orchest
parabol
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orchest | parabol | |
---|---|---|
44 | 33 | |
4,020 | 1,844 | |
0.2% | 1.8% | |
4.5 | 9.8 | |
11 months ago | 3 days ago | |
TypeScript | TypeScript | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
orchest
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Decent low code options for orchestration and building data flows?
You can check out our OSS https://github.com/orchest/orchest
- Build ML workflows with Jupyter notebooks
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Building container images in Kubernetes, how would you approach it?
The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/
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Launch HN: Patterns (YC S21) â A much faster way to build and deploy data apps
First want to say congrats to the Patterns team for creating a gorgeous looking tool. Very minimal and approachable. Massive kudos!
Disclaimer: we're building something very similar and I'm curious about a couple of things.
One of the questions our users have asked us often is how to minimize the dependence on "product specific" components/nodes/steps. For example, if you write CI for GitHub Actions you may use a bunch of GitHub Action references.
Looking at the `graph.yml` in some of the examples you shared you use a similar approach (e.g. patterns/openai-completion@v4). That means that whenever you depend on such components your automation/data pipeline becomes more tied to the specific tool (GitHub Actions/Patterns), effectively locking in users.
How are you helping users feel comfortable with that problem (I don't want to invest in something that's not portable)? It's something we've struggled with ourselves as we're expanding the "out of the box" capabilities you get.
Furthermore, would have loved to see this as an open source project. But I guess the second best thing to open source is some open source contributions and `dcp` and `common-model` look quite interesting!
For those who are curious, I'm one of the authors of https://github.com/orchest/orchest
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Argo became a graduated CNCF project
Haven't tried it. In its favor, Argo is vendor neutral and is really easy to set up in a local k8s environment like docker for desktop or minikube. If you already use k8s for configuration, service discovery, secret management, etc, it's dead simple to set up and use (avoiding configuration having to learn a whole new workflow configuration language in addition to k8s). The big downside is that it doesn't have a visual DAG editor (although that might be a positive for engineers having to fix workflows written by non-programmers), but the relatively bare-metal nature of Argo means that it's fairly easy to use it as an underlying engine for a more opinionated or lower-code framework (orchest is a notable one out now).
- Ideas for infrastructure and tooling to use for frequent model retraining?
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Looking for a mentor in MLOps. I am a lead developer.
If youâd like to try something for you data workflows thatâs vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest
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Is there a good way to trigger data pipelines by event instead of cron?
You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation
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How do you deal with parallelising parts of an ML pipeline especially on Python?
We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest
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Launch HN: Sematic (YC S22) â Open-source framework to build ML pipelines faster
For people in this thread interested in what this tool is an alternative to: Airflow, Luigi, Kubeflow, Kedro, Flyte, Metaflow, Sagemaker Pipelines, GCP Vertex Workbench, Azure Data Factory, Azure ML, Dagster, DVC, ClearML, Prefect, Pachyderm, and Orchest.
Disclaimer: author of Orchest https://github.com/orchest/orchest
parabol
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How to Run a Sprint Retrospective
Parabol: Does much of the heavy lifting of facilitating for you. Applies a pre-defined structure to your retro agenda.
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Retrospective Tools
similar to teamretro: https://www.parabol.co/
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Any recommendations for improving remote only retrospective sessions?
Not sure it helps with the issues you mention, but I found https://www.parabol.co/ to stimulate discussion. Everyone writes their thoughts on their own first, then they get shown to the group, you group them, vote, and discuss in order of most votes.
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PSA don't use Datadog agent in a GraphQL project
We faced something similar. To improve GraphQL performance, we use graphql-jit. We turned off all other tracing that datadog turns on by default. Then, we then wrote a custom tracer to connect graphql-jit to dd-trace. Hopefully this same pattern works for you!
- When you use Parabol to run a meeting, you don't have to be a well-seasoned facilitatorâbut with features that nudge and guide you along the way, you'll feel like a pro in no time! Donât let pricing stop you: Parabol is free for up to 2 teams. Yup, 100% free.
- You donât have to be an agile team to benefit from regularly iterating and improving on projects. Anyone can run great retrospectives and create continuous improvement in their work. - even if you lose track, we won't. Donât let pricing stop you: Parabol is free for up to 2 teams. Yup, 100% free.
- TIL 92% of users agreed that Parabol improves the efficiency of their meetings. By keeping meetings democratic and fair with anonymous voting, they learn what development teams want to talk about giving everyone a voice. Donât let pricing stop you: Parabol is free for up to 2 teams. Yup, 100% free.
- Discover patterns, prioritize what matters as a team, and implement them with multiplayer grouping. Parabolâs AI automates naming groups so scrum masters donât have to, leaving only the change up to you and your team. Donât let pricing stop you: Parabol is free for up to 2 teams. Yup, 100% free.
What are some alternatives?
docker-airflow - Docker Apache Airflow
Baserow - Open source no-code database and Airtable alternative. Create your own online database without technical experience. Performant with high volumes of data, can be self hosted and supports plugins
ploomber - The fastest âĄď¸ way to build data pipelines. Develop iteratively, deploy anywhere. âď¸
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
hookdeck-cli - Manage your Hookdeck workspaces, connections, transformations, filters, and more with the Hookdeck CLI
sgr - sgr (command line client for Splitgraph) and the splitgraph Python library
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
sucrase - Super-fast alternative to Babel for when you can target modern JS runtimes
k6 - A modern load testing tool, using Go and JavaScript - https://k6.io
Node RED - Low-code programming for event-driven applications
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.