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 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 0 through v1.2.1 exposes a Regular Expression Denial of Service (ReDOS) vulnerability. ‣ Known security vulnerabilities: ‣ CVE-2022-32212, CVE-2022-43548, CVE-2023-0286, CVE-2022-32223, CVE-2023-0286

tokenizer word2vec Elmo pb ckpt H5 (Keras) RESTful API RPC API Function test Concurrence test Security test Multi class Multi label preprocessor Traditional --->simple Char replacement Synonym

构的设计也逐渐变得更加 抽象。研究人员开始从单个神经元的角度思考问题，发展到整个层，现在又转向块，重复层的模式。 使用块的想法首先出现在牛津大学的视觉几何组（visual geometry group）91的VGG网络中。通过使用循环 和子程序，可以很容易地在任何现代深度学习框架的代码中实现这些重复的架构。 7.2.1 VGG块 经典卷积神经网络的基本组成部分是下面的这个序列： 1. 带填充以保持分辨率的卷积层； UsingPyTorchIn-Builtscheduler scheduler.step() else: # Usingcustomdefinedscheduler for param_group in trainer.param_groups: param_group['lr'] = scheduler(epoch) print(f'train loss {train_loss:.3f}, train acc {train_acc: 首先，我们定义了一个训练函数train_fine_tuning，该函数使用微调，因此可以多次调用。 # 如果param_group=True，输出层中的模型参数将使用十倍的学习率 def train_fine_tuning(net, learning_rate, batch_size=128, num_epochs=5, param_group=True): train_iter = torch.utils.data.DataLoader(torchvision

the tracking success ratio after initialization. Group A: simple translation Group B: there are loops Group C: slow and nearly pure rotation Group D: fast motion with strong rotation 时间统计 • 台式机上的计算时间 Personal Homepage: http:www.cad.zju.edu.cn/home/gfzhang: Email: zhangguofeng@cad.zju.edu.cn ZJUCVG Group Website: http:www.zjucvg.net:

基于GPU的多级存储训练：更⾼的性价⽐ � 推荐模型GPU训练的挑战 � 显存（A100最⼤80GB）放不下TB级的模型 � GPU多线程并⾏计算能⼒对稀疏数据不友好 � ⽅案 � 原有：内存能够存储的参数->对应的样本量Group � 新增：显存能够存储的参数->对应的样本量Pass � 新增：GPU并⾏操作友好->CSR格式的显存数据访问 SSD磁盘 10TB 全部参数 内存 1TB 即将⽤到的参数 显存 问题：TB模型实时多地传输和加载成本⾼ � ⽅案：⾼低频分别上线 � 更灵活的⽤法：模型多切⽚，按需上线 � Dssm � wdl ... 分布式Serving集群 副本1 副本2 Group 1 Group N 副本1 副本2 推理节点 SDK MB级别DNN部分 Sparse Hotkey TB级别Embedding部分 全量模型，TB级，低峰期（Cos存储） 增量模型，GB级，20分钟（Cos存储） embedding values 痛点： 1. 更少的values： 变⻓Embedding 特征出现次数少，⽤1个float 结合show/click，有效果提升 2. 更少的key： group lasso key级别的稀疏化 3. 更短的values a) 混合精度： float16+int8+int4 b) 量化压缩，1bit或2bit 优点：与优化器⽆关 缺点：1

picture of a snake or a grizzly bear might trigger caution or fear. In a way, we subconsciously group these animals in our head. We don’t necessarily know everything about a dog and cat, but we know that these blocks via a recurrence cell. They fall under the Recurrence group. The efficient transformers under the Memory/Downsampling group use additional parameters to act as a memory. This memory is used during the training process. The transformers that use sparse attention are grouped under the Sparse group. After input sequence and the attention parameters, the next component to attack is the softmax computation

AutoFeature  特征组合 • Count select count(1) group by col • GroupByThenMax/Min/Avg/Sum select max(col2) group by col1 • CrossCount[2] select count (1) group by col1,col2 特征组合 + 特征选择  特征选择

Qwen Qwen is the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Now the large language models have been upgraded to Qwen1.5. Both language models and multimodal "your_model_path" quant_path = "your_quantized_model_path" quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM �→" } # Load your tokenizer and model with AutoAWQ tokenizer "your_model_path" quant_path = "your_quantized_model_path" quantize_config = BaseQuantizeConfig( bits=8, # 4 or 8 group_size=128, damp_percent=0.01, desc_act=False, # set to False can significantly speed up inference but

learning problem Given: N unlabeled examples {x1, · · · , xN}; no. of desired partitions K Goal: Group the examples into K “homogeneous” partitions Loosely speaking, it is classification without ground looks at similarities, no labels are given Without labels, similarity can be hard to define Goal: Group the examples into K “homogeneous” partitions Thus using the right distance/similarity is very important

Normalization ▪ Batch Normalization Batch Norm https://medium.com/syncedreview/facebook-ai-proposes-group-normalization- alternative-to-batch-normalization-fb0699bffae7 Pipeline nn.BatchNorm2d Class variables