CLI (v2) Automated ML text classification job YAML schema

Article
08/28/2024

APPLIES TO: Azure CLI ml extension v2 (current)

Note

The YAML syntax detailed in this document is based on the JSON schema for the latest version of the ML CLI v2 extension. This syntax is guaranteed only to work with the latest version of the ML CLI v2 extension. You can find the schemas for older extension versions at https://azuremlschemasprod.azureedge.net/.

Every Azure Machine Learning entity has a schematized YAML representation. You can create a new entity from a YAML configuration file with a .yml or .yaml extension.

This article provides a reference for some syntax concepts you will encounter while configuring these YAML files for NLP text classification jobs.

The source JSON schema can be found at https://azuremlsdk2.blob.core.windows.net/preview/0.0.1/autoMLNLPTextClassificationJob.schema.json

YAML syntax

Key	Type	Description	Allowed values	Default value
`$schema`	string	Represents the location/url to load the YAML schema. If the user uses the Azure Machine Learning VS Code extension to author the YAML file, including `$schema` at the top of the file enables the user to invoke schema and resource completions.
`type`	const	Required. The type of job.	`automl`	`automl`
`task`	const	Required. The type of AutoML task. Task description of text classification: There are multiple possible classes and each sample can be classified as exactly one class. The task is to predict the correct class for each sample. For example, classifying a movie script as "Comedy" or "Romantic".	`text_classification`
`name`	string	Name of the job. Must be unique across all jobs in the workspace. If omitted, Azure Machine Learning will autogenerate a GUID for the name.
`display_name`	string	Display name of the job in the studio UI. Can be non-unique within the workspace. If omitted, Azure Machine Learning will autogenerate a human-readable adjective-noun identifier for the display name.
`experiment_name`	string	Experiment name to organize the job under. Each job's run record will be organized under the corresponding experiment in the studio's "Experiments" tab. If omitted, Azure Machine Learning will default it to the name of the working directory where the job was created.
`description`	string	Description of the job.
`tags`	object	Dictionary of tags for the job.
`compute`	string	Name of the compute target to execute the job on. To reference an existing compute in the workspace, we use syntax: `azureml:<compute_name>`
`log_verbosity`	number	Different levels of log verbosity.	`not_set`, `debug`, `info`, `warning`, `error`, `critical`	`info`
`primary_metric`	string	The metric that AutoML will optimize for model selection.	`accuracy`, `auc_weighted`, `precision_score_weighted`	`accuracy`
`target_column_name`	string	Required. The name of the column to target for predictions. It must always be specified. This parameter is applicable to `training_data` and `validation_data`.
`training_data`	object	Required. The data to be used within the job. For multi-class classification, the dataset can contain several text columns and exactly one label column.
`validation_data`	object	Required. The validation data to be used within the job. It should be consistent with the training data in terms of the set of columns, data type for each column, order of columns from left to right and at least two unique labels. Note: the column names within each dataset should be unique.
`limits`	object	Dictionary of limit configurations of the job. Parameters in this section: `max_concurrent_trials`, `max_nodes`, `max_trials`, `timeout_minutes`, `trial_timeout_minutes`. See limits for detail.
`training_parameters`	object	Dictionary containing training parameters for the job. See supported hyperparameters for detail. Note: Hyperparameters set in the `training_parameters` are fixed across all sweeping runs and thus don't need to be included in the search space.
`sweep`	object	Dictionary containing sweep parameters for the job. It has two keys - `sampling_algorithm` (required) and `early_termination`. For more information, see model sweeping and hyperparameter tuning sections.
`search_space`	object	Dictionary of the hyperparameter search space. The key is the name of the hyperparameter and the value is the parameter expression. All parameters that are fixable via `training_parameters` are supported here (to be instead swept over). See supported hyperparameters for more detail. There are two types of hyperparameters: - Discrete Hyperparameters: Discrete hyperparameters are specified as a `choice` among discrete values. `choice` can be one or more comma-separated values, a `range` object, or any arbitrary `list` object. Advanced discrete hyperparameters can also be specified using a distribution - `randint`, `qlognormal`, `qnormal`, `qloguniform`, `quniform`. For more information, see this section. - Continuous hyperparameters: Continuous hyperparameters are specified as a distribution over a continuous range of values. Currently supported distributions are - `lognormal`, `normal`, `loguniform`, `uniform`. For more information, see this section. See parameter expressions for the set of possible expressions to use.
`outputs`	object	Dictionary of output configurations of the job. The key is a name for the output within the context of the job and the value is the output configuration.
`outputs.best_model`	object	Dictionary of output configurations for best model. For more information, see Best model output configuration.

Other syntax used in configurations:

Limits

Key	Type	Description	Default value
`max_concurrent_trials`	integer	Represents the maximum number of trials (children jobs) that would be executed in parallel.	`1`
`max_trials`	integer	Represents the maximum number of trials an AutoML nlp job can try to run a training algorithm with different combination of hyperparameters.	`1`
`timeout_minutes`	integer	Represents the maximum amount of time in minutes that the submitted AutoML NLP job can take to run . After this, the job will get terminated. The default timeout in AutoML NLP jobs is 7 days.	`10080`
`trial_timeout_minutes`	integer	Represents the maximum amount of time in minutes that each trial (child job) in the submitted AutoML job can take run. After this, the child job will get terminated.
`max_nodes`	integer	The maximum number of nodes from the backing compute cluster to leverage for the job.	`1`

Supported hyperparameters

The following table describes the hyperparameters that AutoML NLP supports.

