lucazav

$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
type: pipeline
experiment_name: aml_pipeline_cli

compute: azureml:cluster-cpu

inputs:
  data_dir:
    path: azureml:data-fashion-mnist@latest
    type: uri_folder

lucazav

$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
name: component_pipeline_cli_train
version: 3
type: command

inputs:
  data_dir:
    type: uri_folder

outputs:

lucazav

# Schema Reference: https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-deployment-managed-online?view=azureml-api-2
$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json
name: blue
endpoint_name: endpoint-command-cli
model: azureml:model-command-cli@latest
instance_type: Standard_DS3_v2
instance_count: 1

lucazav

# Standard command.
az ml job create -f cloud/azureml-create-command-job-inline-env.yml

# Once the job finishes training the model, if we want to register the model for example, that requires a reference to the job instance. In that case:
run_id=$(az ml job create -f cloud/azureml-create-command-job-inline-env.yml --query name -o tsv)

# az ml job reference: https://learn.microsoft.com/en-us/cli/azure/ml/job?view=azure-cli-latest#az-ml-job-create

# Register the model using the run_id:
az ml model create --name model-command-cli --path "azureml://jobs/$run_id/outputs/model" --type mlflow_model

lucazav

# Upload data from local folder (or remote path)
az ml data create -f cloud/azureml-upload-local-folder.yml

lucazav

$schema: https://azuremlschemas.azureedge.net/latest/amlCompute.schema.json
type: amlcompute
name: cluster-cpu
size: STANDARD_DS3_v2
min_instances: 0
max_instances: 2
idle_time_before_scale_down: 600

lucazav

# Check if you're already logged in
az account show

# Interactive login
az login

# Interactive login with device code
az login --use-device-code

# Sign in with a system-assigned managed identity

lucazav

To install Azure CLI and its ML extension on your local machine, follow these links:
    - Install the Azure CLI by following the instructions in the documentation (https://docs.microsoft.com/en-us/cli/azure/install-azure-cli).
    - Install the ML extension to the Azure CLI by following the "Installation" section of the documentation (https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli).

CLI (v2) core YAML syntax:
https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-core-syntax?view=azureml-api-2

lucazav

# %%
"""
This script demonstrates how to use the `find_best_split_seed` function from the `find_robust_seed` package
implemented in the https://github.com/lucazav/Robust-Multi-Objective-Optimization-for-Train-Test-Splits repository
to identify the most stable random seed for train-test splits. The goal is to ensure that the training 
and test datasets maintain the statistical properties of the original dataset.

Key Features:
- Loads and cleans a dataset (Fish Market) using PyJanitor for column name normalization.
- Uses `find_best_split_seed` to evaluate multiple random seeds and find the most robust split.

lucazav

# Conda environment specification. The dependencies defined in this file will
# be automatically provisioned for runs with userManagedDependencies=False.

# Details about the Conda environment file format:
# https://conda.io/docs/user-guide/tasks/manage-environments.html#create-env-file-manually

name: azureml_scoring_env
dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.