model=AutoModelForCausalLM.from_pretrained(model_path,torch_dtype=torch.bfloat16,trust_remote_code=True,device_map='auto',# pytorch 自适应拆分模型并分配给各个 GPU) 然后执行代码时不用分成N个进程了,设置成一个,一个进程调用N个GPU即可: torchrun --standalone --nnodes=1--nproc_per_node1inferen...
3、设置当前阶段为inference(# predict) model.eval()
'model_fp32.layer1.0.bn2'],['model_fp32.layer1.1.conv1','model_fp32.layer1.1.bn1','...
9、Util：AccuracyCalculator：给定一个 query 和推理嵌入向量（reference embedding），计算数个准确度指标Inference model：utils.inference 包含用于在 batch 或一组 pair 中，找到匹配对（matching pairs ）的类Logging Preset：提供日志数据 hook，模型训练、验证和存储期间的提前停止日志。损失函数可以自定义使用 Distan...
To host an inference endpoint and make predictions using Amazon SageMaker SDK, complete the following steps: Create a model. The model function expects the name of the TorchServe container image and the location of your trained models. See the following code: ...
Input: Classification models: torch.Tensor; NLP models: Masked sentence; OD model: .jpg image Application Metric: Average Inference latency for 100 iterations calculated after 15 warmup iterations Platform: Tiger Lake Number of Nodes: 1 Numa Node ...
在神经网络模型的推断（inference）阶段中，我们不需要进行反向传播，也不需要计算梯度，使用 with torch.no_grad(): 上下文管理器可以有效地减少内存消耗和计算时间 def predict(model, data): model.eval() with torch.no_grad(): output = model(data) pred = output.data.max(1, keepdim=True...
@torch.inference_mode()def p_sample(self, x: torch.Tensor, timestamp: int) -> torch.Tensor:b, *_, device = *x.shape, x.devicebatched_timestamps = torch.full((b,), timestamp, device=device, dtype=torch.long) preds = self.model(x, batch...
def inference(checkpoint_path: str=None,num_time_steps: int=1000,ema_decay: float=0.9999, ):checkpoint = torch.load(checkpoint_path)model = UNET().cuda()model.load_state_dict(checkpoint['weights'])ema = ModelEmaV3(model, decay...
device = device model = build_model(cfg.model) ckpt = torch.load(model_path, map_location=lambda storage, loc: storage) load_model_weight(model, ckpt, logger) self.model = model.to(device).eval() self.pipeline = Pipeline(cfg.data.val.pipeline, cfg.data.val.keep_ratio) def inference(...