DeePMD-kit TensorBoard usage

TensorBoard provides the visualization and tooling needed for machine learning experimentation. A full instruction of 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 viriables

How to use Tensorboard with DeePMD-kit

Before running TensorBoard, make sure you have generated summary data in a log directory by modifying the the input script, set “tensorboard” true in 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",
	"load_ckpt":	"model.ckpt",
	"disp_training":true,
	"time_training":true,
	"tensorboard":	true,
	"tensorboard_log_dir":"log",
	"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”.

tensorboard --logdir path/to/logs

Examples

Tracking and visualizing loss metrics(red:train, blue:test)

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Visualizing deepmd-kit model graph

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Viewing histograms of weights, biases, or other tensors as they change over time

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Viewing summaries of trainable variables

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Atention

Allowing the tensorboard analysis will takes extra execution time.(eg, 15% increasing @Nvidia GTX 1080Ti double precision with default water sample)

TensorBoard can be used in Google Chrome or Firefox. Other browsers might work, but there may be bugs or performance issues.