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argo | debugger | |
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43 | 3 | |
14,259 | 539 | |
1.2% | - | |
9.8 | 0.5 | |
1 day ago | over 2 years ago | |
Go | TypeScript | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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?
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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.
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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)
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Cron alternative that can run a job every two weeks without convoluted tricks
Yea... I personally like the jobber format which is similar to argo workflows ( https://github.com/argoproj/argo-workflows/blob/master/examples/coinflip.yaml ) which can get triggered by an event which can look like cron ( https://argoproj.github.io/argo-workflows/cron-workflows/ ).
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Why can't I label Argo Workflows workflow-controller-metrics service for Prometheus to scrape? Everything works in some cases but fails in most
--filename https://github.com/argoproj/argo-workflows/releases/download/v3.4.4/namespace-install.yaml \
debugger
- Debugging a Mixed Python and C Language Stack
- Best extensions for JupyterLab!!
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What's new in Elyra 2.0
To debug a notebook, associate it with a kernel that supports debugging and enable the debugger. The official debugger example Python notebook provides a short introduction and is highly recommended!
What are some alternatives?
temporal - Temporal service
keda - KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
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
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
lens - Lens - The way the world runs Kubernetes
volcano - A Cloud Native Batch System (Project under CNCF)
devtron - Tool integration platform for Kubernetes
kube-batch - A batch scheduler of kubernetes for high performance workload, e.g. AI/ML, BigData, HPC
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
argo-cd - Declarative Continuous Deployment for Kubernetes