mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
71 lines
3.1 KiB
Markdown
71 lines
3.1 KiB
Markdown
(linux)=
|
|
|
|
# Installing on Linux via `pip`
|
|
|
|
1. Install TensorRT-LLM (tested on Ubuntu 24.04).
|
|
|
|
### Install prerequisites
|
|
|
|
Before the pre-built Python wheel can be installed via `pip`, a few
|
|
prerequisites must be put into place:
|
|
|
|
```bash
|
|
# Optional step: Only required for Blackwell and Grace Hopper
|
|
pip3 install torch==2.7.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
|
|
|
|
sudo apt-get -y install libopenmpi-dev
|
|
```
|
|
|
|
PyTorch CUDA 12.8 package is required for supporting NVIDIA Blackwell and Grace Hopper GPUs. On prior GPUs, this extra installation is not required.
|
|
|
|
```{tip}
|
|
Instead of manually installing the preqrequisites as described
|
|
above, it is also possible to use the pre-built [TensorRT-LLM Develop container
|
|
image hosted on NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/devel)
|
|
(see [here](containers) for information on container tags).
|
|
```
|
|
|
|
### Install pre-built TensorRT-LLM wheel
|
|
|
|
Once all prerequisites are in place, TensorRT-LLM can be installed as follows:
|
|
|
|
```bash
|
|
pip3 install --upgrade pip setuptools && pip3 install tensorrt_llm
|
|
```
|
|
**This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.**
|
|
|
|
2. Sanity check the installation by running the following in Python (tested on Python 3.12):
|
|
|
|
```{literalinclude} ../../../examples/llm-api/quickstart_example.py
|
|
:language: python
|
|
:linenos:
|
|
```
|
|
|
|
**Known limitations**
|
|
|
|
There are some known limitations when you pip install pre-built TensorRT-LLM wheel package.
|
|
|
|
1. MPI in the Slurm environment
|
|
|
|
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.
|
|
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.
|
|
```
|
|
The application appears to have been direct launched using "srun",
|
|
but OMPI was not built with SLURM support. This usually happens
|
|
when OMPI was not configured --with-slurm and we weren't able
|
|
to discover a SLURM installation in the usual places.
|
|
```
|
|
|
|
2. CUDA Toolkit
|
|
|
|
`pip install tensorrt-llm` won't install CUDA toolkit in your system, and the CUDA Toolkit is not required if want to just deploy a TensorRT-LLM engine.
|
|
TensorRT-LLM uses the [ModelOpt](https://nvidia.github.io/TensorRT-Model-Optimizer/) to quantize a model, while the ModelOpt requires CUDA toolkit to jit compile certain kernels which is not included in the pytorch to do quantization effectively.
|
|
Please install CUDA toolkit when you see the following message when running ModelOpt quantization.
|
|
|
|
```
|
|
/usr/local/lib/python3.10/dist-packages/modelopt/torch/utils/cpp_extension.py:65:
|
|
UserWarning: CUDA_HOME environment variable is not set. Please set it to your CUDA install root.
|
|
Unable to load extension modelopt_cuda_ext and falling back to CPU version.
|
|
```
|
|
The installation of CUDA toolkit can be found in [CUDA Toolkit Documentation](https://docs.nvidia.com/cuda/).
|