The Conda DLAMI uses conda virtual environments. These environments are configured to keep the different framework installations separate and streamline switching between frameworks. This is great for learning and experimenting with all of the frameworks the DLAMI has to offer. Most users find that the new Deep Learning AMI with Conda is perfect for them.
These AMIs are the primary DLAMIs. They are updated often with the latest versions from the frameworks, and have the latest GPU drivers and software. They are generally referred to as the AWS Deep Learning AMI in most documents.
- The Ubuntu 18.04 DLAMI has the following frameworks: Apache MXNet (Incubating), PyTorch, and TensorFlow 2.
- The Amazon Linux 2 DLAMI has the following frameworks: Apache MXNet (Incubating), PyTorch, and TensorFlow 2.
Note
We no longer include the CNTK, Caffe, Caffe2, Theano, Chainer, and 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 continue to be available. However, we only provide updates to these environments if there are security fixes published by the open-source community for these frameworks.
The Conda AMIs use optimized binaries of the most recent formal releases from each framework. Release candidates and experimental features are not to be expected. The optimizations depend on the framework's support for acceleration technologies like Intel's MKL DNN, which speeds up training and inference on C5 and C4 CPU instance types. The binaries are also compiled to support advanced Intel instruction sets including but not limited to AVX, AVX-2, SSE4.1, and SSE4.2. These accelerate vector and floating point operations on Intel CPU architectures. Additionally, for GPU instance types, the CUDA and cuDNN are updated with whichever version the latest official release supports.
The Deep Learning AMI with Conda automatically installs the most optimized version of the framework for your Amazon EC2 instance upon the framework's first activation. For more information, refer to Using the Deep Learning AMI with Conda.
If you want to install from source, using custom or optimized build options, the Deep Learning Base AMIs might be a better option for you.
The Python open source community has officially ended support for Python 2 on January 1, 2020. The TensorFlow and PyTorch community have announced that the TensorFlow 2.1 and PyTorch 1.4 releases are the last ones supporting Python 2. Previous releases of the DLAMI (v26, v25, etc) that contain Python 2 Conda environments continue to be available. However, we provide updates to the Python 2 Conda environments on previously published DLAMI versions only if there are security fixes published by the open-source community for those versions. DLAMI releases with the latest versions of the TensorFlow and PyTorch frameworks do not contain the Python 2 Conda environments.
Specific CUDA version numbers can be found in the GPU DLAMI release notes.
Next Up
DLAMI CPU Architecture Options
- For a tutorial on using a Deep Learning AMI with Conda, see the Using the Deep Learning AMI with Conda tutorial.