def __init__(self, f, feature_set):
self.f = f
self.feature_set = feature_set
self.model = M.NNUE(feature_set)
fc_hash = NNUEWriter.fc_hash(self.model)
self.read_header(feature_set, fc_hash)
self.read_int32(feature_set.hash ^
(M.L1 * 2)) # Feature transformer hash
self.read_feature_transformer(self.model.input,
self.model.num_psqt_buckets)
for i in range(self.model.num_ls_buckets):
l1 = nn.Linear(2 * M.L1, M.L2)
l2 = nn.Linear(M.L2, M.L3)
output = nn.Linear(M.L3, 1)
self.read_int32(fc_hash) # FC layers hash
self.read_fc_layer(l1)
self.read_fc_layer(l2)
self.read_fc_layer(output, is_output=True)
self.model.layer_stacks.l1.weight.data[i * M.L2:(i + 1) *
M.L2, :] = l1.weight
self.model.layer_stacks.l1.bias.data[i * M.L2:(i + 1) *
M.L2] = l1.bias
self.model.layer_stacks.l2.weight.data[i * M.L3:(i + 1) *
M.L3, :] = l2.weight
self.model.layer_stacks.l2.bias.data[i * M.L3:(i + 1) *
M.L3] = l2.bias
self.model.layer_stacks.output.weight.data[i:(
i + 1), :] = output.weight
self.model.layer_stacks.output.bias.data[i:(i + 1)] = output.bias
def main():
config = C.Config('config.yaml')
sample_to_device = lambda x: tuple(map(lambda t: t.to(config.device, non_blocking=True), x))
M = model.NNUE().to(config.device)
if (path.exists(config.model_save_path)):
print('Loading model ... ')
M.load_state_dict(torch.load(config.model_save_path))
train_data = ataxx_dataset.AtaxxData(config.train_ataxx_data_path, config)
validation_data = ataxx_dataset.AtaxxData(config.validation_ataxx_data_path, config)
train_data_loader = torch.utils.data.DataLoader(train_data,\
batch_size=config.batch_size,\
num_workers=config.num_workers,\
pin_memory=True,\
worker_init_fn=ataxx_dataset.worker_init_fn)
validation_data_loader = torch.utils.data.DataLoader(validation_data, batch_size=config.batch_size)
validation_data_loader_iter = iter(validation_data_loader)
writer = SummaryWriter(config.visual_directory)
writer.add_graph(M, sample_to_device(next(iter(train_data_loader)))[:3])
opt = optim.Adadelta(M.parameters(), lr=config.learning_rate)
scheduler = optim.lr_scheduler.StepLR(opt, 1, gamma=0.5)
queue = []
for epoch in range(1, config.epochs + 1):
for i, sample in enumerate(train_data_loader):
# update visual data
if (i % config.test_rate) == 0 and i != 0:
step = train_data.cardinality() * (epoch - 1) + i * config.batch_size
train_loss = sum(queue) / len(queue)
validation_sample = next(validation_data_loader_iter, None)
if validation_sample == None:
validation_data_loader_iter = iter(validation_data_loader)
validation_sample = next(validation_data_loader_iter, None)
validation_sample = sample_to_device(validation_sample)
validation_loss = get_validation_loss(M, validation_sample)
writer.add_scalar('train_loss', train_loss, step)
writer.add_scalar('validation_loss', validation_loss, step)
if (i % config.save_rate) == 0 and i != 0:
print('Saving model ...')
M.to_binary_file(config.bin_model_save_path)
torch.save(M.state_dict(), config.model_save_path)
train_step(M, sample_to_device(sample), opt, queue, max_queue_size=config.max_queue_size, report=(0 == i % config.report_rate))
scheduler.step()
def main():
parser = argparse.ArgumentParser(description="Trains the network.")
parser.add_argument("train", help="Training data (.bin or .binpack)")
parser.add_argument("val", help="Validation data (.bin or .binpack)")
parser.add_argument("--architecture", default='normal', help="architecture of model")
parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument("--py-data", action="store_true", help="Use python data loader (default=False)")
parser.add_argument("--lambda", default=1.0, type=float, dest='lambda_', help="lambda=1.0 = train on evaluations, lambda=0.0 = train on game results, interpolates between (default=1.0).")
parser.add_argument("--num-workers", default=1, type=int, dest='num_workers', help="Number of worker threads to use for data loading. Currently only works well for binpack.")
parser.add_argument("--batch-size", default=-1, type=int, dest='batch_size', help="Number of positions per batch / per iteration. Default on GPU = 8192 on CPU = 128.")
parser.add_argument("--threads", default=-1, type=int, dest='threads', help="Number of torch threads to use. Default automatic (cores) .")
parser.add_argument("--seed", default=42, type=int, dest='seed', help="torch seed to use.")
