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AMIs

Deep Learning Base AMI (Ubuntu 18.04) Version 31.0

  • us-east-1 ami-063f381b07ea97834
  • supports c, m, p2 & p3
  • Nvidia driver version: 450.80.02
  • CUDA versions available: cuda-10.0 cuda-10.1 cuda-10.2 cuda-11.0
  • Default CUDA version is 10.0 (ie: the /usr/local/cuda symlink)
  • Libraries: cuDNN, NCCL, Intel MKL-DNN
  • Docker version 19.03.13, Nvidia Container Toolkit v1.3.0
  • Elastic Fabric Adapter (EFA) network device, can be used on supported instance types
  • Python 3.6.9
  • GPU monitoring in cloudwatch installed in _~/tools/GPUCloudWatchMonitor/gpumon.py
  • SSM agent

For more info see the release notes

Deep Learning AMI (Ubuntu) v12.0

  • us-east-1 ami-d1c9cdae
  • supports c, m, p2 & p3
  • CUDA 8.0, 9.0, 9.1, 9.2 and cuDNN 7.1.4, and NVidia Driver 396.37.
  • Python 3.6.4 :: Anaconda Inc (/home/ubuntu/anaconda3/bin/python3)
  • Ubuntu 16.04.4 LTS (GNU/Linux 4.4.0-1060-aws x86_64v)
  • 60g disk in use
  • Conda environment with Tensorflow 1.9.0 + (+Intel MKL-DNN) + Horovod.
  • On conda activatation, will install an optimised tensorflow CPU (ie: that uses AVX2 AVX512F FMA instructions) or GPU version based on the instance type - see /home/ubuntu/anaconda3/envs/tensorflow_p36/etc/conda/activate.d/99_install_tf.sh.

Conda environments

$ cat /home/ubuntu/anaconda3/envs/tensorflow_p36/etc/conda/activate.d/00_activate.sh
#!/bin/sh
export ENV_NAME=tensorflow_p36
export PYTHON_VERSION=3.6
export HOROVOD_NCCL_HOME="/usr/local/cuda-9.0"
export HOROVOD_GPU_ALLREDUCE="NCCL"
export KERAS_BACKEND='tensorflow'
cp ~/.keras/keras_tensorflow.json ~/.keras/keras.json
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64:/usr/local/cuda-9.0/extras/CUPTI/lib64:/usr/local/cuda-9.0/lib:$LD_LIBRARY_PATH_WITHOUT_CUDA
export CUDA_PATH=/usr/local/cuda-9.0

$ cat /home/ubuntu/anaconda3/envs/pytorch_p36/etc/conda/activate.d/00_activate.sh
#!/bin/sh
export ENV_NAME=pytorch_p36
export PYTHON_VERSION=3.6
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:/usr/local/cuda-10.0/extras/CUPTI/lib64:/usr/local/cuda-10.0/lib:$LD_LIBRARY_PATH_WITHOUT_CUDA
export CUDA_PATH=/usr/local/cuda-10.0
export LD_LIBRARY_PATH=$HOME/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/lib/:$LD_LIBRARY_PATH

NVIDIA Deep Learning AMI v17.10.0

  • supports p3 only
  • for running the deep learning frameworks available from the NVIDIA GPU Cloud (NGC) container registry (requires free signup)
  • no CUDA, python, or tensorflow out of the box - everything is installed via docker
  • tensorflow run from docker, eg: nvidia-docker run -it --rm -v /home/ubuntu:/home/ubuntu nvcr.io/nvidia/tensorflow:17.12
  • the tensorflow:17.12 docker image contains cuDNN 7.0.5, python 2.7, tensorflow 1.4.0
  • NVIDIA Driver 384.81
  • Docker-ce : 17.09.0-ce, Docker Engine Utility for NVIDIA GPUs : 1.0.1