# 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...
This document describes Triton’s binary tensor data extension. The binary tensor data extension allows Triton to support tensor data represented in a binary format in the body of an HTTP/REST request. Because this extension is supported, Triton reports “binary_tensor_data” in the extensi...
The following table shows the tensor datatypes supported by the Triton Inference Server. The first column shows the name of the datatype as it appears in the model configuration file. The other columns show the corresponding datatype for the model frameworks supported ...
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Triton helps with a standardized scalable production AI in every data center, cloud, and embedded device. It supports multiple frameworks, runs models on both CPUs and GPUs, handles different types of inference queries, and integrates with Kubernetes and MLOPs platforms. ...
The rapid development of solutions using retrieval augmented generation (RAG) for question-and-answer LLM workflows has led to new types of system... 11 MIN READ Oct 22, 2024 Scaling LLMs with NVIDIA Triton and NVIDIA TensorRT-LLM Using Kubernetes Large language models (LLMs) have been...
Triton Response Cache # In this document aninference requestis the model name, model version, and input tensors (name, shape, datatype and tensor data) that make up a request submitted to Triton. An inference result is the output tensors (name, shape, datatype and tensor data) produced ...