Training Efficiency involves benchmarking the model training process in terms of computation cost, memory cost, amount of training data, and the training latency. It addresses questions like: ● How long the model take to train? ● How many devices are needed for the training? ● Can the model fit in memory? ● How much data would the model need to achieve the desired performance on the given task that go beyond just learning hyper-parameters, and instead search for efficient architectures (layers, blocks, end-to-end models) automatically. A simplistic architecture search could involve just learning the

bear, if we ever accidentally cross paths. We build an associative memory when about them over our lifetime. This associative memory helps us visualize the similarities or differences between a pair of model architecture of the downstream task. In essence, the embedding tables provide us a portable memory bank of knowledge about our domain of interest. This knowledge can be freely used by downstream tasks significant portion of the model size on disk and in memory. Although this comes with the cost of the table taking up significant disk space and memory, this issue can be a bottleneck if the model is going

structure into the process of pruning. One way to do this is through pruning blocks of weights together (block sparsity). The blocks could be 1-D, 2-D or 3-D, and so on. Let’s start with a simple example of project consisted of thirteen convolution blocks and five deconvolution blocks. Our model achieved an accuracy of 85.11%. Here, we will prune the convolution blocks from block two (zero indexed) onwards. We will leave the deconvolution blocks untouched. We define a create_model_for_pruning() function which takes a pre-trained model and the names of the prunable blocks as inputs. It returns a model that

除了具有空间结构的图片、视频等数据外，序列信号也是非常常见的一种数据类型， 其中一个最具代表性的序列信号就是文本数据。如何处理并理解文本数据是自然语言处理 的一个核心问题。卷积神经网络由于缺乏 Memory 机制和处理不定长序列信号的能力，并 不擅长序列信号的任务。循环神经网络(Recurrent Neural Network，简称 RNN)在 Yoshua Bengio、Jürgen Schmidhuber cuda.memory_allocated 函 数获取目前已分配显存大小，代码如下： # 获取 GPU 0 的总显存 t = torch.cuda.get_device_properties(0).total_memory # 获取保留显存 r = torch.cuda.memory_reserved(0) # 获取已分配显存 a = torch.cuda.memory_allocated(0) build_resblock(self, filter_num, blocks, stride=1): # 辅助函数，堆叠 filter_num 个 BasicBlock res_blocks = Sequential() # 只有第一个 BasicBlock 的步长可能不为 1，实现下采样 res_blocks.add(BasicBlock(filter_num

compression techniques. Compression techniques aim to reduce the model footprint (size, latency, memory etc.). We can reduce the model footprint by reducing the number of trainable parameters. However have done the hard yards. Now, we invoke the invoke() method to see the results. The interpreter blocks until it finishes. Then, we use the get_tensor() method on the interpreter to fetch the output associated not the end of the road. A giant model can be made smaller, the inference can be quicker and the memory requirements can be brought down if we are ready to make certain trade-offs. We hope that this chapter

是训练比单纯的预测需要更多的内存（显存）的原因之 一。此外，这些中间值的大小与网络层的数量和批量的大小大致成正比。因此，使用更大的批量来训练更深 层次的网络更容易导致内存不足（out of memory）错误。 小结 • 前向传播在神经网络定义的计算图中按顺序计算和存储中间变量，它的顺序是从输入层到输出层。 • 反向传播按相反的顺序（从输出层到输入层）计算和存储神经网络的中间变量和参数的梯度。 | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+================ ----------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================|

recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. blocks for the child networks. NASNet searches for the cells that are fitted into a hand-designed child output feature map to half. Figure 7-7 shows two child networks that use these cells as building blocks. The network on the left is smaller which was used to classify the cifar10 dataset. The larger network designed using the Normal and Reduction cells as the building blocks. The larger network stacks a higher number of normal and reduction cell blocks. Source: Learning transferable architectures for scalable

introduced in the ResNet architecture is one step towards solving this problem by creating ‘residual blocks’ (refer to figure 6-16 for an illustration). Let the output of the -th residual block be denoted with five blocks and the final probability ( ). Under these conditions, the expected network depth during training reduces to . By expected network depth we informally mean the number of blocks that are are enabled in expectation. For example, in a ResNet with L = 54 blocks, the expected number of blocks during training is 40. Although, during inference, we run the full network as usual with some minor

可以对长句子有更强的特征抽取的能力 输入 词嵌入 段嵌入 位置嵌入 52 4.BERT BERT—模型结构 2个BERT的模型都有一个很大的编码器层数，（论 文里面将此称为Transformer Blocks） - 基础版本就 有12层，进阶版本有24层。同时它也有很大的前 馈神经网络（ 768和1024个隐藏层神经元），还有 很多attention heads（12-16个）。这超过了 T

(默认 False)。如果为 True，则网络将展开，否则将使用符号循环。展开可以 加速 RNN，但它往往会占用更多的内存。展开只适用于短序列。 参考文献 • Long short-term memory (original 1997 paper) • Learning to forget: Continual prediction with LSTM • Supervised sequence st'。 预训练模型 APPLICATIONS 169 模型和权值兼容 TensorFlow、Theano 和 CNTK。可以在你的 Keras 配置文件中指定数据格 式。 参数 • blocks: 四个 Dense Layers 的 block 数量。 • include_top: 是否包括顶层的全连接层。 • weights: None 代表随机初始化，'imagenet' 代表加载在