TensorBoard Usage

November 21, 2022 · View on GitHub

TensorBoard provides the visualization and tooling needed for machine learning experimentation. Full instructions for TensorBoard can be found here.

Highlighted features

DeePMD-kit can now use most of the interesting features enabled by TensorBoard!

Tracking and visualizing metrics, such as l2_loss, l2_energy_loss and l2_force_loss
Visualizing the model graph (ops and layers)
Viewing histograms of weights, biases, or other tensors as they change over time.
Viewing summaries of trainable variables

How to use Tensorboard with DeePMD-kit

Before running TensorBoard, make sure you have generated summary data in a log directory by modifying the input script, setting {ref}tensorboard <training/tensorboard> to true in the training subsection will enable the TensorBoard data analysis. eg. water_se_a.json.

    "training" : {
	"systems":	["../data/"],
	"set_prefix":	"set",    
	"stop_batch":	1000000,
	"batch_size":	1,

	"seed":		1,

	"_comment": " display and restart",
	"_comment": " frequencies counted in batch",
	"disp_file":	"lcurve.out",
	"disp_freq":	100,
	"numb_test":	10,
	"save_freq":	1000,
	"save_ckpt":	"model.ckpt",

	"disp_training":true,
	"time_training":true,
	"tensorboard":	true,
	"tensorboard_log_dir":"log",
	"tensorboard_freq": 1000,
	"profiling":	false,
	"profiling_file":"timeline.json",
	"_comment":	"that's all"
    }

Once you have event files, run TensorBoard and provide the log directory. This should print that TensorBoard has started. Next, connect to http://tensorboard_server_ip:6006.

TensorBoard requires a logdir to read logs from. For info on configuring TensorBoard, run TensorBoard --help. One can easily change the log name with "tensorboard_log_dir" and the sampling frequency with "tensorboard_freq".