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Watson Machine Learning plans and compute usage
Watson Machine Learning plans and compute usage

Watson Machine Learning plans and compute usage

You use Watson Machine Learning resources, measured in capacity unit hours (CUH), when you train AutoAI models, run machine learning models, and request predictions from deployed models. This topic describes the various plans you can choose, what services are included, and provides a list of default computing environments to help you select a plan that matches your needs. This topic describes:

Capacity Unit Hours (CUH)

CUH is a measure of compute resource consumption per unit hour. All Watson Machine Learning plans are consolidated around Capacity Unit Hours, making resource consumption easier to track.

Watson Machine Learning plans

Watson Machine Learning plans govern how you are billed for models you train and deploy with Watson Machine Learning. Choose a plan based on your needs:

  • Lite is a free plan with limited capacity. Choose this plan if you are evaluating Watson Machine Learning and want to try out the capabilities.
  • Standard is a pay-as-you-go plan that gives you the flexibility to build, deploy, and manage models to match your needs.
  • Professional is a high-capacity, flat-rate enterprise plan designed to support all of an organization's machine learning needs. The Professional plan also offers support for HIPAA if provisioned in the Dallas region.

For plan details and pricing, see IBM Cloud Machine Learning.

Note: If you upgrade from Standard to Professional, you cannot roll back to a Standard plan. In this case, contact IBM Support for assistance.

Plans by CUH

Plan Capacity Unit Hours included Overage Max deployment jobs
per space
Lite 20 per month No overage allowed 100
Standard Billed per CUH Billed per CUH 1000
Professional 2500 Billed for extra CUH consumed 3000 (increase by request via support)

Limits and defaults for retaining deployment jobs (Watson Machine Learning)

Watson Machine Learning places limits on the number of deployment jobs retained for each single deployment space. Importantly, with this update, none of your information will be lost, but your user experience when running deployment jobs might need to change.

If you exceed your limit, you will be unable to create new deployment jobs until you delete existing jobs or upgrade your plan. New automation will help you stay within the plan limits. By default, jobs metadata will be auto-delete after 30 days. You can override this value when creating a job. See Managing jobs.

Watson Machine Learning compute usage and pricing

Note: For complete details on pricing, see Watson Machine Learning: Pricing

CUH usage details

  • Machine Learning compute usage is calculated by the number of capacity unit hours (CUH) consumed by an active machine learning instance.
  • The rate of capacity units per hour consumed is determined by the computing requirements of your Machine Learning assets and models. For example, a model with a large, complex data set will consume more training resources than a model with a smaller, simpler data set. Note that scaling a deployment to support more concurrent users and requests also increases CUH consumption.
  • Because there are so many variables that affect resource consumption for a deployment, we recommend you run tests on your models and deployments to analyze CUH consumption.
  • Compute time is calculated to the millisecond. However, there is a one-minute minimum for each distinct operation. That is, a training run that takes 12 seconds is billed as one minute toward the capacity unit hour quota, while a training run that takes 83.555 seconds is billed exactly as calculated.
  • The way that online deployments consume capacity units is based on framework. For some frameworks, CUH is charged for the number of hours the deployment asset is active in a deployment space. For example, SPSS models in online deployment mode that run 24 hours a day for seven days a week consume CUH and are charged for that period. There is no idle time for an active online deployment. For other frameworks, CUH is charged according to scoring duration. See the CUH consumption table for details on how CUH is calculated.

Determining whether a deployment is active

If a deployment does not receive scoring requests for a given duration, it is treated as inactive, or idle, and billing stops for all frameworks other than SPSS. The period used to determine when a deployment is inactive depends on the plan type.

Plan type Time to idle
Lite 1 day
Standard 3 days
Professional 3 days

CUH consumption by deployment and framework type

CUH consumption is calculated using these formulas:

Deployment type Framework CUH calculation
Online AutoAI, AI function, SPSS, Scikit-Learn custom libraries, Tensorflow, RShiny deployment_active_duration no_of_nodes CUH_rate_for_capacity_type_framework
Online Spark, PMML, Scikit-Learn, Pytorch, XGBoost score_duration_in_seconds no_of_nodes CUH_rate_for_capacity_type_framework
Batch all frameworks job_duration_in_seconds no_of_nodes CUH_rate_for_capacity_type_framework

These tables show the capacity units per hour calculation for machine learning environments, by usage type.

Capacity units per hour for training, evaluating, or scoring models

Capacity type Capacity units per hour
Extra small: 1x4 = 1 vCPU and 4 GB RAM 0.5
Small: 2x8 = 2 vCPU and 8 GB RAM 1
Medium: 4x16 = 4 vCPU and 16 GB RAM 2
Large: 8x32 = 8 vCPU and 32 GB RAM 4
Extra large: 16x64 = 16 vCPU and 64 GB RAM 8

 

Capacity units per hour for AutoAI experiments

When data is joined for an AutoAI experiment, capacity units per hour are calculated differently as data is being joined. Once the data is joined, the CUH consumption reverts to the calculations for an experiment with a single data source.

 

Capacity units per hour for AutoAI experiments using a single data source:

Capacity type Capacity units per hour
8 vCPU and 32 GB RAM 20

 

Capacity units per hour for AutoAI experiments while joining data:

Note that consumption increases for each executor you add.

Capacity type Capacity units per hour
Join with 1 Driver: 2 vCPU and 8 GB RAM + 1 Executor: 2 vCPU and 8 GB RAM 10
Join with additional executors add 5 CUH for each additional executor
After data join: 8 vCPU and 32 GB RAM 20

 

Capacity units per hour for Decision Optimization

Capacity type Capacity units per hour
Decision Optimization: 2 vCPU and 8 GB RAM 30
Decision Optimization: 4 vCPU and 16 GB RAM 40
Decision Optimization: 8 vCPU and 32 GB RAM 50
Decision Optimization: 16 vCPU and 64 GB RAM 60

For details on how resources are consumed, see Monitoring account resource usage

Track CUH usage for an account

You can track the runtime usage for an account on the Environment Runtimes page if you are the IBM Cloud account owner or administrator or the Watson Machine Learning service owner. For details, see Monitoring resource.

Track CUH consumption for machine learning in a notebook

To calculate capacity unit hours, use:

CP =  client.service_instance.get_details()
CUH = CUH["entity"]["usage"]["capacity_units"]["current"]/(3600*1000)
print(CUH)

For example:

'capacity_units': {'current': 19773430}

19773430/(3600*1000)

returns 5.49 CUH

For details, see the Service Instances section of the IBM Watson Machine Learning API documentation.

Learn more

Parent topic: Watson Machine Learning