Metadata-Version: 2.1
Name: piperider-python-sdk
Version: 0.0.2
Summary: PipeRider Python SDK
Home-page: https://github.com/InfuseAI/piperider-python-sdk
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/InfuseAI/piperider-python-sdk/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: requests
Requires-Dist: types-requests
Provides-Extra: dev
Requires-Dist: pytest (>=4.6) ; extra == 'dev'
Requires-Dist: pytest-flake8 ; extra == 'dev'
Requires-Dist: flake8 (==3.9.2) ; extra == 'dev'
Requires-Dist: pytest-cov ; extra == 'dev'
Requires-Dist: twine ; extra == 'dev'
Requires-Dist: boto3 ; extra == 'dev'

# 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](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.

```bash
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.

```python
from PipeRider.api.http_client import set_api_key

set_api_key('api-key')

```

## Installation

```bash
pip install piperider-python-sdk
```

## Usage

Import the PipeRider module.

```python
import PipeRider
```

Create a project.

```python
project = PipeRider.project('my-pipe-rider')
```

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

```python
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.

```python
run.add_dataset('golden-dataset')
```

Comments can also be added to the timeline.

```python
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`.

```python
run.win()
```

