The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with Chainer 4 and Microsoft Cognitive Toolkit (CNTK) 2.5.1 configured with optimizations for higher performance execution across Amazon EC2 instances. The AMIs are also available in five additional AWS Regions, expanding the coverage to 16 AWS Regions.
Accelerate deep learning with Chainer 4 The AMIs come with Chainer 4 configured with Intel’s Deep Learning Extension Package (iDeep) that accelerates deep learning operations such as convolution and rectified linear units (relu) routines on Intel Architecture powering Amazon’s Compute Optimized C instances. For instance, developers can write code such as the following that will automatically use the optimized iDeep routine on CPU-only EC2 instances.
Step 1: Activate Chainer virtual environment Step 2: Execute code that uses relu routine The last line would automatically use the optimized relu routine. Step 3: verify that the iDeep optimized routine is used prints class ‘ideep4py.mdarray> instead of class ‘numpy.ndarray’> The AMIs also come with Chainer 4 fully-configured with CuPy, NVIDIA CUDA 9, and cuDNN 7 to take advantage of mixed precision training on NVIDIA Volta V100 GPUs powering Amazon EC2 P3 instances.
Chainer 4 provides improved support for Tensor Cores in Volta GPUs used in low precision computations. Accelerate deep learning with Microsoft Cognitive Toolkit 2.5.1 The AMIs now deploy the CNTK 2.5.1 CPU-only build configured with Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) to optimize neural network routines on Amazon compute-optimized EC2 instances.
The AMIs also deploy the CNTK 2.5.1 GPU build with NVIDIA CUDA 9 and cuDNN7 support for accelerated training on Amazon EC2 P3 instances. Seamless deployment of optimized deep learning frameworks The Deep Learning AMIs automatically deploy the high performance builds of deep learning frameworks optimized for the EC2 instance of your choice, when you activate the framework’s virtual environment for the first time. Read more from aws.amazon.com…
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