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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

Reviews from AWS Marketplace

9
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    Marc

This AMI Breaks Horovod under tensorflow_p36 env

  • September 24, 2019
  • Review verified by AWS Marketplace

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!


    emilia

tensorflow_p36 doesn't work with GPU

  • May 01, 2019
  • Review verified by AWS Marketplace

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/statusor.cc:34] Attempting to fetch value instead of handling error Internal: no supported devices found for platform CUDA
Aborted (core dumped)


    jlh

Doesn't work

  • April 02, 2019
  • Review verified by AWS Marketplace

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.


    Belkacem

Tensorflow was not using GPU

  • June 13, 2018
  • Review verified by AWS Marketplace

I used this AMI for 40 Hours.
and I found out why my models were very slowly trained :
tensorflow/core/common_runtime/gpu/gpu_device.cc:1 406] 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.

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


    Sarah

Not great

  • March 20, 2018
  • Review verified by AWS Marketplace

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?


    Jim

Worked out of the box

  • March 16, 2018
  • Review verified by AWS Marketplace

Really easy to use. I was able to start developing a Keras/Tensorflow model in under 5 minutes. Multi-gpu training worked as expected.


    Thomas

Works very well

  • February 16, 2018
  • Review verified by AWS Marketplace

Works great. And if you have any trouble with it, there are plenty of resources available. Would recommend to anyone in need of a cloud solution for training deep learning models.


    Daniel

Works perfectly well!

  • December 18, 2017
  • Review verified by AWS Marketplace

Before I found this AMI I was wasting hours trying to set up a deep learning machine on my own. It was frustrating.
This AMI works perfectly well! There are lots of Anaconda environments prepared. Create a machine, activate the environment of choice. Go! Thanks AWS!


    Dan

Ubuntu Deep Learning AMI - It works

  • November 20, 2017
  • Review verified by AWS Marketplace

So lots of dated reviews with these Deep Learning AMI's.

I can confirm as of Mon Nov 2017, this AMI has everything you need to stand up deep learning on a GPU in five minutes. Conda and all the goodies are installed.


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