@@ -39,8 +39,6 @@ Docker compilation is recommended. We have prepared the Paddle Serving compilati
We provide five environment development images, namely CPU, CUDA10.1 + CUDNN7, CUDA10.2 + CUDNN7, CUDA10.2 + CUDNN8, CUDA11.2 + CUDNN8. We provide a Serving development image to cover the above environment. At the same time, we also support Paddle development mirroring.
The Serving image name is **paddlepaddle/serving:${Serving development image Tag}** (If the network is not good, you can visit **registry.baidubce.com/paddlepaddle/serving:${Serving development image Tag}**), The name of the Paddle development image is **paddlepaddle/paddle:${Paddle Development Image Tag}**. In order to prevent users from confusing the two sets of mirroring, we explain the origin of the two sets of mirroring separately.
Serving development mirror is the mirror used to compile and debug prediction services provided by Serving suite in order to support various prediction environments. Paddle development mirror is the mirror used for compilation, development, and training models released by Paddle on the official website. In order to allow Paddle developers to use Serving directly in the same container. For developers who have already used Serving users in the previous version, Serving development image should not be unfamiliar. But for developers who are familiar with the Paddle training framework ecology, they should be more familiar with the existing Paddle development mirrors. In order to adapt to the different habits of all users, we have fully supported both sets of mirrors.
| Environment | Serving Dev Image Tag | OS | Paddle Dev Image Tag | OS |
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@@ -55,16 +53,25 @@ We first need to pull related images for the environment we need. Under the **En
You can use Serving Dev Images.
```
docker pull paddlepaddle/serving:${Serving Dev Image Tag}
docker pull registry.baidubce.com/paddlepaddle/serving:${Serving Dev Image Tag}
# For GPU Image
nvidia-docker run --rm -it paddlepaddle/serving:${Serving Dev Image Tag} bash
nvidia-docker run --rm -it registry.baidubce.com/paddlepaddle/serving:${Serving Dev Image Tag} bash
# For CPU Image
docker run --rm -it paddlepaddle/serving:${Serving Dev Image Tag} bash
docker run --rm -it registry.baidubce.com/paddlepaddle/serving:${Serving Dev Image Tag} bash
```
You can also use Paddle Dev Images.
```
docker pull registry.baidubce.com/paddlepaddle/paddle:${Paddle Dev Image Tag}
# For GPU Image
nvidia-docker run --rm -it registry.baidubce.com/paddlepaddle/paddle:${Paddle Dev Image Tag} bash
# For CPU Image
docker run --rm -it registry.baidubce.com/paddlepaddle/paddle:${Paddle Dev Image Tag} bash
```
## Download the Serving Code Repo
**Note: If you are using Paddle to develop the image, you need to manually run `bash env_install.sh` after downloading the code base (as shown in the third line of the code box)**
Install the service whl package. There are three types of client, app and server. The server is divided into CPU and GPU. Choose one installation according to the environment.
- GPU with CUDA10.2 + Cudnn7 + TensorRT6(Recommended)
By default, the domestic Tsinghua mirror source is turned on to speed up the download. If you use a proxy, you can turn it off（`-i https://pypi.tuna.tsinghua.edu.cn/simple`).
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@@ -89,10 +89,10 @@ The paddle-serving-client and paddle-serving-app installation packages support L
**You only need to install it when you use the `paddle_serving_client.convert` command or the `Python Pipeline framework`. **
**Note**: If your CUDA version is not 10.2 or if you want to use TensorRT(CUDA10.2 included), please do not execute the above commands directly, you need to refer to [Paddle-Inference official document-download and install the Linux prediction library](https://paddleinference.paddlepaddle.org.cn/master/user_guides/download_lib.html#python) Select the URL link of the corresponding GPU environment and install it. Assuming that you use Python3.6, please follow the codeblock.
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.
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@@ -27,15 +27,15 @@ for most users, we do not need to read this section. But if you deploy your Padd
