Google Releases Deep Learning Containers into Beta

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.