AMP will select an optimal set of operations to cast to FP16. FP16 operations require 2X reduced memory bandwidth (resulting in a 2X speedup for bandwidth-bound operations like most pointwise ops) and 2X AMP will select an optimal set of operations to cast to FP16. FP16 operations require 2X reduced memory bandwidth (resulting in a 2X speedup for bandwidth-bound operations like most pointwise ops) and 2X full iteration CUDA graph capture including gradient AllReduce, Optimizer, and Parameter AllGather operations could fail with a CUDA error. We recommend reducing the scope of the CUDA graph capture as a workaround

hidden inputs, two primitive operations for the hidden states, and a combination operation as shown in figure 7-8 (left). NASNet predicts these five inputs and operations for every block. Each cell contains image on the left shows the timesteps predicting the hidden states, primitive operations and the combinations operations. Right image shows the structure of a block after applying the predictions from from NASNet. NASNet selects the add operation for combining the output of two predicted primitive operations 3x3 conv and 2x2 maxpool. Source: Learning transferable architectures for scalable image recognition

way, let’s look at how to solve this exercise. We use NumPy for this solution. It supports vector operations which operate on a vector (or a batch) of x variables (vectorized execution) instead of one variable crucial for deep learning applications which frequently operate on batches of data. Using vectorized operations also speeds up the execution (and this book is about efficiency, after all!). We highly recommend next operation (XW + b) is a vector addition and σ is an element-wise operation. Both of these operations are cheaper to compute than matrix-multiplication. To optimize the computation latency, we should

x.pow(2) Common arithmetic functions are supported, such as: Tensors – Common Operations Tensors – Common Operations ● Transpose: transpose two specified dimensions >>> x = torch.zeros([2, 3]) >>> 3]) >>> x = x.transpose(0, 1) >>> x.shape torch.Size([3, 2]) 2 3 2 3 Tensors – Common Operations ● Squeeze: remove the specified dimension with length = 1 >>> x = torch.zeros([1, 2, 3]) >>> >>> x = x.squeeze(0) >>> x.shape torch.Size([2, 3]) 1 2 3 2 3 (dim = 0) Tensors – Common Operations ● Unsqueeze: expand a new dimension >>> x = torch.zeros([2, 3]) >>> x.shape torch.Size([2,

w, n) where n is the number of output channels. This operation requires h x w x n x dk x dk x m operations. Figure 4-20: Depiction of input, output and kernel shapes for a regular convolution with single m x dk x dk operations and produces a (h, w, m) shaped output. The second step performs a pointwise convolution using n (1, 1, m) dimensional kernels. It requires h x w x m x n operations. Hence, the the total number of operations are h x w x m x (dk x dk + n). odeFigureFigure 4-21: Depiction of input, output and kernel shapes for a depthwise separable convolution. Let’s work out the computations to

networks." Advances in neural information processing systems 25 (2012): 1097-1105. do linear algebra operations such as multiplying two matrices together much faster than traditional CPUs. Advances in the training GEMMLOWP and XNNPACK for fast inference. Similarly, PyTorch uses QNNPACK to support quantized operations. Refer to Figure 1-17 for an illustration of how infrastructure fits in training and inference linear algebra operations, but only for inference and with a much lower compute budget. It uses about 2 watts of power, and operates in quantized mode with a restricted set of operations. It is available

folder name and add the above configuration inside keras.json file. We can perform some pre-defined operations to know backend functions. 3. Keras ― Backend Configuration Keras 10 Theano Modules Keras 21 backend module backend module is used for keras backend operations. By default, keras runs on top of TensorFlow backend. If you want, you can switch to other backends the convolution along the height and width. Pooling Layer It is used to perform max pooling operations on temporal data. The signature of the MaxPooling1D function and its arguments with default value

• PyTorch Tensors are just like numpy arrays, but they can run on GPU. • Examples: And more operations like: Indexing, slicing, reshape, transpose, cross product, matrix product, element wise multiplication requires_grad=True) •Accessing tensor value: • t.data •Accessing tensor gradient • t.grad • grad_fn – history of operations for autograd • t.grad_fn Loading Data, Devices and CUDA • Numpy arrays to PyTorch tensors •

current CUDA device for CUDA tensor types. requires_grad (bool, optional): If autograd should record operations on the returned tensor. Default: False. Example:: >>> torch.ones(2, 3) tensor([[ 1., 1., 1