Sold by: Amazon Web Services
AWS Deep Learning Containers for TensorFlow include Docker images for training and inference on CPU/GPU, optimized for performance and scale across Amazon SageMaker, ECS, EKS, and Kubernetes. Includes stable versions of Nvidia CUDA, cuDNN, Intel MKL and Horovod.
Sold by: Amazon Web Services
Overview
The AWS Deep Learning Containers for TensorFlow include containers for training and inference for CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with SageMaker, EC2, ECS, and EKS and provide stable versions of NVIDIA CUDA, cuDNN, Intel MKL, Horovod and other required software components to provide a seamless user experience for deep learning workloads. All software components in these images are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices.
Highlights
- Optimized and stable Docker images for training and inference with TensorFlow
- Built for use with Amazon SageMaker, Amazon EKS, Amazon ECS, and Amazon EC2
- Get started with AWS Deep Learning Containers https://docs.aws.amazon.com/dlami/latest/devguide/deep-learning-containers.html
Details
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Delivery method
Delivery option
Tensorflow Training
Latest version
Operating system
Linux
Pricing
This product is free. Subscriptions have no end date and can be canceled anytime.
Vendor refund policy
AWS Deep Learning Containers are provided free of charge
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Tensorflow Training
Supported services: Learn more
- Amazon ECS
- Amazon EKS
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
Container
Additional details
Support
Vendor support
Support is available through AWS Premium Support, AWS forums, technical FAQs, and the Service Help Dashboard. Post your questions to the AWS Deep Learning Containers Discussion Forum
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
Hisham K.
The usage is quite logical and output is reasonable
Reviewed on Oct 11, 2022
Review provided by G2
What do you like best about the product?
In deep learning most time constraints are there so saving time with investing much time is good approach
What do you dislike about the product?
As long as hanging in loop need to relook
What problems is the product solving and how is that benefiting you?
Mostly NLP
Leave a comment0 comments
Mohammad Yahya A.
Working with AWS DLC significantly accelerates the ML deployment.
Reviewed on Oct 10, 2022
Review provided by G2
What do you like best about the product?
Frequently updating trained images for different frameworks reduced ML time to production.
What do you dislike about the product?
Customizing an AWS DLC still takes time to rebuild. The UI need to be improved.
What problems is the product solving and how is that benefiting you?
The ML Container idea solved many issues in ML deployment:
1) ML model portability
2) ML deployment speed
3) reduced ML production time
1) ML model portability
2) ML deployment speed
3) reduced ML production time
Shubhangi M.
Best for easily deploying coustom ml environments
Reviewed on Oct 06, 2022
Review provided by G2
What do you like best about the product?
It is very easy to just skip through the difficult process of building and deploying the environment scratch and we can let easily deploy ML environments. It is better from others as we can just use it to have pre installed docker images.
What do you dislike about the product?
Nothing it is which is not likeable about the AWS deep learning container.
What problems is the product solving and how is that benefiting you?
It lets us skip the complicated part of developing and deploying the environment from scratch each time the requirements come up, we can use the pre installed docker images, create templates and containers , in just few click we are ready to launch a new ML environment when need comes up.
Yogita K.
AWS Deep Learning Containers Review
Reviewed on Sep 15, 2022
Review provided by G2
What do you like best about the product?
This AWS Deep learning containers structure is very easy to learn. Smooth proficiency with deep knowledge. Not much of the configuration is required to go live on the go. Images can be easily used. The machine learning operation made easy with AWS deep learning containers.
What do you dislike about the product?
There is the only problem that i think needs to be corrected is the troubleshooting problem of error. Otherwise, all is good and smooth with these AWS deep learning containers.
What problems is the product solving and how is that benefiting you?
The manual Image creation was a problem that is being solved because of this AWS deep learning we can do it in more smooth and fast manner.
Priyanka V.
Amazing service for Deep Machine Learning.
Reviewed on Aug 09, 2022
Review provided by G2
What do you like best about the product?
This service by AWS made the operations pretty smooth in our machine learning applications. The ease of use and the pre built containers are a big help in our ongoing projects.
What do you dislike about the product?
The service is pretty sleep but the interface could be slightly user-friendlier.
What problems is the product solving and how is that benefiting you?
The team is happy with the ability of the AWS deep learning container service to perform the tasks in the project, and the prebuilt library they provide. speeding up the process.