PaddlePaddle / VisualDL

**中文** Introduction VisualDL, a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters, and visualizes model structures, data samples, histograms of tensors, PR curves , ROC curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently. VisualDL provides various visualization functions, including **tracking metrics in real-time, visualizing the model structure, displaying the data sample, visualizing the relationship between hyperparameters and model metrics, presenting the changes of distributions of tensors, showing the pr curves, projecting high-dimensional data to a lower dimensional space and more.** Additionally, VisualDL provides VDL.service, which enables developers easily to save, track and share visualization results of experiments. For specific guidelines of each function, please refer to **VisualDL User Guide**. For up-to-date experience, please feel free to try our **Online Demo**. Currently, VisualDL iterates rapidly and new functions will be continuously added. Browsers supported by VisualDL are: • Google Chrome ≥ 79 • Firefox ≥ 67 • Microsoft Edge ≥ 79 • Safari ≥ 11.1 VisualDL natively supports the use of Python. Developers can retrieve plentiful visualization results by simply adding a few lines of Python code into the model before training. Contents • Key Highlights • Installation • Usage Guideline • Function Preview • Frequently Asked Questions • Contribution • More Details • Technical Communication Key Highlights Easy to Use The high-level design of API makes it easy to use. Only one click can initiate the visualization of model structures. Various Functions The function contains the visualization of training parameters, data samples, graph structures, histograms of tensors, PR curves and high-dimensional data distributions. High Compatibility VisualDL provides the visualization of the mainstream model structures such as Paddle, ONNX, Caffe, widely supporting visual analysis for diverse users. Fully Support By Integrating into PaddlePaddle and related modules, VisualDL allows developers to use different components without obstructions, and thus to have the best experience in the PaddlePaddle ecosystem. Installation Install by PiP Install by Code Please note that Python 2 is no longer maintained officially since January 1, 2020. VisualDL now only supports Python 3 in order to ensure the usability of codes. Usage Guideline VisualDL stores the data, parameters and other information of the training process in a log file. Users can launch the panel to observe the visualization results. • Log The Python SDK is provided at the back end of VisualDL, and a logger can be customized through LogWriter. The interface description is shown as follows: Interface Parameters | parameters | type | meaning | | --------------- | ------- | ------------------------------------------------------------ | | logdir | string | The path location of log file. VisualDL will create a log file under this path to record information generated by the training process. If not specified, the path will be as default. | | max_queue | int | The maximum capacity of the data generated before recording in a log file. Default value is 10. If the capacity is reached, the data are immediately written into the log file. | | flush_secs | int | The maximum cache time of the data generated before recording in a log file. Default value is 120. When this time is reached, the data are immediately written to the log file. (When the log message queue reaches the maximum cache time or maximum capacity, it will be written to the log file immediately)| | filename_suffix | string | Add a suffix to the default log file name. | | display_name | string | This parameter is displayed in the location of in the panel. If not set, the default name is .(When is too long or needed to be hidden). | | file_name | string | Set the name of the log file. If the file_name already exists, setting the file_name will be new records in the same log file, which will continue to be used. Note that the name should include 'vdlrecords'. | Example Create a log file and record scalar values: • Launch Panel In the above example, the log has recorded three sets of scalar values. Developers can view the visualization results of the log file through launching the visualDL panel. There are two ways to launch the log file: Launch by Command Line Use the command line to launch the VisualDL panel： Parameter details: | parameters | meaning | | --------------- | ------------------------------------------------------------ | | --logdir | Set one or more directories of the log. All the logs in the paths or subdirectories will be displayed on the VisualDL Board indepentently. | | --model | Set a path to the model file (not a directory). VisualDL will visualize the model file in Graph page. PaddlePaddle、ONNX、Keras、Core ML、Caffe and other model formats are supported. Please refer to Graph - Functional Instructions. | | --host | Specify IP address. The default value is . Specify it as or public IP address so that other machines can visit VisualDL Board. | | --port | Set the port. The default value is . | | --cache-timeout | Cache time of the backend. During the cache time, the front end requests the same URL multiple times, and then the returned data are obtained from the cache. The default cache time is 20 seconds. | | --language | The language of the VisualDL panel. Language can be specified as 'en' or 'zh', and the default is the language used by the browser. | | --public-path | The URL path of the VisualDL panel. The default path is '/app', meaning that the access address is 'http://<host>:<port>/app'. | | --api-only | Decide whether or not to provide only API. If this parameter is set, VisualDL will only provides API service without displaying…