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 the following ways:

Python client library: Use the hardware_specifications.store function from the Python client library. For more information, see Python client library reference
Environemtns API: Use POST /v2/hardware_specifications from the Environments API to create a hardware specification. For more information, see Environments API reference.

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

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

You can use the XS configuration to deploy:

Python functions
Python scripts
R scripts
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 .

Use the following predefined hardware specifications for GPU inferencing:

Learn more

Monitoring account resource usage

Parent topic: Managing predictive deployments