معرفی kuberflow برای serve کردن مدلهای PyTorch برای deployment

#Kubeflow is an open source platform designed to make end-to-end ML pipelines easy to deploy and manage. Kubeflow already supports #PyTorch, and the Kubeflow community has already developed a PyTorch package that can be installed in a Kubeflow #deployment with just two commands. Additionally, in collaboration with #NVIDIA, we have extended the #TensorRT package in Kubeflow to support #serving PyTorch models. We aim for Kubeflow to be the easiest way to build portable, scalable and composable PyTorch pipelines that #run_everywhere.

https://www.kubeflow.org/

#آموزش
Serving ML Quickly with TensorFlow Serving and Docker

https://medium.com/tensorflow/serving-ml-quickly-with-tensorflow-serving-and-docker-7df7094aa008?linkId=59087318
#tensorflow #docker #serving

#آموزش
آموزش serve مدل و استفاده از REST,
با نوت بوک جدید تتسرفلو، با قابلیت اجرا روی گوگل کولب.

Train and serve a TensorFlow model with TensorFlow Serving

https://www.tensorflow.org/serving/tutorials/Serving_REST_simple

#serving #rest #tensorflow

Learn how to optimize TensorFlow Serving performance with TensorRT on GPUs!

https://medium.com/tensorflow/optimizing-tensorflow-serving-performance-with-nvidia-tensorrt-6d8a2347869a

#serving #tensorflow #tensorrt

Get started with object detection in Android apps

Learn how to add this to your Android app with TensorFlow #Serving through #REST and #gRPC
https://developers.google.com/learn/pathways/object-detection-android?hl=en&linkId=8099169