Parameter name	Description	Syntax
gradient_accumulation_steps	The number of backward operations whose gradients are to be summed up before performing one step of gradient descent by calling the optimizer’s step function. This is leveraged to use an effective batch size which is gradient_accumulation_steps times larger than the maximum size that fits the GPU.	Must be a positive integer.
learning_rate	Initial learning rate.	Must be a float in the range (0, 1).
learning_rate_scheduler	Type of learning rate scheduler.	Must choose from `linear, cosine, cosine_with_restarts, polynomial, constant, constant_with_warmup`.
model_name	Name of one of the supported models.	Must choose from `bert_base_cased, bert_base_uncased, bert_base_multilingual_cased, bert_base_german_cased, bert_large_cased, bert_large_uncased, distilbert_base_cased, distilbert_base_uncased, roberta_base, roberta_large, distilroberta_base, xlm_roberta_base, xlm_roberta_large, xlnet_base_cased, xlnet_large_cased`.
number_of_epochs	Number of training epochs.	Must be a positive integer.
training_batch_size	Training batch size.	Must be a positive integer.
validation_batch_size	Validation batch size.	Must be a positive integer.
warmup_ratio	Ratio of total training steps used for a linear warmup from 0 to learning_rate.	Must be a float in the range [0, 1].
weight_decay	Value of weight decay when optimizer is sgd, adam, or adamw.	Must be a float in the range [0, 1].

Training or validation data

Key	Type	Description	Allowed values	Default value
`description`	string	The detailed information that describes this input data.
`path`	string	The path from where data should be loaded. Path can be a `file` path, `folder` path or `pattern` for paths. `pattern` specifies a search pattern to allow globbing(`` and `*`) of files and folders containing data. Supported URI types are `azureml`, `https`, `wasbs`, `abfss`, and `adl`. For more information on how to use the `azureml://` URI format, see core yaml syntax. URI of the location of the artifact file. If this URI doesn't have a scheme (for example, http:, azureml: etc.), then it's considered a local reference and the file it points to is uploaded to the default workspace blob-storage as the entity is created.
`mode`	string	Dataset delivery mechanism.	`direct`	`direct`
`type`	const	In order to generate nlp models, the user needs to bring training data in the form of an MLTable. For more information, see preparing data	mltable	mltable

Best model output configuration

Key	Type	Description	Allowed values	Default value
`type`	string	Required. Type of best model. AutoML allows only mlflow models.	`mlflow_model`	`mlflow_model`
`path`	string	Required. URI of the location where the model-artifact file(s) are stored. If this URI doesn't have a scheme (for example, http:, azureml: etc.), then it's considered a local reference and the file it points to is uploaded to the default workspace blob-storage as the entity is created.
`storage_uri`	string	The HTTP URL of the Model. Use this URL with `az storage copy -s THIS_URL -d DESTINATION_PATH --recursive` to download the data.

Remarks

The az ml job command can be used for managing Azure Machine Learning jobs.

Examples

Examples are available in the examples GitHub repository. Examples relevant to text classification job are linked below.

YAML: AutoML text classification job

$schema: https://azuremlsdk2.blob.core.windows.net/preview/0.0.1/autoMLJob.schema.json

type: automl
experiment_name: dpv2-cli-text-classification-newsgroup-experiment
description: A text classification job using newsgroup dataset

compute: azureml:gpu-cluster

task: text_classification
primary_metric: accuracy
log_verbosity: debug

limits:
  timeout_minutes: 60

target_column_name: 'y'
training_data:
  type: mltable
  path: "./training-mltable-folder"
validation_data:
  type: mltable
  path: "./validation-mltable-folder"

featurization:
  dataset_language: "eng"

YAML: AutoML text classification pipeline job

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

description: Pipeline using AutoML Text Classification task

display_name: pipeline-with-text-classification
experiment_name: pipeline-with-automl

settings:
  default_compute: azureml:gpu-cluster

inputs:
  text_classification_training_data:
    type: mltable
    path: ./training-mltable-folder
  text_classification_validation_data:
    type: mltable
    path: ./validation-mltable-folder

jobs:
  preprocessing_node:
    type: command
    component: file:./components/component_preprocessing.yaml
    inputs:
      train_data: ${{parent.inputs.text_classification_training_data}}
      validation_data: ${{parent.inputs.text_classification_validation_data}}
    outputs:
      preprocessed_train_data:
        type: mltable
      preprocessed_validation_data:
        type: mltable
  text_classification_node:
    type: automl
    task: text_classification
    log_verbosity: info
    primary_metric: accuracy
    limits:
      max_trials: 1
    target_column_name: "y"
    training_data: ${{parent.jobs.preprocessing_node.outputs.preprocessed_train_data}}
    validation_data: ${{parent.jobs.preprocessing_node.outputs.preprocessed_validation_data}}
    featurization:
      dataset_language: eng
    # currently need to specify outputs "mlflow_model" explicitly to reference it in following nodes
    outputs:
      best_model:
        type: mlflow_model
  register_model_node:
    type: command
    component: file:./components/component_register_model.yaml
    inputs:
      model_input_path: ${{parent.jobs.text_classification_node.outputs.best_model}}
      model_base_name: newsgroup_model

Next steps

Install and use the CLI (v2)

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