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Create a step

A step is an atomic component defined by its input and output parameters and by the processing it applies. Steps are the building blocks of Pipelines. In practice, a step is a function with inputs and outputs coded in Python. They are assembled with each other to create a complete ML pipeline. The Python code (only available language for the moment) used by the step is stored on a Git repository.

An input of a step is an object you can use inside the code.

An output of a step is defined from the results of the step function.

You will be able to connect inputs & outputs of a step with another step to compose a complete ML pipeline by using a directed acyclic graph (DAG).

Each step is considered as a specific container that is executed on Kubernetes.

The steps are stored in a specific environment, and only people with access to this environment can read and write the steps.

createStep_1

Summary

  1. Prepare your code a Git repository
  2. Define step inputs and outputs
  3. Create a step
Function name Method Return type Description
Input Input(name, data_type="string", description="", is_required=False, default_value=None) Input SDK Object Create an Input object to give at create_steps() function for step a step input.
Output Output(name, data_type="string", description="") Output SDK Object Create an Output object to give at create_steps() function for step a step output.
create_step create_step(step_name, function_path, function_name, repository_branch=None, description=None, timeout_s=180, container_config=None, inputs=None, outputs=None) list of dict[str, str] Create pipeline steps from a source code located on a remote repository.

Prepare your code a Git repository

Prerequisites: Before the creation of your first step, make sure you have already done this :

  • Setup Project & Environment
  • Git repository link to the project

❓ Why do you need to put your code on a Git repository ? This simplifies the access to the source code by the Craft AI platform. Indeed, the platform will be able to directly fetch your code from the repository, without the need for you to send it directly each time you change it, you just have to push it to your GitHub / GitLab repository.

Currently, you can create a step via the Python SDK and not with graphical interface. But, after the creation, you will be able to see the step on the UI platform.

If it’s not already done, put the code of the step into a GitHub / GitLab repository linked to the platform. The file with the entry function of your step can be anywhere in your Git repository.

Example tree file in repo :

| requirements.txt
| src
   | my_entry_function_step.py
...

Example my_entry_function_step.py :

import numpy as np
# and other import

def entryStep(dataX_input, dataY_input) :

    # Some machine learning code

    return result_output

Define step inputs and outputs

A step may need to receive some information or give some result (just like a function). To do that, we use Input and Output object. These objects allow defining the properties of the input or output that will be expected in the step. The input and output objects thus created must be given as a parameter of the step creation. Each input is defined as an Input object and, each Output is defined as an Output object, through a class available in the SDK.

Input object definition

from craft_ai_sdk.io import Input

Input(
   name="*your_input_name*",
   data_type="*your_io_data_type*",
   description="",
   is_required=True
   default_value="*default_value*"
)

Parameters

  • name just a name for identifying the input later.

  • data_type, one of the following possible types:

  • file: reference to binary data, equivalent to a file’s content. If the input/output is not available, an empty stream.

  • json: JSON-serializable Python object. The following sub-types are provided for more precise type checking, but they are all JSON

  • string

  • number

  • array of JSON

  • boolean

    If the input/output is not available, None in Python

  • default_value (optional) - If the parameter is empty, this value will be set by default. If a deployment receives an empty parameter and already put a default value in the input, the default value of deployment will be keep.

  • is_required (optional, True by default) - Push an error is the input is empty.

  • description (optional) - This parameter precise what it’s expected in this input. It’s not read by the machine, it’s like a comment.

Return

No return

Output object definition

from craft_ai_sdk.io import Output

Output(
   name="*your_input_name*",
   data_type="*your_io_data_type*",
   description="",
)

Parameters

  • name just a name for identifying the input later.

  • data_type, one of the following possible types:

  • file: reference to binary data, equivalent to a file’s content. If the input/output is not available, an empty stream.

  • json: JSON-serializable Python object. The following sub-types are provided for more precise type checking, but they are all JSON
  • string
  • number
  • array of JSON
  • boolean

If the input/output is not available, None in Python

  • description (optional) - This parameter precise what it’s expected in this input. It’s not read by the machine, it’s like a comment.

Return

No return

Note

You can use craft_ai_sdk.INPUT_OUTPUT_TYPES to get all possible types in Input and Output objects.

List of all possible types :

  • ARRAY = "array"
  • BOOLEAN = "boolean"
  • FILE = "file"
  • JSON = "json"
  • NUMBER = "number"
  • STRING = "string"

Example :

from craft_ai_sdk.io import Input, INPUT_OUTPUT_TYPES

Input(
   name="inputName",
   data_type=INPUT_OUTPUT_TYPES.JSON,
)

Example for input and output

Input(
    name="inputName",
    data_type="string",
    description="A parameter for step input",
    is_required=True,
    default_value="default_content_here"
)

Output(
    name="inputName",
    data_type="string",
    description="A parameter for step input",
)

Warning

The size of the I/O must not exceed 0.06MB (except for file type).

Create a step

Function definition

Create pipeline steps from a source code located on a remote repository.

sdk.create_step(
    function_path="src/my_reusable_funtion.py",
    function_name="my_function",
    inputs=[Input(...)],
    outputs=[Output(...)],
    name="step-name", # by default its the function name
    description="text desciption",
    repository_branch="*your-git-branch* or *your-git-tag*",
    timeout_s=180,
    container_config = {
        language="python:3.8-slim",
        repository_url="your-git-url",
        repository_deploy_key="your-private_key",
        requirements_path="your-path-to-requirements.txt",
        included_folders=["your-list-of-path-to-sources"],
        system_dependencies=["package_1", "package_2"],
        dockerfile_path="dockerfile",
    },
)

Parameters

  • function_path (str) – Path to access to the file who had the entry function of the step.

