monogon
Kedro
monogon | Kedro | |
---|---|---|
4 | 29 | |
366 | 9,362 | |
27.0% | 0.7% | |
9.6 | 9.7 | |
2 days ago | 8 days ago | |
Go | 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.
monogon
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Monogon: A Linux userland in pure Go
It's somewhere in my git stack :).
Until I get to publishing it, the proto/gRPC definitions for node management are a good enough start: https://github.com/monogon-dev/monogon/blob/main/metropolis/...
And the top level API to actually deploy workloads is plain Kubernetes.
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Ask HN: Who is hiring? (May 2021)
Monogon, a fully remote, self-funded and engineer-led technology company, is hiring software engineers to work on Metropolis, an open source [1], secure, distributed cluster operating system based on Linux and Kubernetes.
Metropolis runs on a fleet of bare metal or cloud machines and provides users with a hardened, production ready Kubernetes - without the overhead of traditional Linux distributions or configuration management systems. It does away with the scripting/YAML duct tape and configuration drift inherent to traditional deployments, and instead provides a stable, API-driven, secure and vendor-lock-in-free platform for companies to build their products upon.
We're looking for senior candidates who can design, implement and verify complex systems that will make up part of Metropolis. We offer a kind and honest work environment in which we prioritize quality over quantity. You'll be the fourth member of a team working on an ambitious, industry-challenging product.
Our ideal candidate is a generalist with deeper knowledge in one or more of the following areas:
- Distributed systems;
- Software engineering of systems built to last;
- Security engineering, especially experience with secure boot chains;
- Low-level programming and debugging (C, Linux Kernel, …);
- Kubernetes, especially practical experience of running bare-metal production deployments;
- Platform development, ie. running a 'Company A' style infrastructure/DevOps team [2].
Our codebase is mostly Go (including pid1!), so knowledge of the language is a plus, but not a requirement (given the seniority of the position, we expect any candidate to be able to ramp up on Go within a few weeks).
To get in touch, email me at at nexantic.com.
[1] - https://github.com/monogon-dev/monogon
[2] - https://rachelbythebay.com/w/2020/05/19/abc/
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Ask HN: Who is hiring? (April 2021)
- Platform development, ie. running a 'Company A' style infrastructure/DevOps team [2].
Our codebase is mostly Go (including pid1!), so knowledge of the language is a plus, but not a requirement (given the seniority of the position, we expect any candidate to be able to ramp up on Go within a few weeks).
To get in touch, email me at at nexantic.com.
[1] - https://github.com/monogon-dev/monogon
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?
talos - Talos Linux is a modern Linux distribution built for Kubernetes.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
trivy - Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more
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.
starboard - Moved to https://github.com/aquasecurity/trivy-operator
Dask - Parallel computing with task scheduling
postgrest - REST API for any Postgres database
cookiecutter-pytorch - A Cookiecutter template for PyTorch Deep Learning projects.
u-bmc - Open-source firmware for your baseboard management controller (BMC)
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
appsmith - Platform to build admin panels, internal tools, and dashboards. Integrates with 25+ databases and any API.
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!