kubeflow
pipelines
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| kubeflow | pipelines | |
|---|---|---|
| 3 | 2 | |
| 13,658 | 3,436 | |
| 1.5% | 1.5% | |
| 8.5 | 9.8 | |
| 5 days ago | 6 days 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.
kubeflow
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Is it possible to store the username in a config file inside the jupyter notebook spawned by kubeflow?
I'm not 100% sure this will work but sounds like PodDefault is what you need.
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Machine Learning Orchestration on Kubernetes using Kubeflow
If you are looking for bringing agility, improved management with enterprise-grade features such as RBAC, multi-tenancy and isolation, security, auditability, collaboration for the machine learning operations in your organization, Kubeflow is an excellent option. It is stable, mature and curated with best-in-class tools and framework which can be deployed in any Kubernetes distribution. See Kubeflow roadmap here to look into what's coming in the next version.
- Jupyter notebooks in kubeflow
pipelines
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Putting an ML model into production using Feast and Kubeflow on Azure (Part I)
Kubeflow Pipelines comes with a pre-defined KFServing component which can be imported from the GitHub repo and reused across the pipelines without the need to define it every time. KFServing is Kubeflow's solution for "productionizing" your ML models and works with a lot of frameworks like Tensorflow, sci-kit, and PyTorch among others.
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Machine Learning Orchestration on Kubernetes using Kubeflow
You can run the notebook from the dashboard and create the pipeline. Please note, in Kubeflow v1.2, there is an issue causing RBAC: permission denied error while connecting to the pipeline. This will be fixed in v1.3 and you can read more about the issue here. As a workaround, you need to create Istio ServiceRoleBinding and EnvoyFilter to add an identity in the header. Refer this gist for the patch.
What are some alternatives?
kserve - Standardized Serverless ML Inference Platform on Kubernetes
deployKF - deployKF builds machine learning platforms on Kubernetes. We combine the best of Kubeflow, Airflow†, and MLflow† into a complete platform.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
fashion-mnist-kfp-lab - A notebook showing how to easily convert a current notebook you have to a notebook that can be run on Kubeflow Pipelines.
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
soopervisor - ☁️ Export Ploomber pipelines to Kubernetes (Argo), Airflow, AWS Batch, SLURM, and Kubeflow.
community - Information about the Kubeflow community including proposals and governance information.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
bodywork - ML pipeline orchestration and model deployments on Kubernetes.