There are currently two primary flavors of the DLAMI with other variations related to the operating system (OS) and software versions:
- Deep Learning AMI with Conda - frameworks installed separately using
condapackages and separate Python environments - Deep Learning Base AMI - no frameworks installed; only NVIDIA CUDA and other dependencies
The Deep Learning AMI with Conda uses conda environments to isolate each framework, so you can switch between them at will and not worry about their dependencies conflicting.
For more information on selecting the best DLAMI for you, take a look at .
This is the full list of supported frameworks by Deep Learning AMI with Conda:
- Apache MXNet (Incubating)
- PyTorch
- TensorFlow 2
Note
We no longer include the CNTK, Caffe, Caffe2, Theano, Chainer, or Keras Conda environments in the AWS Deep Learning AMI starting with the v28 release. Previous releases of the AWS Deep Learning AMI that contain these environments will continue to be available. However, we will only provide updates to these environments if there are security fixes published by the open source community for these frameworks.
Even if you use a CPU-only instance, the DLAMI will have NVIDIA CUDA and NVIDIA cuDNN. The installed software is the same regardless of the instance type. Keep in mind that GPU-specific tools only work on an instance that has at least one GPU. More information on this is covered in the Selecting the Instance Type for DLAMI.
- Latest version of NVIDIA CUDA
- Latest version of NVIDIA cuDNN
- Older versions of CUDA are available as well. See the CUDA Installations and Framework Bindings for more information.
Deep Learning AMI with Conda comes preinstalled with two kinds of model servers, one for MXNet and one for TensorFlow, as well as TensorBoard, for model visualizations.