light-weight Gang Execution and Service Discovery
8. Easy to leverage HivedScheduler to achieve GPU Multi-Tenant, Topology-Aware, Priority and Gang Scheduling
9. Compatible with other Kubernetes features, such as Kubernetes Service, Gpu Scheduling, Volume, Logging
10. Idiomatic with Kubernetes official controllers, such as Pod Spec
11. Aligned with Kubernetes Controller Design Guidelines and API Conventions

1. A Kubernetes cluster, v1.14.2 or above, on-cloud or on-premise.

1. Run Controller
2. Submit Framework

1. User Manual
2. Known Issue and Upcoming Feature
3. FAQ
4. Release Note

• DockerHub

A specialized wrapper can be built on top of FrameworkController to optimize for a specific kind of application:

• OpenPAI Controller Wrapper (Job RestServer): A wrapper client optimized for AI applications
• NNI Controller Wrapper (TrainingService): A wrapper client optimized for AutoML applications

FrameworkController can directly leverage many Kubernetes Schedulers and among them we recommend these best fits:

• Kubernetes Default Scheduler: A General-Purpose Kubernetes Scheduler
• HivedScheduler: A Kubernetes Scheduler Extender optimized for GPUs

• YARN FrameworkLauncher: Similar offering natively supports Apache YARN

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.