jupyterlab-lsp
argo
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jupyterlab-lsp | argo | |
---|---|---|
17 | 43 | |
1,726 | 14,259 | |
2.4% | 1.4% | |
9.5 | 9.8 | |
11 days ago | 3 days ago | |
TypeScript | Go | |
BSD 3-clause "New" or "Revised" License | 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.
jupyterlab-lsp
- Does Jupyter labs or jupyter notebook have a way to expose python (or C++) objects?
- [D] Why is no one talking about the disadvantages of Colab ?
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Is there anything like a running Jupyter Kernel LSP?
I've recently seen JupyterLab LSP that bring the static analysis aspect of LSP to the notebook, and been wondering if there is anything similar that try to bridge a running kernel back into Neovim.
- Improving Jupyter Lab code entry and editing - recommendations?
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Alternatives to Rstudio
JupyterLab is optimised for handling R, Python and Julia. Code intelligence-wise it requires installing jupyterlab-lsp to get all the best features.
- Good examples of well formatted Jupyter notebooks?
- Jupyterlab-Lsp: Coding Assistance for JupyterLab Using Language Server Protocol
- IDE for data scientists
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Best debugging tool for python
Pylance is really a great LSP in VS Code and it's pretty fast, and Jupyter Lab has an LSP that is also great depending on your use case. VS Code has remote host support for Docker, VMs, etc. depending on what your plans are.
- JupyterLab LSP 3.8 (coding assistance, better autocompletion) released
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.
- 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)
What are some alternatives?
polynote - A better notebook for Scala (and more)
temporal - Temporal service
Spyder - Official repository for Spyder - The Scientific Python Development Environment
keda - KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes
ansible-language-server - 🚧 Ansible Language Server codebase is now included in vscode-ansible repository
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.
julia-snail - An Emacs development environment for Julia
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
jupyter-black - Black formatter for Jupyter Notebook
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.