NVIDIA CUDA GPU Installation
The following installation methods are available when your environment meets these requirements:
- GPU Driver >= 535
- CUDA >= 12.3
- CUDNN >= 9.5
- Python >= 3.10
- Linux X86_64
1. Pre-built Docker Installation (Recommended)
Notice: The pre-built image only supports SM80/90 GPU(e.g. H800/A800),if you are deploying on SM86/89GPU(L40/4090/L20), please reinstall fastdpeloy-gpu
after you create the container.
docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-cuda-12.6:2.0.0
2. Pre-built Pip Installation
First install paddlepaddle-gpu. For detailed instructions, refer to PaddlePaddle Installation
python -m pip install paddlepaddle-gpu==3.1.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
Then install fastdeploy. Do not install from PyPI. Use the following methods instead:
For SM80/90 architecture GPUs(e.g A100/H100):
# Install stable release
python -m pip install fastdeploy-gpu -i https://www.paddlepaddle.org.cn/packages/stable/fastdeploy-gpu-80_90/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
# Install latest Nightly build
python -m pip install fastdeploy-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/fastdeploy-gpu-80_90/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
For SM86/89 architecture GPUs(e.g 4090/L20/L40):
# Install stable release
python -m pip install fastdeploy-gpu -i https://www.paddlepaddle.org.cn/packages/stable/fastdeploy-gpu-86_89/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
# Install latest Nightly build
python -m pip install fastdeploy-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/fastdeploy-gpu-86_89/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
3. Build from Source Using Docker
- Note:
dockerfiles/Dockerfile.gpu
by default supports SM 80/90 architectures. To support other architectures, modifybash build.sh 1 python false [80,90]
in the Dockerfile. It's recommended to specify no more than 2 architectures.
git clone https://github.com/PaddlePaddle/FastDeploy
cd FastDeploy
docker build -f dockerfiles/Dockerfile.gpu -t fastdeploy:gpu .
4. Build Wheel from Source
First install paddlepaddle-gpu. For detailed instructions, refer to PaddlePaddle Installation
python -m pip install paddlepaddle-gpu==3.1.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
Then clone the source code and build:
git clone https://github.com/PaddlePaddle/FastDeploy
cd FastDeploy
# Argument 1: Whether to build wheel package (1 for yes, 0 for compile only)
# Argument 2: Python interpreter path
# Argument 3: Whether to compile CPU inference operators
# Argument 4: Target GPU architectures
bash build.sh 1 python false [80,90]
The built packages will be in the FastDeploy/dist
directory.
Environment Verification
After installation, verify the environment with this Python code:
import paddle
from paddle.jit.marker import unified
# Verify GPU availability
paddle.utils.run_check()
# Verify FastDeploy custom operators compilation
from fastdeploy.model_executor.ops.gpu import beam_search_softmax
If the above code executes successfully, the environment is ready.