lineapy
nbdev
| lineapy | nbdev | |
|---|---|---|
| 7 | 45 | |
| 656 | 4,744 | |
| 0.5% | 0.6% | |
| 2.0 | 6.5 | |
| 9 months ago | 7 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
lineapy
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Rant: Jupyter notebooks are trash.
There are a few projects that can help close this gap between notebook prototype -> production. One of them is ipyflow (https://github.com/ipyflow/ipyflow), another is lineapy (https://github.com/linealabs/lineapy).
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The hand-picked selection of the best Python libraries and tools of 2022
LineaPy — notebooks in production
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Model artifacts mess and how to deal with it?
If you are mainly using python, there is a library called lineapy that is pretty much trying to solve all the challenges you just listed.
- lineapy: Data engineering, simplified. LineaPy creates a frictionless path for taking your data science artifact from development to production.
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Overwhelmed about consolidating code
Hi, I'm a contributor of LineaPy. We're building a tool that solves this problem. Our goal is to reduce the friction between developing Jupyter notebooks(or python scripts) and production codes.
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When to use Jupyter Notebooks vs. “Organized” Python Code?
I think you might want to give LineaPy a try! It is a tool trying to bridge the gap between Jupyter notebooks and production pipelines. One of the feature it provides is extracting codes only related to objects(you've selected) from your notebook into a python script and I think it is helpful for anyone who is using both Jupyter notebooks and python scripts.
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Introducing LineaPy!
GitHub
nbdev
- The Jupyter+Git problem is now solved
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What is literate programming used for?
One example I've seen is ML/DL folks using jupyter notebooks to develop DL libraries in jupyter notebooks, see https://github.com/fastai/nbdev
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GitHub Accelerator: our first cohort and what's next
- https://github.com/fastai/nbdev: Increase developer productivity by 10x with a new exploratory programming workflow.
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Startups are in first batch of GitHub OS Accelerator
9. Nbdev: Boost developer productivity with an exploratory programming workflow - https://nbdev.fast.ai/
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Start learning python for a Statistician with SAS experience and little R experience
See if you like nbdev way of working with data through python and jupyter. nbdev is an optional part that will create python packages from jupyter notebooks. Also even the simple tutorials are opinionated and will guide you to unit test your code and write CICD pipelines.
- FastKafka - free open source python lib for building Kafka-based services
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isn't this just too much for a take home assignment?
You probably don’t have time for this for the purposes of your task, but I will also throw in the recommendation of nbdev especially if you’re a Python person. I haven’t had a project to use it on yet, but I’ve gone through the docs and the walkthrough and it seems like a great framework for starting potential projects with all the infrastructure needed for if/when they eventually get big and need all the packaging and stuff
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Any experience dealing with a non-technical manager?
nbdev: jupyter notebooks -> python package
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Resources to bridge the gap between jupyter notebooks and regular python development
Take a look at https://github.com/fastai/nbdev - haven't used it but supposedly the whole if fast.ai library was written that way. It sounds like a natural direction in your scenario - allowing your to keep working in a familiar environment and still producing production ready code (will, at least in paper 😅)
- Rant: Jupyter notebooks are trash.
What are some alternatives?
ruff - An extremely fast Python linter and code formatter, written in Rust.
papermill - 📚 Parameterize, execute, and analyze notebooks
lingua-py - The most accurate natural language detection library for Python, suitable for short text and mixed-language text
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
dbt - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. [Moved to: https://github.com/dbt-labs/dbt-core]
python-benedict - :blue_book: dict subclass with keylist/keypath support, built-in I/O operations (base64, csv, html, ini, json, pickle, plist, query-string, toml, xls, xml, yaml), s3 support and many utilities.
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
ipyflow - A reactive Python kernel for Jupyter notebooks.
rr - Record and Replay Framework
whylogs - An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
Jupyter-PowerShell - Jupyter Kernel for PowerShell