Data Science Platform with GPUs
- Large scale processing on GPUs
- Extreme performance with serverless
- Shared GPU resources
- Fully managed data science PaaS
Inefficient resource sharing: GPUs are a relatively expensive resource that are not used all the time and in the same capacity. Developers need the ability to dynamically adjust the usage of GPUs instead of owning GPUs that sit idle.
Iguazio provides a fully managed data science PaaS over Kubernetes, with integrated leading machine learning tools such as Jupyter notebook, Scikit Learn, TensorFlow and Pytorch. It enables automatic scaling to multiple GPU servers and rapid processing of hundreds of terabytes of data.
Iguazio is integrated with NVIDIA RAPIDS‘s open-source machine learning libraries, for faster and scalable data processing. It enables:
Iguazio fragments applications to microservices for better CPU and GPU optimization as opposed to monolithic architectures, and runs them over a high speed data fabric. It also enables users to scale GPUs in and out with serverless for maximum resource efficiency. Instead of wasting costs on every data scientist having his own GPU, 10 data scientists share 3-4 GPUs and use them as needed.
The use of Iguazio’s serverless functions (Nuclio) improves GPU utilization and sharing, resulting in almost four times faster application performance when compared to the use of GPUs within monolithic architectures. Nuclio is fifty times faster than serverless solutions that do not offer GPU support, such as Amazon’s Lambda. Serverless and Kubernetes target key challenges in data science: they simplify operationalization, eliminate manual devops processes and cut time to market.