# Azure Machine Learning Designer Python SDK
The `mldesigner` package provide the SDK interface which work along with Azure ML Designer (drag-n-drop ML) UI experience.
- [Azure ML Designer (drag-n-drop ML)](https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer): designer is a UI tool in the Azure ML workspace for visually connecting datasets and components on an interactive canvas to create machine learning pipelines. To learn how to get started with the designer, see [Tutorial: Predict automobile price with the designer](https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score).
Especially, the package ease the authoring experience of resources like `Components` & `Pipelines`:
- [Components](https://docs.microsoft.com/en-us/azure/machine-learning/concept-component): self-contained piece of code that does one step in a machine learning pipeline: data preprocessing, model training, model scoring, a hyperparameter tuning run, etc. Such that it can be parameterized and then used in different contexts.
- [Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines): independently executable workflow of machine learning tasks composed by Components.
# Change Log
## [v0.1.0b18](https://pypi.org/project/mldesigner/0.1.0b18/) (2024.01.31)
Recommended to work with azure-ai-ml==1.13
**New Features**
- `mldesigner compile` supports to compile [parallel component](https://azureml-pipelines-doc.azurewebsites.net/features/components/parallel.html).
- `mldesigner generate` supports to generate package with provided authentication.
## [v0.1.0b17](https://pypi.org/project/mldesigner/0.1.0b17/) (2023.11.14)
Recommended to work with azure-ai-ml==1.12
**Improvements**
- [Compile] Update compiled command component schema path to `https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json`.
**Fixed Bugs**
- [Compile] Fixed the error when source component inputs is empty.
## [v0.1.0b16](https://pypi.org/project/mldesigner/0.1.0b16/) (2023.10.18)
Recommended to work with azure-ai-ml==1.11
**New Features**
- `mldesigner compile` supports to compile with [.amlignore](https://azureml-pipelines-doc.azurewebsites.net/features/component_authoring/ignore_files.html):
- `mldesigner compile --source xxx.yaml --amlignore-file <path-to>/.amlignore`
## [v0.1.0b15](https://pypi.org/project/mldesigner/0.1.0b15/) (2023.09.13)
Recommended to work with azure-ai-ml==1.10
**New Features**
- `mldesigner compile` support v1.5 spark components.
- `is_control=True` is not required for primitive type output.
- `generate` package support specify version for registry component and `generate` by sdk support remote url.
```python
from mldesigner import generate
generate(
source="azureml://registries/{registry_name}/components/{component_name}/versions/0.0.8",
)
```
## [v0.1.0b14](https://pypi.org/project/mldesigner/0.1.0b14/) (2023.07.31)
Recommended to work with azure-ai-ml==1.9.0
**New Features**
- Support primitive type as component return annotation and not need to write `is_control=True`.
- Support `Annotated` type as component input or component return annotation.
- Support `Annotated` type in `@group`.
```python
from mldesigner import command_component, Output, Meta
from typing_extensions import Annotated
@command_component()
def my_component1(input_int: int) -> int:
return input_int
@command_component()
def my_component2(input_int: Annotated[int, Meta(description="test annotation int", min=1, max=10, default=1)],) \
-> Annotated[int, Meta(description="test annotation int")]: # using Meta class to configure more meta data
return input_int
```
## [v0.1.0b13](https://pypi.org/project/mldesigner/0.1.0b13/) (2023.05.16)
Recommended to work with azure-ai-ml==1.7.0
**Improvements**
- Support to generate components with specified version or label:
- azureml://subscriptions/<subscription>/resourcegroups/<resource_group>/workspaces/<workspace>/components/<component_name>/labels/<label>
- azureml://subscriptions/<subscription>/resourcegroups/<resource_group>/workspaces/<workspace>/components/<component_name>/versions/<version>
- Refine the log content of generate component package.
## [v0.1.0b12](https://pypi.org/project/mldesigner/0.1.0b12/) (2023.03.28)
Recommended to work with azure-ai-ml==1.5.0
**Improvements**
- Support multiple types of identity for dynamic pipeline creation, the priority is user identity > managed identity > others.
- Refine `mldesigner execute`:
- When component name is not specified, if there is only one component in the source file, execute this component, raise error when there are multiple components.
- If required parameters are not provided, list all of them and raise error.
- Improve support for `enum` values in `generate_package`.
- Support enum values with no `\w` characters, e.g. empty string, `-`, `+` and `\t`.
