In the industrial scenario, it is usually the remote periodic output model, and the online server needs to pull down the new model to update the old model without service interruption.
Paddle Serving provides an automatic monitoring script. After the remote address updates the model, the new model will be pulled to update the local model. At the same time, the `fluid_time_stamp` in the local model folder will be updated to realize model hot loading.
Currently, the following types of Monitors are supported:
| General | Without authentication, you can directly access the download file by `wget` (such as FTP and BOS which do not need authentication) | `general_host` General remote host. |
| HDFS | The remote is HDFS, and relevant commands are executed through HDFS binary | `hdfs_bin` Path of HDFS binary file. |
| FTP | The remote is FTP, and relevant commands are executed through `ftplib`(Using this monitor, you need to install `ftplib` with command `pip install ftplib`) | `ftp_host` FTP remote host.<br>`ftp_port` FTP remote port.<br>`ftp_username` FTP username. Not used if anonymous access.<br>`ftp_password` FTP password. Not used if anonymous access. |
| AFS | The remote is AFS, and relevant commands are executed through Hadoop-client | `hadoop_bin` Path of Hadoop binary file.<br>`hadoop_host` AFS host. Not used if set in Hadoop-client.<br>`hadoop_ugi` AFS ugi, Not used if set in Hadoop-client. |
| `remote_path` | Specify the base path for the remote | / |
| `remote_model_name` | Specify the model name to be pulled from the remote | / |
| `remote_donefile_name` | Specify the donefile name that marks the completion of the remote model update | / |
| `local_path` | Specify local work path | / |
| `local_model_name` | Specify local model name | / |
| `local_timestamp_file` | Specify the timestamp file used locally for hot loading, The file is considered to be placed in the `local_path/local_model_name` folder. | `fluid_time_file` |
| `local_tmp_path` | Specify the path of the folder where temporary files are stored locally. If it does not exist, it will be created automatically. | `_serving_monitor_tmp` |
| `interval` | Specify the polling interval in seconds. | `10` |
| `unpacked_filename` | Monitor supports the `tarfile` packaged remote model file. If the remote model is in a packaged format, you need to set this option to tell monitor the name of the extracted file. | `None` |
The following is an example of HDFSMonitor to show the model hot loading of Paddle Serving.
## HDFSMonitor example
In this example, the production model is uploaded to HDFS in `product_path` folder, and the server hot loads the model in `server_path` folder:
```shell
.
├── product_path
└── server_path
```
### Product model
Run the following Python code products model in `product_path` folder. Every 60 seconds, the package file of Boston house price prediction model `uci_housing.tar.gz` will be generated and uploaded to the path of HDFS `/`. After uploading, the timestamp file `donefile` will be updated and uploaded to the path of HDFS `/`.
The above code monitors the remote timestamp file `/donefile` of the remote HDFS address `/` every 10 seconds by polling. When the remote timestamp file changes, the remote model is considered to have been updated. Pull the remote packaging model `/uci_housing.tar.gz` to the local temporary path `./_tmp/uci_housing.tar.gz`. After unpacking to get the model file `./_tmp/uci_housing_model`, update the local model `./uci_housing_model` and the model timestamp file `./uci_housing_model/fluid_time_file` of Paddle Serving.
#### View server logs
View the running log of the server with the following command:
```shell
tail-f log/serving.INFO
```
The log shows that the model has been hot loaded:
```shell
I0330 09:38:40.087316 7361 server.cpp:150] Begin reload framework...
W0330 09:38:40.087399 7361 infer.h:656] Succ reload version engine: 18446744073709551615
