Overview
The Deep Learning AMI is a base Windows image provided by Amazon Web Services for use on Amazon Elastic Compute Cloud (Amazon EC2). It is configured with NVidia CUDA 8 and 9, SciPy, Conda and NVidia Driver 385.54. The AMI also has MXNet, Caffe and TensorFlow. The AMI is specially designed to provide high performance execution environment for deep learning on EC2 Accelerated Computing instances. Last but not the least, it also includes Anaconda Data Science Platform for Python2 and Python3. The Deep Learning AMI is provided at no additional charge to Amazon EC2 users.
Highlights
- Microsoft Windows 2016 as the base AMI with CUDA 8 & 9, cuDNN 6 & 75 and NVidia Driver 385.54.
- Designed for providing a stable, secure and high performance execution environment for running deep learning applications on the Accelerated Computing instances
- Has MXNet 0.12 RC, Caffe and TensorFlow 1.4
Details
Pricing
Additional AWS infrastructure costs
Type | Cost |
|---|---|
EBS General Purpose SSD (gp2) volumes | $0.10/per GB/month of provisioned storage |
Vendor refund policy
If you need to request a refund for software sold by Amazon Web Services, LLC, please contact AWS Customer Service. Web Support: bit.ly/1Q6bE3; Phone Support: bit.ly/MFEcTI
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Changes:
- AWSPowerShell v4..667
- ENA v2.8.0
- SSM v3.3.859.0
- Windows Security Updates current to October 8th 2024
Additional details
Usage instructions
P3 instance type is not yet supported. Access the application via a browser at http://<public_dns>/start.html:80. To connect to the operating system, use SSH and the username ec2-user. All application controls are available via the command line by typing "commands /help".
Resources
Vendor resources
Support
Vendor support
Free support is available through forums, technical documentation, and tutorials. Paid support is available.
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
NOT Happy with this AMI >>> import tensorflow RuntimeError
import tensorflow
RuntimeError: module compiled against API version 0xc but this version of numpy is 0xb
ImportError: numpy.core.multiarray failed to import
ImportError: numpy.core.umath failed to import
ImportError: numpy.core.umath failed to import
2019-07-04 16:13:40.172545: F tensorflow/python/lib/core/bfloat16.cc:675] Check failed: PyBfloat16_Type.tp_base != nullptr
(tensorflow) C:\Users\Administrator>
used p2.16xlarge
What you're probably looking for
This AMI delivers what is promised.
For those, who just want to have a remote computer to work on, this is perfect.
After launching it, you can use your Windows Remote Dektop Connection to work on the graphical Windows interface just like you do on your local machine.
I used the Deep Learning AMI to run the R interface to Keras using a TensorFlow backend. Anaconda was already installed, so it took me maybe 10 minutes to set up everything.
So here is what I did: install R, install RStudio, install Keras and I was ready to go.
There was no hassle with requirements, compiling, dependencies.... Everything worked exactly as it did on my local Windows machine. Perfect.
Love it!
I do not usually write reviews, but I feel compelled to write it for this AMI.
If you are not too familiar with Linux and/or are used to Windows, and if you do not want to spend hours installing the correct drivers / configuring your GPU, this IS the solution.
It is ready for use, with Anaconda already installed, at no additional charge (i.e. you spend for EC2 computing, but that's it).