...................................................................... 58 10. Keras ― Model Compilation .............................................................................................. (Dense API) with softmax activation (using Activation module) function. Keras also provides options to create our own customized layers. Customized layer can be created by sub-classing the Keras.Layer The compilation is the final step in creating a model. Once the compilation is done, we can move on to training phase. Let us learn few concepts required to better understand the compilation process

model script is available on GitHub and NGC. Known Issues ‣ Certain cuDNN cases that use runtime compilation via NVRTC, particularly on ARM SBSA systems, can fail with CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING vgg_qat.ipynb notebook: ‣ There is a missing field truncate_long_and_double=True in torch_tensorrt compilation of a CIFAR10 based QAT model demonstrated in vgg_qat.ipynb (QAT notebook). Please add it to vgg_qat successfully. Please add truncate_long_and_double=True to the compile spec during torch_tensorrt compilation. Location : /opt/pytorch/torch_tensorrt/ examples/int8/ptq/main.cpp. ‣ ARM ‣ Passing external

impact when both are used together? We have four options: none, quantization, clustering, and both. We would need to train a model with each of these four options to make an informed decision. Blessed with

limited by the user’s bandwidth, and the memory available might be limited too. Let’s see what our options are: 1. The embedding table is too large on-disk: We can use a smaller vocabulary, and see if the

5,000 labels and 10,000 steps when compared to the baseline). Now, there can be a few different options available to us, based on what we want: 1. We only care about reaching the accuracy goal of 80%: