dagster-sklearn
Dask
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| dagster-sklearn | Dask | |
|---|---|---|
| 3 | 32 | |
| 40 | 11,999 | |
| - | 1.6% | |
| 0.0 | 9.6 | |
| about 1 year ago | 3 days ago | |
| Python | Python | |
| - | BSD 3-clause "New" or "Revised" License |
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.
dagster-sklearn
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Scheduling tools for ETL and ML flow
I would give dagster a look. It has a built-in native scheduler and is cross-platform. It is general purpose, so your team can grow with it and tackle broader set of use cases if needed. If you struggle to get started after reading their docs/tutorials, you can take a look at my personal repo. Ive gotten a few feedback that my example has been very useful in getting started. I know they revamped their docs recently, but havent looked at their tutorial again or looked to see if they provided an intermediate level full example yet, so I need to get back in there to see.
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Dagster Tutorials/Presentations
Hey! I've recently started to use dagster and it's been great with its 0.11.x releases. I am still a newbie with it and maybe only use 20% of its features and abstractions. Here's my work-in-progress personal Github repo. Not sure if you'll learn much from it.
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Is anyone trying to switch out of data science, and if so, what jobs are you applying for?
I have created a trivial, contrived scikit-learn example using dagster so that people have an idea of how it can be used.
Dask
- The Distributed Tensor Algebra Compiler (2022)
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A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
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What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
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Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
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msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
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What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
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Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
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Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
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Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/
What are some alternatives?
dagster - An orchestration platform for the development, production, and observation of data assets.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Numba - NumPy aware dynamic Python compiler using LLVM
yellowbrick - Visual analysis and diagnostic tools to facilitate machine learning model selection.
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.
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
NetworkX - Network Analysis in Python
dagster-example-pipeline - Template Dagster repo using poetry and a single Docker container; works well with CICD
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
statsmodels - Statsmodels: statistical modeling and econometrics in Python