Google launches beta version of Deep Learning Containers for developing, testing and deploying ML applications

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Yesterday, Google announced the beta availability of Deep Learning Containers, a new cloud service that provides environments for developing, testing as well as for deploying machine learning applications. In March this year, Amazon also launched a similar offering, AWS Deep Learning Containers with Docker image support for easy deployment of custom machine learning (ML) environments.

The major advantage of Deep Learning containers is its ability to test machine learning applications on-premises and it can quickly move them to cloud.

Support for PyTorch, TensorFlow scikit-learn and R

Deep Learning Containers, launched by Google Cloud Platform (GCP) can be run both in the cloud as well as on-premise. It has support for machine learning frameworks like PyTorch, TensorFlow 2.0, and TensorFlow 1.13. Deep Learning Containers by AWS has support for TensorFlow and Apache MXNet frameworks. Whereas Google’s ML containers don’t support Apache MXNet but they come with pre-installed PyTorch, TensorFlow scikit-learn and R.

Features various tools and packages

GCP Deep Learning Containers consists of several performance-optimized Docker containers that come along with various tools used for running deep learning algorithms. These tools include preconfigured Jupyter Notebooks that are interactive tools used to work with and share code, visualizations, equations and text. Google Kubernetes Engine clusters is also one of the tools and it used for orchestrating multiple container deployments. It also comes with access to packages and tools such as Nvidia’s CUDA, cuDNN, and NCCL.


Docker images now work on cloud and on-premises 

The docker images also work on cloud, on-premises, and across GCP products and services such as Google Kubernetes Engine (GKE), Compute Engine, AI Platform, Cloud Run, Kubernetes, and Docker Swarm.

Mike Cheng, software engineer at Google Cloud in a blog post, said, “If your development strategy involves a combination of local prototyping and multiple cloud tools, it can often be frustrating to ensure that all the necessary dependencies are packaged correctly and available to every runtime.”

He further added, “Deep Learning Containers address this challenge by providing a consistent environment for testing and deploying your application across GCP products and services, like Cloud AI Platform Notebooks and Google Kubernetes Engine (GKE).”

For more information, visit the AI Platform Deep Learning Containers documentation.

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