orchest
metaflow
Our great sponsors
orchest | metaflow | |
---|---|---|
44 | 24 | |
4,016 | 7,494 | |
0.2% | 2.3% | |
4.5 | 9.2 | |
10 months ago | 1 day ago | |
TypeScript | Python | |
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.
orchest
-
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/
-
Launch HN: Patterns (YC S21) – 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
-
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).
-
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
-
Prefect vs other things question
If you’re looking for something with a great UI experience you can check out our open source project called Orchest. It might be what you seek from a simplicity perspective. https://github.com/orchest/orchest
-
Airflow's Problem
Argo is pretty amazing if you want to take advantage of the work Kubernetes has done to scale resource efficiently across a cluster of compute nodes.
If you’re looking for something that’s a bit more high level and friendly to expose directly to your data team (data scientists/data engineers/data analysts) you can check out https://github.com/orchest/orchest
You can think of it as a browser UI/workbench for Argo scheduled pipelines. Disclaimer: author of the project
-
How are you guys validating your data?
+1 on a lightweight version of GE to more easily make part of an existing pipeline. Would like it for internal use (our data pipelines), but also for our open source users (https://github.com/orchest/orchest).
- Apache Hop 2.0
-
I reviewed 50+ open-source MLOps tools. Here’s the result
You might want to add https://github.com/orchest/orchest/ to the Pipeline orchestration category (disclaimer: I work at the company making it)
metaflow
- FLaNK Stack 05 Feb 2024
-
metaflow VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
- In Need of Guidance: Implementing MLOps in a Complex Organization as a Junior Data Engineer
-
What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
-
Needs advice for choosing tools for my team. We use AWS.
1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling.
-
Selfhosted chatGPT with local contente
even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.
-
[OC] Gender diversity in Tech companies
They had to figure out video compression that worked at the volume that they wanted to deliver. They had to build and maintain their own CDN to be able to have a always available and consistent viewing experience. Don’t even get me started on the resiliency tools like hystrix that they were kind enough to open source. I mean, they have their own fucking data science framework and they’re looking into using neural networks to downscale video.. Sound familiar? That’s cause that’s practically the same thing as Nvidia’s DLSS (which upscales instead of downscales).
-
Model artifacts mess and how to deal with it?
Check out Metaflow by Netflix
-
Going to Production with Github Actions, Metaflow and AWS SageMaker
Github Actions, Metaflow and AWS SageMaker are awesome technologies by themselves however they are seldom used together in the same sentence, even less so in the same Machine Learning project.
-
Small to Reasonable Scale MLOps - An Approach to Effective and Scalable MLOps when you're not a Giant like Google
It's undeniable that leadership is instrumental in any company and project success, however I was intrigued with one of their ML tool choices that helped them reach their goal. I was so curious about this choice that I just had to learn more about it, so in this article will be talking about a sound strategy of effectively scaling your AI/ML undertaking and a tool that makes this possible - Metaflow.
What are some alternatives?
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
kedro-great - The easiest way to integrate Kedro and Great Expectations
clearml - ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
docker-airflow - Docker Apache Airflow
great_expectations - Always know what to expect from your data.
dvc - 🦉 ML Experiments and Data Management with Git
feast - Feature Store for Machine Learning
Poetry - Python packaging and dependency management made easy
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
hookdeck-cli - Manage your Hookdeck workspaces, connections, transformations, filters, and more with the Hookdeck CLI