The command line is a convenient way to start an A2ML project even if you plan to use the API.

Creating a New A2ML Project

Create a new A2ML project with the new command by supplying a project name. A2ML will create a directory which has a default set of configuration files that can be more specifically configured.

$ a2ml new test_app

Configuring Your A2ML Project

To use the Python API or the command line interface for a specific PREDIT pipeline, configure the project first.

This includes both general options that apply to all vendors and vendor specific options in separate YAML files.

All Vendors

  • config.yaml

name: the name of the project
provider: the AutoML provider. **GC (for Google Cloud)**, **AZ (for Microsoft Azure)**, or **Auger**
source: the CSV or Parquet file to train with. Can be a local file path (for Auger or Azure). Can be a hosted file URL. Can be URL for Google Cloud Storage ("gs://...") for Google Cloud AutoML.
source_format: csv(default), parquet. Set it if source is url or file has no extension
exclude: features from the dataset to exclude from the model
target: the feature which is the target
model_type: Can be regression or classification
budget: the time budget in milliseconds to train

Vendor Specfic

  • auger.yaml

  • azure.yaml

  • google.yaml

region: the region for the AutoML providers compute clusters, each vendor has different names for their regions
metric: how to measure the accuracy of the model to perform the search of algorithms, each vendor has different names for their regions


Here is an example with options that apply to all AutoML providers:


source: ./moneyball/train.csv
exclude: Team,League,Year
target: RS
model_type: regression
  cross_validation_folds: 5
  max_total_time: 60
  max_eval_time: 5
  max_n_trials: 10
  use_ensemble: true


  metric: r2_score

  region: eastus2
  name: cpucluster
  min_nodes: 0
  max_nodes: 4
  type: STANDARD_D2_V2

dataset: train.csv


region: us-central1
project: automl-test-237311
dataset_id: TBL1889796605356277760
operation_id: TBL2145477039279308800
operation_name: projects/291533092938/locations/us-central1/operations/TBL4473943599746121728
model_name: projects/291533092938/locations/us-central1/models/TBL1517370026795991040


project: moneyball
dataset: train.csv

  cross_validation_folds: 5
  max_total_time: 60
  max_eval_time: 1
  max_n_trials: 10
  use_ensemble: true
  metric: f1_macro

A2ML CLI Commands

Below are the full set of commands provided by A2ML. Command line options are provided for each stage in the PREDIT Pipeline.



  • new Create new A2ML application.

  • import Import data for training.

  • train Train the model.

  • evaluate Evaluate models after training.

  • deploy Deploy trained model.

  • predict Predict with deployed model.

  • review Review specified model info.

  • project Project(s) management.

  • dataset Dataset(s) management.

  • experiment Experiment(s) management.

  • model Model(s) management.

To get detailed information on available options for each command, please run:

$ a2ml command --help