deepmd.train package

Submodules

deepmd.train.run_options module

Module taking care of important package constants.

class deepmd.train.run_options.RunOptions(init_model: Optional[str] = None, init_frz_model: Optional[str] = None, restart: Optional[str] = None, log_path: Optional[str] = None, log_level: int = 0, mpi_log: str = 'master')[source]

Bases: object

Class with inf oon how to run training (cluster, MPI and GPU config).

Attributes

gpus: Optional[List[int]]: list of GPUs if any are present else None
is_chief: bool: in distribured training it is true for tha main MPI process in serail it is always true
world_size: int: total worker count
my_rank: int: index of the MPI task
nodename: str: name of the node
node_list_List[str]: the list of nodes of the current mpirun
my_device: str: deviice type - gpu or cpu

Methods

print_resource_summary()

Print build and current running cluster configuration summary.

gpus: Optional[List[int]]

property is_chief: Whether my rank is 0.

my_device: str

my_rank: int

nodelist: List[int]

nodename: str

print_resource_summary()[source]: Print build and current running cluster configuration summary.

world_size: int

deepmd.train.trainer module

class deepmd.train.trainer.DPTrainer(jdata, run_opt, is_compress=False)[source]

Bases: object

Methods

save_compressed()

Save the compressed graph

build
get_evaluation_results
get_feed_dict
get_global_step
print_header
print_on_training
save_checkpoint
train
valid_on_the_fly

build(data=None, stop_batch=0)[source]

get_evaluation_results(batch_list)[source]

get_feed_dict(batch, is_training)[source]

get_global_step()[source]

static print_header(fp, train_results, valid_results)[source]

static print_on_training(fp, train_results, valid_results, cur_batch, cur_lr)[source]

save_checkpoint(cur_batch: int)[source]

save_compressed()[source]: Save the compressed graph

train(train_data=None, valid_data=None)[source]

valid_on_the_fly(fp, train_batches, valid_batches, print_header=False)[source]