PaddlePaddle Cloud is a combination of PaddlePaddle and Kubernetes. It supports fault-recoverable and fault-tolerant large-scaled distributed deep learning. We can deploy it on public cloud and on-premise clusters.
PaddlePaddle Cloud includes the following components:
- paddlectl: A command-line tool that talks to paddlecloud and paddle-fs.
- paddlecloud: An HTTP server that exposes Kubernetes as a Web service.
- paddle-fs: An HTTP server that exposes the CephFS distributed filesystem as a Web service.
- EDL (elastic deep learning): A Kubernetes controller that supports elastic scheduling of deep learning jobs and other jobs.
- Fault-tolerant distributed deep learning: This part is in the Paddle repo.
- Build PaddlePaddle Cloud
- Deploy PaddlePaddle Cloud
- Elastic Deep Learning using EDL
- PaddlePaddle Cloud on Minikube
.
├── demo: distributed version of https://github.com/PaddlePaddle/book programs
├── doc: documents
├── docker: scripts to build Docker image to run PaddlePaddle distributed
├── go
│ ├── cmd
│ │ ├── edl: entry of EDL controller binary
│ │ ├── paddlecloud: the command line client of PaddlePaddle Cloud (will be deprecated)
│ │ ├── paddlectl: the command line client of PaddlePaddle Cloud
│ │ └── pfsserver: entry of PaddleFS binary
│ ├── edl: EDL implementation
│ ├── filemanager: PaddleFS implementation
│ ├── paddlecloud: command line client implement (will be deprecated)
│ ├── paddlectl: command line client implement
│ ├── scripts: scripts for Go code generation
├── k8s: YAML files to create different components of PaddlePaddle Cloud
│ ├── edl: TPR definition and EDL controller for TraningJob resource
│ │ ├── autoscale_job: A sample TrainingJob that can scale
│ │ └── autoscale_load: A sample cluster job demonstrating a common workload
│ ├── minikube: YAML files to deploy on local mini-kube environment
│ └── raw_job: A demo job demonstrates how to run PaddlePaddle jobs in cluster
└── python: PaddlePaddle Cloud REST API server
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