paradigm
aws-sfn-resume-from-any-state
| paradigm | aws-sfn-resume-from-any-state | |
|---|---|---|
| 9 | 1 | |
| 36 | 56 | |
| - | - | |
| 7.6 | 10.0 | |
| 11 months ago | over 1 year ago | |
| Python | 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.
paradigm
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Deploying speech recognition models at scale
I built Paradigm from scratch to deploy any model at scale. It deploys the model on Kubernetes with load balancers. If you run into any issues, I'm happy to guide you on how to use it.
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Which is the best MLOps tool for getting started?
I started with paradigm. I got a deeper understanding about argo workflows through it as well. Helps to get a proper grab of industry standards from the beginning.
- What are some open-source ML pipeline managers that are easy to use?
- I use this OS tool to deploy LLMs on Kubernetes.
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Serving Scikit-Learn model on EC2 instance and Scaling
For scalability, it should be on Kubernetes. This is the best solution I have come across. You can deploy the model as a service with a LoadBalancer. You can refer to Kubernetes services or use a tool such as this one that handles building the service for you.
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Who wants to run ML pipelines on Kubernetes? This might be the simplest tool for the job.
I came across this tool today and checked it out, I feel this can get the job done very quickly without so many complex features. It is also very small in size, so does not take up a lot of space in the cluster as well.
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[P] I found the simplest tool to run ML pipelines on Kubernetes. Github link in comments.
Link - https://github.com/ParadigmAI/paradigm It seems to be a pretty new project. But this has high usability.
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Airflow + Slurm for ML Training Pipelines?
Prefect is a good choice, But I wanted a much simpler tool. Hence, I built a barebone workflow controller here.
aws-sfn-resume-from-any-state
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Airflow + Slurm for ML Training Pipelines?
A state machine is not easy to resume from where it failed. So you either need to rerun it entirely or use this to recreate the previous run dynamically which is a bit too hacky to my taste or you simply string the remaining batch jobs together manually
What are some alternatives?
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
flecs - A fast entity component system (ECS) for C & C++
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
dagster - An orchestration platform for the development, production, and observation of data assets.
wenet - Production First and Production Ready End-to-End Speech Recognition Toolkit
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
omegaml - MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle