Python Jupyter kernel using Poetry for reproducible notebooks

Overview

Poetry Kernel

Use per-directory Poetry environments to run Jupyter kernels. No need to install a Jupyter kernel per Python virtual environment!

The idea behind this project is to allow you to capture the exact state of your environment. This means you can email your work to your peers, and they'll have exactly the same set of packages that you do! Reproducibility!

Why not virtual environments (venvs)?

Virtual environments were (and are) an important advancement to Python's package management story, but they have a few shortcomings:

  • They are not great for reproducibility. Usually, you'll create a new virtual environment using a requirements.txt which includes all the direct dependencies (numpy, pandas, etc.), but not transient dependencies (pandas depends on pytz for timezone support, for example). And usually, even the direct dependencies are specified only as minimum (or semver) ranges (e.g., numpy>=1.21) which can make it hard or impossible to accurately recreate the venv later.
  • With Jupyter, they usually require that the kernels be installed globally. This means you'll need need to have a separate kernelspec for every venv you want to use with Jupyter.

Poetry uses venvs transparently under the hood by constructing them from the pyproject.toml and poetry.lock files. The poetry.lock file records the exact state of dependencies (and transient dependencies) and can be used to more accurately reproduce the environment.

Additionally, Poetry Kernel means you only have to install one kernelspec. It then uses the pyproject.toml file from the directory of the notebook (or any parent directory) to choose which environment to run the notebook in.

Shameless plug

The reason we created this package was to make sure that the code environments created for running student code on Pathbird exactly match your development environment. Interested in developing interactive, engaging, inquiry-based lessons for your students? Check out Pathbird for more information!

Usage

  1. Install Poetry if not yet installed.
  2. Install this package:
    # NOTE: Do **NOT** install this package in your Poetry project, it should be
    # installed at the system or user level.
    pip3 install --user poetry-kernel
  3. Initialize a Poetry project (only required if you do not have an existing Poetry project ready to use):
    poetry init -n
  4. IMPORTANT: Add ipykernel to your project's dependencies:
    # In the directory of your Poetry project
    poetry add ipykernel
  5. Start a "Poetry" Jupyter kernel and see it in action! Jupyter launcher screenshot

Troubleshooting

Kernel isn't starting ("No Kernel" message)

Pro-tip: Check the output of the terminal window where you launched Jupyter. It will usually explain why the kernel is failing to start.

  1. Make sure that you are launching a notebook in a directory/folder that contains a Poetry project (pyproject.toml and poetry.lock files). You can turn a directory into a Poetry project by running:
poetry init -n
  1. Make sure that you've installed ipykernel into your project:
poetry add ipykernel
  1. Make sure the Poetry project is installed! This is especially important for projects that you have downloaded from others (warning: installing a Poetry project could run arbitrary code on your computer, make sure you trust your download first!):

    poetry install
  2. Still can't figure it out? Open an issue!

A package I added won't import properly

If you added the package after starting the kernel, you might need to restart the kernel for it to see the new package.

FAQ

See FAQ.md.

Comments
  • Windows Does not have SIGKILL

    Windows Does not have SIGKILL

    I believe the following line needs more attention to be compatible with Windows considering windows does not have SIGKILL: https://github.com/pathbird/poetry-kernel/blob/main/poetry_kernel/main.py#L39

    bug good first issue prs accepted 
    opened by amirhessam88 4
  • FileNotFoundError: [Errno 2] No such file or directory: 'poetry'.

    FileNotFoundError: [Errno 2] No such file or directory: 'poetry'.

    Hey, cool project!

    I got the following error, not sure how informative it is for you. Happy to dig into logs etc. I'm using a Gitpod workspace with a public github repo too, so could potentially offer a completely reproducible clean environment.

