CLI¶
The AnalyticOps Accelerator Command Line Interface is a tool that allows to interact with the AnalyticOps platform form a terminal.
Overview of CLI¶
The cli can be used to perform a number of interactions and guides the user to perform those actions.
> aoa -h
usage: aoa [-h] [--debug] [--version] {list,add,retire,run,init,clone,configure,message,connection} ...
AOA CLI
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
--version Display the version of this tool
actions:
valid actions
{list,add,retire,run,init,clone,configure,message,connection}
list List projects, models, local models or datasets
add Add model to working dir
retire Retire active deployments for all users/projects
run Train and Evaluate model locally
init Initialize model directory with basic structure
clone Clone Project Repository
configure Configure AOA client
message Send a message to AOA message broker
connection Manage local connections
aoa configure¶
If not already performed the configuration step, start by configuring the client for your user and your environment. This allows you to set the AOA API endpoint and the authentication information for the client (basic or kerberos). The cli stores this configuration information in the users home directory under ~/.aoa/config.yaml. Note if you are using Kerberos, you will need to install an additional library (see the Kerberos section).
You can also use the configure command with the --repo argument to set repository level configuration such as the projectId of the repo. This only needs to be set once and can be committed and pushed to the repository. Note that this configuration is stored in the .aoa/config.yaml of the repository directory!
> aoa configure -h
usage: aoa configure [-h] [--debug] [--repo]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
--repo Configure the repo only
To configure for first time, log in the AnalyticOps browser interface and get to Session Details at the top right.
From there you can copy the OAuth CLI Environment Variabble needed for aoa to connect to the AnalyticOps Service. You just need to paste and execute the copied code in the Command line where aoa is installed.
aoa clone¶
The clone command provides a convenient way to perform a git clone of the repository associated with a given project. The command can be run interactively and will allow you to select the project you wish to clone. Note that by default it clones to the current working directory so you either need to make sure you create an empty folder and run it from within there or else provide the --path argument.
> aoa clone -h
usage: aoa clone [-h] [--debug] [-id PROJECT_ID] [-p PATH]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-id PROJECT_ID, --project-id PROJECT_ID
Id of Project to clone
-p PATH, --path PATH Path to clone repository to
aoa init¶
When you create a git repository, its empty by default. The init command allows you to initialize the repository with the structure required by the AOA. It also adds a default README.md and HOWTO.md.
> aoa init -h
usage: aoa init [-h] [--debug]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
aoa list¶
Allows to list the aoa resources. In the cases of listing models (pushed / committed) and datasets, it will prompt the user to select a project prior showing the results. In the case of local models, it lists both committed and non-committed models.
> aoa list -h
usage: aoa list [-h] [--debug] [-p] [-m] [-lm] [-t] [-d] [-c]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-p, --projects List projects
-m, --models List registered models (committed / pushed)
-lm, --local-models List local models. Includes registered and non-
registered (non-committed / non-pushed)
-t, --templates List dataset templates
-d, --datasets List datasets
-c, --connections List local connections
All results are shown in the format
[index] (id of the resource) name
for example:
List of models for project Demo:
--------------------------------
[0] (03c9a01f-bd46-4e7c-9a60-4282039094e6) Diabetes Prediction
[1] (74eca506-e967-48f1-92ad-fb217b07e181) IMDB Sentiment Analysis
aoa add¶
Adding a new model to a given repository requires a number of steps. You need to create the folder structure, configuration files, generate a modelId, etc. The add command is intended to simplify this for the user. It will interactively prompt you for the model name, language, description and even allow you to use a model template to get you started. This can really help reduce the boilerplate required and ensure you get started developing quicker while maintaining a standard repository structure.
> aoa add
model name: my new model
model description: to show adding new models
These languages are supported: R, python, sql
model language: python
templates available for python: empty, sklearn
template type (leave blank for the default one):
aoa run¶
The cli can be used to validate the model training and evaluation logic locally before committing to git. This simplifies the development lifecycle and allows you to test and validate many options. It also enables you to avoid creating the dataset definitions in the AOA UI until you are ready and have a finalised version.
