| `ir_optim` | - | - | Enable analysis and optimization of calculation graph |
| `use_mkl` (Only for cpu version) | - | - | Run inference with MKL |
| `use_trt` (Only for trt version) | - | - | Run inference with TensorRT |
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/).
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.
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@@ -136,7 +154,10 @@ execute `make install` to put targets under directory `./output`
| CUDA 8 | 8.0.61 | CuDNN 7.1.2 for CUDA 8.0 | 2.1.4 |
| CUDA 9 | 9.0.176 | CuDNN 7.3.1 for CUDA 9.0 | 2.2.12 |
| post9 | 9.0 | CuDNN 7.3.1 for CUDA 9.0 | |
| post10 | 10.0 | CuDNN 7.5.1 for CUDA 10.0| |
| trt | 10.1 | CuDNN 7.5.1 for CUDA 10.1| 6.0.1.5 |
### How to make the compiler detect the CuDNN library
Download the corresponding CUDNN version from NVIDIA developer official website and decompressing it, add `-DCUDNN_ROOT` to cmake command, to specify the path of CUDNN.
### How to make the compiler detect the nccl library
After downloading the corresponding version of the nccl2 library from the NVIDIA developer official website and decompressing it, add the following environment variables (take nccl2.1.4 as an example):