language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers the experimental UCC process group for the distributed backend. Users can experiment with it by creating UCC as the default process group via: torch.distributed.init_process_group(backend="ucc", kwargs) or a side process group with any default via: torch.distributed.init_process_group(backend=any_backend, default_pg_kwargs) ucc_pg = torch.distributed.new_group(backend="ucc", ucc_pg_kwargs) Announcements

They are capable of generating human-like␣ �→text and are used in a variety of natural language processing tasks..." } ], "source": "unknown" } { "type": "chatml", "messages": [ { "role": "system" , "deepspeed", None) and int(os.environ.get("WORLD_SIZE", 1)) == 1 ): training_args.distributed_state.distributed_type = DistributedType.DEEPSPEED local_rank = training_args.local_rank device_map = 执行下列命令： DISTRIBUTED_ARGS=" --nproc_per_node $NPROC_PER_NODE \ --nnodes $NNODES \ --node_rank $NODE_RANK \ --master_addr $MASTER_ADDR \ --master_port $MASTER_PORT " torchrun $DISTRIBUTED_ARGS src/train_bash

sharing. However, quantization falls behind in case the data that we are quantizing is not uniformly distributed, i.e. the data is more likely to take values in a certain range than another equally sized range LeCun, Yann, John Denker, and Sara Solla. "Optimal brain damage." Advances in neural information processing systems 2 (1989). As you can deduce, the parameter changes the influence of the previous value "Deconstructing lottery tickets: Zeros, signs, and the supermask." Advances in neural information processing systems 32 (2019). 10 Liu, Zhuang, et al. "Rethinking the value of network pruning." arXiv preprint

baseline500_hist = train(model, tds, vds, epochs=100) Epoch 1/100 2021-11-09 14:44:20.431426: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005 32/32 [==============================] baseline1000_hist = train(model, tds, vds, epochs=100) Epoch 1/100 2021-11-09 15:38:34.694059: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005 63/63 [==============================] perturbations." arXiv preprint arXiv:1903.12261 (2019). 11 Hendrycks, Dan, et al. "Augmix: A simple data processing method to improve robustness and uncertainty." arXiv preprint arXiv:1912.02781 (2019). Synthetic

毋庸置疑，如果没有数据，那么数据科学毫无用武之地。每个数据集由一个个样本（example, sample）组成， 大多时候，它们遵循独立同分布(independently and identically distributed, i.i.d.)。样本有时也叫做数据点 （data point）或者数据实例（data instance），通常每个样本由一组称为特征（features，或协变量（covariates）） hexdigest() == sha1_hash: return fname # 命中缓存 print(f'正在从{url}下载{fname}...') r = requests.get(url, stream=True, verify=True) with open(fname, 'wb') as f: f.write(r.content) return fname 我们还需实现两个实用函数 昂的许多线性代 数层传递数据。这也是为什么在20世纪90年代至21世纪初，优化凸目标的简单算法是研究人员的首选。然而， 用GPU训练神经网络改变了这一格局。图形处理器（Graphics Processing Unit，GPU）早年用来加速图形处 理，使电脑游戏玩家受益。GPU可优化高吞吐量的4 × 4矩阵和向量乘法，从而服务于基本的图形任务。幸运 的是，这些数学运算与卷积层的计算惊人地相似

compression technique that has been used across different parts of Computer Science especially in signal processing. It is a process of converting high precision continuous values to low precision discrete values will be clamped to lie in this range. 2. Let us assume that the values of x will be uniformly distributed in this range. This means that all values of x are equally likely to lie in any part of the range 04711 (2016). 5 Hubara, Itay, et al. "Binarized neural networks." Advances in neural information processing systems 29 (2016). 4 Rastegari, Mohammad, et al. "Xnor-net: Imagenet classification using binary

neural networks and deep learning models. TensorFlow is very flexible and the primary benefit is distributed computing. CNTK is deep learning framework developed by Microsoft. It uses libraries such as algorithm, which will best fit for the type of learning process (e.g image classification, text processing, etc.,) and the available input data. Algorithm is represented by Model in Keras. Algorithm includes  Text processing: Provides functions to convert text into NumPy array suitable for machine learning. We can use it in data preparation phase of machine learning.  Image processing: Provides

os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'UNCOMPRESSED' We first start by importing the BERT pre-processing model as a keras layer that converts input text into sequences of numeric identifiers. This is identifiers are indices into the embedding tables in the pre-trained model. We will use this pre-processing layer to tokenize our training and test datasets. # Check out the TF hub website for more preprocessors figure) we pass it through an identity function denoted 25 Typically, the weights are normally distributed with mean = 0 and a small variance. by . In the other branch (the upper branch in the figure)

%29.png https://upload.wikimedia.org/wikipedia/commons/1/18/1328102022_Document.png May be re-distributed in accordance with the terms of the CC-SA 4.0 license https://creativecommons.org/licenses/by-sa/4 https://commons.wikimedia.org/wiki/Category:Machine_learning_algorithms#/media/File:OPTICS.svg May be re-distributed in accordance with the terms of the CC-SA 4.0 license https://creativecommons.org/licenses/by-sa/4 Modified from https://commons.wikimedia.org/wiki/File:Cats_Petunia_and_Mimosa_2004.jpg May be re-distributed in accordance with the terms of the CC-SA 4.0 license https://creativecommons.org/licenses/by-sa/4

distribution parameter Given: m independent and identically distributed (i.i.d.) samples of the data D = {d(i)}i=1,··· ,m Independent and Identically Distributed Given θ, each sample is independent of all other