IBM Watson Machine Learning Accelerator is a deep learning platform that data scientists can use to build, train, and deploy deep learning models.
Watson Machine Learning Accelerator can be connected to Watson Machine Learning to take advantage of the multi-tenant resource plans that manage resource sharing across Watson Machine Learning projects. With this integration, data scientists can use the Watson Machine Learning Experiment Builder and Watson Machine Learning Accelerator hyperparameter optimization.
Watson Machine Learning Accelerator provides the following benefits on Cloud Pak for Data:
- Distributed deep learning architecture that simplifies the process of training deep learning models across a cluster for faster time to results.
- Large model support that helps increase the amount of memory available for deep learning models up to 16 GB or 32 GB per network layer, enabling more complex models with larger, more high-resolution data inputs.
- Advanced Kubernetes scheduling including consumer resource plans, ability to run parallel jobs and dynamically allocate GPU resources.
- Powerful model development tools, including real-time training visualization and runtime monitoring of accuracy and hyper-parameter search and optimization, for faster model development.
- Included ready-to-use deep learning frameworks, such as TensorFlow and PyTorch.
- An administrative service console for GPU cluster management and monitoring.