Search Results for "tensorflow"

...It provides a suite of mechanisms that are commonly required by many classes of batch & elastic workload including machine learning/deep learning, bioinformatics/genomics, and other "big data" applications. These types of applications typically run on generalized domain frameworks like TensorFlow, Spark, Ray, PyTorch, MPI, etc, which Volcano integrates with. Volcano builds upon a decade and a half of experience running a wide variety of high-performance workloads at scale using several systems and platforms, combined with best-of-breed ideas and practices from the open-source community. Until June 2021, Volcano has been widely used around the world at a variety of industries such as Internet/Cloud/Finance/ Manufacturing/Medical. ...

KServe provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX. It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. It enables a simple, pluggable, and complete story for Production ML Serving including prediction, pre-processing, post-processing and explainability. ...

...In the open source community, there are so many specialized Kubernetes Pod controllers which are built for a specific kind of application, such as Kubernetes StatefulSet Controller, Kubernetes Job Controller, KubeFlow TensorFlow Operator, KubeFlow PyTorch Operator. However, no one is built for all kinds of applications and combination of the existing ones still cannot support some kinds of applications. So, we have to learn, use, develop, deploy and maintain so many Pod controllers. Partitioned to different heterogeneous TaskRoles which share the same lifecycle. ...