luigi
Kedro
luigi | Kedro | |
---|---|---|
14 | 29 | |
17,327 | 9,362 | |
0.5% | 0.7% | |
6.3 | 9.7 | |
9 days ago | 8 days ago | |
Python | 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.
luigi
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Ask HN: What is the correct way to deal with pipelines?
I agree there are many options in this space. Two others to consider:
- https://airflow.apache.org/
- https://github.com/spotify/luigi
There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…
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In the context of Python what is a Bob Job?
Maybe if your use case is “smallish” and doesn’t require the whole studio suite you could check out apscheduler for doing python “tasks” on a schedule and luigi to build pipelines.
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Lessons Learned from Running Apache Airflow at Scale
What are you trying to do? Distributed scheduler with a single instance? No database? Are you sure you don't just mean "a scheduler" ala Luigi? https://github.com/spotify/luigi
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Apache Airflow. How to make the complex workflow as an easy job
It's good to know what Airflow is not the only one on the market. There are Dagster and Spotify Luigi and others. But they have different pros and cons, be sure that you did a good investigation on the market to choose the best suitable tool for your tasks.
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DevOps Fundamentals for Deep Learning Engineers
MLOps is a HUGE area to explore, and not surprisingly, there are many startups showing up in this space. If you want to get it on the latest trends, then I would look at workflow orchestration frameworks such as Metaflow (started off at Netflix, is now spinning off into its own enterprise business, https://metaflow.org/), Kubeflow (used at Google, https://www.kubeflow.org/), Airflow (used at Airbnb, https://airflow.apache.org/), and Luigi (used at Spotify, https://github.com/spotify/luigi). Then you have the model serving itself, so there is Seldon (https://www.seldon.io/), Torchserve (https://pytorch.org/serve/), and TensorFlow Serving (https://www.tensorflow.org/tfx/guide/serving). You also have the actual export and transfer of DL models, and ONNX is the most popular here (https://onnx.ai/). Spark (https://spark.apache.org/) still holds up nicely after all these years, especially if you are doing batch predictions on massive amount of data. There is also the GitFlow way of doing things and Data Version Control (DVC, https://dvc.org/) is taken a pole position there.
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Data pipelines with Luigi
At Wonderflow we're doing a lot of ML / NLP using Python and recently we are enjoying writing data pipelines using Spotify's Luigi.
- Noobie who is trying to use K8s needs confirmation to know if this is the way or he is overestimating Kubernetes.
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Open Source ETL Project For Startups
💡【About Luigi】 https://github.com/spotify/luigi Luigi was built at Spotify since 2012, it's open source and mainly used for getting data insights by showing recommendations, toplists, A/B test analysis, external reports, internal dashboards, etc.
- Resources/tutorials to help me learn about ETL?
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Using Terraform to make my many side-projects 'pick up and play'
So to sum that up, I went from having nothing for my side-project set up in AWS to having a Kubernetes cluster with the basic metrics and dashboard, a proper IAM-linked ServiceAccount support for a smooth IAM experience in K8s, and Luigi deployed so that I could then run a Luigi workflow using an ad-hoc run of a CronJob. That's quite remarkable to me. All that took hours to figure out and define when I first did it, over six months ago.
Kedro
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Nextflow: Data-Driven Computational Pipelines
Interesting, thanks for sharing. I'll definitely take a look, although at this point I am so comfortable with Snakemake, it is a bit hard to imagine what would convince me to move to another tool. But I like the idea of composable pipelines: I am building a tool (too early to share) that would allow to lay Snakemake pipelines on top of each other using semi-automatic data annotations similar to how it is done in kedro (https://github.com/kedro-org/kedro).
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A Polars exploration into Kedro
# pyproject.toml [project] dependencies = [ "kedro @ git+https://github.com/kedro-org/kedro@3ea7231", "kedro-datasets[pandas.CSVDataSet,polars.CSVDataSet] @ git+https://github.com/kedro-org/kedro-plugins@3b42fae#subdirectory=kedro-datasets", ]
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What are some open-source ML pipeline managers that are easy to use?
So there's 2 sides to pipeline management: the actual definition of the pipelines (in code) and how/when/where you run them. Some tools like prefect or airflow do both of them at once, but for the actual pipeline definition I'm a fan of https://kedro.org. You can then use most available orchestrators to run those pipelines on whatever schedule and architecture you want.
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How do data scientists combine Kedro and Databricks?
We have set up a milestone on GitHub so you can check in on our progress and contribute if you want to. To suggest features to us, report bugs, or just see what we're working on right now, visit the Kedro projects on GitHub.
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How do you organize yourself during projects?
you could use a project framework like kedro to force you to be more disciplined about how you structure your projects. I'd also recommend checking out this book: Edna Ridge - Guerrilla Analytics: A Practical Approach to Working with Data
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Futuristic documentation systems in Python, part 1: aiming for more
Recently I started a position as Developer Advocate for Kedro, an opinionated data science framework, and one of the things we're doing is exploring what are the best open source tools we can use to create our documentation.
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Python projects with best practices on Github?
You can also check out Kedro, it’s like the Flask for data science projects and helps apply clean code principles to data science code.
- Data Science/ Analyst Zertifikate für den Job Markt?
- What are examples of well-organized data science project that I can see on Github?
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Dabbling with Dagster vs. Airflow
An often overlooked framework used by NASA among others is Kedro https://github.com/kedro-org/kedro. Kedro is probably the simplest set of abstractions for building pipelines but it doesn't attempt to kill Airflow. It even has an Airflow plugin that allows it to be used as a DSL for building Airflow pipelines or plug into whichever production orchestration system is needed.
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Dask - Parallel computing with task scheduling
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Pinball
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
streamparse - Run Python in Apache Storm topologies. Pythonic API, CLI tooling, and a topology DSL.
lightning-bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers.