Kubeflow offers ML model development environments optimized for cloud, enabling Kubernetes-based linking with various open source software.
The standardized environments support a range of machine learning frameworks from TensorFlow, PyTorch, scikit-learn, and Keras. The pipeline for the entire development, learning and deployment processes of machine learning models are automated to ensure simple configuration/creation as well as reuse of the models.
Expanded open source Kubeflow from distributed learning job execution/monitoring to inference service management/analysis and job queue management are provided on Samsung Cloud Platform. Users can also enjoy job schedulers (FIFO, Bin-packing, and Gang-based), GPU fraction, GPU resource monitoring, Kubeflow engine logging and more add-on features that are usually unavailable on open source software.
- Request : Automatic deployment and service configuration for the requested Kubernetes clusters
- View : Offering list, Kubeflow version and resource status
- Delete : Delete created Kubeflow modules
- Basic features
· Jupyter Notebook (model development, learning, and inference)
· Workflow automation (based on machine learning pipelines)
- Additional features of Samsung Cloud Platform
· Manage GPU job scheduling and job queue
· Conduct GPU resource monitoring and GPU fraction
· Provide Kubeflow engine monitoring/logging and distributed learning job execution/monitoring
· Build and manage ML framework images (TensorFlow, PyTorch, etc.) and ML images
· Manage/analyze inference services and manage model experiments/learning nodes
· Work with user authentication of Samsung Cloud Platform and manage users/projects/announcements
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