    Failed to start the Kernel. 
    FileNotFoundError: [Errno 2] No such file or directory: 'poetry'. 
    
    opened by andrewcstewart 3
  • Check in parent_dirs for pyproject.toml

    Check in parent_dirs for pyproject.toml

    This now loops through parent directories to check for pyproject.toml to allow for kernel execution when running notebooks in sub directories. I used the same code from poetry itself to ensure compatibility

    opened by nick-gorse 3
  • modified forward_signals to be compatible with windows

    modified forward_signals to be compatible with windows

    in regard to issue #3

    removed signal.SIGTERM from forward_signals, as well as two others as they could not be used by signal.signal (ValueError: invalid signal value)

    from https://docs.python.org/3/library/signal.html signal.CTRL_C_EVENT The signal corresponding to the Ctrl+C keystroke event. This signal can only be used with os.kill()

    same for signal.CTRL_BREAK_EVENT

    opened by gpfv 0
  • Check in parent_dirs for pyproject.toml

    Check in parent_dirs for pyproject.toml

    This now loops through parent directories to check for pyproject.toml to allow for kernel execution when running notebooks in sub directories. I used the same code as used poetry itself to ensure compatibility

    opened by nick-gorse 0
  • poetry-kernel uses Jupyter env instead of notebook env

    poetry-kernel uses Jupyter env instead of notebook env

    If a poetry pyproject.toml is used to create the environment for the Jupyter lab (adding poetry-kernel as a dependency), then no matter in which working directory the jupyter server is started, any notebook that is opened with the "poetry" kernel will have the Jupyter environment, not the environment of the notebook's project.

    Minimal example: https://github.com/drakesiardxy/poetry-kernel-bug To replicate: Create the jupyer-base env and the kernel_a env separately, then start the jupyter server with the first environment and attempt to run kernel_a.ipynb using the "Poetry" kernel. pandas will be missing, because the notebook will have been launched with the environment of the server, not the environment belonging to the notebook's project.

    opened by drakesiardxy 0
  • Is it possible to make poetry-kernel work with JupyterHub?

    Is it possible to make poetry-kernel work with JupyterHub?

    Hi!

    We have a multi-user JupyterHub instance and we would like to use your library. So far we have only been able to see the poetry-kernel button in the kernel selection screen by installing poetry kernel at the user level (if we install poetry kernel from the user that launches JupyterHub, the root user, the button does not show). But the problem that we have is that when we press the poetry button and create a new notebook in a folder with a poetry project (and ipykernel installed), the kernel is never connected so no code can be executed. The generated logs are the following:

    Apr 26 15:30:26 labs-ubuntu-20-04 python3[446728]: [I 2022-04-26 15:30:26.298 SingleUserLabApp restarter:66] AsyncIOLoopKernelRestarter: restarting kernel (2/5), new random ports
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]: Traceback (most recent call last):
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:   File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:     return _run_code(code, main_globals, None,
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:   File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:     exec(code, run_globals)
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:   File "/home/marta/.local/lib/python3.8/site-packages/poetry_kernel/__main__.py", line 68, in <module>
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:     main()
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:   File "/home/marta/.local/lib/python3.8/site-packages/poetry_kernel/__main__.py", line 37, in main
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:     proc = subprocess.Popen(cmd)
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:   File "/usr/lib/python3.8/subprocess.py", line 858, in __init__
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:     self._execute_child(args, executable, preexec_fn, close_fds,
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:   File "/usr/lib/python3.8/subprocess.py", line 1704, in _execute_child
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]:     raise child_exception_type(errno_num, err_msg, err_filename)
    Apr 26 15:30:26 labs-ubuntu-20-04 python3[1028846]: FileNotFoundError: [Errno 2] No such file or directory: 'poetry'
    

    This looks like poetry is not being found by the library although it is indeed installed both at the root and user level. Is there a way to solve this? Or is this case out of the scope of the library for now?

    opened by MsLimon 2
Releases(v0.1.2)
  • v0.1.2(Mar 30, 2022)

    What's Changed

    • modified forward_signals to be compatible with windows by @gpfv in https://github.com/pathbird/poetry-kernel/pull/4

    New Contributors

    • @gpfv made their first contribution in https://github.com/pathbird/poetry-kernel/pull/4

    Full Changelog: https://github.com/pathbird/poetry-kernel/compare/v0.1.1...v0.1.2

    Source code(tar.gz)
    Source code(zip)
Owner
Pathbird
Pathbird is a platform for instructors to build interactive, engaging, inquiry-based lessons for computational courses.
Pathbird
E-RAFT: Dense Optical Flow from Event Cameras