  • function_name (str) – Function name of entry function step.

  • inputs (list<Input>) – List of step inputs.

  • outputs (list<Output>) – List of step outputs.

  • name (str) – Step name. By default, it’s the function name. The name must be unique inside an environment and without special character ( - _ & / ? …)

  • description (str, optional) – Description of the step, it’s no use by the code, it’s only for user.

  • repository_branch (str, optional) – Branch name for Git repository. Defaults to None.

  • timeout_s (int, optional) – Maximum time to wait for the step to be created. 3min by default, and must be at least 2min.

  • container_config (dict, optional) – Dict Python object where each key can override default parameter values for this step defined at project level.

  • language (str, optional) – Language and version used for the step. Defaults to falling back on project information. The accepted formats are python:3.X-slim, where 3.X is a supported version of Python, and python-cuda:3.X-Y.Z for GPU environments, where Y.Z is a supported version of CUDA. The list of supported versions is available here.

  • repository_url (str, optional) – Remote repository URL.
  • repository_deploy_key (str, optional) – Private SSH key related to the repository.
  • requirements_path (str, optional) – Path to the file requirement for Python dependency.
  • included_folders (list, optional) – List of folders that need to be accessible from step code.
  • system_dependencies (list, optional) – List of APT Linux packages to install.
  • dockerfile_path (str, optional) – Path to a docker-file for having a custom config in step. (see the part after for more detail)

Note

The repository_branch parameters as well as the container_config elements (except dockerfile_path) can take one of the STEP_PARAMETER object's values in addition to theirs.

In fact, STEP_PARAMETER allows us to specify at the step level whether we want to take the project's values (default behavior) or define a null value:

  • STEP_PARAMETER.FALLBACK_PROJECT : Allows to take the value defined in the project parameters (default behavior if the field is not defined).
  • STEP_PARAMETER.NULL : Allows to set the field to null value and not to take the value defined in the project.

Example with a code step that does not need a requirement.txt and does not take the one defined in the project settings:

from craft_ai_sdk import STEP_PARAMETER

# Code for init SDK here ...

sdk.create_step(
  function_path="src/helloWorld.py",
  function_name="helloWorld",
  step_name="stepName",
  container_config = {
      "requirements_path": STEP_PARAMETER.NULL,
   }
)

Warning

The size of the embedded code from your repository must not exceed 5MB. You can select the part of your repository to import using the included_folders parameter.

If the data you want to import is larger than 5MB, you can use the data store to store it and then import it into your step.

Returns

The return type is a dict with the following keys :

  • parameters (dict): Information used to create the step with the following keys:
  • step_name (str): Name of the step.
  • function_path (str): Path to the file that contains the function.
  • function_name (str): Name of the function in that file.
  • repository_branch (str): Branch name.
  • description (str): Description.
  • inputs (list of dict): List of inputs represented as a dict with the following keys:
    • name (str): Input name.
    • data_type (str): Input data type.
    • is_required (bool): Whether the input is required.
    • default_value (str): Input default value.
  • outputs (list of dict): List of outputs represented as a dict with the following keys:
    • name (str): Output name.
    • data_type (str): Output data type.
    • description (str): Output description.
  • container_config (dict[str, str]): Some step configuration, with the following optional keys:
    • language (str): Language and version used for the step. Defaults to falling back on project information. The accepted formats are python:3.X-slim, where 3.X is a supported version of Python, and python-cuda:3.X-Y.Z for GPU environments, where Y.Z is a supported version of CUDA. The list of supported versions is available here.
    • repository_url (str): Remote repository url.
    • included_folders (list[str]): List of folders and files in the repository required for the step execution.
    • system_dependencies (list[str]): List of system dependencies.
    • dockerfile_path (str): Path to the Dockerfile.
    • requirements_path (str): Path to the requirements.txt file.
  • creation_info (dict): Information about the step creation:
  • created_at (str): The creation date in ISO format.
  • updated_at (str): The last update date in ISO format.
  • commit_id (str): The commit id on which the step was built.
  • status (str): The step status, if the step creation process is under 2m40s (most of the time it is), is always Ready when this function returns.

Note

Once our step is created, we need to create the pipeline that wraps the step. It is mandatory to create a pipeline once the step is created to be able to use it later. This technical choice was made in anticipation of future multistep functionality. This forces the use of a pipeline to contain the steps.

Liste of language available

When using a CPU environment, the language parameter must be :

  • python:3.8-slim.
  • python:3.9-slim
  • python:3.10-slim

When using a GPU environment, the language parameter must be :

  • For cuda v11.8
  • python-cuda:3.8-11.8
  • python-cuda:3.9-11.8

  • python-cuda:3.10-11.8

  • For cuda v12.1

  • python-cuda:3.9-12.1
  • python-cuda:3.10-12.1

You can also use the CPU image in a GPU environment if you don't need access to the GPU.

Example: Create step from scratch

Function usage

from craft_ai_sdk import Input, Output

input1 = Input(
    name="input1",
    data_type="string",
    description="A parameter named input1, its type is a string",
    is_required=True,
)

input2 = Input(
    name="input2",
    data_type="file",
    description="A parameter named input2, its type is a file"
)

input3 = Input(
    name="input3",
    data_type="number",
)

prediction_output = Output(
    name="prediction",
    data_type="file",
    default_value="default,content,here",
)

step = sdk.create_step(
        function_path="src/my_reusable_funtion.py",
        function_name="my_function",
    inputs_list=[input1, input2, input3],
        outputs_list=[prediction_output],
    description="Apply the model to the sea",
        ## ...
)

Note

If you need to create a step with more specific configuration, you can do this with a custom dockerfile, more detail about here.