orchest
proposals
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orchest | proposals | |
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44 | 60 | |
4,020 | 63 | |
0.2% | - | |
4.5 | 4.0 | |
11 months ago | 22 days ago | |
TypeScript | ||
Apache License 2.0 | MIT License |
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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
proposals
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Is there an alternative for Airflow for running thousands of dynamic tasks?
Check out temporal.io open source project. It was built at Uber for large scale business-level processes. So any data pipelines are low-rate use cases by definition.
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KuFlow as a Temporal.io-based Workflow Orchestrator
With KuFlow it is also possible to work with serverless workflows apart from Temporal.io, we explain it in this blog entry, but in summary, almost as a no-code tool, the correct use It would be a rather low-code tool; in just a matter of minutes with our drag-and-drop tool, you can have a workflow that interacts with one or more users of the organization.
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How to handle background jobs in Rust?
Otherwise you may want to look into Kafka or Fluvio to ensure that task runs at least once. If you're doing something like batch operations as a background task, Temporal is another great option.
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No-code or Workflow as code? Better both
The runtime is developed using Temporal, which is one of the main tools that we are currently using at KuFlow. Thanks to, all the workflow executions are robust: your application will be durable, reliable, and scalable.
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Temporal Programming, a new name for an old paradigm
Hmmm I got confused by the name. I thought it's related to https://temporal.io/
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Possible innovations in Event Sourcing frameworks.
Have you looked at temporal.io open source platform? It uses event sourcing as an implementation detail. But it greatly simplifies the user experience compared to "raw event sourcing."
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After Airflow. Where next for DE?
Rewrite Airflow on top of temporal.io. This way, you get unlimited scalability and very high reliability out of the box and would be able to innovate on the features that matter for DE.
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Show HN: Retool Workflows â Cronjobs, but better
Hi all, founder @ Retool here. Over the past year, weâve been working on Retool Workflows; a fast way for engineers to automate tasks with code. We started building the product because we ourselves (as developers) were looking for something in-between writing cron jobs (which involves a lot of boilerplate) and Zapier (which oftentimes isnât customizable enough, since it doesnât _really_ support writing code).
Workflows is a code-first automation tool: youâre _expected_ to write code, but we handle all the boilerplate for you. For example: out-of-the-box integration with 80+ resources (you probably donât want to be trying to figure out OAuth 2.0 with Salesforce!), monitoring and observability (so you can see the output of every run in the past, and immediately be notified if something goes wrong), and permissions (e.g. some Okta groups can see the outputs of Workflows, but canât change the code itself).
Right now, the product is cloud-only, but weâre hard at work at an on-prem, self-hosted version (in a Docker image). If youâre interested in that version, feel free to email us at [email protected]. We aim to get it out in the next few weeks. Self-hosted Retool is responsible for a large portion of our usage today, and weâre excited to be supporting Workflows too.
All Retool plans now include 1GB of Workflows throughput, which we think is quite generous (80% of active Workflows users are below 1GB). We donât bill by run at all, so youâre welcome to run as many workflows as you want.
We use a bunch of interesting technology for Workflows; we are, for example, using Temporal (https://temporal.io/) under the hood. Thatâs something weâre going to be writing a blog post about later. (Weâve been hard at work on the launch, hah.)
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How KuFlow supports Temporal as a worfkows engine for our processes?
In such a diverse world, it would be boring to have a single way of doing things. That's why at KuFlow we support different ways to implement the logic of our processes and tasks. And in this post, we will talk about one of them, the orchestration through Temporal, which gives us a powerful way to manage our workflows.
- Library for manage tasks when make a workflow automation.
What are some alternatives?
docker-airflow - Docker Apache Airflow
conductor - Conductor is a microservices orchestration engine.
hookdeck-cli - Manage your Hookdeck workspaces, connections, transformations, filters, and more with the Hookdeck CLI
temporalite-archived - An experimental distribution of Temporal that runs as a single process
ploomber - The fastest âĄď¸ way to build data pipelines. Develop iteratively, deploy anywhere. âď¸
zenml - ZenML đ: Build portable, production-ready MLOps pipelines. https://zenml.io.
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
kubemq-community - KubeMQ is a Kubernetes native message queue broker
Node RED - Low-code programming for event-driven applications
nextjs-cron - Cron jobs with Github Actions for Next.js apps on Vercelâ˛