The Triton TensorRT-LLM Backend. Contribute to aiutarmi/tensorrtllm_backend development by creating an account on GitHub.
The Triton TensorRT-LLM Backend. Contribute to dongs0104/tensorrtllm_backend development by creating an account on GitHub.
编译好engine后,会生成/work/trtModel/llama/1-gpu,后续会用到。 然后克隆https://github.com/triton-inference-server/tensorrtllm_backend: 执行以下命令: cd tensorrtllm_backend mkdir triton_model_repo # 拷贝出来模板模型文件夹 cp -r all_models/inflight_batcher_llm/* triton_model_repo/ # 将刚才生成...
这个file内容很复杂,步骤非常多,tensorrtllm_backend官网给出了简单的方法Option2,在docker外去编译此环境,测试的时候发现这个dockerfile报错就容易找不到位置。(如果你的环境允许可以参考:GitHub - triton-inference-server/tensorrtllm_backend: The Triton TensorRT-LLM Backend) --我这里是在23.10-py3 docker内手动...
dockerrun--rm-it--nethost--shm-size=2g\--ulimitmemlock=-1--ulimitstack=67108864--gpusall\-v</path/to/engines>:/engines\nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3 Prepare TensorRT-LLM engines# You can skip this step if you already have the engines ready...
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git clone -b v0.11.0 https://github.com/triton-inference-server/tensorrtllm_backend.git cd tensorrtllm_backend git submodule update --init --recursive git lfs install git lfs pull Launch Triton TensorRT-LLM container Launch Triton docker container nvcr.io/nvidia/triton...
{MODEL_NAME} export VILA_PATH="tmp/hf_models/VILA" git clone https://github.com/Efficient-Large-Model/VILA.git ${VILA_PATH} # For LLaVA OneVision pip install -r all_models/multimodal/requirements-llava-onevision.txt export MODEL_NAME="llava-onevision-qwen2-7b-ov-hf" git clone https...
The Triton TensorRT-LLM Backend. Contribute to triton-inference-server/tensorrtllm_backend development by creating an account on GitHub.
git config submodule.tensorrt_llm.url https://github.com/NVIDIA/TensorRT-LLM.git git submodule update --init --recursive 2.修改文件 在构建的过程中可能会涉及网络的问题,我这里是修改了下面的几个文件 1)build_wheel.py 路径tensorrtllm_backend/tensorrt_llm/scripts/build_wheel.py ...