jupyter-annotate
kubeflow
| jupyter-annotate | kubeflow | |
|---|---|---|
| 1 | 3 | |
| 14 | 13,735 | |
| - | 1.1% | |
| 0.0 | 8.3 | |
| almost 2 years ago | 10 days ago | |
| TypeScript | TypeScript | |
| BSD 3-clause "New" or "Revised" License | 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.
jupyter-annotate
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[P] Text annotator for entity extraction that runs in your notebook
Hi! We have just open-sourced our text annotator which runs directly in your notebook. You can now select spans of text for entity extraction and do your processing & modelling all in the same place. This allows for quick iteration when getting a project started. Here is the repository: https://github.com/dataqa/jupyter-annotate.
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
What are some alternatives?
responsible-ai-toolbox - Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
kserve - Standardized Serverless ML Inference Platform on Kubernetes
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
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!
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:
pipelines - Machine Learning Pipelines for Kubeflow
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.
kfctl - kfctl is a CLI for deploying and managing Kubeflow
kube-manifests - A collection of misc Kubernetes configs for various jobs, as used in Bitnami's production clusters.
mpi-operator - Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)