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Command line tool for the Auger AI platform.

Project description

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Auger CLI

A command line tool for the Auger AI platform.


Create Auger account

Please create account and organization to start working with CLI:


Latest Python:

Install pip:

Auger CLI python package

pip3 install auger-cli

Auger CLI sources

# Pull latest version
git clone

cd auger-cli
pip3 install .

Usage scenarios


To access the usage information, simply add the --help option to any command or sub-command. For example:

$ auger --help
Usage: auger [OPTIONS] COMMAND [ARGS]...

  Auger command line interface.

  --help  Show this message and exit.

  auth        Authentication with Auger.
  experiments Manage Auger Experiments.
  orgs        Manage Auger Organizations.
  instances   Display available instance types for clusters.
  clusters    Manage Auger Clusters.
  projects    Manage Auger Projects.
$ auger auth --help
Usage: auger auth [OPTIONS] COMMAND [ARGS]...

  --help  Show this message and exit.

  login   Login to Auger.
  logout  Logout from Auger.


The complete example iris config file described below can be found in experiments/iris_train folder.


The first step you'll need to do is login to Auger with:

auger auth login

Note you can login to a different Auger hub instance by passing the --url argument:

auger auth login --url

To get current login information:

auger auth whoami

To logout:

auger auth logout


Organization allocates S3 bucket where all data can be stored between cluster runs.

To start using it you should be a member of any organization, check it with:

auger orgs

To create your own organization go to


Experiment definition

To start working with Auger experiment create folder with experiment name and place file 'auger_experiment.yml' there. This file contain definition of experiment.

For more details see

auger_experiment.yml fields:

  # Path to file with data. May be URL or path in project files folder 
  # For Google drive use the following format:<shared file ID>
  data_path: files/iris_data_sample.csv

  # List of features from data file to be used to evaluate ML models
  - sepal_length
  - sepal_width
  - petal_length
  - petal_width

  # Target feature to build ML model for
  target_feature: class

  # If some of your features are strings, add them to the categoricals, so they will be one-hot encoded
  - class

  # If you want some categoricals whould be hashed instead of one-hot encoded add them to label encoded list
  label_encoding_features: []

  # List of features of datetime type
  datetime_features: []

  # List of time series features, usually one
  # If provided, when time series preprocessor and models will be used. See:
  time_series_features: []

  # Define type of ML models. true for 'classification', false for 'regression'
  classification: true

  # If target has two unique values, set it to true 
  binary_classification: false

  # Score used to optimize ML model.
  # Supported scores for classification: accuracy, f1_macro, f1_micro, f1_weighted, neg_log_loss, precision_macro, precision_micro, precision_weighted, recall_macro, recall_micro, recall_weighted
  # Supported scores for binary classification: accuracy, average_precision, f1, f1_macro, f1_micro, f1_weighted, neg_log_loss, precision, precision_macro, precision_micro, precision_weighted, recall, recall_macro, recall_micro, recall_weighted, roc_auc, cohen_kappa_score, matthews_corrcoef
  # Supported scores for regression: explained_variance, neg_median_absolute_error, neg_mean_absolute_error, neg_mean_squared_error, neg_mean_squared_log_error, r2, neg_rmsle, neg_mase, mda, neg_rmse

  scoring: accuracy

  # Number of K-folds: is a cross validation technique for splitting data into train/test
  cross_validation_folds: 5

  # Max Total Time Minutes, the maximum time in minutes an entire training can run for before it is stopped.
  max_total_time_mins: 60

  # Max Trial Time Minutes, this is the maximum time in minutes an individual fold in trial can run before it is stopped.
  max_eval_time_mins: 10

  # Max Trials, this is the maximum number of trials to be run before training stops.
  max_n_trials: 10

  # Build ensembles models after plain models completed. See : 
  use_ensemble: true

  # OPTIONAL evaluation parameters  

  # To see list of available algorithms call 'auger experiments search_space'
    # Use algorithm with parameters range defined in Auger ML
    #sklearn.ensemble.AdaBoostClassifier: {}

    # Modify some of algorithm parameters
    #sklearn.ensemble.GradientBoostingClassifier: {"max_depth": {"bounds": [10, 20]}}

    # Use limited set of algorithm parameters, all other parameters will be not tunable and set to default values
    #sklearn.ensemble.GradientBoostingClassifier: {'_no_default_params': True, "max_depth": {"bounds": [10, 20]}}

    # Add new parameter to algorithm. If one value is present, it will be passed as default value to algorithm
    #lightgbm.LGBMClassifier: {"feature_border_type": {"values": ['Median'], "type": "categorical", "tunable": True}}

  # To see list of available optimizers call 'auger experiments search_space'
  #optimizers_names: []

  #data_extension: ".csv"
  #data_compression: gzip

  #split_options: {}
  #oversampling: {}
  #use_ensemble: true
  #preprocessors: {}

# OPTIONAL parameters  

# Specify organization name, if you have more then one organization.
# By default Auger will use your first organization.
# Organization must exist. See Installation section
#organization: <organization name>

# Experiment name
# By default Auger will use folder name of auger_experiment.yml
# Will be generated automatically if not exists
#experiment: <experiment name>

