sidenotes
ploomber
sidenotes | ploomber | |
---|---|---|
27 | 121 | |
44 | 3,374 | |
- | 0.3% | |
0.0 | 7.4 | |
over 1 year ago | 24 days ago | |
TypeScript | Python | |
MIT 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.
sidenotes
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Do I need to learn LaTeX? Are there better options?
try out curvenote.com it's not latest but has a lot of powerful features -- including latex support for equations
- Is there a CodePen/OverLeaf equivalent for sharing and viewing Jupyter Notebooks/Labs
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Is there a way to share a Latex file to Word users
try using curvenote.com instead - write in something like a sciencey good docs interface, output to Tex, PDF or word whenever you want. Has full support for math, cross referencing, citations, bibtex etc.,, and output to latex templates for specific journals etc...
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Interactive journal articles/Latex documents
probably curvenote.com ?
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Im a new 1st year PhD candidate starting Monday… I would really appreciate any tips or things you wish you had known at the beginning of your PhD. I have worked with my supervisor before so have a pretty good relationship although I am not naive to the fact this will change over time.
citation manager, keep a regular schedule, stay fit and use tools that help you - paperpile.com curvenote.com
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What are some "work smart, not hard" ways of writing a Thesis?
take a look at curvenote.com
- Finished my PhD… :)
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Tools that allow college students to collaborate on math problems - non-LaTeX based
Try curvenote.com it's a visual editor like google docs but block-based (a little like notion) and has maths support via latex.
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Should I have to edit my PhD students dissertation for basic grammar?
Grammarly doesn't work in overleaf does it? nor in MS Word locally? it does on other online tools like curvenote.com or google docs though, and maybe MS Office but I haven't tried. I'm totally comfortable using grammarly
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do you know a program that will help me write math homework more quickly?
have a look at mathpix.com and curvenote.com both interesting, the first for going from handwriting to LaTeX and the second for a WYSIWYG editor for writing with latex maths support
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?
floating-ui - A JavaScript library to position floating elements and create interactions for them.
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.
tlk - Group video call for the web. No signups. No downloads. [Moved to: https://github.com/vasanthv/talk]
papermill - 📚 Parameterize, execute, and analyze notebooks
tex-rs - A port of TeX82 to Rust. (WIP)
dagster - An orchestration platform for the development, production, and observation of data assets.
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
dvc - 🦉 ML Experiments and Data Management with Git
RusTeX - A (somewhat experimental) implementation of a TeX engine in rust, used to convert LaTeX documents to xhtml.
argo - Workflow Engine for Kubernetes
publish - Collection of various things I deem helpful for publishing
MLflow - Open source platform for the machine learning lifecycle