Tensorboard

TensorBoard is a visualization tool provided by Google TensorFlow, designed to help understand, debug, and optimize machine learning models. It presents various metrics and data from the training process through a graphical interface, allowing developers to gain a more intuitive understanding of the model's performance and behavior.

You can use swanlab convert to convert Tensorboard-generated Tfevent files into SwanLab experiments.

Method 1: Command Line Conversion

swanlab convert -t tensorboard -tb_logdir [TFEVENT_LOGDIR]

Here, [TFEVENT_LOGDIR] refers to the path of the log files generated when you previously recorded experiments using Tensorboard.

The SwanLab Converter will automatically detect tfevent files in the specified path and its subdirectories (default subdirectory depth is 3) and create a SwanLab experiment for each tfevent file.

Method 2: Conversion Within Code

from swanlab.converter import TFBConverter

tfb_converter = TFBConverter(convert_dir="[TFEVENT_LOGDIR]")
tfb_converter.run()

This method achieves the same effect as the command line conversion.

Parameter List

Parameter	Corresponding CLI Argument	Description
convert_dir	-	Path to Tfevent files
project	-p, --project	SwanLab project name
workspace	-w, --workspace	SwanLab workspace name
cloud	--cloud	Whether to use the cloud version, default is True
logdir	-l, --logdir	SwanLab log file save path

Example:

from swanlab.converter import TFBConverter

tfb_converter = TFBConverter(
    convert_dir="./runs",
    project="Tensorboard-Converter",
    workspace="SwanLab",
    logdir="./logs",
    )
tfb_converter.run()

The equivalent CLI command:

swanlab convert -t tensorboard --tb_logdir ./runs -p Tensorboard-Converter -w SwanLab -l ./logs

Executing the above script will create a project named Tensorboard-Converter under the SwanLab workspace, convert the tfevent files in the ./runs directory into individual swanlab experiments, and save the logs generated during the swanlab runtime in the ./logs directory.