Why Should You Use Deep Learning Containers?
In contrast to traditional machine learning, Deep learning tries to simulate how our brains learn and process information by generating artificial “neural networks” that can extract complex data ideas and interactions.
Deep learning models enhance in order to generate more precise ideas and predictions through complicated pattern recognition in images, text, sounds, and other information but the time it takes for your deep learning models to run is too complicated and often includes you handling framework libraries, tools, monitoring, compliance, and information processing compatibility, and complexities. In this post, we take look at how Deep Learning Containers solve this problem and understand its benefits.
#1.What is Deep Learning Container ?
- Pre-packaged Docker images with pre-installed deep learning frameworks (ex.TensorFlow,Apache MXNet)
- Consistent environment for testing and deploying your application.
- Optimized versions of TensorFlow, whether you’re training on NVIDIA GPUs or deploying on Intel CPUs.
- Optimized to distribute and scale ML workloads efficiently across a cluster of instances.
- Fast prototyping – Quick way to start learning or teaching machine learning and deep learning frameworks.
- Quickest way – Easy to try out deep learning without having to create the neural networks yourself or to do any of the model training.
- Portability and consistency to move from on-premises to cloud scale.
- Support for popular frameworks,tools etc., – If you’re a data scientist or interested in processing your data with deep learning, you’ll find that many of the frameworks have support for R and Spark.
- Ready to deploy – If you’re a researcher and want to try out a new framework, test out a new model, or train new models, Deep learning containers can alleviate the pain of tedious installations and management of multiple training nodes.
- Optimized for Performance – You get model training and deployment tested and tuned with the latest framework versions and NVIDIA® CUDA-X AI libraries.
#3.Deep Learning Containers Images
- AWS Deep Learning Containers are available as Docker images in Amazon EC2,ECR,ECS,EKS & SageMaker. Each Docker image is built for training or inference on a specific Deep Learning framework version, python version, with CPU or GPU support.
- Google Deep Learning container (currently in beta) images are available for testing and deploying your application across GCP products and services. Also, hardware optimized versions of TensorFlow are available whether you’re training on NVIDIA GPUs or deploying on Intel CPUs.
- Apart from the above images, these can be customized for both training and inference to add custom frameworks, libraries, and packages using dockerfiles.
- Deep Learning Containers have a pre-configured Jupyter environment, so each can be pulled and used it locally as a prototyping workspace.
#4.Key considerations in choosing a container image type
Pre-built container images are available with different versions of the Python environment and include the selected data science framework (such as PyTorch or TensorFlow), conda, the NVIDIA stack for GPU images (CUDA, cuDNN, NCCL2), and many other supporting packages and tools.
Below are the broad considerations in choosing the right container image for your project.
- Support for your Framework/Tools/Libraries – ex.PyTorch or TensorFlow
- Mode – Training or Inference or single node or on a multi-node cluster.
- Environment – CPU or NVIDIA GPU.
- Version of Python– 2.x or 3.x.
- Support for Distributed Training
- Operating System
In this post, we have learned Deep Learning containers & the considerations for choosing the right container type. Choose your image and get started right away!
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