# Convert Triton types to numpy types self.output0_dtype = pb_utils.triton_string_to_numpy(output0_config['data_type']) self.output1_dtype = pb_utils.triton_string_to_numpy(output1_config['data_type']) def execute(self, requests): """ requests : list A list of pb_utils.InferenceRe...
{name:"input__0"# 输入名字,对于torch来说名字于代码的名字不需要对应,但必须是<name>__<index>的形式,注意是2个下划线,写错就报错data_type:TYPE_INT64# 类型,torch.long对应的就是int64,不同语言的tensor类型与triton类型的对应关系可以在官方文档找到dims:[-1]# -1 代表是可变维度,虽然输入是二维的,但是...
The first column shows the name of the datatype as it appears in the model configuration file. The next four columns show the corresponding datatype for supported model frameworks. If a model framework does not have an entry for a given datatype, then Triton does n...
Triton supports multiple scheduling and batching algorithms that can be selected independently for each model. This section describesstateless,statefulandensemblemodels and how Triton provides schedulers to support those model types. For a given model, the selection and configuration o...
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It supports model-parallel embedding tables and data-parallel neural networks and their variants, such as Wide and Deep Learning (WDL), Deep Cross Network (DCN), DeepFM, and Deep Learning Recommendation Model (DLRM). NVIDIA Triton™ Inference Server and NVIDIA® TensorRT™ accelerate ...
Triton Inference Server is open source and provides a single standardized inference platform that can support multi framework model inferencing in different deployments such as datacenter, cloud, embedded devices, and virtualized environments. It supports different types of inference queries through advanced...
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Introduction to TensorRT and Triton: A Walkthrough of Optimizing Your First Deep Learning Inference Model NVIDIA TensorRT is a deep learning platform that optimizes neural network models and speeds up inference across GPU-accelerated platforms running in the data center and embedded devices. We'll pr...
High performance: Supports both CPU and GPU. Triton can use GPUs to accelerate inference, significantly reduce latency, and increase throughput. Cost effectiveness: Offers dynamic batching and concurrent model execution features to maximize GPU utilization and improve inference throughput. ...