config-aml.yml

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

variables:

  ap_vm_image: ubuntu-20.04

# Training pipeline settings

  # Training dataset settings
  training_dataset_name: uci-credit
  training_dataset_description: uci_credit
  training_dataset_local_path: data/training/
  training_dataset_path_on_datastore: data/training/
  training_dataset_type: local
  training_dataset_storage_url: 'https://azureaidemostorage.blob.core.windows.net/data/'
  
  # Training AzureML Environment name
  training_env_name: credit-training

  # Training AzureML Environment conda yaml
  training_env_conda_yaml: data-science/environment/train.yml

  # Name for the training pipeline
  training_pipeline_name: credit-training

  # Compute target for pipeline
  training_target: cpu-cluster
  training_target_sku: STANDARD_D2_V2
  training_target_min_nodes: 0
  training_target_max_nodes: 4

  # Training arguments specification
  training_arguments: ''

  # Training datasets specification
  # Syntax: <name>:<version>:<mode>:<steps (names separated by +)>
  training_datasets: uci-credit:1:download:prep

  # Name under which the model will be registered
  model_name: credit-ci

# Batch pipeline settings

  # Batch scoring dataset settings
  scoring_dataset_name: credit-batch-input
  scoring_dataset_description: credit-batch-input
  scoring_dataset_local_path: data/scoring/
  scoring_dataset_path_on_datastore: data/scoring/
  scoring_dataset_type: local
  scoring_dataset_storage_url: 'https://azureaidemostorage.blob.core.windows.net/data/'

  # Batch AzureML Environment name
  batch_env_name: credit-batch

  # Batch AzureML Environment conda yaml
  batch_env_conda_yaml: data-science/environment/batch.yml

  # Name for the batch scoring pipeline
  batch_pipeline_name: credit-batch-scoring

  # Compute target for pipeline
  batch_target: cpu-cluster
  #not needed because batch uses the same target as training
  # batch_target_sku: STANDARD_D2_V2
  # batch_target_min_nodes: 0
  # batch_target_max_nodes: 4

  # Input batch dataset
  batch_input_dataset_name: credit-batch-input

  # Output dataset with results
  batch_output_dataset_name: credit-batch-output
  batch_output_path_on_datastore: credit-batch-scoring-results/{run-id}
  batch_output_filename: results.csv

  # Parallelization settings
  batch_mini_batch_size: 8
  batch_error_threshold: 1
  batch_process_count_per_node: 1
  batch_node_count: 1

  # Monitoring settings
  scoring_table_name: scoringdata
  training_table_name: mlmonitoring