GS-aiflow AI/ML Workflow Management Framework
GS-aiflow is the workflow management Framework for machine learning operations - pipelines, training and inferences. When workflows are defined, they become more maintainable, versionable, testable, and collaborative. With GS-aiflow you can use workflows as directed acyclic graphs (DAGs) of tasks. The GS-aiflow scheduler executes your tasks on an array of workers while following the specified dependencies.
Aiflow offers a set of lightweight environments that can be used with any existing machine learning application or library (TensorFlow, PyTorch, Keras, ONNX etc), wherever you currently run ML/DL code (e.g. in notebooks, standalone applications).
| Main version (dev) | Stable version (1.5) | |
|---|---|---|
| Python | 3.7, 3.8, 3.9 | 3.7, 3.8, 3.9 |
| Docekr | 18.09.x, 20.10.x | 18.09.x, 20.10.x |
| Kubernetes | 1.20, 1.19 | 1.22 |
| NVIDIA Docker | 20.10.17 | 20.10.17 |
- check the readme inside app directory
- manage workflow with DAG
- automate configuration and management of tasks
- work with Kubernetes
- optimize and accelerate ML/DL inferencing and training for fastest responce time
- automate configuration Runtime Enviroment for M/L
- Login
- User
- Manage Project(Workflow) List
- DAG Workflow Monitoring and Launching
- DAG Editing
- Manage user storage by jupyter
- Admin
- User Management
- All user project monitoring and init & stop
If you're interested in being a contributor and want to get involved in developing the GEdge Platform code, please see DOCUMENTATIONs for details on submitting patches and the contribution workflow.
We have a project site for the GEdge Platform. If you're interested in being a contributor and want to get involved in developing the Cloud Edge Platform code, please visit GEdge Plaform Project site
GEdge Platform is under the Apache 2.0 license. See the LICENSE file for details.