Kedro
DurableTask
Kedro | DurableTask | |
---|---|---|
29 | 9 | |
9,362 | 1,444 | |
0.7% | 1.6% | |
9.7 | 7.8 | |
10 days ago | 3 days ago | |
Python | C# | |
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.
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.
DurableTask
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Show HN: Windmill – fastest open-source workflow engine – the how
Might want to checkout DurableTasks[1] for that
[1] https://github.com/Azure/durabletask
- Temporal .NET – Deterministic Workflow Authoring in .NET
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.NET Modern Task Scheduler
Azure Durable Tasks are great for this. It’s open source too: https://github.com/Azure/durabletask
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Dabbling with Dagster vs. Airflow
AWS Simple Workflows or Azure Logic Apps are both services that let you define S2S workflows however you like without any particular bias to CI/CD or business operations.
If you want to go even lower level, a framework like DTFx lets you define long-running, distributed and resilient orchestrations in code:
https://github.com/Azure/durabletask
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Resetting your Durable Functions Task Hub state
The fast and less official way to clean up state is to use the underlying Durable Task Framework storage APIs directly. Durable Functions has an internal dependency on the Durable Task Framework, so no new packages need to be added to your app to access them. Here's an example function that demonstrates how to clean up Azure Storage state quickly (disclaimer: use any of my code samples at your own risk).
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How to use Netherite Storage Provider in Durable Functions
Netherite is a distributed workflow execution engine for Durable Functions (DF) and the Durable Task Framework (DTFx).
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What was your stupidest mistake? (As a C# dev)
To give context - this table would track long running operations that were initiated and being executed by our worker machines (we use this c sharp framework).
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Azure Durable Functions - Developing Serverless Stateful Workflow
Behind the scenes, the Durable Functions extension is built on top of the Durable Task Framework, an open-source library on GitHub that's used to build workflows in code. Like Azure Functions is the serverless evolution of Azure WebJobs, Durable Functions is the serverless evolution of the Durable Task Framework. Microsoft and other organizations use the Durable Task Framework extensively to automate mission-critical processes. It's a natural fit for the serverless Azure Functions environment.
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A 10+ year Journey
I could clearly see that the developers building applications on Microsoft Azure were facing eerily similar challenges to what I had seen back at AWS. The same challenges we had tried to address with SWF. So I used one of the internal team hackathons as an opportunity to pair up with Affan Dar and take another stab at solving the problem. Affan had a very deep understanding of Azure ServiceBus so he was the perfect person to build the backend for the stateful C# experience I had in mind. Microsoft had recently added async/await capabilities into C# and it turned out to be an amazing fit for writing stateful applications which need to orchestrate calls among microservices. Since Java lacked an async/await like primitive, we had to rely on Promise-based async approach when building SWF. But with C#, we were able to deliver a much cleaner and synchronous programming model using async/await. This hackathon project resulted in Azure Durable Task Framework as an OSS client SDK which uses Azure ServiceBus as the backend to provide a stateful workflow-as-code experience for applications. I'm so glad to see Microsoft has continued investing in the experience with Azure Durable Functions as the latest reincarnation of the original effort. An effort which started with that hackathon project.
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Workflow Core - Lightweight workflow engine for .NET Standard
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
QuartzNet - Quartz Enterprise Scheduler .NET
Dask - Parallel computing with task scheduling
FluentScheduler - Automated job scheduler with fluent interface for the .NET platform.
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
temporal - Temporal service
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
Chroniton - A library for running tasks(jobs) on schedules.
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!
NCrontab - Crontab for .NET