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

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()

%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

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 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 likelihood of . Can we do better such that we assign more bits to regions where more of our data is distributed, and fewer bits to the sparser regions? Recall that huffman encoding does this by trying to create

, "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

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 75224d4c48d7ca), all batch norm multiplier is initialized as constant 1, instead of uniformly distributed between 0 and 1, as it was previously. This has caused accuracy issue for our TACOTRON2 model.

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

Compressed Communication for Distributed Deep Learning: Survey and Quantitative Evaluation [ICLR2018]Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Dense参数，每次 都⽤，快速收敛

方案复杂 图像 搜索 推荐 语音 视频理解 NLP 广告 CNN RNN GNN MLP Tensorflow PyTorch Parameter Server MPI TreeModel SQL MapReduce Blink  场景丰富: 图像/视频/推荐/搜索  大数据+大模型: Model Zoo  跨场景+跨模态  开箱即用: 封装复杂性  白盒化, 可扩展性强