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34 lines
1.4 KiB
Plaintext
34 lines
1.4 KiB
Plaintext
(linux)=
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# Installing on Linux
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1. Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit).
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2. Install TensorRT-LLM.
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```bash
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# Obtain and start the basic docker image environment (optional).
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docker run --rm --runtime=nvidia --gpus all --entrypoint /bin/bash -it nvidia/cuda:12.1.0-devel-ubuntu22.04
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# Install dependencies, TensorRT-LLM requires Python 3.10
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apt-get update && apt-get -y install python3.10 python3-pip openmpi-bin libopenmpi-dev git
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# Install the latest preview version (corresponding to the main branch) of TensorRT-LLM.
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# If you want to install the stable version (corresponding to the release branch), please
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# remove the `--pre` option.
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pip3 install tensorrt_llm -U --pre --extra-index-url https://pypi.nvidia.com
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# Check installation
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python3 -c "import tensorrt_llm"
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```
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3. Install the requirements inside the Docker container.
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```bash
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git clone https://github.com/NVIDIA/TensorRT-LLM.git
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cd TensorRT-LLM
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pip install -r examples/bloom/requirements.txt
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git lfs install
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```
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Beyond the local execution, you can also use the NVIDIA Triton Inference Server to create a production-ready deployment of your LLM as described in this [Optimizing Inference on Large Language Models with NVIDIA TensorRT-LLM](https://developer.nvidia.com/blog/optimizing-inference-on-llms-with-tensorrt-llm-now-publicly-available/) blog.
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