trainml 0.1.3

trainML client SDK and command line utilities

trainML Python SDK and Command Line Tools

Provides programmatic access to trainML platform.

Installation

Python 3.8 or above is required.

pip install trainml

Authentication

Prerequisites

You must have a valid trainML account. On the account settings page click the Create button in the API Keys section. This will automatically download a credentials.json file. This file can only be generated once per API key. Treat this file as a password, as anyone with access to your API key will have the ability to create and control resources in your trainML account. You can deactivate any API key by clicking the Remove button.

Creating resources on the trainML platform requires a non-zero credit balance. To purchase credits or sign-up for automatic credit top-ups, visit the billing page.

Methods

Credentials File

The easiest way to authenticate is to place the credentials file downloaded into the .trainml folder of your home directory and ensure only you have access to it. From the directory that the credentials.json file was downloaded, run the following command:

mkdir -p ~/.trainml
mv credentials.json ~/.trainml/credentials.json
chmod 600 ~/.trainml/credentials.json

Environment Variables

You can also use environment variables TRAINML_USER and TRAINML_KEY and set them to their respective values from the credentials.json file.

export TRAINML_USER=<'user' field from credentials.json>
export TRAINML_KEY=<'key' field from credentials.json>
python create_job.py

Environment variables will override any credentials stored in ~/.trainml/credentials.json

Runtime Variables

API credentials can also be passed directly to the TrainML object constructor at runtime.

import trainml
trainml = trainml.TrainML(user="user field from credentials.json",key="key field from credentials.json>")
await trainml.jobs.create(...)

Passing credentials to the TrainML constructor will override all other methods for setting credentials.