| gcc | 4.8.5(Cuda 9.0 and 10.0) and 8.2(Others) |
| gcc-c++ | 4.8.5(Cuda 9.0 and 10.0) and 8.2(Others) |
| cmake | 3.2.0 and later |
| Python | 2.7.2 and later / 3.6 and later |
| Python | 2.7.2 and later / 3.5.1 and later |
| Go | 1.9.2 and later |
| git | 2.17.1 and later |
| glibc-static | 2.17 |
...
...
@@ -19,19 +18,13 @@
| bzip2-devel | 1.0.6 and later |
| python-devel / python3-devel | 2.7.5 and later / 3.6.8 and later |
| sqlite-devel | 3.7.17 and later |
| patchelf | 0.9 and later |
| patchelf | 0.9 |
| libXext | 1.3.3 |
| libSM | 1.2.2 |
| libXrender | 0.9.10 |
It is recommended to use Docker for compilation. We have prepared the Paddle Serving compilation environment for you, see [this document](DOCKER_IMAGES.md).
This document will take Python2 as an example to show how to compile Paddle Serving. If you want to compile with Python3, just adjust the Python options of cmake:
- Set `DPYTHON_INCLUDE_DIR` to `$PYTHONROOT/include/python3.6m/`
- Set `DPYTHON_LIBRARIES` to `$PYTHONROOT/lib64/libpython3.6.so`
- Set `DPYTHON_EXECUTABLE` to `$PYTHONROOT/bin/python3.6`
# for example, the path of python is /usr/bin/python, you can set /usr as PYTHONROOT
export PYTHONROOT=/usr/
# For example, the path of python is /usr/bin/python, you can set PYTHONROOT
export PYTHONROOT=/usr
```
In the default centos7 image we provide, the Python path is `/usr/bin/python`. If you want to use our centos6 image, you need to set it to `export PYTHONROOT=/usr/local/python2.7/`.
If you are using a Docker development image, please follow the following to determine the Python version to be compiled, and set the corresponding environment variables
@@ -59,14 +80,11 @@ In the default centos7 image we provide, the Python path is `/usr/bin/python`. I
pip install-r python/requirements.txt
```
If Python3 is used, replace `pip` with `pip3`.
If you use other Python version, please use the right `pip` accordingly.
## GOPATH Setting
The default GOPATH is set to `$HOME/go`, you can also set it to other values. **If it is the Docker environment provided by Serving, you do not need to set up.**
## Compile Arguments
The default GOPATH is `$HOME/go`, which you can set to other values.
```shell
export GOPATH=$HOME/go
export PATH=$PATH:$GOPATH/bin
...
...
@@ -100,52 +118,42 @@ make -j10
you can execute `make install` to put targets under directory `./output`, you need to add`-DCMAKE_INSTALL_PREFIX=./output`to specify output path to cmake command shown above.
### Integrated GPU version paddle inference library
### CUDA_PATH is the cuda install path,use the command(whereis cuda) to check,it should be /usr/local/cuda.
### CUDNN_LIBRARY && CUDA_CUDART_LIBRARY is the lib path, it should be /usr/local/cuda/lib64/
Compared with CPU environment, GPU environment needs to refer to the following table,
**It should be noted that the following table is used as a reference for non-Docker compilation environment. The Docker compilation environment has been configured with relevant parameters and does not need to be specified in cmake process. **
### Integrated TRT version paddle inference library
| cmake environment variable | meaning | GPU environment considerations | whether Docker environment is needed |
| CUDA_TOOLKIT_ROOT_DIR | cuda installation path, usually /usr/local/cuda | Required for all environments | No
(/usr/local/cuda) |
| CUDNN_LIBRARY | The directory where libcudnn.so.* is located, usually /usr/local/cuda/lib64/ | Required for all environments | No (/usr/local/cuda/lib64/) |
| CUDA_CUDART_LIBRARY | The directory where libcudart.so.* is located, usually /usr/local/cuda/lib64/ | Required for all environments | No (/usr/local/cuda/lib64/) |
| TENSORRT_ROOT | The upper level directory of the directory where libnvinfer.so.* is located, depends on the TensorRT installation directory | Cuda 9.0/10.0 does not need, other needs | No (/usr) |
```
If not in Docker environment, users can refer to the following execution methods. The specific path is subject to the current environment, and the code is only for reference.
execute `make install` to put targets under directory `./output`
**Attention：** After the compilation is successful, you need to set the path of `SERVING_BIN`. See [Note](https://github.com/PaddlePaddle/Serving/blob/develop/doc/COMPILE.md#Note) for details.
