1. Yandex Cloud Functions for AI Model Inference.

    Python

    To set up a Yandex Cloud Function for AI Model inference, you'll first need to define the function in a Pulumi program written in Python. Yandex Cloud Functions are serverless, which means they automatically scale based on the incoming request load, and you only pay for compute time you actually consume—which, for AI inference tasks that can run quickly, may be cost-efficient.

    Here's how you might accomplish this step-by-step:

    1. Define the Cloud Function: You need to create a function, upload your code, and specify an entry point (the function your code calls when the service starts). You'll also need to set up a trigger if you want the function to run in response to events like an HTTP request or a new message in a queue.

    2. Configure the Environment: This includes setting the necessary environment variables, selecting a runtime, and setting the memory allocated to the function.

    3. Set Up Permissions: You might need to assign appropriate roles to the service account that will execute the function.

    4. Define Triggers (optional): If your function needs to respond automatically to certain events (HTTP requests, incoming messages, etc.), you will need to define triggers.

    Below is a basic Pulumi program that defines a Yandex Cloud Function with some of these elements. We'll use a zip archive for the code content, and set basic configuration like memory and runtime. We need to replace placeholders like YOUR_FOLDER_ID and YOUR_ENTRYPOINT with actual values that apply to your Yandex Cloud account and AI model code.

    import pulumi import pulumi_yandex as yandex # Replace these variables with your actual configuration details folder_id = 'YOUR_FOLDER_ID' source_code_path = 'path/to/your/function/code.zip' entrypoint = 'YOUR_ENTRYPOINT' # The function in your code to call # Create a Yandex Cloud Function ai_function = yandex.Function('ai-inference-function', folder_id=folder_id, runtime='python37', # Example runtime, choose one that matches your code entrypoint=entrypoint, memory=256, # Memory allocated to the function in MB content=yandex.FunctionContentArgs( zip_filename=source_code_path, ), description='AI Model Inference Function', # Environment variables can pass necessary configuration environment={ 'MODEL_NAME': 'your_model_name', } ) # Export the function ID and HTTP invoke URL pulumi.export('function_id', ai_function.id) # If you've set up an API Gateway or other triggers, you would similarly export their URLs

    In this program:

    • We import pulumi and pulumi_yandex, which are necessary to interact with the Pulumi framework and Yandex resources respectively.
    • The yandex.Function class is initialized to create a new cloud function.
    • The code for the function is assumed to be a zip file present at source_code_path, and entrypoint specifies the function within your code that should be called when the function is triggered.
    • The memory argument sets how much memory will be available to your function. You should adjust this according to your model's requirements.
    • The environment dictionary can be used to set environment variables for your function. You can use these environment variables inside your function code to configure the AI model, or for other purposes.

    Please replace YOUR_FOLDER_ID, path/to/your/function/code.zip, and YOUR_ENTRYPOINT with your specific details. The entry point should be the name of the function your code executes, and the zip file should contain your AI inference code.

    Keep in mind this is just an example of deploying a standalone Yandex Cloud Function. If you have additional requirements, like connecting the function to a storage bucket, or setting up a retry policy for failed executions, you would need to define additional resources and configurations.


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