Python client for Teradata AnalyticOps Accelerator (AOA)
Project description
Teradata AnalyticOps Client
Python client for Teradata AnalyticOps Accelerator. It is composed of both an client API implementation to access the AOA Core APIs and a command line interface (cli) tool which can be used for many common tasks.
Installation
You can install via pip. The minimum python version required is 3.5+
pip install aoa
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] {add,run,init,clone,configure} ...
AOA CLI
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
actions:
valid actions
{add,run,init,clone,configure}
add Add model
run Train and Evaluate model
init Initialize model directory with basic structure
clone Clone Project Repository
configure Configure AOA client
To see the details or help for a specific action, just select the action and add -h
> aoa run -h
usage: aoa run [-h] [-id MODEL_ID] [-m MODE] [-d DATA]
optional arguments:
-h, --help show this help message and exit
-id MODEL_ID, --model_id MODEL_ID
Which model_id to use (prompted to select if not provided)
-m MODE, --mode MODE The model (train or evaluate) (prompted to select if not provided)
-d DATA, --data DATA Json file containing data configuration (prompted to select if not provided)
Client API
We have a client implementation for all of the entities exposed in the AOA API. We provide the RESTful and RPC client usage for this. We'll show an example of the Dataset API here but the same applies for all.
To configure the client you simply run the cli which will guide you through the process and create the configuration file .aoa/config.yaml
in your home directly. Note you can override this configuration at runtime via environment variables or constructor arguments.
aoa configure
Create the client. Note there are a number of options to specify the client information and credentials. The example here is where you specify everything in the constructor.
from aoa import AoaClient
from aoa import DatasetApi
client = AoaClient()
client.set_project_id("23e1df4b-b630-47a1-ab80-7ad5385fcd8d")
dataset_api = DatasetApi(aoa_client=client)
Now, find all datasets or a specific dataset
import pprint
datasets = dataset_api.find_all()
pprint.pprint(datasets)
dataset = dataset_api.find_by_id("11e1df4b-b630-47a1-ab80-7ad5385fcd8c")
pprint.pprint(dataset)
Add a dataset
dataset_definition = {
"name": "my dataset",
"description": "adding sample dataset",
"metadata": {
"url": "http://nrvis.com/data/mldata/pima-indians-diabetes.csv",
"test_split": "0.2"
}
}
dataset = dataset_api.save(dataset=dataset_definition)
pprint.pprint(dataset)
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