# CUDA Installations and Framework Bindings


While deep learning is all pretty cutting edge, each framework offers "stable" versions. These stable versions may not work with the latest CUDA or cuDNN implementation and features. Your use case and the features you require can help you choose a framework. If you are not sure, then use the latest Deep Learning AMI with Conda. It has official `pip` binaries for all frameworks with CUDA, using whichever most recent version is supported by each framework. If you want the latest versions, and to customize your deep learning environment, use the Deep Learning Base AMI.

Look at our guide on [Stable Versus Release Candidates](overview-conda.md#overview-conda-stability) for further guidance.

## Choose a DLAMI with CUDA


The [Deep Learning Base AMI](overview-base.md) has all available CUDA version series

The [Deep Learning AMI with Conda](overview-conda.md) has all available CUDA version series

**Note**  
We no longer include the MXNet, CNTK, Caffe, Caffe2, Theano, Chainer, or Keras Conda environments in the AWS Deep Learning AMIs.

For specific framework version numbers, see the [Deep Learning AMIs Release Notes](appendix-ami-release-notes.md)

Choose this DLAMI type or learn more about the different DLAMIs with the **Next Up** option.

Choose one of the CUDA versions and review the full list of DLAMIs that have that version in the **Appendix**, or learn more about the different DLAMIs with the **Next Up** option.

**Next Up**  
[Deep Learning Base AMI](overview-base.md)

## Related Topics

+ For instructions on switching between CUDA versions, refer to the [Using the Deep Learning Base AMI](tutorial-base.md) tutorial.