| `ir_optim` | bool | `False` | Enable analysis and optimization of calculation graph |
| `use_mkl` (Only for cpu version) | bool | `False` | Run inference with MKL |
Here, we use `curl` to send a HTTP POST request to the service we just started. Users can use any python library to send HTTP POST as well, e.g, [requests](https://requests.readthedocs.io/en/master/).
This document will take Python2 as an example to show how to compile Paddle Serving. If you want to compile with Python 3, just adjust the Python options of cmake.
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`
## Get Code
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@@ -54,7 +58,7 @@ make -j10
execute `make install` to put targets under directory `./output`
**Attention：**After the compilation is successful, the serving binary file will be generated in the ./core/general-server directory. Before starting the server, export SERVING_BIN = $ {path / to / serving / bin} is required to allow the server to use the compiled serving binary file.
**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.
If you want to have more detection models, please refer to [Paddle Detection Model Zoo](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/docs/MODEL_ZOO_cn.md)
*the port of server side in this example is 9393, the sample data used by client side is in the folder ./data. These parameter can be modified in practice*
