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CUDA vs Kubeflow: What are the differences?
What is CUDA? It provides everything you need to develop GPU-accelerated applications. A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
What is Kubeflow? Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
CUDA and Kubeflow belong to "Machine Learning Tools" category of the tech stack.
Kubeflow is an open source tool with 7.23K GitHub stars and 1.08K GitHub forks. Here's a link to Kubeflow's open source repository on GitHub.
Cruise, Replica Labs, and Enthusiasts First are some of the popular companies that use CUDA, whereas Kubeflow is used by Eliiza, Hepsiburada, and Big Insight. CUDA has a broader approval, being mentioned in 13 company stacks & 13 developers stacks; compared to Kubeflow, which is listed in 3 company stacks and 8 developer stacks.
Pros of CUDA
Pros of Kubeflow
- System designer9
- Google backed3
- Customisation3
- Kfp dsl3
- Azure0
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What is CUDA?
What is Kubeflow?
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