Tensorlake functions are the building blocks of workflows. They are Python functions decorated with the @tensorlake_function decorator.

from tensorlake import tensorlake_function

image = Image()
  .run("pip install transformers")
  .build()

@tensorlake_function(image=image, input_encoding="json")
def my_function(data: str) -> int:
    return len(data)

You can write any Python code inside the function, and depend on any Python package. The functions are run inside a container of an image built from the specification of the Image object.

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Dynamic Routing

Functions can route data to different nodes based on custom logic, enabling dynamic branching.

@tensorlake_function()
def handle_error(text: str):
    # Logic to handle error messages
    pass

@tensorlake_function()
def handle_normal(text: str):
    # Logic to process normal text
    pass

# The function routes data into the handle_error and handle_normal based on the
# logic of the function.
@tensorlake_router()
def analyze_text(text: str) -> List[Union[handle_error, handle_normal]]:
    if 'error' in text.lower():
        return [handle_error]
    else:
        return [handle_normal]