- Support enum values with duplicate sanitized names, e.g. `A` and `a`.
- Use `curated environment` as default mldesigner component environment:
- For command component: `azureml://registries/azureml/environments/mldesigner-minimal/labels/latest`
- For dynamic pipeline: `azureml://registries/azureml/environments/mldesigner/labels/latest`
- Support `mltable` type in `@dynamic` outputs
**Fixed Bugs**
- Fix wrong command error when mldesigner component code is set to be a file instead of a folder.
## [v0.1.0b11](https://pypi.org/project/mldesigner/0.1.0b11/) (2023.02.09)
Recommended to work with azure-ai-ml==1.4.0
**Improvements**
- Add python version requirements `<4.0,>=3.7`
## [v0.1.0b10](https://pypi.org/project/mldesigner/0.1.0b10/) (2023.01.20)
Recommended to work with azure-ai-ml==1.3.0
**New Features**
- Support primitive type as component return annotation.
- Support `@group` as component return annotation.
- Add validation for component execution results according to return annotation.
**Improvements**
- Return component execution result as `mldesigner execute` result.
**Fixed Bugs**
- Fixed missing keyword arguments when executing component in an environment that has both mldesigner and azure-ai-ml
- Fixed compile error when compiling pipeline that has a node uses remote registered component either from workspace or registry.
- Fixed parse error when input string is a json serialized string.
## [v0.1.0b9](https://pypi.org/project/mldesigner/0.1.0b9/) (2022.12.06)
Recommended to work with azure-ai-ml==1.2.0
**Improvements**
- Enable creating component with auto-incremented version if no version specified.
- Update mldesigner default environment image to be `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04`
- Mldesigner compile improvements:
- Exits with code 1 when there is failed compilation.
- Output yaml file with original file name if the input is a yaml.
- Generated pipeline node keys is ordered.
**Fixed Bugs**
- Fixed mldesigner executor does not pass `**kwargs` to component func
## [v0.1.0b8](https://pypi.org/project/mldesigner/0.1.0b8/) (2022.10.26)
**Fixed Bugs**
- Fixed dependency issue when importing `typing_extensions`
## [v0.1.0b7](https://pypi.org/project/mldesigner/0.1.0b7/) (2022.10.25)
**New Features**
- Support mldesigner compile: `mldesigner compile`
- Support list type component input for generate_package.
**Fixed Bugs**
- Fixed execute error when input string has space or "=" inside.
- Fixed execute error for bool parameter parsing.
**Improvements**
- Remove `mode` client default value `ro_mount/rw_mount` from mldesigner `Input/Output` class.
- Refine error message when defining a component in `.ipynb`.
- Refine error message when failed to create component entity.
## [v0.1.0b6](https://pypi.org/project/mldesigner/0.1.0b6/) (2022.09.19)
**Improvements:**
- Adopted optional input new format: '$[[]]' instead of old '[]'.
- Support Enum for mldesigner input.
- Raise error if input has no annotation specified.
**Fixed Bugs:**
- Fixed import error when used along with azure-ai-ml<0.1.0b7.
## [v0.1.0b5](https://pypi.org/project/mldesigner/0.1.0b5/) (2022.09.08)
**New Features**
- Enable using @command_component without brackets when no additional parameters.
```python
from mldesigner import command_component
@command_component
def my_component():
pass
# equals to
@command_component()
def my_component():
pass
```
**Fixed Bugs:**
- Fixed component command execution error when no inputs or outputs specified.
- Fixed incompatible issue with azure-ai-ml >=0.1.0b7: error when trying to import azure-ai-ml constants
## [v0.1.0b4](https://pypi.org/project/mldesigner/0.1.0b4/) (2022.08.22)
**New Features:**
- Support mldesigner generate:
- Generate component package from local yaml files or remote source
- See reference doc: [mldesigner generate doc](https://github.com/Azure/azureml-pipelines-doc/tree/main/user_guide/component_import_experience)
- Sample notebook: [mldesigner generate samples](https://github.com/Azure/azureml-pipelines-doc/blob/main/user_guide/component_import_experience/component-import-experience.ipynb)
- Support mldesigner execute: execute component in local host environment.
- CLI example: `mldesigner execute --source ./components.py --name my_component inputs a=1 b=2`
- SDK example:
```python
from mldesigner import execute
from components import my_component
node = my_component(a=1, b=2)
res = execute(node)
```
- More information: `mldesigner execute --help`
**Improvements:**
- Fix bump version config.
