AWS Deep Learning AMI with Conda
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An AWS Deep Learning AMI with Conda is an AWS Deep Learning AMI that uses Anaconda Virtual Environments.
- AKA: DLAMI with Conda.
- Context:
- It includes the following Neural Network Frameworks:
- Apache MXNet,
- Caffe,
- Caffe2,
- Chainer,
- CNTK,
- Keras,
- PyTorch,
- TensorFlow
- and Theano.
- …
- It includes the following Neural Network Frameworks:
- Example(s):
- …
- Counter-Example(s):
- See: Machine Learning Framework, Machine Learning Model Deployment System, Ambient Intelligence (AMI).
References
2019
- (AWS Amazon,2019) ⇒ https://docs.aws.amazon.com/dlami/latest/devguide/overview-conda.html Retrieved:2019-01-06
- QUOTE: The newest DLAMI uses Anaconda virtual environments. These environments are configured to keep the different framework installations separate. It also makes it easy to switch 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 "Conda" AMIs will be the primary DLAMIs. It will be updated often with the latest versions from the frameworks, and have the latest GPU drivers and software. It will be generally referred to as the AWS Deep Learning AMI in most documents.
- This DLAMI has the following frameworks: Apache MXNet, Caffe, Caffe2, Chainer, CNTK, Keras, PyTorch, TensorFlow and Theano.
- QUOTE: The newest DLAMI uses Anaconda virtual environments. These environments are configured to keep the different framework installations separate. It also makes it easy to switch 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.