auger-cli 0.1.1

Command line tool for the Auger AI platform.

[](https://circleci.com/gh/deeplearninc/auger-cli)

# Auger CLI

A command line tool for the [Auger AI platform](https://auger.ai).

Please create account and organization to start working with CLI.

# Installation

```sh
# Pull latest version
git clone [e-mail]:deeplearninc/auger-cli.git

cd auger-cli
pip3 install -e .

# Now `auger` command should be accessible
auger --help
```

# Usage scenarios

## Help

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

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

Auger command line interface.

Options:
--help Show this message and exit.

Commands:
auth Authentication with Auger.
experiments Manage Auger Experiments.
orgs Manage Auger Organizations.
help
instances Display available instance types for clusters.
clusters Manage Auger Clusters.
projects Manage Auger Projects.
schema Display current Auger schema.

```

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

Options:
--help Show this message and exit.

Commands:
login Login to Auger.
logout Logout from Auger.
whoami Display the current logged in user.
```

## Login

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

```sh
auger auth login
```

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

```sh
auger auth login --url https://test-instance.auger.ai
```

To get current login information:

```sh
auger auth whoami
```

To logout:

```sh
auger auth logout
```

## Organizations

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:
```sh
auger orgs
```

To create your own organization go to https://auger.ai:

## Experiments
### 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 https://docs.auger.ai/docs/experiments/evaluation-options

auger_experiment.yml fields:

```yml
evaluation_options:
# Path to file with data. May be URL or path in project files folder
data_path: files/iris_data_sample.csv

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

# Target feature to build ML model for
targetFeature: class

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

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

# List of features of datetime type
datetimeFeatures: []

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

# If target has two unique values, set it to true
binaryClassification: 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
crossValidationFolds: 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 trial can run before it is stopped.
max_eval_time_mins: 1

# 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 : https://docs.auger.ai/docs/machine-learning/ensembles
use_ensemble: true

# OPTIONAL evaluation parameters
#data_extension: ".csv"
#data_compression: gzip

#optimizers_names: []
#splitOptions: {}
#oversampling: {}
#search_space:
#use_ensemble: true
#preprocessors: {}

# OPTIONAL parameters

#Specify organization name, if you have more then one organization
#organization: <organization name>

#You may use one project/cluster for different experiments
#project: <project name>

# Cluster settings
cluster:
# Number of nodes to run on cluster
#worker_count : 2

# To list available types call: `auger instances`
# instance_type: c5.large

# 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:
```sh
auger experiments run
```

Display leaderboard from last run:
```sh
auger experiments leaderboard
```

To export model locally:
```sh
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:
```sh
auger experiments deploy_model -t <trial id>
```

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

To call predict using deployed model:
```sh
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

Display information about experiment:
```sh
auger experiments show
```

## Clusters

To display cluster information.
```sh
auger clusters show <cluster id>
```

To terminate cluster. It will free all paid AWS resources related with this cluster.
```sh
auger clusters delete <cluster id>
```

## Projects

To display project information.
```sh
auger projects show -p <project name>
```

To open project in web browser:
```sh
auger projects open_project -p <project name>
```

To download file from project cluster:
```sh
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:
```sh
auger project logs <project_id>
```

To Create project:
```sh
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 delete project:
```sh
auger projects delete -p <project name>
```