> aoa run -h
usage: aoa run [-h] [--debug] [-id MODEL_ID] [-m MODE] [-d DATASET_ID]
[-t DATASET_TEMPLATE_ID] [-ld LOCAL_DATASET] [-lt LOCAL_DATASET_TEMPLATE]
[-c CONNECTION]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-id MODEL_ID, --model-id MODEL_ID
Id of model
-m MODE, --mode MODE Mode (train or evaluate)
-d DATASET_ID, --dataset-id DATASET_ID
Remote datasetId
-t DATASET_TEMPLATE_ID, --dataset-template-id DATASET_TEMPLATE_ID
Remote datasetTemplateId
-ld LOCAL_DATASET, --local-dataset LOCAL_DATASET
Path to local dataset metadata file
-lt LOCAL_DATASET_TEMPLATE, --local-dataset-template LOCAL_DATASET_TEMPLATE
Path to local dataset template metadata file
-c CONNECTION, --connection CONNECTION
Local connection id
You can run all of this as a single command or interactively by selecting some optional arguments, or none of them.
For example, if you want to run the cli interactively you just select aoa run but if you wanted to run it non interactively to train a given model with a given datasetId you would expect
> aoa run -id <modelId> -m <mode> -d <datasetId>
And if you wanted to select the model interactively but use a specific local dataset definition, you would execute
> aoa run -ld /path/to/my_test_dataset.json
aoa retire¶
The cli can be used to retire deployments and to archive projects. Deployments can be retired based on the project id, filtered by project name or all that are active. In addition, those projects can also be archived.
> aoa retire -h
usage: aoa retire [-h] [--debug] [-A] [-a] [-r REGEX] [-p PROJECT]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-A, --all Retire all
-a, --archive Retire and archive project
-r REGEX, --regex REGEX
Filter project name by regex
-p PROJECT, --project PROJECT
Retire from specified project
The archive (-a) and regex (-r) arguments can be used in combination with project (-p) and retire all (-A) parameters. Though, retire all (-A) takes precedence over project (-p).
aoa connection¶
As for security reasons, the connection credentials stored in the core service can not be accessed locally through the client. In order to solve that, users may list, create and remove local connection credentials to be used with the run command.
> aoa connection -h
usage: aoa connection [-h] {list,add,remove,export} ...
optional arguments:
-h, --help show this help message and exit
actions:
valid actions
{list,add,remove}
list List all local connections
add Add a local connection
remove Remove a local connection
export Export a local connection to be used as a shell script
aoa connection list¶
List all local connections.
> aoa connection list -h
usage: aoa connection list [-h] [--debug]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
aoa connection add¶
Add a local connection.
> aoa connection add -h
usage: aoa connection add [-h] [--debug] [-n NAME] [-H HOST] [-u USERNAME] [-p PASSWORD]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-n NAME, --name NAME Connection name
-H HOST, --host HOST Connection host
-u USERNAME, --username USERNAME
Connection username
-p PASSWORD, --password PASSWORD
Connection password
aoa connection remove¶
Remove a local connection.
> aoa connection remove -h
usage: aoa connection remove [-h] [--debug] [-c CONNECTION]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-c CONNECTION, --connection CONNECTION
Local connection id
aoa connection export¶
Export a local connection to be used as a shell script.
> aoa connection export -h
usage: aoa connection export [-h] [--debug] [-c CONNECTION]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-c CONNECTION, --connection CONNECTION
Local connection id
Kerberos¶
If you are using kerberos, you will need to install some libraries separately. We do not include this as a default dependency as it has a large dependency stack and is not trivial to install. It can be annoying for non Kerberos installations, so we leave it to the specific environment. Note that on OSX, you should use version 1.1.14 of pykerberos. For your linux env, it may vary.
First install the libraries with:
sudo apt update && sudo apt install -y krb5-multidev
Then install or reinstall the package with the option kerberos:
pip install --force-reinstall --upgrade aoa[kerberos]
NOTE: some other libraries may be required in the host OS in order for kerberos to be fully functional.