## [v0.1.0b3](https://pypi.org/project/mldesigner/0.1.0b3/) (2022.07.14)
**New Features:**
- Support optional input for mldesigner Input class.
```python
from mldesigner import command_component, Input
@command_component()
def my_component(optional_param: Input(type="integer", optional=True)):
pass
```
- Support io descriptions inferring from docstring for pipeline & component.
**Improvements:**
- Handle compatibility issue for future changes. Less imports of private functions
- Remove default property for mldesigner Input class.
- Enable mldesigner to use argparser to parse incoming args.
- Compatibility handling: old azure ai ml package use different way to load component.
- Component input like "int_param=3", no longer to be processed as optional input when registering to remote.
## [v0.1.0b2](https://pypi.org/project/mldesigner/0.1.0b2/) (2022.05.23)
**Improvements:**
- Refine code terminologies, replace old dsl with new mldesigner.
## [v0.1.0b1](https://pypi.org/project/mldesigner/0.1.0b1/) (2022.05.20)
**New Features:**
- Support using decorator @command_component to define a component.
- Create a component:
```python
from mldesigner import command_component, Input, Output
@command_component()
def hello_world(input: Input, output: Output, param='str_param'):
print("Hello World!")
```
- Register the component to server:
```python
from azure.ai.ml import MLClient
client = MLClient.from_config(credential=credential)
client.components.create_or_update(hello_world)
```
- Use sdk component in pipeline:
```python
from azure.ai.ml import dsl
@dsl.pipeline()
def my_pipeline():
node = hello_world()
return {"pipeline_output": node.outputs.output}
pipeline = my_pipeline()
client.jobs.create_or_update(pipeline)
```
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"description": "# Azure Machine Learning Designer Python SDK\n\nThe `mldesigner` package provide the SDK interface which work along with Azure ML Designer (drag-n-drop ML) UI experience.\n\n- [Azure ML Designer (drag-n-drop ML)](https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer): designer is a UI tool in the Azure ML workspace for visually connecting datasets and components on an interactive canvas to create machine learning pipelines. To learn how to get started with the designer, see [Tutorial: Predict automobile price with the designer](https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score).\n\n\nEspecially, the package ease the authoring experience of resources like `Components` & `Pipelines`:\n\n- [Components](https://docs.microsoft.com/en-us/azure/machine-learning/concept-component): self-contained piece of code that does one step in a machine learning pipeline: data preprocessing, model training, model scoring, a hyperparameter tuning run, etc. Such that it can be parameterized and then used in different contexts.\n- [Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines): independently executable workflow of machine learning tasks composed by Components.\n\n\n# Change Log\n\n## [v0.1.0b18](https://pypi.org/project/mldesigner/0.1.0b18/) (2024.01.31)\n\nRecommended to work with azure-ai-ml==1.13\n\n**New Features**\n- `mldesigner compile` supports to compile [parallel component](https://azureml-pipelines-doc.azurewebsites.net/features/components/parallel.html).\n- `mldesigner generate` supports to generate package with provided authentication.\n\n## [v0.1.0b17](https://pypi.org/project/mldesigner/0.1.0b17/) (2023.11.14)\n\nRecommended to work with azure-ai-ml==1.12\n\n**Improvements**\n- [Compile] Update compiled command component schema path to `https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json`.\n\n**Fixed Bugs**\n- [Compile] Fixed the error when source component inputs is empty.\n\n## [v0.1.0b16](https://pypi.org/project/mldesigner/0.1.0b16/) (2023.10.18)\n\nRecommended to work with azure-ai-ml==1.11\n\n**New Features**\n- `mldesigner compile` supports to compile with [.amlignore](https://azureml-pipelines-doc.azurewebsites.net/features/component_authoring/ignore_files.html):\n - `mldesigner compile --source xxx.yaml --amlignore-file <path-to>/.amlignore`\n\n\n## [v0.1.0b15](https://pypi.org/project/mldesigner/0.1.0b15/) (2023.09.13)\n\nRecommended to work with azure-ai-ml==1.10\n\n**New Features**\n- `mldesigner compile` support v1.5 spark components.\n- `is_control=True` is not required for primitive type output.\n- `generate` package support specify version for registry component and `generate` by sdk support remote url.