piperider-python-sdk 0.0.3

PipeRider Python SDK

PipeRider SDK For Python

The PipeRiper SDK for Python enables you to access and manipulate project resources in your PipeRider account.

Configuration

To use the SDK you will need to obtain an API key. This can be done from the User Settings page of your account.

https://app.piperider.io/settings

Configure your API key using one of the following methods.

Store API key in an environment variable

Users can set the API key as an environment variable.

export PIPERIDER_API_KEY=<user_api_key>

Configure API key using the set_api_key function

Set your API key using the set_api_key function.

from PipeRider.api.http_client import set_api_key

set_api_key('api-key')

Installation

pip install piperider-python-sdk

Usage

Import the PipeRider module.

import PipeRider

Create a project.

project = PipeRider.create_project('my-project')

Use an existing project.

project = PipeRider.project_by_uid('6f9f6eb9-8657-4761-9e32-116f1b8cfb82')

Create a run and save the settings for a machine learning project.

with project.runs.create(name='Awesome Run') as run:
    run.config = {
        'learning_rate': 0.02,
        'architecture': 'CNN',
        'dataset': 'TKNV-users',
    }

    run.params = {
        'batch_size': 64,
        'epoch': 100,
        'learning_rate': 0.005
    }

Add a dataset. This will enable you to more easily trace your model lineage.

run.add_dataset('golden-dataset')

Comments can also be added to the timeline.

project.comment('it is a good idea.')

If the run achieves the desired performance it can be registered as a model by declaring it a win.

run.win()