Launch Triton docker container nvcr.io/nvidia/tritonserver:<xx.yy>-trtllm-python-py3 with TensorRT-LLM backend. Make an engines folder outside docker to reuse engines for future runs. Make sure to replace the <xx.yy> with the version of Triton that you want to u...
Below is an example of how to serve a TensorRT-LLM model with the Triton TensorRT-LLM Backend on a 4-GPU environment. The example uses the GPT model from theTensorRT-LLM repositorywith theNGC Triton TensorRT-LLM container. Make sure you are cloning the same version of TensorRT-LLM backend ...
python3 tensorrtllm_backend/tools/fill_template.py -i${TRITON_REPO}/tensorrt_llm/config.pbtxt${OPTIONS} # 建立 /data/model 的软链(TIONE在线服务中,模型默认挂载到此处) mkdir-p /data ln-s${TRITON_REPO}/data/model # 本地启动 Triton 推理服务调试 ...
设置好之后进入tensorrtllm_backend执行: python3 scripts/launch_triton_server.py --world_size=1 --model_repo=triton_model_repo 顺利的话就会输出: root@6aaab84e59c0:/work/code/tensorrtllm_backend# I1105 14:16:58.286836 2561098 pinned_memory_manager.cc:241] Pinned memory pool is created at '0x...
name:"tensorrt_llm"backend:"${triton_backend}"max_batch_size:${triton_max_batch_size} trtllm-build中的max_batch_size: 这个是指trtllm在编译engine的时候,engine支持的最大batch_size。使用过TensorRT的同学们应该对这个参数非常熟悉了。如果太大,可能会导致在编译engine阶段就OOM。
● 支持多种开源框架的部署,包括TensorFlow/PyTorch/ONNX Runtime/TensorRT 等,同时也支持用户提供自定义backend扩展解码引擎; ● 支持多个模型同时运行在 GPU 上,以提高 GPU 设备的利用率; ● 支持 HTTP/gRPC 协议,提供二进制格式扩展来压缩发送请求大小; ...
name: "tensorrt_llm"backend: "${triton_backend}"max_batch_size: ${triton_max_batch_size} trtllm-build中的max_batch_size: 这个是指trtllm在编译engine的时候,engine支持的最大batch_size。使用过TensorRT的同学们应该对这个参数非常熟悉了。如果太大,可能会导致在编译engine阶段就OOM。
Before Triton 23.10 release, please useOption 3 to build TensorRT-LLM backend via Docker. Run the Pre-built Docker Container Starting with Triton 23.10 release, Triton includes a container with the TensorRT-LLM Backend and Python Backend. This container should have everything to run a TensorRT-LL...
首先,创建一个模型库,以便Triton可以读取模型和任何相关元数据。tensorrtllm_backend存储库包含all_models/inflight_batcher_llm/下适当模型存储库的骨架。该目录中有以下子文件夹,其中包含模型执行过程不同部分的构件: /preprocessing和/postprocessing:包含适用于 Python 的 Triton 后端,用于在字符串和模型运行...
cd ..git clone git@github.com:triton-inference-server/tensorrtllm_backend.gitcd tensorrtllm_backend 运行 llama 7b 的端到端工作 初始化 TRT-LLM 子模块：git lfs installgit submodule update --init --recursive 从 HuggingFace 下载 LLaMa 模型：huggingface-cli loginhuggingface-cli download meta-llama/...