FastDeploy/examples/splitwise

Run the Examples on NVIDIA CUDA GPU

Prepare the Environment

Refer to NVIDIA CUDA GPU Installation to pull the docker image, such as:

docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-cuda-12.6:2.3.0

In the docker container, the NVIDIA MLNX_OFED and Redis are pre-installed.

Build and install FastDeploy

git clone https://github.com/PaddlePaddle/FastDeploy
cd FastDeploy

export ENABLE_FD_RDMA=1

# 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]

Run the Examples

Run the shell scripts in this directory, bash start_v0_tp1.sh or bash start_v1_tp1.sh

Note that, there are two methods for splitwise deployment:

• v0: using splitwise_scheduler or dp_scheduler, in which the requests are scheduled in the engine.
• v1: using router, in which the requests are scheduled in the router.

Run the Examples on Kunlunxin XPU

Coming soon...