Execute `make install` to put the target output in the `./output` directory.
**Note:** After the compilation is successful, you need to set the `SERVING_BIN` path, see the following [Notes](COMPILE.md#Notes) ).
## Compile Client
...
...
@@ -208,7 +216,6 @@ Please use the example under `python/examples` to verify.
Running a CUDA container requires a machine with at least one CUDA-capable GPU and a driver compatible with the CUDA toolkit version you are using.
The machine running the CUDA container **only requires the NVIDIA driver**, the CUDA toolkit doesn't have to be installed.
The machine running the CUDA container **only requires the NVIDIA driver**, the CUDA toolkit does not have to be installed.
For the relationship between CUDA toolkit version, Driver version and GPU architecture, please refer to [nvidia-docker wiki](https://github.com/NVIDIA/nvidia-docker/wiki/CUDA).
# (Attachment) The List of All the Docker images
Develop Images:
| Env | Version | Docker images tag | OS | Gcc Version |
nvidia-docker run -p 9292:9292 --nametest-dit hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
nvidia-docker run -p 9292:9292 --nametest-dit hub.baidubce.com/paddlepaddle/serving:latest-cuda10.2-cudnn8-devel
nvidia-docker exec-ittest bash
```
or
```bash
docker run --gpus all -p 9292:9292 --nametest-dit hub.baidubce.com/paddlepaddle/serving:latest-cuda9.0-cudnn7
docker run --gpus all -p 9292:9292 --nametest-dit hub.baidubce.com/paddlepaddle/serving:latest-cuda10.2-cudnn8-devel
docker exec-ittest bash
```
...
...
@@ -69,7 +69,7 @@ The `-p` option is to map the `9292` port of the container to the `9292` port of
The mirror comes with `paddle_serving_server_gpu`, `paddle_serving_client`, and `paddle_serving_app` corresponding to the mirror tag version. If users don’t need to change the version, they can use it directly, which is suitable for environments without extranet services.
If you need to change the version, please refer to the instructions on the homepage to download the pip package of the corresponding version.
If you need to change the version, please refer to the instructions on the homepage to download the pip package of the corresponding version.[LATEST_PACKAGES](./LATEST_PACKAGES.md)
If you have saved model files using Paddle's `save_inference_model` API, you can use Paddle Serving's` inference_model_to_serving` API to convert it into a model file that can be used for Paddle Serving.
| `dirname` | str | - | Path of saved model files. Program file and parameter files are saved in this directory. |
| `serving_server` | str | `"serving_server"` | The path of model files and configuration files for server. |
| `serving_client` | str | `"serving_client"` | The path of configuration files for client. |
| `model_filename` | str | None | The name of file to load the inference program. If it is None, the default filename `__model__` will be used. |
| `params_filename` | str | None | The name of file to load all parameters. It is only used for the case that all parameters were saved in a single binary file. If parameters were saved in separate files, set it as None. |
**Demo: Convert From Dynamic Graph**
PaddlePaddle 2.0 provides a new dynamic graph mode, so here we use imagenet ResNet50 dynamic graph as an example to teach how to export from a saved model and use it for real online inference scenarios.
You can see that the prediction has been successfully executed. The above is the content predicted by the dynamic graph ResNet50 model on Serving. The use of other dynamic graph models is similar.
## Save from training or prediction script (Static Graph Mode)
Currently, paddle serving provides a save_model interface for users to access, the interface is similar with `save_inference_model` of Paddle.
If you have saved model files using Paddle's `save_inference_model` API, you can use Paddle Serving's` inference_model_to_serving` API to convert it into a model file that can be used for Paddle Serving.
| `dirname` | str | - | Path of saved model files. Program file and parameter files are saved in this directory. |
| `serving_server` | str | `"serving_server"` | The path of model files and configuration files for server. |
| `serving_client` | str | `"serving_client"` | The path of configuration files for client. |
| `model_filename` | str | None | The name of file to load the inference program. If it is None, the default filename `__model__` will be used. |
| `params_filename` | str | None | The name of file to load all parameters. It is only used for the case that all parameters were saved in a single binary file. If parameters were saved in separate files, set it as None. |
