direktiv
ploomber
direktiv | ploomber | |
---|---|---|
13 | 121 | |
464 | 3,380 | |
1.5% | 0.5% | |
10.0 | 7.4 | |
4 days ago | 26 days ago | |
TypeScript | 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.
direktiv
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Preloading Ollama Models
That went okay, but there is still the startup problem - it took ages to run the lifecycle hook, plus it won't function on Kubernetes nodes with no internet access. At Direktiv were are using Knative a lot as well which does not support lifecycle events. So, my plan was to create a container using the Ollama image as base with the model pre-downloaded.
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Knative Serverless in 2024
When deciding which option to choose, consider your specific environment, requirements, and preferences. At Direktiv, we typically opt for Contour due to its simplicity. However, your choice may vary depending on your use case and infrastructure setup.
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Lessons Learned from Running Apache Airflow at Scale
So being completely transparent, we're the creators of Direktiv (https://github.com/direktiv/direktiv). We're genuinely curious to have users who have previously used Airflow and other DAGs (mentioned in here is Argo workflows) try Direktiv and give us more feedback.
- direktiv runs containers as part of workflows from any compliant container registry, passing JSON structured data between workflow states.
- Encrypting server-side emails using serverless workflows using Direktiv
- Encrypting server-side emails using serverless workflows
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Step Functions Wait Loop w/ Timeout Feature
If you want a portable step functions take a look at https://github.com/vorteil/direktiv developers are very helpful and responsive
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Direktiv: Docker development environment, VSCode plugin & Infrastructure-as-a-Chatbot
Another update to our Direktiv event-driven serverless workflow engine - but this one focused on development. Release v0.3.1 included some bug fixes, improved stability and security enhancements, but more notably:
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Update to our serverless workflow engine Direktiv
We've previously posted on our serverless workflow / automation engine called Direktiv and wanted to share a couple of updates:
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?
direktiv-apps - Direktiv Application Containers
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.
states-language-cadence - States Language on Cadence
papermill - 📚 Parameterize, execute, and analyze notebooks
argo - Workflow Engine for Kubernetes
dagster - An orchestration platform for the development, production, and observation of data assets.
windmill - Open-source developer platform to turn scripts into workflows and UIs. Fastest workflow engine (5x vs Airflow). Open-source alternative to Airplane and Retool.
dvc - 🦉 ML Experiments and Data Management with Git
toil - A scalable, efficient, cross-platform (Linux/macOS) and easy-to-use workflow engine in pure Python.
stepwise - Clojure AWS Step Functions library
MLflow - Open source platform for the machine learning lifecycle