\n ```python\n from mldesigner import generate\n generate(\n source=\"azureml://registries/{registry_name}/components/{component_name}/versions/0.0.8\",\n )\n ```\n\n## [v0.1.0b14](https://pypi.org/project/mldesigner/0.1.0b14/) (2023.07.31)\n\nRecommended to work with azure-ai-ml==1.9.0\n\n**New Features**\n- Support primitive type as component return annotation and not need to write `is_control=True`.\n- Support `Annotated` type as component input or component return annotation.\n- Support `Annotated` type in `@group`.\n ```python\n from mldesigner import command_component, Output, Meta\n from typing_extensions import Annotated\n\n @command_component()\n def my_component1(input_int: int) -> int:\n return input_int\n\n @command_component()\n def my_component2(input_int: Annotated[int, Meta(description=\"test annotation int\", min=1, max=10, default=1)],) \\\n -> Annotated[int, Meta(description=\"test annotation int\")]: # using Meta class to configure more meta data\n return input_int\n ```\n\n## [v0.1.0b13](https://pypi.org/project/mldesigner/0.1.0b13/) (2023.05.16)\n\nRecommended to work with azure-ai-ml==1.7.0\n\n**Improvements**\n- Support to generate components with specified version or label:\n - azureml://subscriptions/<subscription>/resourcegroups/<resource_group>/workspaces/<workspace>/components/<component_name>/labels/<label>\n - azureml://subscriptions/<subscription>/resourcegroups/<resource_group>/workspaces/<workspace>/components/<component_name>/versions/<version>\n- Refine the log content of generate component package.\n\n## [v0.1.0b12](https://pypi.org/project/mldesigner/0.1.0b12/) (2023.03.28)\n\nRecommended to work with azure-ai-ml==1.5.0\n\n**Improvements**\n- Support multiple types of identity for dynamic pipeline creation, the priority is user identity > managed identity > others.\n- Refine `mldesigner execute`:\n - When component name is not specified, if there is only one component in the source file, execute this component, raise error when there are multiple components.\n - If required parameters are not provided, list all of them and raise error.\n- Improve support for `enum` values in `generate_package`.\n - Support enum values with no `\\w` characters, e.g. empty string, `-`, `+` and `\\t`.\n - Support enum values with duplicate sanitized names, e.g. `A` and `a`.\n- Use `curated environment` as default mldesigner component environment:\n - For command component: `azureml://registries/azureml/environments/mldesigner-minimal/labels/latest`\n - For dynamic pipeline: `azureml://registries/azureml/environments/mldesigner/labels/latest`\n- Support `mltable` type in `@dynamic` outputs\n\n**Fixed Bugs**\n- Fix wrong command error when mldesigner component code is set to be a file instead of a folder.\n\n\n## [v0.1.0b11](https://pypi.org/project/mldesigner/0.1.0b11/) (2023.02.09)\n\nRecommended to work with azure-ai-ml==1.4.0\n\n**Improvements**\n- Add python version requirements `<4.0,>=3.7`\n\n\n## [v0.1.0b10](https://pypi.org/project/mldesigner/0.1.0b10/) (2023.01.20)\n\nRecommended to work with azure-ai-ml==1.3.0\n\n**New Features**\n- Support primitive type as component return annotation.\n- Support `@group` as component return annotation.\n- Add validation for component execution results according to return annotation.\n\n**Improvements**\n- Return component execution result as `mldesigner execute` result.\n\n**Fixed Bugs**\n- Fixed missing keyword arguments when executing component in an environment that has both mldesigner and azure-ai-ml\n- Fixed compile error when compiling pipeline that has a node uses remote registered component either from workspace or registry.\n- Fixed parse error when input string is a json serialized string.\n\n## [v0.1.0b9](https://pypi.org/project/mldesigner/0.1.0b9/) (2022.12.06)\n\nRecommended to work with azure-ai-ml==1.2.0\n\n**Improvements**\n- Enable creating component with auto-incremented version if no version specified.\n- Update mldesigner default environment image to be `mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04`\n- Mldesigner compile improvements:\n - Exits with code 1 when there is failed compilation.\n - Output yaml file with original file name if the input is a yaml.\n - Generated pipeline node keys is ordered.