Watson Studio environments compute usage

Compute usage is calculated by the number of capacity unit hours (CUH) consumed by an active environment runtime in Watson Studio. Watson Studio plans govern how you are billed for the resources you consume.

A set amount of capacity units is included in each plan. With the Standard and Enterprise plans, you can pay for more processing usage.

Feature Lite Standard Enterprise
Processing usage 50 CUH 50 CUH + pay for more 5000 CUH + pay for more

 

Capacity units per hour for notebooks

Name Capacity type Capacity units per hour
Default Python 3.5 Free 1 vCPU and 4 GB RAM 0
Default Python 3.6 Free 1 vCPU and 4 GB RAM 0
Default Python 3.5 XS 2 vCPU and 8 GB RAM 1
Default Python 3.6 XS 2 vCPU and 8 GB RAM 1
Default Python 3.6 XS + DO 2 vCPU and 8 GB RAM 21
Default Python 3.5 S 4 vCPU and 16 GB RAM 2
Default Python 3.6 S 4 vCPU and 16 GB RAM 2
Default R 3.4 XS 2 vCPU and 8 GB RAM 1
Default R 3.4 S 4 vCPU and 16 GB RAM 2
Default Spark Python 3.5 XS 2 Executors each: 1 vCPU and 4 GB RAM; Driver: 1 vCPU and 4 GB RAM 1.5
Default Spark Python 3.6 XS 2 Executors each: 1 vCPU and 4 GB RAM; Driver: 1 vCPU and 4 GB RAM 1.5
Default Spark R 3.4 2 Executors each: 1 vCPU and 4 GB RAM; Driver: 1 vCPU and 4 GB RAM 1.5
Default Spark Scala 2.11 2 Executors each: 1 vCPU and 4 GB RAM; Driver: 1 vCPU and 4 GB RAM 1.5

 

The rate of capacity units per hour consumed is determined for:

  • Default Python or R environments by the hardware size and the number of users in a project using one or more runtimes

    For example: The Default Python 3.6 XS with 2 vCPUs will consume 1 CUH if it runs for one hour. If you have a project with 7 users working on notebooks 8 hours a day, 5 days a week, all using the Default Python 3.6 XS environment, and everyone shuts down their runtimes when they leave in the evening, runtime consumption is 5 x 7 x 8 = 280 CUH per week.

    The CUH calculation becomes more complex when different environments are used to run notebooks in the same project and if users have multiple active runtimes, all consuming their own CUHs. Additionally, there might be notebooks, which are scheduled to run during off-hours, and long-running jobs, likewise consuming CUHs.

  • Default Spark environments by the hardware configuration size of the driver, and the number of executors and their size.

Capacity units per hour for notebooks with Decision Optimization

The rate of capacity units per hour consumed is determined by the hardware size and the price for Decision Optimization.

Capacity type Language Capacity units per hour
1 vCPU and 4 GB RAM Python 3.6 + Decision Optimization 20.5
2 vCPU and 8 GB RAM Python 3.6 + Decision Optimization 21
4 vCPU and 16 GB RAM Python 3.6 + Decision Optimization 22
8 vCPU and 32 GB RAM Python 3.6 + Decision Optimization 24
16 vCPU and 64 GB RAM Python 3.6 + Decision Optimization 28

 

Capacity units per hour for SPSS Modeler flows

Name Capacity type Capacity units per hour
Default SPSS XS 4 vCPU 16 GB RAM 2

 

Capacity units per hour for Spark MLlib modeler flows

Name Capacity type Capacity units per hour
Default Spark Python 3.6 XS 2 Executors each: 1 vCPU and 4 GB RAM; Driver: 1 vCPU and 4 GB RAM 1.5
Default Spark R 3.4 2 Executors each: 1 vCPU and 4 GB RAM; Driver: 1 vCPU and 4 GB RAM 1.5
Default Spark Scala 2.11 2 Executors each: 1 vCPU and 4 GB RAM; Driver: 1 vCPU and 4 GB RAM 1.5

 

Capacity units per hour for Data Refinery and Data Refinery flows

Name Capacity type Capacity units per hour
Default Data Refinery XS runtime 3 vCPU and 12 GB RAM 1.5
Default Spark R 3.4 2 Executors each: 1 vCPU and 4 GB RAM; Driver: 1 vCPU and 4 GB RAM 1.5

 

Capacity units per hour for RStudio

Name Capacity type Capacity units per hour
Default RStudio XS 2 VCPU and 8 GB RAM 1
Default RStudio M 8 vCPU and 32 GB RAM 4
Default RStudio L 16 vCPU and 64 GB RAM 8

 

Capacity units per hour for GPU environments

Name Number of GPUs Capacity units per hour
1/2 x NVIDIA TESLA K80 1 1
1 x NVIDIA TESLA V100 1 4

During the beta phase, you can only consume a maximum of 100 CUHs a month. After this amount has been consumed, you must wait for the next month before you can consume capacity units for a GPU environment again. During beta, you will not be charged for the 100 capacity units you consumed in the month.

Runtime capacity limit

You are notified when you’re about to reach the runtime capacity limit for your Watson Studio service plan. When this happens, you can:

  • Continue using the Default Python 3.5 Free. You can create any number of these small environment runtimes but only one free environment can be active at any one time.
  • Stop active runtimes you don’t need.
  • Upgrade your service plan. For up-to-date information, see the Watson Studio pricing plans.

Remember: The CUH counter continues to increase while a runtime is active so stop the runtimes you aren’t using. If you don’t explicitly stop a runtime, the runtime is stopped after an idle timeout. During the idle time, you will continue to consume CUHs for which you are billed.

Usage limitation for the free environment

Only one free environment can be active per account at any one time. When you start a notebook with the free environment runtime, you are notified if the free runtime is already active for your account. This can happen, for example, when you create and customize several of these small runtime environments in one project or even across projects you created, or when multiple users in the same project share environment definitions.

If the free runtime is already active:

  • As account admin of the project, you can see which runtimes are active for your account across all of your projects from the Environment Runtimes page and stop the other free runtime. Then try opening your notebook again. You can also see which runtimes are active and stop runtimes from the Environments page of your projects.
  • As project admin of the project, you can stop the other active free runtime from the project’s Environments page.
  • You can change the environment of your notebook to an environment which consumes capacity units from the Assets page of your project:
    1. Select your notebook from the Notebooks section.
    2. Click Actions > Change environment and select an environment which consumes capacity units.

Track runtime usage for a project

You can view the environment runtimes that are currently active in a project, and monitor usage for the project from the project’s Environments page.

Track runtime usage for an account

The CUH consumed by the active runtimes in a project are billed to the account that the project creator has selected in his or her profile settings at the time the project is created. This account can be the account of the project creator, or another account that the project creator has access to. If other users are added to the project and use runtimes, their usage is also billed against the account that the project creator chose at the time of project creation.

You can track the runtime usage for an account on the Environment Runtimes page if you are the IBM Cloud account owner or administrator.

To view the total runtime usage across all of the projects and see how much of your plan you have currently used, choose Manage > Environment Runtimes.

A list of the active runtimes billed to your account is displayed. You can see who created the runtimes, when, and for which projects, as well as the capacity units that were consumed by the active runtimes at the time you view the list.

Learn more