Functions can depend on any python or system packages. We provide APIs to express these dependencies in a declarative manner. When you deploy a graph, images are automatically build as part of the deploy process.

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Images

Specify the commands to install dependencies in the image used to run functions. You can choose any base image, and install any system or python dependencies. An image can be used to run multiple functions.

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Step 1: Define the Image

from tensorlake import Image

image = (
    Image()
    .name("my-pdf-parser-image")
    .base_image("ubuntu:22.04")
    .run("apt update")
    .run("pip install torch")
    .run("pip install langchain")
)

This defines an Image object and specify the name of the image. We then run commands to install the dependencies. You can use any base image, the default being python:3.11.10-slim-bookworm.

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Step 2: Use the Image in a Function

from tensorlake import tensorlake_function

@tensorlake_function(image=image)
def parse_pdf(pdf_path: str) -> str:
    ...

In the function decorator, we pass the image object. This tells Tensorlake to run the function in the specified image.