# You may use one project/cluster for different experiments
# By default Auger will use folder name of auger_experiment.yml
# Will be generated automatically if not exists
#project: <project name>

# Cluster settings with default values
# cluster:
  # Number of nodes to run on cluster. Minimum of 2, the more workers deployed the more jobs that can be run in parallel.
  # workers_count : 2

  # To list available types call: `auger instances`
  # worker_type_id: 1

  # Number of workers per computer node. Setting it lower then CPU count, increase amoutn of memory available for worker
  # workers_per_node_count: 2

  # Cluster will be terminated after period of inactivity
  # autoterminate_minutes: 30

Run experiment:

auger experiments run

Display leaderboard from last run:

auger experiments leaderboard

Display individual model parameters from last run:

auger trials show <id_from_leaderboard>

To call predict using deployed model:

auger experiments predict -p <pipeline id> -t <trial id> -f <csv file path>

Pipeline ID is optional, if missed model with trial id will be automatically deployed Trial ID to export model for the last experiment session, if missed best trial used. CSV file path should point to local file with data for predcition

To call predict using locally exported model:

auger experiments predict -e -t <trial id> -f <csv file path>

Pipeline ID is optional, if missed model with trial id will be automatically deployed Trial ID to export model for the last experiment session, if missed best trial used. CSV file path should point to local file with data for predcition

To call predict proba using locally exported model:

auger experiments predict -e -t <trial id> -f <csv file path> --threshold 0.5

Prediction data will contain additional proba_<class name> columns per each target class. Target calculation: if proba(class1) > threshold then class1 else class0

To export model locally:

auger experiments export_model -t <trial id>

Trial ID to export model for the last experiment session, if missed best trial used.

Model zip file will be downloaded into models folder. Unzip it and see readme file inside how to use it.

To deploy model to Auger HUB:

auger experiments deploy_model -t <trial id>

Trial ID to export model for the last experiment session, if missed best trial used.

Display information about experiment:

auger experiments show

Display information about experiment settings:

auger experiments settings

Display information about Auger ML oprimizers and algorithms:

auger experiments search_space


To display cluster information.

auger clusters show <cluster id>

To terminate cluster. It will free all paid AWS resources related with this cluster.

auger clusters delete <cluster id>


To display project information.

auger projects show -p <project name>

To open project in web browser:

auger projects open_project -p <project name>

To download file from project cluster:

auger projects download_file <remote path> -l <local path> -p <project name>

Remote path may be full path or relative path on cluster. For examble: files/iris_data_sample.csv

Local path is optional, by default file will be downloaded to 'files' folder in current directory

Project name is optional, if missed project name will be retrieve from auger_experiment.yml

To read project log:

auger projects logs <project_id>

To Create project:

auger project create --project <project name> --organization-id <organization id>

The project name must be unique within the organization. This means that a project can be deployed to a cluster, the cluster can be terminated, and the project can be deployed to another one. NOTE: If you delete the project, another project with the same name can be used.

To open project in the browser:

auger projects open -p <project name>

To delete project:

auger projects delete -p <project name>

Auger Python API

To start working with Auger Python API, follow installation instructions for Auger CLI.

Getting started

Create AugerClient and login:

    # To read login information from experiment dir:
    #config_settings={'login_config_path': "./iris_train"}

    # To use root user dir to read login information
    # You may specify any properties from auger_experiment.yml

    # Read experiment setting from iris_train\auger_experiment.yml 
    client = AugerClient(AugerConfig(config_dir="./iris_train", 

    # To login to Auger:
    # You may login using CLI and store login credentials in user dir
    # OR login direcly
    # url is optional parameter, hub_url may be specified in config_settings
    #auth.login(client, "user", "pwd")

Run experiment and wait for completion:

    # Experiment run, after finish, save experiment session parameters to .auger_experiment_session.yml

    while True:
        leaderboard, info = experiments.read_leaderboard(client)

        if info.get("Status") == 'error':
            raise Exception("Iris dataset train failed: %s"%info.get("Error"))

        if info.get("Status") != 'completed':


Predict using pipeline model:

    # Create pipeline based on best trial    
    pipeline_id = experiments.export_model(client, trial_id=leaderboard[0]['id'], deploy=True)

    # Pipeline can ber reused multiple time, predict can be called without cluster run
    result = experiments.predict_by_file(client, pipeline_id=pipeline_id, file='./iris_train/files/iris_data_test.csv', save_to_file=False)

Predict using locally exported model:

    result = experiments.predict_by_file_locally(client, file='./iris_train/files/iris_data_test.csv', trial_id=leaderboard[0]['id'], save_to_file=False, pull_docker=True)

Export model locally:

    # Export model to local zip file, see readme inside how to call predict     
    file_path = experiments.export_model(client, trial_id=leaderboard[0]['id'], deploy=False)

How to update Python package:

  1. update version in

  2. commit

  3. create tag with convention 'vX.X.X' (like v0.1.2)

git tag v0.1.4

  1. git push --tags

circleci will build it and upload the tagged build to

  1. Review new package here:

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