This article is part of the Learning path: Get started with Watson Machine Learning Accelerator series.
|100||An introduction to Watson Machine Learning Accelerator||Article|
|101||Classify images with Watson Machine Learning Accelerator||Article + notebook|
|201||Elastic Distributed Training in Watson Machine Learning Accelerator||Article + notebook|
|202||Drive higher GPU utilization and throughput||Tutorial|
|203||Expedite retail price prediction with Watson Machine Learning Accelerator hyperparameter optimization||Tutorial|
|301||Accelerate your deep learning and machine learning||Article + notebook|
The adoption of artificial intelligence (AI) has been increasing across all business sectors as more industry leaders understand the value that data and machine learning models can bring to their business. Faster times to create accurate models are essential to driving value for time to market. IBM® Watson™ Machine Learning Accelerator is an environment for data science as a service that enables businesses or organizations to bring AI applications into production and makes deep learning and machine learning more accessible.
This article gives an overview of Watson Machine Learning Accelerator. In it, we’ll provide:
- Getting started with Watson Machine Learning Accelerator
- An overview of the capabilities of Watson Machine Learning Accelerator
What is Watson Machine Learning Accelerator?
Watson Machine Learning Accelerator is an enterprise AI infrastructure to make deep learning and machine learning more accessible, and brings the benefits of AI to your business. It combines popular open source deep learning frameworks with efficient AI development tools, and is available in both accelerated IBM Power Systems™ servers and Intel® servers.
Data scientists can accelerate their AI journey by scaling out their workload such as tuning their hyperparameters, while sharing GPU resources in an elastic manner with a growing number of data scientists. The following video gives you an overview of getting started, focusing on why you as a data scientist should care about Watson ML Accelerator.
Watson Machine Learning Accelerator capabilities
The following video offers an overview of the key capabilities of Watson Machine Learning Accelerator.
The key capabilities are:
- Elastic Distributed Training: Simplifies the distribution of training workloads for data scientists
- Automated Hyperparameter Optimization: Helps data scientists optimize the speed of training by automating hyperparameter searches in parallel
- Accelerated machine learning library with SnapML: An efficient, scalable machine learning library that enables fast training of various machine learning models
- Elastic Distributed Inference: Publish inference models as services
Installation and configuration
Installing and configuring Watson Machine Learning Accelerator is documented in the IBM Knowledge Center. However, if you prefer, the installation and configuration can be fully automated, without human interaction, by using the scripts found here: https://github.com/IBM/spectrum-installs/tree/master/wmla/1.2.2.
These scripts support:
- Watson Machine Learning Accelerator 1.2.2
- x86_64 and ppc64le architectures
- Air-gapped environment
- Installing management and compute hosts in parallel
- Local and shared installation
- Configuring High Availability shared directory and master candidates list
- Enabling GPU configuration
- Option to add a new Anaconda distribution
- Option to create a user environment with Spark 2.4.3, Watson Machine Learning Community Edition 1.7.0, Jupyter notebook, and Deep Learning Impact
This article provided an overview of Watson Machine Learning Accelerator and its capabilities. The article is part of the Learning path: Get started with Watson Machine Learning Accelerator series. Continue the series with the next article, Classify images with Watson Machine Learning Accelerator.