nbdev
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nbdev | ploomber | |
---|---|---|
45 | 121 | |
4,740 | 3,374 | |
0.9% | 1.0% | |
6.5 | 7.4 | |
about 1 month ago | 20 days ago | |
Jupyter Notebook | Python | |
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.
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.
ploomber
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Show HN: JupySQL – a SQL client for Jupyter (ipython-SQL successor)
- One-click sharing powered by Ploomber Cloud: https://ploomber.io
Documentation: https://jupysql.ploomber.io
Note that JupySQL is a fork of ipython-sql; which is no longer actively developed. Catherine, ipython-sql's creator, was kind enough to pass the project to us (check out ipython-sql's README).
We'd love to learn what you think and what features we can ship for JupySQL to be the best SQL client! Please let us know in the comments!
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Runme – Interactive Runbooks Built with Markdown
For those who don't know, Jupyter has a bash kernel: https://github.com/takluyver/bash_kernel
And you can run Jupyter notebooks from the CLI with Ploomber: https://github.com/ploomber/ploomber
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Rant: Jupyter notebooks are trash.
Develop notebook-based pipelines
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Who needs MLflow when you have SQLite?
Fair point. MLflow has a lot of features to cover the end-to-end dev cycle. This SQLite tracker only covers the experiment tracking part.
We have another project to cover the orchestration/pipelines aspect: https://github.com/ploomber/ploomber and we have plans to work on the rest of features. For now, we're focusing on those two.
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New to large SW projects in Python, best practices to organize code
I recommend taking a look at the ploomber open source. It helps you structure your code and parameterize it in a way that's easier to maintain and test. Our blog has lots of resources about it from testing your code to building a data science platform on AWS.
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A three-part series on deploying a Data Science Platform on AWS
Developing end-to-end data science infrastructure can get complex. For example, many of us might have struggled to try to integrate AWS services and deal with configuration, permissions, etc. At Ploomber, we’ve worked with many companies in a wide range of industries, such as energy, entertainment, computational chemistry, and genomics, so we are constantly looking for simple solutions to get them started with Data Science in the cloud.
- Ploomber Cloud - Parametrizing and running notebooks in the cloud in parallel
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Is Colab still the place to go?
If you like working locally with notebooks, you can run via the free tier of ploomber, that'll allow you to get the Ram/Compute you need for the bigger models as part of the free tier. Also, it has the historical executions so you don't need to remember what you executed an hour later!
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Alternatives to nextflow?
It really depends on your use cases, I've seen a lot of those tools that lock you into a certain syntax, framework or weird language (for instance Groovy). If you'd like to use core python or Jupyter notebooks I'd recommend Ploomber, the community support is really strong, there's an emphasis on observability and you can deploy it on any executor like Slurm, AWS Batch or Airflow. In addition, there's a free managed compute (cloud edition) where you can run certain bioinformatics flows like Alphafold or Cripresso2
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Saving log files
That's what we do for lineage with https://ploomber.io/
What are some alternatives?
papermill - 📚 Parameterize, execute, and analyze notebooks
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
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]
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
dagster - An orchestration platform for the development, production, and observation of data assets.
rr - Record and Replay Framework
dvc - 🦉 ML Experiments and Data Management with Git
Jupyter-PowerShell - Jupyter Kernel for PowerShell
argo - Workflow Engine for Kubernetes
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
MLflow - Open source platform for the machine learning lifecycle