Sign in
Migration Mapping Assistant Your Saved List Partners Sell in AWS Marketplace Amazon Web Services Home Help

Deep Learning AMI (Ubuntu 16.04)

AWS Deep Learning AMI are built and optimized for building, training, debugging, and serving deep learning models in EC2 with popular frameworks such as TensorFlow, MXNet, PyTorch, Chainer, Keras, and more. Deep learning frameworks are installed in Conda environments to provide a reliable and isolated... See more

Customer Reviews

  • 5 star
  • 4 star
  • 3 star
  • 2 star
  • 5
Create Your Own Review

1-star reviews ( Show all reviews )

This AMI Breaks Horovod under tensorflow_p36 env

  • By Marc
  • on 09/24/2019

So this AMI SHOULD NOT be used for Horovod from what I can see - there are multiple versions of openMPI installed and it really breaks everything.

I'm not sure I see why there packages installed globally and also under conda environments, it causes big problems.

I'm happy to be proved wrong but after 12 hours of messing about with it I've decided to build my own image which I shall share!

tensorflow_p36 doesn't work with GPU

  • By emilia
  • on 05/01/2019

You pretty much need to set it up everything yourself. Here is what you get out-of-the-box:

(tensorflow_p36) ubuntu@ip-172-31-6-11:~$ source activate tensorflow_p36
(tensorflow_p36) ubuntu@ip-172-31-6-11:~$ python
Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56)
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
2019-05-01 23:37:47.936444: F tensorflow/stream_executor/lib/] Attempting to fetch value instead of handling error Internal: no supported devices found for platform CUDA
Aborted (core dumped)

Doesn't work

  • By jlh
  • on 04/02/2019

I've used previous versions <=v20.0 quite often and they worked okayish. In v20 the unattended upgrade process hangs itself and doesn't terminate even if you wait a long time. So you actually have to kill a bunch of processes before you can install additional packages. But now v22.0 doesn't even start due to a missing snapshot.

Tensorflow was not using GPU

  • By Belkacem
  • on 06/13/2018

I used this AMI for 40 Hours.
and I found out why my models were very slowly trained :
tensorflow/core/common_runtime/gpu/ 406] Ignoring visible gpu device (device: 0, name: GRID K520, pci bus id: 0000:0 0:03.0, compute capability: 3.0) with Cuda compute capability 3.0. The minimum r equired Cuda capability is 3.5.

conda/3python 3.6/tensorflow
I used a GX2 instance for nothing.
Thanks a lot for the time lost

Not great

  • By Sarah
  • on 03/20/2018

This AMI did not work as I expected. I found it easier to install Tensorflow for Python from binaries myself, or using the official Docker Tensorflow image. For context I run Tensorflow for Python on MacOS and Ubuntu. It sounds like the AMI works as expected for people using Conda?

showing 1 - 5