clerk
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clerk | ploomber | |
---|---|---|
22 | 121 | |
1,697 | 3,374 | |
1.6% | 1.0% | |
8.5 | 7.4 | |
6 days ago | 20 days ago | |
Clojure | Python | |
ISC License | 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.
clerk
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The Current State of Clojure's Machine Learning Ecosystem
Something I really like in the Clojure data science stack that isn't mentioned is Clerk* — an interesting take on notebooks. I think it's a good gateway into Clojure for those coming from a Python or R background.
*https://clerk.vision/
- Improve Jupyter Notebook Reruns by Caching Cells
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Critique of Lazy Sequences in Clojure
Clojure's lazy sequences by default are wonderful ergonomically, but it provides many ways to use strict evaluation if you want to. They aren't really a hassle either. I've been doing Clojure for the last few years and have a few grievances, but overall it's the most coherent, well thought out language I've used and I can't recommend it enough.
There is the issue of startup time with the JVM, but you can also do AOT compilation now so that really isn't a problem. Here are some other cool projects to look at if you're interested:
Malli: https://github.com/metosin/malli
Babashka: https://github.com/babashka/babashka
Clerk: https://github.com/nextjournal/clerk
- Moldable Live Programming for Clojure
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Morse, an open-source interactive tool for inspecting Clojure
I'm really enjoying using Clojure with Clerk: https://github.com/nextjournal/clerk
It's a bit like a Jupyter notebook, but you get to use your own editor, you still have a normal Clojure REPL, it's stored in git like "normal" code, etc.
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Adding Clerk to a Leiningen Project
Hey all, I'm new to Clojure and would appreciate your help with a few questions I had getting started. I'm using Leiningen to setup my projects and manage my packages as recommended in Brave & True. So far I've been able to add any dependencies I've needed without much issue, Neanderthal, tech.v3.dataset, etc. I'm interested in data science, and was hoping to set up a notebook environment to be able to quickly produce data visualizations on the fly since I'm used to working with Jupyter. I came across Clerk, but I'm having some trouble adding it to my project. Here's what I tried:
- Clojure Turns 15 panel discussion video
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The program is the database is the interface
Clojure also has Clerk, which is like Jupyter, but more befitting Clojure's overall philosophy: https://clerk.vision/
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Clojure conventions for writing complicated mathematical calculations?
If I were working long enough with gnarly enough equations I'd look into using Clerk to visualize the equations with MathJax or similar, probably following Sam Ritchie's footsteps with SICMUtils. To me this is the true readability answer: lisp notation for precise implementations, compiling to a rich & familiar visual representation.
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?
next-auth - Authentication for the Web.
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.
portal - A clojure tool to navigate through your data.
papermill - 📚 Parameterize, execute, and analyze notebooks
libpython-clj - Python bindings for Clojure
dagster - An orchestration platform for the development, production, and observation of data assets.
JD Esurvey - JD eSurvey is an open source enterprise survey web application written in Java and based on the Spring Framework. Check out the tutorial videos to find out more about the application features.
dvc - 🦉 ML Experiments and Data Management with Git
leo-editor - Leo is an Outliner, Editor, IDE and PIM written in 100% Python.
argo - Workflow Engine for Kubernetes
pytudes - Python programs, usually short, of considerable difficulty, to perfect particular skills.
MLflow - Open source platform for the machine learning lifecycle