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escrowai_jupyter

Github Actions Status An extension to encrypt and upload the working directory to EscrowAI

Requirements

  • JupyterLab >= 4.0.0 or Jupyter Notebook >= 6.0.0

Install

To install the extension, execute:

pip install escrowai_jupyter
jupyter server extension enable escrowai_jupyter

Uninstall

To remove the extension, execute:

pip uninstall escrowai_jupyter

Usage

To use the package, the following prerequisites must be met:

  1. Working directory must be the algorithm to be uploaded to EscrowAI.
cd {algorithm_directory}
  1. Variables CONTENT_ENCRYPTION_KEY and PROJECT_PRIVATE_KEY must be present in the environment and base64 encoded. The CEK must match an uploaded WCEK on the given project, and the Project Private Key must be an RSA-4096 private key matching a Project Public Access Key on the given project.

  2. A config.yaml file must be present in the working directory, with the following format:

BEEKEEPER_USERNAME: username
BEEKEEPER_PROJECT_ID: project_id
BEEKEEPER_ORGANIZATION_ID: organization_id
BEEKEEPER_ENVIRONMENT: dev | tst | stg | prod (default = prod)
ALGORITHM_TYPE: validation | training (default = validation)
ALGORITHM_NAME: name (default = Jupyter Algorithm)
ALGORITHM_DESCRIPTION: description (default = null)
VERSION_DESCRIPTION: version_description (default = null)
ENTRYPOINT: algorithm_executable (default = run.py)
EXCLUDED_FILES: (optional)
 - ignored_file_1
 - ignored_file_2
 - ...

Once prerequisites are met, open the JupyterLab command pallete and select "Upload to EscrowAI". The algorithm directory will then be automatically encrypted and uploaded to EscrowAI.

Contributing

Development install

Note: You will need NodeJS to build the extension package.

The jlpm command is JupyterLab's pinned version of yarn that is installed with JupyterLab. You may use yarn or npm in lieu of jlpm below.

# Clone the repo to your local environment
# Change directory to the escrowai_jupyter directory
# Install package in development mode
pip install -e "."
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Rebuild extension Typescript source after making changes
jlpm build

You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.

# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter lab

With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).

By default, the jlpm build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:

jupyter lab build --minimize=False

Development uninstall

pip uninstall escrowai_jupyter

In development mode, you will also need to remove the symlink created by jupyter labextension develop command. To find its location, you can run jupyter labextension list to figure out where the labextensions folder is located. Then you can remove the symlink named escrowai-jupyter within that folder.

Packaging the extension

See RELEASE

Metadata

Release files for escrowai-jupyter 1.0.5

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for escrowai-jupyter 1.0.5
File Interpreter ABI Platform
escrowai_jupyter-1.0.5-py3-none-any.whl Python 3 none any Details

Release files / escrowai_jupyter-1.0.5-py3-none-any.whl

Download URL escrowai_jupyter-1.0.5-py3-none-any.whl
Size 37.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
8f94cff7583529410f1c57308e6419b2407b3c53b06e2098176c9950a9daafe4
BLAKE2b-256 checksum
How to use checksums
d20b0dca279afc79b1909dd4d4b37fca25359597f1eded31fb36903103f0c6e7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.10.12

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