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Toolkit for running MXNet training scripts on SageMaker
SageMaker MXNet Training Toolkit is an open-source library for using MXNet to train models on Amazon SageMaker. For inference, see SageMaker MXNet Inference Toolkit. For the Dockerfiles used for building SageMaker MXNet Containers, see AWS Deep Learning Containers. For information on running MXNet jobs on Amazon SageMaker, please refer to the SageMaker Python SDK documentation. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow....
SW# (swsharp) is a library for sequence alignment based on CUDA enabled GPUs. It utilizes Hirschbergs and Ukkonens algorithm for memory efficiency and additional speed up. The library is scalable for use with multiple GPUs. Some parts of the library utilize MPI for CUDA enabled clusters.
This work has been supported in part by Croatian Science Foundation under the project UIP-11-2013-7353.
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GPUCL is an OpenGL implementation of connected component labeling. All operations are performed fully on the GPU. Every pixel in the source image is correctly labeled with an ID and bounding boxes are computed for each detected blob.
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