mlrun
orchest
| mlrun | orchest | |
|---|---|---|
| 3 | 44 | |
| 1,301 | 4,022 | |
| 4.2% | 0.1% | |
| 9.9 | 4.5 | |
| about 24 hours ago | 11 months ago | |
| Python | TypeScript | |
| 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.
mlrun
- Discussion on Need of Feature Stores
-
I reviewed 50+ open-source MLOps tools. Here’s the result
You should also add MLRun: https://github.com/mlrun/mlrun
- Has anyone here been able to deploy Mlrun successfully on Kubernetes cluster?
orchest
-
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
-
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).
- Ideas for infrastructure and tooling to use for frequent model retraining?
-
Looking for a mentor in MLOps. I am a lead developer.
If you’d like to try something for you data workflows that’s vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest
-
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
-
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
-
Launch HN: Sematic (YC S22) – 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
What are some alternatives?
feast - Feature Store for Machine Learning
docker-airflow - Docker Apache Airflow
dagster-example-pipeline - Template Dagster repo using poetry and a single Docker container; works well with CICD
hookdeck-cli - Manage your Hookdeck workspaces, connections, transformations, filters, and more with the Hookdeck CLI
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
SmartSim - SmartSim Infrastructure Library.
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
phidata - Add memory, knowledge and tools to LLMs
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
mosec - A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
Node RED - Low-code programming for event-driven applications