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https://github.com/NVIDIA/TensorRT-LLM.git
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69 lines
2.8 KiB
Markdown
69 lines
2.8 KiB
Markdown
(linux)=
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# Installing on Linux via `pip`
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1. Install TensorRT LLM (tested on Ubuntu 24.04).
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### Install prerequisites
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Before the pre-built Python wheel can be installed via `pip`, a few
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prerequisites must be put into place:
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Install CUDA Toolkit following the [CUDA Installation Guide for Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/) and
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make sure `CUDA_HOME` environment variable is properly set.
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```{tip}
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:name: installation-linux-tip-cuda-version
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TensorRT LLM 1.1 supports both CUDA 12.9 and 13.0. The wheel package release only supports CUDA 12.9, while CUDA 13.0 is only supported through NGC container release.
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```
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```bash
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# Optional step: Only required for NVIDIA Blackwell GPUs and SBSA platform
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pip3 install torch==2.7.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
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sudo apt-get -y install libopenmpi-dev
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# Optional step: Only required for disagg-serving
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sudo apt-get -y install libzmq3-dev
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```
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PyTorch CUDA 12.8 package is required for supporting NVIDIA Blackwell GPUs and SBSA platform. On prior GPUs or Linux x86_64 platform, this extra installation is not required.
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```{tip}
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Instead of manually installing the preqrequisites as described
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above, it is also possible to use the pre-built [TensorRT LLM Develop container
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image hosted on NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/devel)
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(see [here](containers) for information on container tags).
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```
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### Install pre-built TensorRT LLM wheel
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Once all prerequisites are in place, TensorRT LLM can be installed as follows:
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```bash
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pip3 install --upgrade pip setuptools && pip3 install tensorrt_llm
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```
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**This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.**
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2. Sanity check the installation by running the following in Python (tested on Python 3.12):
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```{literalinclude} ../../../examples/llm-api/quickstart_example.py
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:language: python
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:linenos:
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```
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**Known limitations**
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There are some known limitations when you pip install pre-built TensorRT LLM wheel package.
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1. MPI in the Slurm environment
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If you encounter an error while running TensorRT LLM in a Slurm-managed cluster, you need to reconfigure the MPI installation to work with Slurm.
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The setup methods depends on your slurm configuration, pls check with your admin. This is not a TensorRT LLM specific, rather a general mpi+slurm issue.
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```
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The application appears to have been direct launched using "srun",
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but OMPI was not built with SLURM support. This usually happens
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when OMPI was not configured --with-slurm and we weren't able
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to discover a SLURM installation in the usual places.
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```
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