Several examples of training can be found in the examples directory:
$ cd $deepmd_source_dir/examples/water/se_e2_a/After switching to that directory, the training can be invoked by
$ dp train input.jsonwhere input.json is the name of the input script.
By default, the verbosity level of the DeePMD-kit is INFO, one may see a lot of important information on the code and environment showing on the screen. Among them two pieces of information regarding data systems are worth special notice.
DEEPMD INFO ---Summary of DataSystem: training -----------------------------------------------
DEEPMD INFO found 3 system(s):
DEEPMD INFO system natoms bch_sz n_bch prob pbc
DEEPMD INFO ../data_water/data_0/ 192 1 80 0.250 T
DEEPMD INFO ../data_water/data_1/ 192 1 160 0.500 T
DEEPMD INFO ../data_water/data_2/ 192 1 80 0.250 T
DEEPMD INFO --------------------------------------------------------------------------------------
DEEPMD INFO ---Summary of DataSystem: validation -----------------------------------------------
DEEPMD INFO found 1 system(s):
DEEPMD INFO system natoms bch_sz n_bch prob pbc
DEEPMD INFO ../data_water/data_3 192 1 80 1.000 T
DEEPMD INFO --------------------------------------------------------------------------------------The DeePMD-kit prints detailed information on the training and validation data sets. The data sets are defined by {ref}training_data <training/training_data> and {ref}validation_data <training/validation_data> defined in the {ref}training <training> section of the input script. The training data set is composed of three data systems, while the validation data set is composed by one data system. The number of atoms, batch size, the number of batches in the system and the probability of using the system are all shown on the screen. The last column presents if the periodic boundary condition is assumed for the system.
During the training, the error of the model is tested every {ref}disp_freq <training/disp_freq> training steps with the batch used to train the model and with {ref}numb_btch <training/validation_data/numb_btch> batches from the validating data. The training error and validation error are printed correspondingly in the file {ref}disp_file <training/disp_file> (default is lcurve.out). The batch size can be set in the input script by the key {ref}batch_size <training/training_data/batch_size> in the corresponding sections for the training and validation data set. An example of the output
# step rmse_val rmse_trn rmse_e_val rmse_e_trn rmse_f_val rmse_f_trn lr
0 3.33e+01 3.41e+01 1.03e+01 1.03e+01 8.39e-01 8.72e-01 1.0e-03
100 2.57e+01 2.56e+01 1.87e+00 1.88e+00 8.03e-01 8.02e-01 1.0e-03
200 2.45e+01 2.56e+01 2.26e-01 2.21e-01 7.73e-01 8.10e-01 1.0e-03
300 1.62e+01 1.66e+01 5.01e-02 4.46e-02 5.11e-01 5.26e-01 1.0e-03
400 1.36e+01 1.32e+01 1.07e-02 2.07e-03 4.29e-01 4.19e-01 1.0e-03
500 1.07e+01 1.05e+01 2.45e-03 4.11e-03 3.38e-01 3.31e-01 1.0e-03The file contains 8 columns, from left to right, which are the training step, the validation loss, training loss, root mean square (RMS) validation error of energy, RMS training error of energy, RMS validation error of force, RMS training error of force and the learning rate. The RMS error (RMSE) of the energy is normalized by the number of atoms in the system. One can visualize this file with a simple Python script:
import numpy as np
import matplotlib.pyplot as plt
data = np.genfromtxt("lcurve.out", names=True)
for name in data.dtype.names[1:-1]:
plt.plot(data["step"], data[name], label=name)
plt.legend()
plt.xlabel("Step")
plt.ylabel("Loss")
plt.xscale("symlog")
plt.yscale("log")
plt.grid()
plt.show()Checkpoints will be written to files with the prefix {ref}save_ckpt <training/save_ckpt> every {ref}save_freq <training/save_freq> training steps.
:::{warning}
It is warned that the example water data (in folder examples/water/data) is of very limited amount, is provided only for testing purposes, and should not be used to train a production model.
:::