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In Azure Machine Learning prompt flow, you use a compute session to run flows.
Compute sessions
In prompt flow, compute sessions serve as computing resources that enable customers to execute their flows seamlessly. A compute session is equipped with a prebuilt Docker image that includes our built-in tools, ensuring that all necessary tools are readily available for execution. Compute session is managed by Azure Machine Learning, providing users with a convenient and efficient way to execute their flows without the need to manage the underlying infrastructure.
Within the Azure Machine Learning workspace, you can create a compute session by using the predefined base image. This base image is set up to reference the prebuilt Docker image, so you can get started easily and efficiently. We regularly update the base image to ensure it aligns with the latest version of the Docker image. You can also add Python packages to the base image through the requirements.txt file, which are installed during the creation of the compute session, and manually install them in a running compute session.
If you want more customization, prompt flow lets you create a custom base image. By using our prebuilt Docker image as a foundation, you can easily customize your image by adding your preferred packages, configurations, or other dependencies. After you customize the environment, you can publish it as a custom base image within the Azure Machine Learning workspace, so you can create a compute session based on your custom base image.
In addition to running flows, the compute session also validates and ensures the accuracy and functionality of the tools incorporated within the flow when you update the prompt or code content.