0.1.14

ailab-lite

AI Lab is a cloud-based enterprise software application built by Fathom Solutions that enables building data workflows, managing experiments and deploying models. The ailab-lite library enables us to harness the power of the AI Lab product in a simple Jupyter Notebook setting. By adding this extension, the user can exploit the GUI node editor to create and test complicated data pipelines. Below is a link to a visualisation of how to generate an example workflow in Jupyter Notebook using the ailab-lite extension:

https://github.com/fathom-io/ailab-lite/blob/master/graphics/workflow.gif

The main idea of this product resolves around using the UI to construct a graph, which is later used to generate a data processing pipeline. The following image shows an example visualisation of a particular graph in the AI Lab product.

Next , there is the same graph generated in the Jupyter Notebook extension.

Each node represents a certain data transformation, model or validation process. Thinking of all these elements as one large graph lets us encapsulate our whole data processing and prediction pipeline into one object. Communication between each node is assured thanks to using a similar API to the ones used in libraries:

• scikit-learn https://scikit-learn.org/stable/developers/develop.html
• MLlib https://spark.apache.org/docs/latest/ml-pipeline.html

Buttons

Buttons in the editor are revealed after a dataset and at least one component are added to the graph. Before running an experiment, validation must be performed. After clicking the validation button and reveiving positive feedback, the run option appears. When running an experiment the results are printed below the GUI and additional files are saved in the same folder as the opened notebook.

Installation

To install use pip:

$ pip install ailab_lite

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Instructions

Jupyter Notebook/Lab

Import node editor widget:

from ailab_lite import NodeEditorWidget

Import pandas:

import pandas as pd

Decalare dataset:

example = pd.read_csv("example.csv")

Run widget:

NodeEditorWidget(env=globals())

We initialize it with globals() so all previously defined datasets are available in node editor.

We can pass workflow definition directly when initializing widget:

NodeEditorWidget(env=globals(), workflow_definition="definition")

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Running example

The examples directory contain example of widget usage. It contain predefined workflow. To run it, simply cd to the directory and run jupyter notebook or jupyter lab command.

(In order to run widget in the example first time it is required to run all cells. Other way the widget won't render)

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Resolving issues

Sometimes the extension is not enabled by default after installing from pip install ailab-lite. It manifests itself by returing 404 status for ailab-lite.js file The solution for this is simple and requires running one command: jupyter nbextension enable ailab-lite/extension After that reload the page and restart the kernel and the widget will work.

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Development

For a development installation (requires Node.js and Yarn version 1),

$ git clone https://github.com/fathoms-io/ailab-lite.git
$ cd ailab-lite
$ pip install -e .
$ jupyter nbextension install --py --symlink --overwrite --sys-prefix ailab_lite
$ jupyter nbextension enable --py --sys-prefix ailab_lite

When actively developing your extension for JupyterLab, run the command:

$ jupyter labextension develop --overwrite ailab_lite

Then you need to rebuild the JS when you make a code change:

$ cd js
$ yarn run build

You then need to refresh the JupyterLab page when your javascript changes.