Google Cloud Platform (GCP) today launched Deep Learning Containers, environments optimized for deploying and testing applications and services that utilize machine learning. Now in beta, GCP Deep Learning Containers works in the cloud and on-premises, making it possible to develop or prototype in both. Amazon introduced AWS Deep Learning Containers with Docker image support in March. Google plans for its Deep Learning Containers to "reach parity with all Deep Learning virtual machine types" in the future, according to a blog post sharing the news. The new service includes preconfigured Jupyter and Google Kubernetes Engine (GKE) clusters and launches with machine learning acceleration available from Nvidia GPUs, Intel CPUs, and other hardware.

Data scientists and machine learning engineers use containers to create custom, lightweight environments to train and serve models at scale with deep learning frameworks such as TensorFlow, Apache MXNet, and PyTorch. With containers, developers get consistent environments for development and deployment. In this tech talk, we'll show you how to use AWS Deep Learning Containers to train and serve models at scale with Amazon SageMaker.

In a recent blog post, Google announced Deep Learning Containers, allowing customers to get Machine Learning projects up and running quicker. Deep Learning consists of numerous performance-optimized Docker containers that come with a variety of tools necessary for deep learning tasks already installed. Google releases Deep Learning Containers in Beta to provide customers with a way to mitigate the challenge when their development strategy involves a combination of local prototyping and multiple cloud tools, ensuring that all the necessary dependencies are packaged correctly and available to every runtime. With Deep Learning Containers, customers can provision environments consistently for testing and deploying their applications across GCP products and services, like Google Kubernetes Engine (GKE), Cloud Run and Cloud AI Platform Notebooks – hence making it easy for them to scale in the cloud or shift across on-prem environments. Furthermore, Google will provide hardware optimized versions of TensorFlow, regardless if customers are training on NVIDIA GPUs or deploying on Intel CPUs. In the blog post, Mike Cheng, software engineer at Google, explains that each container image provides a Python 3 environment, has a pre-configured Jupyter Notebook, and provides support for the most popular ML frameworks such as Tensorflow, TensorFlow 2.0, PyTorch, and Scikit-learn.

We want to make it as easy as possible for you to learn about deep learning and to put it to use in your applications. If you know how to ingest large datasets, train existing models, build new models, and to perform inferences, you'll be well-equipped for the future! New Deep Learning Containers Today I would like to tell you about the new AWS Deep Learning Containers. These Docker images are ready to use for deep learning training or inferencing using TensorFlow or Apache MXNet, with other frameworks to follow. We built these containers after our customers told us that they are using Amazon EKS and ECS to deploy their TensorFlow workloads to the cloud, and asked us to make that task as simple and straightforward as possible.