, "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 = 文件中的列应为： "dataset_name": { "file_name": "dataset_name.json", "columns": { "prompt": "instruction", "query": "input", "response": "output", "system": "system", "history": "history" } } • 对于 sharegpt 格式的数据集，dataset_info 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

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with GPU support for NGC containers, when you run a container, the following occurs: ‣ The Docker engine loads the image into a container which runs the software. ‣ You define the runtime resources of Deep Learning Framework containers are no longer tested on Pascal GPU architectures. ‣ Transformer Engine is a library for accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating which provides better training and inference performance with lower memory utilization. Transformer Engine also includes a collection of highly optimized modules for popular Transformer architectures and

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 sparsifying activation maps to produce robust models. Rhu et al., through their work on Compression DMA Engine12, observed that a non-trivial fraction of activation values for ReLU activation function are naturally In this scenario, the dequantization error would be large for ranges where the data is densely distributed. Quantization-aware training can mitigate some of the losses by making the network resilient to

用户数 据 推理结 果 推理服务 数据抽样 和整理 样本 训练 模型 模型评估 AVA深度学习平台 Caching IO Distributed System Docker Orchestration Storage HDFS SQL NoSQL Caffe MXNet Tensorflow Data Clean Iterative training Semi-supervised

- Nov 2015, Research Fellow, National University of Singapore, Singapore. Research Interests: Distributed Algorithms and Systems, Wireless Net- works, Mobile Computing, Internet of Things. Feng Li (SDU) teacher. “Near miss” examples Learner can query an oracle about class of an unlabeled example in the environment Learner can construct an arbitrary example and query an oracle for its label Learner can design Basic idea: Traditional supervised learning algorithms passively accept training data. Instead, query for annotations on informative images from the unlabeled data. Theoretical results show that large

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）） 各种机器学习问题 25 办比赛14来完成这项工作。 搜索 有时，我们不仅仅希望输出一个类别或一个实值。在信息检索领域，我们希望对一组项目进行排序。以网络 搜索为例，目标不是简单的“查询（query）‐网页（page）”分类，而是在海量搜索结果中找到用户最需要的 那部分。搜索结果的排序也十分重要，学习算法需要输出有序的元素子集。换句话说，如果要求我们输出字 母表中的前5个字母，返回“A、 ，就会对图像中内容的推断造成极大的困难。 最重要的是，到目前为止我们默认数据都来自于某种分布，并且所有样本都是独立同分布的（independently and identically distributed，i.i.d.）。然而，大多数的数据并非如此。例如，文章中的单词是按顺序写的，如 果顺序被随机地重排，就很难理解文章原始的意思。同样，视频中的图像帧、对话中的音频信号以及网站上 的浏览行

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 Recurrent neural networks(RNN). It is defined as shown below: Keras 49 keras.engine.base_layer.wrapped_fn() It supports the following parameters:  cell refers an instance. 

取 pd.read_sql() | 从 SQL 表 或 数 据 库 读 取 pd.read_json() | 从JSON格式的URL或文件读取 pd.read_clipboard() | 从剪切板读取 将DataFrame写入⽂件 df.to_csv() | 写入CSV文件 df.to_excel() | 写入Excel文件 df.to_sql() | 写入SQL表或数据库 df.to_json()

取 pd.read_sql() | 从 SQL 表 或 数 据 库 读 取 pd.read_json() | 从JSON格式的URL或文件读取 pd.read_clipboard() | 从剪切板读取 将DataFrame写入⽂件 df.to_csv() | 写入CSV文件 df.to_excel() | 写入Excel文件 df.to_sql() | 写入SQL表或数据库 df.to_json()