What is a Generalized Compute Code Object?

Understanding Generalized Compute

In the Rhino Health Federated Computing Platform (FCP), the Generalized Compute (GC) Code Object represents a versatile and powerful way to execute pre-built container images within the FCP environment. This Code type enables you to run custom code, computations, or processes that are encapsulated within container images. With GC, you can harness the full potential of distributed computing while tailoring your computations to suit your specific needs.

Key Features of Generalized Compute:

• Flexible Container Execution: GC empowers you to execute container images containing diverse computational tasks, irrespective of programming language or complexity. This allows you to leverage existing tools, libraries, and applications within the FCP ecosystem.
• Batch Processing: Ideal for tasks that do not require real-time interactivity, GC excels at batch processing scenarios. You can execute computationally intensive tasks, data transformations, or complex analyses efficiently across distributed datasets.
• Secure Data Access: GC can only access data provided via the FCP, with no external internet connectivity. GC accesses input Datasets located under the /input directory and generates output Datasets under the /output directory. This ensures secure data access and maintains the privacy of sensitive healthcare information.

Use Cases for Generalized Compute:

• Data Transformation: Execute data transformation and normalization tasks across distributed healthcare datasets.
• Custom Analytics: Run complex statistical analyses, machine learning processes, or custom algorithms on distributed data.
• Scientific Simulations: Perform large-scale scientific simulations or simulations requiring substantial computational resources.
• Resource-Intensive Tasks: Process resource-intensive tasks, such as image processing, signal analysis, or simulations.

Benefits of Generalized Compute:

• Adaptability: Leverage various programming languages and tools within your container, ensuring flexibility in your computational tasks.
• Distributed Processing: Distribute computation tasks across multiple sites, enhancing efficiency and reducing processing time.
• Customization: Craft computations tailored to your project's unique requirements, enhancing the depth of insights you can derive.

In summary, Generalized Compute empowers you to harness the versatility and power of containerized computations within the Rhino Health FCP. By executing custom code across distributed datasets, you can streamline tasks, gain valuable insights, and contribute to advanced healthcare research in a secure and privacy-preserving manner

Key Components of a Generalized Compute

Attributes

1. Name: The name of the defined Code
2. Description (Optional): The description of the Code
3. Version(s): The version of the Code. After creating the initial Code Object you can edit the existing Code and create a new version instead of creating a whole new Code Object
4. Date Created: The date the Code version was created
5. Runs: The number of times the Code version has been run and produced a Code Run. Note: If you delete a Code Run for a Code Object, the number of runs will also be reduced by the number of Code Runs you delete
6. Latest Run (UTC): The timestamp of the latest run in the UTC timezone for the particular Code version
7. Input Data Schema: The input data schema that was defined when creating the particular Code version
8. Output Data Schema: The output data schema that was defined when creating the particular Code version
9. Creator:  The creator of the Code version
10. UUID:  The unique identifier for a specific Code version within the Rhino FCP
11. Code Type: The type of Code that has been defined during creation—Generalized Compute
12. Container: The Docker container used when creating the Code. This could come from under the section Workgroup Images if you pushed to your workgroups ECR or under the section Rhino Health Images if you are using a stock Rhino Health provided container image

Actions

1. Creating a New Generalized Compute Code or Code Version: Create a new Generalized Compute Code or new version of existing Generalized Compute Code
2. Viewing a Generalized Compute Code's Configuration: Viewing the configuration that was provided while creating specific Generalized Compute Code
3. Running Generalized Compute Code: Running a specific version of a certain Generalized Compute Code Object
4. Deleting a Generalized Compute Code or Code Version: Delete a single version or a whole Generalized Compute Code Object

Interfaces

Below are a series of screenshots that detail how you can interact with Generalized Compute Code within the Rhino FCP

Main Code Page

The main interface for initiating the creation of Generalized Compute Code .

Creating New Generalized Compute Code

For more information about creating new Generalized Compute Code, please refer to Creating New Generalized Compute Code or Code Versions.

Creating New Generalized Compute Code - Selecting Your Container Image

For more information about selecting your container image while creating new Generalized Compute Code, please refer to Creating New Generalized Compute Code or Code Versions.

Running Generalized Compute Code

For more information about running Generalized Compute Code, please refer to Running Generalized Compute Code.