batch_size = 50 for i in range(0, len(df), batch_size): docs = nlp.pipe(df['text'][i:i+batch_size]) for j, doc in enumerate(docs): for col, values in extract_nlp(doc).items(): df[col].iloc[i+j] = values In the inner loop we extract the features from the processed...
for i in range(batch_size): data = np.random.randn(mem_max_seq_len, memory_hidden_dim) * 0.01 for j in range(memory_sequence_length[i], mem_max_seq_len): data[j] = outter_embbeding memory.append(data) memory = np.asarray(memory) memory = paddle.to_tensor(memory, dtype=dtype)...
For offline inference, you can set the max batch size usingmax_num_batched_tokensormax_num_seqs. This parameter can be passed in bothEngineorLLMclass. vllm/vllm/engine/arg_utils.py Lines 28 to 29 in1a2bbc9 max_num_batched_tokens:Optional[int]=None ...
Refer to the I/O Formats section for more details. 2.7. Dynamic Shapes By default, TensorRT optimizes the model based on the input shapes (batch size, image size, and so on) at which it was defined. However, the builder can be configured to allow the input dimensions to be...
epoch_acc = 0 for i, batch in enumerate(dataloader): # 标签形状为 (batch_size, 1) label = batch["label"] text = batch["text"] # tokenized_text 包括 input_ids, token_type_ids, attention_mask tokenized_text = tokenizer(text, max_length=100, add_special_tokens=True, truncation=True,...
Microsoft.Azure.Batch v16.2.0 Source: IBatchRequest.cs 获取或设置对 Batch 服务的请求的客户端超时。 C# publicTimeSpan Timeout {get;set; } 属性值 TimeSpan 注解 此超时适用于单个 Batch 服务请求;如果指定了重试策略,则每次重试都将获得此值的完整持续时间。
程序集: Microsoft.Azure.Management.Batch.dll 包: Microsoft.Azure.Management.Batch v15.0.0 获取有关与订阅关联的 Batch 帐户的信息。 C# 复制 public System.Threading.Tasks.Task<Microsoft.Rest.Azure.AzureOperationResponse<Microsoft.Rest.Azure.IPage<Microsoft.Azure.Manage...
KMP_AFFINITY=granularity=fine, verbose, compact,1,0Number of CPU coresConsider the impact on inference performance based on the number of CPU cores being used, as follows:When batchsize is small (in online services for instance) the increase in inference throughput gradually weakens...
Using ADD EXTRACT options you can perform the operations that are summarized in "ADD EXTRACT options summary". ADD EXTRACT group_name { [, BEGIN time |, AUDSEQNO seq_num, AUDRBA rba] | [[, EXTTRAILSOURCE trail_name {BEGIN time |, EXTSEQNO seq_num, EXTRBA rba}] | [, LOGTRAIL...
num_labels = 2 learning_rate = 1e-5 weight_decay = 1e-2 epochs = 2 batch_size = 16 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # 文件路径 data_path = ".\\sentiment\\" vocab_file = data_path+"vocab.txt" # 词汇表 ...