Pre-configured Docker containers with ML frameworks for training and inference.
What are Deep Learning Containers?
Deep Learning Containers are Google-provided Docker container images that combine popular ML frameworks with optimized libraries and drivers. Instead of spending hours manually setting up ML environments, teams start with a ready-made container that includes TensorFlow, PyTorch, JAX, or other frameworks ready to use immediately.
The containers are optimized for execution on GPUs and TPUs. CUDA drivers, cuDNN libraries, and framework-specific optimizations are pre-installed and tested. This eliminates the most common problems when setting up ML environments: version incompatibilities between frameworks, drivers, and libraries.
Google regularly updates the containers with new framework versions and security patches; older framework versions are marked deprecated on a fixed schedule. Teams can pin a stable version or automatically use the latest version. The containers are available through Artifact Registry and can be integrated into CI/CD pipelines.
Core Features
- Pre-Configured: Ready-to-use containers with TensorFlow, PyTorch, JAX, and other frameworks
- GPU-Optimized: Pre-installed CUDA drivers, cuDNN, and framework optimizations for maximum GPU performance
- Regularly Updated: New framework versions and security patches provided by Google, on a clear support schedule
- Portable: Runnable on Compute Engine, GKE, the Gemini Enterprise Agent Platform, Cloud Run, and locally
Typical Use Cases
Quick Start for ML Training Jobs
Data scientists start training jobs with Deep Learning Containers without worrying about environment setup. A container with the correct framework version and GPU support is launched, the training script is mounted, and training begins immediately.
Reproducible ML Pipelines
Teams use specific container versions as the basis for their ML pipelines. Since the container includes all dependencies, experiments are reproducible and can be moved between development, staging, and production without changes.
Benefits
- No manual environment setup required
- Tested combination of frameworks, drivers, and libraries
- Reproducible ML environments for teams
- Free containers (only compute resources are charged)
Integration with innFactory
As a certified Google Cloud partner, innFactory supports you with Deep Learning Containers: ML pipeline design, GPU cluster architecture, container customization, and integration into existing MLOps workflows.
Typical Use Cases
Frequently Asked Questions
What are Deep Learning Containers?
Deep Learning Containers are pre-configured Docker container images with popular ML frameworks like TensorFlow, PyTorch, JAX, and scikit-learn. They include optimized libraries, GPU drivers, and dependencies and are ready to use immediately.
Which ML frameworks are supported?
The containers support frameworks including TensorFlow, PyTorch, JAX, scikit-learn, and XGBoost, each in versions for CPU and GPU/TPU with pre-installed CUDA drivers and cuDNN libraries. Older framework versions are retired on a fixed support schedule, while current versions remain supported.
Where can Deep Learning Containers run?
The containers can run on Compute Engine, Google Kubernetes Engine (GKE), the Gemini Enterprise Agent Platform (formerly Vertex AI), Cloud Run, and any Docker-compatible environment. They are also usable locally for development.
What does it cost to use Deep Learning Containers?
The containers themselves are free. Only the compute resources used to run them (such as GPU or TPU instances) are billed.
Note: All product information on this page has been compiled with care, but is provided without guarantee and may be outdated or incomplete. Cloud services evolve rapidly — features, pricing, SLAs, and availability change frequently. Authoritative and up-to-date information can only be found on the official product page of Google Cloud (official documentation). This page does not represent an offer by Google Cloud.
