In your workspace, you can create a cloud analysis environment via the "Apps" tab. Cloud environments are responsible for providing the necessary computing resources for using applications on the Researcher Workbench. For example, Jupyter environments power JupyterLab and use of the terminal. They consist of CPUs, RAM, and a storage disk, which are all required for different aspects of the environment.
CPUs, or central processing units, are responsible for executing instructions and performing calculations. RAM, or random access memory, is used to temporarily store data that is being actively processed. Environments are unique for each user in a workspace, so every collaborator can have their own separate customized environment. Cost is incurred while the app is running or paused and is based on your configuration.
To learn more about using apps in the Researcher Workbench, see these resources:
Creating Environments
- To create an cloud analysis environment in your workspace, please navigate to the "Apps" tab.
- Select "+New app instance"
- Select the app of your choice and click "Next"
- Chose your configuration and customize your analysis profiles. Note, the GCP calculator on the right is an estimate of cloud environment cost while running the app. It is not an exhaustive amount for total analysis. To learn more about cost, see the support article Getting Started and What to Know About Costs
- You will have the option to adjust the environment's disk size and configure the autostop duration. To help minimize cloud costs, we strongly recommend enabling autostop and choosing an appropriate disk size for your analysis.
- Enter a instance ID (optional), and select "Create app"
- Once the app is successfully provisioned, it will be listed under your "App" tab as running.
Editing, Pausing and deleting an environment
When you're finished actively working in an environment, we recommend pausing or deleting the environment in order to save costs. A paused environment will still incur some costs because the environment isn't completely gone and can be easily restarted, but will generally be significantly lower than when active. Deleting the environment will ensure you're no longer being charged for the environment, but you would lose any data that isn't saved to a workspace bucket.
To pause an environment, select "Stop." This will pause the environment, which still incurs some cost for being in a paused state. While the app is being 'stopped', you cannot restart the app; you can only edit its name and description, or delete it.
To edit an environment, to include the description, the configuration or autostop, select the "three dots" on the right and select "Edit"
To delete your environment, select the "three dots" on the right and select "Delete." Note: before deleting an environment be sure you have saved your files into the workspace bucket. Files not saved to the bucket may be lost with environment deletion.
Dataproc clusters
Dataproc clusters must be enabled to run Hail, a popular open-source framework for genomic analysis that can be used in the Researcher Workbench. By leveraging dataproc scalability and managed infrastructure, users can easily process large-scale genomic datasets with Hail, enabling efficient and parallelized analysis. Unlike General Analysis environments, Dataproc clusters can utilize workers to speed up the processing of some computations. In addition to being required for Hail, these environments can be used for other types of processing, though they're more expensive than General Analysis environments.
To access a dataproc cluster, you can select the "JupyterLab Spark Cluster for AoU" environment.
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