@haidark/experiments_ext
v1.0.0
Published
Define cell execution orders for different experiments in a single notebook
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JupyterLab-Experiments
Adds "experiments" to JupyterLab notebooks that allow a user to share experimental design and results easily in a single notebook.
Experiments are custom cell execution orders defined by the user that show exactly how a result is generated in the notebook. The experiments are saved as part of the meta-data of the notebook and can be easily shared.
This extension adds a small set of UI elements to JupyterLab:
- A dropdown menu showing the available experiments
- Cell index labels
Although experiments are usually added after the code in a notebook is written, adding/removing/moving cells are smartly handled and the existing experiments are automatically updated to reflect changes to the notebook.
Basic Usage
- Click on the "Select Experiment" button and use the dropdown menu to select an experiment to run or add an experiment.
- Adding an experiment requires a label ("L2 Regularization") and a cell execution order ("4-5-6-9-12-13-14-8"). The cell execution order must be a list of valid cell indices separated by the dash (-) character.
- Click the "fast forward" icon to run the selected experiment.
Handling Changes
- Once a set of experiments are defined, changes to the order of the cells (add/remove/move) in the notebook will automatically update all experiments so that they point to the same cells.
- If all the cells that are part of an experiment are removed, the experiment will also be removed.
- To edit an existing experiment, enter the experiment label in the input field and assign it a new cell execution order.
Prerequisites
- JupyterLab
Installation
jupyter labextension install @haidark/experiments_ext
Development
For a development install (requires npm version 4 or later), do the following in the repository directory:
npm install
npm run build
jupyter labextension link .
To rebuild the package and the JupyterLab app:
npm run build
jupyter lab build
Authors
This extension was developed at the Rensselaer Polytechnic Institute Data Science Research Center by Haidar Khan Ph.D and Sadia Khan.