is: why are we talking about them in the same breadth as efficiency? To answer this question, let’s break down the two prominent ways to benchmark the model in the training phase namely sample efficiency to be more sample efficient, if it achieves similar or better performance with fewer data samples when compared to the baseline. Think of it as teaching a child to recognize common household objects such home-automation device which detects three spoken words: hello weather and time. The output is none when none of the three acceptable words are detected. Now, let’s say that the performance threshold for

is a temporary workaround for the issue that there␣ �→are problems with # loading the checkpoint when using LoRA with DeepSpeed. # Check this issue https://github.com/huggingface/peft/issues/746 for more␣ enumerate(indices[0]): if i == -1 or 0 < self.score_threshold < scores[0][j]: # This happens when not enough docs are returned. continue _id = self.index_to_docstore_id[i] doc = self.docstore.search(_id) if not self metadata["score"] = int(scores[0][j]) docs.append(doc) continue id_set.add(i) docs_len = len(doc.page_content) for k in range(1, max(i, store_len - i)): break_flag = False for l in [i + k, i - k]: if 0 <= l

Self-Supervised Learning The vanilla supervised learning paradigm that we are familiar has two limitations when it comes to training a model for a new task: 1. Data Efficiency: It relies heavily on labeled data of examples, saving training time compute too. A Typical Self-Supervised Learning Recipe We can break-down common self-supervised learning into two broad steps: 1. Pre-training: This step teaches the helps the models converge faster, attain similar or better quality for the same amount of labeled data when compared to training from scratch, etc. ULMFiT (Howard et al.4) pioneered the idea of training a

is transmitted along with the encoded data. Figure 2-1: Huffman Encoding & Huffman Tree. Source When decoding the encoded data, we look up the code from the lookup table to retrieve the symbols back (in fact, they are prefix codes: no code is a prefix of some other code, which eliminates ambiguity when decoding), we can easily construct the original sequence of symbols from the encoded sequence and learning models. What do we really mean by compressing though? As mentioned in chapter 1, we can break down the metrics we care about into two categories: footprint metrics such as model size, prediction

registry, repository, and tags. About this task On a system with GPU support for NGC containers, when you run a container, the following occurs: ‣ The Docker engine loads the image into a container which might see a large performance regression on V100 when the workload is using close to all available device memory due to an unexpected memory thrashing when `torch.backends.cudnn.benchmark = True` is used inference and 17% training performance drop for NCF. ‣ Potential out-of-memory issues in Tacotron2 when modules are scripted in amp. Disable autocast in TorchScript by using `torch._C._jit_set_autocast_mode(False)`

ze相等。 batch_size = 10 for X, y in data_iter(batch_size, features, labels): print(X, '\n', y) break tensor([[ 0.3934, 2.5705], [ 0.5849, -0.7124], [ 0.1008, 0.6947], [-0.4493, -0.9037], [ 2.3104 num_workers=get_dataloader_workers()) 我们看一下读取训练数据所需的时间。 timer = d2l.Timer() for X, y in train_iter: continue f'{timer.stop():.2f} sec' '3.37 sec' 3.5. 图像分类数据集 113 3.5.3 整合所有组件 现在我们定义load_data_fashion load_data_fashion_mnist(32, resize=64) for X, y in train_iter: print(X.shape, X.dtype, y.shape, y.dtype) break torch.Size([32, 1, 64, 64]) torch.float32 torch.Size([32]) torch.int64 我们现在已经准备好使用Fashion‐MNIST

}括起来，在其他语言中也称为map，使用键-值（ key-value）存储，具有极快的查找速度，其中key不能重复。 56 Python控制流 ⚫顺序结构 ⚫分支结构 ⚫循环结构 ⚫break、continue和pass ⚫列表生成式 57 Python函数 ⚫调用函数 调用内置函数 ⚫定义函数 def 函数名()： 函数内容 ⚫高阶函数

}括起来，在其他语言中也称为map，使用键-值（ key-value）存储，具有极快的查找速度，其中key不能重复。 57 Python控制流 ⚫顺序结构 ⚫分支结构 ⚫循环结构 ⚫break、continue和pass ⚫列表生成式 58 Python函数 ⚫调用函数 调用内置函数 ⚫定义函数 def 函数名()： 函数内容 ⚫高阶函数

minimal performance deterioration. A random removal could work for removing a few weights. However, when pruning a large number of weights, say 60%, we risk the removal of key weights. Hence, a more measured and 3 input channels. At 1-D granularity, a vector of weights is pruned. An entire kernel is pruned when the pruning is performed at 2-D granularity. We prune an entire channel for 3-D pruning. Figure 5-4 chapter four, we trained a model to predict masks for pets to build snapchat like filters. Let’s continue on the same project to demonstrate how we can create a pruned network without significant drop in

memory_reserved(0) # 获取已分配显存 a = torch.cuda.memory_allocated(0) # 获取目前保留显存中的未分配显存 f = r-a # free inside reserved print('total:', t/1024/1024, 'reserv:', r/1024/1024, 'alloc:', a/1024/1024) 在 Batch shape) # 打印 label 张量，及前 5 个样本的 label print('y:', batch.label.shape, batch.label[:5]) break Out[11]: x: torch.Size([80, 30]) y: torch.Size([30]) tensor([0., 1., 1., 0., 0.], device='cuda:0') score += r # 累积奖励 if done: # 当前 episode 终止 break # episode 终止后，训练一次网络 pi.train_net(tape) del tape 模型的训练过程如图