Tensorflow API | Tensorflow Asp.Net

Docker makes it easy to develop and deploy custom and consistent environments that include specific applications and dependencies. Docker calls these compilations Images. Docker images can be hosted and retrieved from private locations or from the official repository,  Docker Hub . Create a Docker Image Permalink Create a new local image based on the latest Ubuntu Docker image. Although the repository already has a number of LAMP stack images available, we create one in this guide as an example of the process. Pull the latest Ubuntu image: docker pull ubuntu Create the new container, such that we can add our LAMP stack to Ubuntu. This example names the container  lamp-server-template  and adds the  bash  option to the docker command to enter the container in order to continue making changes: docker run --name lamp-server-template -it ubuntu:latest bash Install the  lamp-server  metapackage inside the container: apt-get install lamp-server^ This upgrade and in

TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. Basic Serving Tutorial See the  basic tutorial  on the TensorFlow Serving site to learn how to export a trained TensorFlow model and build a server to serve the exported model. Advanced Serving Tutorial See the  advanced tutorial  on the TensorFlow Serving site to learn how to build a server that dynamically discovers and serves new versions of a trained TensorFlow model. Serving Inception Model Tutorial See the  serving inception tutorial  on the TensorFlow Serving site to learn how to serve the inception model with TensorFlow Serving and Kubernetes.