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Latest Wheel Packages

CPU server

Python 3

# Compile by gcc8.2
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server-0.0.0-py3-none-any.whl

Python 2

# Compile by gcc8.2
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server-0.0.0-py2-none-any.whl

GPU server

Python 3

#cuda 9.0, Compile by gcc4.8
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post9-py3-none-any.whl
#cuda 10.0, Compile by gcc4.8
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post10-py3-none-any.whl
#cuda10.1 with TensorRT 6, Compile by gcc8.2
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post101-py3-none-any.whl
#cuda10.2 with TensorRT 7, Compile by gcc8.2
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post102-py3-none-any.whl
#cuda11.0 with TensorRT 7 (beta), Compile by gcc8.2
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post11-py3-none-any.whl

Python 2

#cuda 9.0, Compile by gcc4.8
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post9-py2-none-any.whl
#cuda 10.0, Compile by gcc4.8
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post10-py2-none-any.whl
#cuda10.1 with TensorRT 6, Compile by gcc8.2
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post101-py2-none-any.whl
#cuda10.2 with TensorRT 7, Compile by gcc8.2
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post102-py2-none-any.whl
#cuda11.0 with TensorRT 7 (beta), Compile by gcc8.2
https://paddle-serving.bj.bcebos.com/whl/paddle_serving_server_gpu-0.0.0.post11-py2-none-any.whl

Tips: If you want to use CPU server and GPU server at the same time, you should check the gcc version, only Cuda10.1/10.2/11 can run with CPU server owing to the same gcc version(8.2).

Client

Python 3.6

https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp36-none-any.whl

Python 3.8

https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp38-none-any.whl

Python 3.7

https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp37-none-any.whl

Python 3.5

https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp35-none-any.whl

Python 2.7

https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp27-none-any.whl

App

Python 3

https://paddle-serving.bj.bcebos.com/whl/paddle_serving_app-0.0.0-py3-none-any.whl

Python 2

https://paddle-serving.bj.bcebos.com/whl/paddle_serving_app-0.0.0-py2-none-any.whl

ARM user

for ARM user who uses PaddleLite can download the wheel packages as follows. And ARM user should use the xpu-beta docker DOCKER IMAGES We only support Python 3.6 for Arm Users.

Wheel Package Links

# Server 
https://paddle-serving.bj.bcebos.com/whl/xpu/paddle_serving_server_gpu-0.0.0.postarm_xpu-py3-none-any.whl
# Client
https://paddle-serving.bj.bcebos.com/whl/xpu/paddle_serving_client-0.0.0-cp36-none-any.whl 
# App
https://paddle-serving.bj.bcebos.com/whl/xpu/paddle_serving_app-0.0.0-py3-none-any.whl

Binary Package

for most users, we do not need to read this section. But if you deploy your Paddle Serving on a machine without network, you will encounter a problem that the binary executable tar file cannot be downloaded. Therefore, here we give you all the download links for various environment.

Bin links

# CPU AVX MKL
https://paddle-serving.bj.bcebos.com/bin/serving-cpu-avx-mkl-0.0.0.tar.gz
# CPU AVX OPENBLAS
https://paddle-serving.bj.bcebos.com/bin/serving-cpu-avx-openblas-0.0.0.tar.gz
# CPU NOAVX OPENBLAS
https://paddle-serving.bj.bcebos.com/bin/serving-cpu-noavx-openblas-0.0.0.tar.gz
# Cuda 9
https://paddle-serving.bj.bcebos.com/bin/serving-gpu-cuda9-0.0.0.tar.gz
# Cuda 10
https://paddle-serving.bj.bcebos.com/bin/serving-gpu-cuda10-0.0.0.tar.gz
# Cuda 10.1
https://paddle-serving.bj.bcebos.com/bin/serving-gpu-101-0.0.0.tar.gz
# Cuda 10.2
https://paddle-serving.bj.bcebos.com/bin/serving-gpu-102-0.0.0.tar.gz
# Cuda 11
https://paddle-serving.bj.bcebos.com/bin/serving-gpu-cuda11-0.0.0.tar.gz

How to setup SERVING_BIN offline?

  • download the serving server whl package and bin package, and make sure they are for the same environment
  • download the serving client whl and serving app whl, pay attention to the Python version.
  • pip install the serving and tar xf the binary package, then export SERVING_BIN=$PWD/serving-gpu-cuda10-0.0.0/serving (take Cuda 10.0 as the example)