In this section, we will take $deepmd_source_dir/examples/water/se_e2_a/input.json as an example of the input file. ## 4.2.1. Learning rate The learning_rate section in input.json is given as follows  "learning_rate" :{ "type": "exp", "start_lr": 0.001, "stop_lr": 3.51e-8, "decay_steps": 5000, "_comment": "that's all" }  • start_lr gives the learning rate at the beginning of the training. • stop_lr gives the learning rate at the end of the training. It should be small enough to ensure that the network parameters satisfactorily converge. • During the training, the learning rate decays exponentially from start_lr to stop_lr following the formula. lr(t) = start_lr * decay_rate ^ ( t / decay_steps )  where t is the training step. ## 4.2.2. Training parameters Other training parameters are given in the training section.  "training": { "training_data": { "systems": ["../data_water/data_0/", "../data_water/data_1/", "../data_water/data_2/"], "batch_size": "auto" }, "validation_data":{ "systems": ["../data_water/data_3"], "batch_size": 1, "numb_btch": 3 }, "numb_step": 1000000, "seed": 1, "disp_file": "lcurve.out", "disp_freq": 100, "save_freq": 1000 }  The sections "training_data" and "validation_data" give the training dataset and validation dataset, respectively. Taking the training dataset for example, the keys are explained below: • systems provide paths of the training data systems. DeePMD-kit allows you to provide multiple systems with different numbers of atoms. This key can be a list or a str. • list: systems gives the training data systems. • str: systems should be a valid path. DeePMD-kit will recursively search all data systems in this path. • At each training step, DeePMD-kit randomly pick batch_size frame(s) from one of the systems. The probability of using a system is by default in proportion to the number of batches in the system. More optional are available for automatically determining the probability of using systems. One can set the key auto_prob to • "prob_uniform" all systems are used with the same probability. • "prob_sys_size" the probability of using a system is in proportional to its size (number of frames). • "prob_sys_size; sidx_0:eidx_0:w_0; sidx_1:eidx_1:w_1;..." the list of systems are divided into blocks. The block i has systems ranging from sidx_i to eidx_i. The probability of using a system from block i is in proportional to w_i. Within one block, the probability of using a system is in proportional to its size. • An example of using "auto_prob" is given as below. The probability of using systems[2] is 0.4, and the sum of the probabilities of using systems[0] and systems[1] is 0.6. If the number of frames in systems[1] is twice as system[0], then the probability of using system[1] is 0.4 and that of system[0] is 0.2.  "training_data": { "systems": ["../data_water/data_0/", "../data_water/data_1/", "../data_water/data_2/"], "auto_prob": "prob_sys_size; 0:2:0.6; 2:3:0.4", "batch_size": "auto" }  • The probability of using systems can also be specified explicitly with key "sys_prob" that is a list having the length of the number of systems. For example  "training_data": { "systems": ["../data_water/data_0/", "../data_water/data_1/", "../data_water/data_2/"], "sys_prob": [0.5, 0.3, 0.2], "batch_size": "auto:32" }  • The key batch_size specifies the number of frames used to train or validate the model in a training step. It can be set to • list: the length of which is the same as the systems. The batch size of each system is given by the elements of the list. • int: all systems use the same batch size. • "auto": the same as "auto:32", see "auto:N" • "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N. • The key numb_batch in validate_data gives the number of batches of model validation. Note that the batches may not be from the same system Other keys in the training section are explained below: • numb_step The number of training steps. • seed The random seed for getting frames from the training data set. • disp_file The file for printing learning curve. • disp_freq The frequency of printing learning curve. Set in the unit of training steps • save_freq The frequency of saving check point. ## 4.2.3. Options and environment variables Several command line options can be passed to dp train, which can be checked with $ dp train --help


An explanation will be provided

positional arguments:
INPUT                 the input json database

optional arguments:
-h, --help            show this help message and exit

--init-model INIT_MODEL
Initialize a model by the provided checkpoint

--restart RESTART     Restart the training from the provided checkpoint

--init-frz-model INIT_FRZ_MODEL
Initialize the training from the frozen model.


--init-model model.ckpt, initializes the model training with an existing model that is stored in the checkpoint model.ckpt, the network architectures should match.

--restart model.ckpt, continues the training from the checkpoint model.ckpt.

--init-frz-model frozen_model.pb, initializes the training with an existing model that is stored in frozen_model.pb.

On some resources limited machines, one may want to control the number of threads used by DeePMD-kit. This is achieved by three environmental variables: OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS and TF_INTER_OP_PARALLELISM_THREADS. OMP_NUM_THREADS controls the multithreading of DeePMD-kit implemented operations. TF_INTRA_OP_PARALLELISM_THREADS and TF_INTER_OP_PARALLELISM_THREADS controls intra_op_parallelism_threads and inter_op_parallelism_threads, which are Tensorflow configurations for multithreading. An explanation is found here.

For example if you wish to use 3 cores of 2 CPUs on one node, you may set the environmental variables and run DeePMD-kit as follows:

export OMP_NUM_THREADS=6
dp train input.json


One can set other environmental variables:

Environment variables

Allowed value

Default value

Usage

DP_INTERFACE_PREC

high, low

high

Control high (double) or low (float) precision of training.