Types of environments

You can use these types of environments for running notebook:

• Anaconda CPU environments for standard workloads.
• Spark environments for parallel processing that is provided by the platform or by other services.
• GPU environments for compute-intensive machine learning models.

Most environment types for notebooks have default environment templates so you can get started quickly. Otherwise, you can create custom environment templates.

Runtime releases

The default environments for notebooks are added as an affiliate of a runtime release and prefixed with Runtime followed by the release year and release version.

A runtime release specifies a list of key data science libraries and a language version, for example Python 3.10. All environments of a runtime release are built based on the library versions defined in the release, thus ensuring the consistent use of data science libraries across all data science applications.

The Runtime 22.2 release is available for Python 3.10 and R 4.2.

While a runtime release is supported, IBM will update the library versions to address security requirements. Note that these updates will not change the <Major>.<Minor> versions of the libraries, but only the <Patch> versions. This ensures that your notebook assets will continue to run.

CPU environment templates

You can select any of the following default CPU environment templates for notebooks. The default environment templates are listed under Templates on the Environments page on the Manage tab of your project.

DO Indicates that the environment templates includes the CPLEX and the DOcplex libraries to model and solve decision optimization problems that exceed the complexity that is supported by the Community Edition of the libraries in the other default Python environments. See Decision Optimization notebooks.

NLP Indicates that the environment templates includes the Watson Natural Language Processing library with pre-trained models for language processing tasks that you can run on unstructured data. See Using the Watson Natural Language Processing library. This default environment should be large enough to run the pre-trained models.

~ Indicates that the environment templates requires the Watson Studio Professional plan. See Offering plans.

# Indicates that the environment template is in constricted mode and can't be selected to create a notebook.

You should stop all active CPU runtimes when you no longer need them to prevent consuming extra capacity unit hours (CUHs). See CPU idle timeout.

Spark environment templates

You can select any of the following default Spark environment templates for notebooks. The default environment templates are listed under Templates on the Environments page on the Manage tab of your project.

# Indicates that the environment template is in constricted mode and can't be selected to create a notebook.

You should stop all active Spark runtimes when you no longer need them to prevent consuming extra capacity unit hours (CUHs). See Spark idle timeout.

GPU environment templates

You can select the following GPU environment template for notebooks. The environment templates are listed under Templates on the Environments page on the Manage tab of your project.

The GPU environment template names indicate the accelerator power. The GPU environment templates include the Watson Natural Language Processing library with pre-trained models for language processing tasks that you can run on unstructured data. See Using the Watson Natural Language Processing library.

~ Indicates that the environment template requires the Watson Studio Professional plan. See Offering plans.

You should stop all active GPU runtimes when you no longer need them to prevent consuming extra capacity unit hours (CUHs). See GPU idle timeout.

Default hardware specifications for scoring models with Watson Machine Learning

When you invoke the Watson Machine Learning API within a notebook, you consume compute resources from the Watson Machine Learning service as well as the compute resources for the notebook kernel.

You can select any of the following hardware specifications when you connect to Watson Machine Learning and create a deployment.

Data files in notebook environments

If you are working with large data sets, you should store the data sets in smaller chunks in the IBM Cloud Object Storage associated with your project and process the data in chunks in the notebook. Alternatively, you should run the notebook in a Spark environment.

Be aware that the file system of each runtime is non-persistent and cannot be shared across environments. To persist files in Watson Studio, you should use IBM Cloud Object Storage. The easiest way to use IBM Cloud Object Storage in notebooks in projects is to leverage the project-lib package for Python or the project-lib package for R.

Compute usage by service

The notebook runtimes consumes compute resources as CUH from Watson Studio, while running default or custom environments. You can monitor the Watson Studio CUH consumption in the project on the Resource usage page on the Manage tab of the project.

Notebooks can also consume CUH from the Watson Machine Learning service when the notebook invokes the Watson Machine Learning to score a model. You can monitor the total monthly amount of CUH consumption for the Watson Machine Learning service on the Resource usage page on the Manage tab of the project.

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

'capacity_units': {'current': 19773430}

19773430/(3600*1000)

Runtime scope

Environment runtimes are always scoped to an environment template and a user within a project. If different users in a project work with the same environment, each user will get a separate runtime.

If you select to run a version of a notebook as a scheduled job, each scheduled job will always start in a dedicated runtime. The runtime is stopped when the job finishes.

Changing the environment of a notebook

You can switch environments for different reasons, for example, you can:

• Select an environment with more processing power or more RAM
• Change from using an environment without Spark to a Spark environment

You can only change the environment of a notebook if the notebook is unlocked. You can change the environment:

• From the notebook opened in edit mode:

  1. Save your notebook changes.
  2. Click the Notebook Info icon () from the notebook toolbar and then click Environment.
  3. Select another template with the compute power and memory capacity from the list.
  4. Select Change environment. This stops the active runtime and starts the newly selected environment.

• From the Assets page of your project:

  1. Select the notebook in the Notebooks section, click Actions > Change Environment and select another environment. The kernel must be stopped before you can change the environment. This new runtime environment will be instantiated the next time the notebook is opened for editing.

• In the notebook job by editing the job template. See Editing job settings.

Next steps

• Creating a notebook
• Creating your own environment template
• Customizing an environment template
• Stopping active notebook runtimes

Learn more

• Monitoring account resource usage

Parent topic: Compute resources for tools