azureml-infra-tools 0.1.1

azureml-infra-tools is a Python package providing high-level APIs for Azure Machine Learning. It simplifies setup of Azure ML infrastructures, manages datasets, and streamlines authentication. Designed for AI researchers, data scientists, and ML engineers, it boosts productivity and accelerates Azure ML projects.

azureml-infra-tools

azureml-infra-tools is a Python utility package designed to simplify and streamline your Azure Machine Learning (Azure ML) workflows. Our goal is to provide a set of high-level APIs that help set up Azure ML infrastructure, manage authentication, and handle datasets in an intuitive and user-friendly manner.

Whether you are an AI researcher, a data scientist, or a machine learning engineer, azureml-infra-tools can help you seamlessly leverage the power of Azure ML and accelerate your machine learning projects.

You can check the project in GitHub page. Also, you can find more information about Azure ML here.

Installation

You can install the azureml-infra-tools package via pip:

pip install azureml-infra-tools

Or via poetry:

poetry add azureml-infra-tools

Usage

Simple usage

Here is a simple example of how to use azureml-infra-tools:

from azureml_infra_tools import setup_infrastructure
import hydra
from omegaconf import DictConfig

@hydra.main(version_base="1.2", config_path="conf", config_name="config")
def main(cfg: DictConfig):
    """ Main function to run the pipeline
    @param cfg: hydra configuration file
    """
    # get original directory of the root of the project
    original_dir = hydra.utils.get_original_cwd()

    # create o setup with environment to run pipeline, client, cluster and data
    azure_credential, data_asset, cpu_cluster, pipeline_env = setup_infrastructure(cfg, original_dir)

Example of hydra configuration file:

data:
  name: rpa-chatbot-assistant-intentions-csv
  description: RPA Chatbot Assistant Intentions
  version: 0.1.0
  data_path: data/processed/data.csv

cluster:
  name: rpachat-cluster-m60
  type: amlcompute
  size: Standard_NV6
  min_instances: 0
  max_instances: 18
  idle_time_before_scale_down: 180
  tier: Dedicated

environment:
  name: rpachat-custom-env
  dependencies_dir: src/dependencies
  version: 1.2.0
  tags: { "datasets": "2.13.1", "transformers": "4.30.2", "torch": "2.0.1" }
  description: Custom environment for RPA Chatbot Assistant Intentions pipeline

Advanced usage

Here is an advanced example of how to use azureml-infra-tools:

# Get a handle to the workspace
azure_credential = AzureCredential(subscription_id=os.environ.get("SUBSCRIPTION_ID"),
                                   resource_group_name=os.environ.get("RESOURCE_GROUP_NAME"),
                                   workspace_name=os.environ.get("WORKSPACE_NAME"))

# Upload the data to Azure ML Studio
data_asset: Data = AzureData(azure_credential=azure_credential,
                             data_path=cfg.data.data_path,
                             data_name=cfg.data.name,
                             data_description=cfg.data.description,
                             data_version=cfg.data.version).upload_data()

# Create the cluster
cpu_cluster: AmlCompute = AzureCluster(azure_credential,
                                       cfg.cluster.name,
                                       cfg.cluster.type,
                                       cfg.cluster.size,
                                       cfg.cluster.min_instances,
                                       cfg.cluster.max_instances,
                                       cfg.cluster.idle_time_before_scale_down,
                                       cfg.cluster.tier).create_cluster()

# Create the environment
pipeline_env: Environment = AzureEnvironment(azure_credential,
                                             cfg.environment.name,
                                             f"{original_dir}/{cfg.environment.dependencies_dir}",
                                             cfg.environment.version,
                                             cfg.environment.tags,
                                             cfg.environment.description).create_environment()

NOTE: In both cases you need to create a .env file with SUBSCRIPTION_ID, RESOURCE_GROUP_NAME and WORKSPACE_NAME. Then gather the information from the .env file and pass it to the AzureCredential class as follows:

import os
from dotenv import load_dotenv

load_dotenv()

os.environ.get("SUBSCRIPTION_ID")
os.environ.get("RESOURCE_GROUP_NAME")
os.environ.get("WORKSPACE_NAME")

Please note that you need to provide your own directory and configuration parameters.

Contributing

We appreciate all contributions. If you're planning to contribute back bug-fixes, please create an issue describing the bug. If you plan to contribute new features, utility functions, or extensions, please first open an issue and discuss the feature with us.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Please modify the contents to better match your project requirements and details. For instance, you might want to add more usage examples, a section about the project's dependencies, instructions for how to run tests, etc.

Versioning

The versioning system that we use is known as semantic versioning (SemVer). It's a versioning scheme for software that aims to convey meaning about the underlying changes in a release.

In general, SemVer's structure is MAJOR.MINOR.PATCH, where:

• MAJOR version increments indicate incompatible API changes.
• MINOR version increments indicate the addition of functionality in a backwards-compatible manner.
• PATCH version increments indicate backwards-compatible bug fixes. For the versions available, see the tags on this repository.

Contact

For more information on this project, you can visit the project's GitHub page.