Managing hardware specifications for deployments

When you deploy certain assets in Watson Machine Learning, you can choose the type, size, and power of the hardware configuration that matches your computing needs.

Creating hardware specifications for deployments

You can create hardware specifications for your deployments in one of the following ways:

By using the hardware_specifications.store function from the Python client
By using the Environments API.

Deployment types that require hardware specifications

Selecting a hardware specification is available for all batch deployment types. For online deployments, you can select a specific hardware specification if you're deploying:

Python Functions
Tensorflow models
Models with custom software specifications

Hardware configurations available for deploying assets

XXS: 1x2 = 1 CPU and 2 GB RAM
XS: 1x4 = 1 CPU and 4 GB RAM
S: 2x8 = 2 CPU and 8 GB RAM
M: 4x16 = 4 CPU and 16 GB RAM
L: 8x32 = 8 CPU and 32 GB RAM
XL: 16x64 = 16 CPU and 64 GB RAM

You can use the XXS and XS configurations to deploy:

Python functions
Python scripts
R scripts
Shiny apps
Models based on custom libraries and custom images

For Decision Optimization deployments, you can use these hardware specifications:

S
M
L
XL

Hardware specifications for GPU inferencing

Beginning Cloud Pak for Data version 4.8.5, you can select GPU hardware specifications for CUDA software specifications from the user interface on x86 platform when you create a deployment. For more information, see Configuring MIG support in Red Hat OpenShift.

Use the following predefined hardware specifications for GPU inferencing:

Hardware specifications for GPU inferencing
Size	Hardware definition
GPUx1	1GPU, 1 CPU and 4 GB RAM
GPUx2	2GPU, 2 CPU and 8 GB RAM
GPUx3	3GPU, 2 CPU and 12 GB RAM
GPUx4	4GPU, 2 CPU and 16 GB RAM

Learn more

Configuring MIG support in Red Hat OpenShift

Parent topic: Managing predictive deployments