E-RAFT: Dense Optical Flow from Event Cameras This is the code for the paper E-RAFT: Dense Optical Flow from Event Cameras by Mathias Gehrig, Mario Mi

Robotics and Perception Group 71 Dec 12, 2022
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization Official PyTorch implementation for our URST (Ultra-Resolution Sty

czczup 148 Dec 27, 2022
Codes for paper "KNAS: Green Neural Architecture Search"

KNAS Codes for paper "KNAS: Green Neural Architecture Search" KNAS is a green (energy-efficient) Neural Architecture Search (NAS) approach. It contain

90 Dec 22, 2022
A public available dataset for road boundary detection in aerial images

Topo-boundary This is the official github repo of paper Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images

Zhenhua Xu 79 Jan 04, 2023
A Repository of Community-Driven Natural Instructions

A Repository of Community-Driven Natural Instructions TLDR; this repository maintains a community effort to create a large collection of tasks and the

AI2 244 Jan 04, 2023
Unifying Global-Local Representations in Salient Object Detection with Transformer

GLSTR (Global-Local Saliency Transformer) This is the official implementation of paper "Unifying Global-Local Representations in Salient Object Detect

11 Aug 24, 2022
GUI for a Vocal Remover that uses Deep Neural Networks.

GUI for a Vocal Remover that uses Deep Neural Networks.

4.4k Jan 07, 2023
DA2Lite is an automated model compression toolkit for PyTorch.

DA2Lite (Deep Architecture to Lite) is a toolkit to compress and accelerate deep network models. ⭐ Star us on GitHub — it helps!! Frameworks & Librari

Sinhan Kang 7 Mar 22, 2022
Pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments

Cascaded-FCN This repository contains the pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments the liver and its lesions out of

300 Nov 22, 2022
City-seeds - A random generator of cultural characteristics intended to spark ideas and help draw threads

City Seeds This is a random generator of cultural characteristics intended to sp

Aydin O'Leary 2 Mar 12, 2022
Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021)

Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021) In this repository we provide PyTorch implementations for GeMCL; a

4 Apr 15, 2022
Epidemiology analysis package

zEpid zEpid is an epidemiology analysis package, providing easy to use tools for epidemiologists coding in Python 3.5+. The purpose of this library is

Paul Zivich 111 Jan 08, 2023
Understanding Convolutional Neural Networks from Theoretical Perspective via Volterra Convolution

nnvolterra Run Code Compile first: make compile Run all codes: make all Test xconv: make npxconv_test MNIST dataset needs to be downloaded, converted

1 May 24, 2022
Raptor-Multi-Tool - Raptor Multi Tool With Python

Promises 🔥 20 Stars and I'll fix every error that there is 50 Stars and we will

Aran 44 Jan 04, 2023
Official implementation of "Implicit Neural Representations with Periodic Activation Functions"

Implicit Neural Representations with Periodic Activation Functions Project Page | Paper | Data Vincent Sitzmann*, Julien N. P. Martel*, Alexander W. B

Vincent Sitzmann 1.4k Jan 06, 2023
Code accompanying the paper "Knowledge Base Completion Meets Transfer Learning"

Knowledge Base Completion Meets Transfer Learning This code accompanies the paper Knowledge Base Completion Meets Transfer Learning published at EMNLP

14 Nov 27, 2022
A Pytorch Implementation of ClariNet

ClariNet A Pytorch Implementation of ClariNet (Mel Spectrogram -- Waveform) Requirements PyTorch 0.4.1 & python 3.6 & Librosa Examples Step 1. Downlo

Sungwon Kim 286 Sep 15, 2022
Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Zhengxia Zou 1.5k Dec 28, 2022
Weakly-supervised object detection.

Wetectron Wetectron is a software system that implements state-of-the-art weakly-supervised object detection algorithms. Project CVPR'20, ECCV'20 | Pa

NVIDIA Research Projects 342 Jan 05, 2023
Official PyTorch implementation of the paper "Graph-based Generative Face Anonymisation with Pose Preservation" in ICIAP 2021

Contents AnonyGAN Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Evaluation Acknowledgments Citat

Nicola Dall'Asen 10 May 24, 2022