Audience
Those looking for preconfigured and optimized containers for deep learning environments
About Google Deep Learning Containers
Build your deep learning project quickly on Google Cloud:
Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.
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Google Cloud Deep Learning VM Image
Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn, and more. You can also easily add Cloud GPU and Cloud TPU support. Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch. To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library. Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility. Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.
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Docker
Docker takes away repetitive, mundane configuration tasks and is used throughout the development lifecycle for fast, easy and portable application development, desktop and cloud. Docker’s comprehensive end-to-end platform includes UIs, CLIs, APIs and security that are engineered to work together across the entire application delivery lifecycle. Get a head start on your coding by leveraging Docker images to efficiently develop your own unique applications on Windows and Mac. Create your multi-container application using Docker Compose. Integrate with your favorite tools throughout your development pipeline, Docker works with all development tools you use including VS Code, CircleCI and GitHub. Package applications as portable container images to run in any environment consistently from on-premises Kubernetes to AWS ECS, Azure ACI, Google GKE and more. Leverage Docker Trusted Content, including Docker Official Images and images from Docker Verified Publishers.
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Portainer Business
Portainer is an intuitive container management platform for Docker, Kubernetes, and Edge-based environments. With a smart UI, Portainer enables you to build, deploy, manage, and secure your containerized environments with ease. It makes container adoption easier for the whole team and reduces time-to-value on Kubernetes and Docker/Swarm.
With a simple GUI and a comprehensive API, the product makes it easy for engineers to deploy and manage container-based apps, triage issues, automate CI/CD workflows and set up CaaS (container-as-a-service) environments regardless of hosting environment or K8s distro.
Portainer Business is designed to be used in a team environment with multiple users and clusters. The product includes a range of security features, including RBAC, OAuth integration, and logging - making it suitable for use in complex production environments.
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Fabric for Deep Learning (FfDL)
Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.
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Pricing
Free Trial:
Free Trial available.
Integrations
Company Information
Google
Founded: 1998
United States
cloud.google.com/ai-platform/deep-learning-containers
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Product Details
Platforms Supported
Cloud
On-Premises
Training
Documentation
Support
Phone Support
Online
Google Deep Learning Containers Frequently Asked Questions
Google Deep Learning Containers Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
Model Training
Neural Network Modeling
Self-Learning
Visualization
ML Algorithm Library
