argo
flight-delay-notebooks
argo | flight-delay-notebooks | |
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43 | 1 | |
14,314 | 14 | |
0.9% | - | |
9.8 | 0.0 | |
7 days ago | over 1 year ago | |
Go | Jupyter Notebook | |
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.
argo
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StackStorm – IFTTT for Ops
Like Argo Workflows?
https://github.com/argoproj/argo-workflows
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Creators of Argo CD Release New OSS Project Kargo for Next Gen Gitops
Dagger looks more comparable to Argo Workflows: https://argoproj.github.io/argo-workflows/ That's the first of the Argo projects, which can run multi-step workflows within containers on Kubernetes.
For what it's worth, my colleagues and I have had great luck with Argo Workflows and wrote up a blog post about some of its advantages a few years ago: https://www.interline.io/blog/scaling-openstreetmap-data-wor...
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Practical Tips for Refactoring Release CI using GitHub Actions
Despite other alternatives like Circle CI, Travis CI, GitLab CI or even self-hosted options using open-source projects like Tekton or Argo Workflow, the reason for choosing GitHub Actions was straightforward: GitHub Actions, in conjunction with the GitHub ecosystem, offers a user-friendly experience and access to a rich software marketplace.
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(Not) to Write a Pipeline
author seems to be describing the kind of patterns you might make with https://argoproj.github.io/argo-workflows/ . or see for example https://github.com/couler-proj/couler , which is an sdk for describing tasks that may be submitted to different workflow engines on the backend.
it's a little confusing to me that the author seems to object to "pipelines" and then equate them with messaging-queues. for me at least, "pipeline" vs "workflow-engine" vs "scheduler" are all basically synonyms in this context. those things may or may not be implemented with a message-queue for persistence, but the persistence layer itself is usually below the level of abstraction that $current_problem is really concerned with. like the author says, eventually you have to track state/timestamps/logs, but you get that from the beginning if you start with a workflow engine.
i agree with author that message-queues should not be a knee-jerk response to most problems because the LoE for edge-cases/observability/monitoring is huge. (maybe reach for a queue only if you may actually overwhelm whatever the "scheduler" can handle.) but don't build the scheduler from scratch either.. use argowf, kubeflow, or a more opinionated framework like airflow, mlflow, databricks, aws lamda or step-functions. all/any of these should have config or api that's robust enough to express rate-limit/retry stuff. almost any of these choices has better observability out-of-the-box than you can easily get from a queue. but most importantly.. they provide idioms for handling failure that data-science folks and junior devs can work with. the right way to structure code is just much more clear and things like structuring messages/events, subclassing workers, repeating/retrying tasks, is just harder to mess up.
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what technologies are people using for job scheduling in/with k8s?
Argo Workflows + Argo Events
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What are some good self-hosted CI/CD tools where pipeline steps run in docker containers?
Drone, or Tekton, Argo Workflows if you’re on k8s
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job scheduling for scientific computing on k8s?
Check out Argo Workflows.
- Orchestration poll
- What's the best way to inject a yaml file into an Argo workflow step?
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Which build system do you use?
go-git has a lot of bugs and is not actively maintained. The bug even affects Argo Workflow, which caused our data pipeline to fail unexpectedly (reference: https://github.com/argoproj/argo-workflows/issues/10091)
flight-delay-notebooks
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What's new in Elyra 2.0
Use the forum to ask general questions or share things you've created, like Nick Burdakous did. He recently published a pipeline viewer for Google Chrome and Mozilla Firefox. This viewer visualizes pipeline files on GitHub, like shown in the screen shot below. The depicted pipeline was created by Nick Pentreath and presented at the Big Things conference in November 2020.
What are some alternatives?
temporal - Temporal service
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
keda - KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
elyra - Elyra extends JupyterLab with an AI centric approach.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
rapaio-jupyter-kernel - Java jupyter kernel
StackStorm - StackStorm (aka "IFTTT for Ops") is event-driven automation for auto-remediation, incident responses, troubleshooting, deployments, and more for DevOps and SREs. Includes rules engine, workflow, 160 integration packs with 6000+ actions (see https://exchange.stackstorm.org) and ChatOps. Installer at https://docs.stackstorm.com/install/index.html
xeus-python - Jupyter kernel for the Python programming language
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
jupyterlab-lsp - Coding assistance for JupyterLab (code navigation + hover suggestions + linters + autocompletion + rename) using Language Server Protocol