VMware Machine Learning Platform
Our goal is to provide an end-to-end ML platform for Data Scientists to perform their job more effectively by running ML workloads on top of VMware infrastructure.
Using vMLP allows to:
- Save the costs by enabling efficient use of shared GPUs for ML workfloads
- Reduce the risks of broken Data Science workflows by leveraging well-tested and ready-to-use demos and project templates
- Faster "go-to-market" for ML models by utilizing end-to-end oriented tooling including fast and easy model deployment and serving via standardized REST API
Quickly setup a virtualized cloud infrastructure to conduct Data Science experiments:
- Virtualized environment based on VMware and Kubernetes
- Familiar Jupyter Notebooks and distributed model training based on Open Source Kubeflow 1.0 GA and Horovod
- GPU support available on Tanzu Kubernetes GRID using vGPU and NVIDIA Kubernetes Device Plugin
- Quick turn-around on model testing based on the built-in deployment framework
Utilize a set of example Notebooks and libraries for common Data Science tasks, including:
- Data collection (extract data from various sources, and describe the data semantics using metadata)
- Data cleansing and transformation (clean up collected data and transform them from its raw form to a structured form more suitable for analytic processing)
- Model training (develop predictive and optimization machine learning models)
- Model serving (deploy model into a runtime environment where an online REST API request will be served)
Share metadata about your Data Sources to enable team collaboration using our Data Manager component.
Store and reliably track your experiments for reproducibility using MLflow model repository.
Leverage JupyterLab extensions to achieve smooth model training and deployment experience.
You must have the VMware Cloud Foundation 3.8 GA
Please see the readme file in the download after unzipping.
- Added support for vSphere with Kubernetes and Tanzu Kubernetes GRID in addition to VMware
- Cloud Foundation/PKS
- Upgraded to Kubeflow 1.0 GA
- Added support for GPUs
- Introduced a new data registry component called Data Manager
- Upgraded minor components/libraries to the latest versions
- Added an easy-to-use installer
- Lots of bug fixes