\n\n**Fixed Bugs**\n- Fixed mldesigner executor does not pass `**kwargs` to component func\n\n## [v0.1.0b8](https://pypi.org/project/mldesigner/0.1.0b8/) (2022.10.26)\n\n**Fixed Bugs**\n- Fixed dependency issue when importing `typing_extensions`\n\n## [v0.1.0b7](https://pypi.org/project/mldesigner/0.1.0b7/) (2022.10.25)\n\n**New Features**\n- Support mldesigner compile: `mldesigner compile`\n- Support list type component input for generate_package.\n\n**Fixed Bugs**\n- Fixed execute error when input string has space or \"=\" inside.\n- Fixed execute error for bool parameter parsing.\n\n**Improvements**\n- Remove `mode` client default value `ro_mount/rw_mount` from mldesigner `Input/Output` class.\n- Refine error message when defining a component in `.ipynb`.\n- Refine error message when failed to create component entity.\n\n## [v0.1.0b6](https://pypi.org/project/mldesigner/0.1.0b6/) (2022.09.19)\n\n**Improvements:**\n- Adopted optional input new format: '$[[]]' instead of old '[]'.\n- Support Enum for mldesigner input.\n- Raise error if input has no annotation specified.\n\n**Fixed Bugs:**\n- Fixed import error when used along with azure-ai-ml<0.1.0b7.\n\n## [v0.1.0b5](https://pypi.org/project/mldesigner/0.1.0b5/) (2022.09.08)\n\n**New Features**\n- Enable using @command_component without brackets when no additional parameters.\n ```python\n from mldesigner import command_component\n @command_component\n def my_component():\n pass\n\n # equals to\n @command_component()\n def my_component():\n pass\n ```\n\n**Fixed Bugs:**\n- Fixed component command execution error when no inputs or outputs specified.\n- Fixed incompatible issue with azure-ai-ml >=0.1.0b7: error when trying to import azure-ai-ml constants\n\n## [v0.1.0b4](https://pypi.org/project/mldesigner/0.1.0b4/) (2022.08.22)\n\n**New Features:**\n- Support mldesigner generate:\n - Generate component package from local yaml files or remote source\n - See reference doc: [mldesigner generate doc](https://github.com/Azure/azureml-pipelines-doc/tree/main/user_guide/component_import_experience)\n - Sample notebook: [mldesigner generate samples](https://github.com/Azure/azureml-pipelines-doc/blob/main/user_guide/component_import_experience/component-import-experience.ipynb)\n- Support mldesigner execute: execute component in local host environment.\n - CLI example: `mldesigner execute --source ./components.py --name my_component inputs a=1 b=2`\n - SDK example:\n ```python\n from mldesigner import execute\n from components import my_component\n node = my_component(a=1, b=2)\n res = execute(node)\n ```\n - More information: `mldesigner execute --help`\n\n**Improvements:**\n- Fix bump version config.\n\n## [v0.1.0b3](https://pypi.org/project/mldesigner/0.1.0b3/) (2022.07.14)\n\n**New Features:**\n- Support optional input for mldesigner Input class.\n ```python\n from mldesigner import command_component, Input\n @command_component()\n def my_component(optional_param: Input(type=\"integer\", optional=True)):\n pass\n ```\n- Support io descriptions inferring from docstring for pipeline & component.\n\n**Improvements:**\n- Handle compatibility issue for future changes. Less imports of private functions\n- Remove default property for mldesigner Input class.\n- Enable mldesigner to use argparser to parse incoming args.\n- Compatibility handling: old azure ai ml package use different way to load component.\n- Component input like \"int_param=3\", no longer to be processed as optional input when registering to remote.\n\n\n\n## [v0.1.0b2](https://pypi.org/project/mldesigner/0.1.0b2/) (2022.05.23)\n\n**Improvements:**\n- Refine code terminologies, replace old dsl with new mldesigner.\n\n## [v0.1.0b1](https://pypi.org/project/mldesigner/0.1.0b1/) (2022.05.20)\n\n**New Features:**\n- Support using decorator @command_component to define a component.\n - Create a component:\n ```python\n from mldesigner import command_component, Input, Output\n\n @command_component()\n def hello_world(input: Input, output: Output, param='str_param'):\n print(\"Hello World!\")\n ```\n - Register the component to server:\n ```python\n from azure.ai.ml import MLClient\n client = MLClient.from_config(credential=credential)\n client.components.create_or_update(hello_world)\n ```\n - Use sdk component in pipeline:\n ```python\n from azure.ai.ml import dsl\n\n @dsl.pipeline()\n def my_pipeline():\n node = hello_world()\n return {\"pipeline_output\": node.outputs.output}\n\n pipeline = my_pipeline()\n client.jobs.create_or_update(pipeline)\n\n ```\n",
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