parser.add_argument("--smart-fen-skipping", action='store_true', dest='smart_fen_skipping', help="If enabled positions that are bad training targets will be skipped during loading. Default: False")
args = parser.parse_args()
if args.architecture.lower() == "leiser":
data_name = halfkp.LEISER_NAME
model_inputs = halfkp.LEISER_INPUTS
elif args.architecture.lower() == "normal":
data_name = halfkp.NAME
model_inputs = halfkp.INPUTS
else:
raise Exception("Incorrect architecture name")
nnue = M.NNUE(num_inputs=model_inputs, lambda_=args.lambda_)
print("Training with {} validating with {}".format(args.train, args.val))
pl.seed_everything(args.seed)
print("Seed {}".format(args.seed))
batch_size = args.batch_size
if batch_size <= 0:
batch_size = 128 if args.gpus == 0 else 8192
print('Using batch size {}'.format(batch_size))
print('Smart fen skipping: {}'.format(args.smart_fen_skipping))
if args.threads > 0:
print('limiting torch to {} threads.'.format(args.threads))
t_set_num_threads(args.threads)
if args.py_data:
print('Using python data loader')
train, val = data_loader_py(args.train, args.val, batch_size)
else:
print('Using c++ data loader')
train, val = data_loader_cc(args.train, args.val, data_name, args.num_workers, batch_size, args.smart_fen_skipping)
logdir = args.default_root_dir if args.default_root_dir else 'logs/'
print('Using log dir {}'.format(logdir), flush=True)
tb_logger = pl_loggers.TensorBoardLogger(logdir)
checkpoint_callback = pl.callbacks.ModelCheckpoint(save_top_k=1, save_last=True, monitor='val_loss', filename='best_model')
trainer = pl.Trainer.from_argparse_args(args, callbacks=[checkpoint_callback], logger=tb_logger)
trainer.fit(nnue, train, val)
def __init__(self, f, num_inputs):
self.f = f
self.model = M.NNUE(num_inputs=num_inputs)
self.read_header()
self.read_int32(0x5d69d7b8) # Feature transformer hash
self.read_feature_transformer(self.model.input)
self.read_int32(0x63337156) # FC layers hash
self.read_fc_layer(self.model.l1)
self.read_fc_layer(self.model.l2)
self.read_fc_layer(self.model.output, is_output=True)
def __init__(self, f, feature_set):
self.f = f
self.feature_set = feature_set
self.model = M.NNUE(feature_set)
self.read_header(feature_set)
self.read_int32(feature_set.hash) # Feature transformer hash
self.read_feature_transformer(self.model.input)
self.read_int32(FC_HASH) # FC layers hash
self.read_fc_layer(self.model.l1)
self.read_fc_layer(self.model.l2)
self.read_fc_layer(self.model.output, is_output=True)
def __init__(self, f, feature_set):
self.f = f
self.feature_set = feature_set
self.model = M.NNUE(feature_set)
fc_hash = NNUEWriter.fc_hash(self.model)
self.read_header(feature_set, fc_hash)
self.read_int32(feature_set.hash ^
(M.L1 * 2)) # Feature transformer hash
self.read_feature_transformer(self.model.input)
self.read_int32(fc_hash) # FC layers hash
self.read_fc_layer(self.model.l1)
self.read_fc_layer(self.model.l2)
self.read_fc_layer(self.model.output, is_output=True)
def main():
parser = argparse.ArgumentParser(
description="Converts files between ckpt and nnue format.")
parser.add_argument("source",
help="Source file (can be .ckpt, .pt or .nnue)")
parser.add_argument("target", help="Target file (can be .pt or .nnue)")
parser.add_argument("--architecture",
default="normal",
help="model architecture (leiser or normal)")
args = parser.parse_args()
if args.architecture.lower() == "leiser":
num_inputs = halfkp.LEISER_INPUTS
elif args.architecture.lower() == "normal":
num_inputs = halfkp.INPUTS
else:
raise Exception("Non-valid architecture selection" + args.architecture)
print('Converting %s to %s' % (args.source, args.target))
if args.source.endswith(".pt") or args.source.endswith(".ckpt"):
if not args.target.endswith(".nnue"):
raise Exception("Target file must end with .nnue")
if args.source.endswith(".pt"):
nnue = torch.load(args.source)
else:
nnue = M.NNUE(num_inputs=num_inputs)
checkpoint = torch.load(args.source)
nnue.load_state_dict(checkpoint['state_dict'])
nnue.eval()
#test(nnue)
writer = NNUEWriter(nnue)
with open(args.target, 'wb') as f:
f.write(writer.buf)
elif args.source.endswith(".nnue"):
if not args.target.endswith(".pt"):
raise Exception("Target file must end with .pt")
with open(args.source, 'rb') as f:
reader = NNUEReader(f, num_inputs)
torch.save(reader.model, args.target)
else:
raise Exception('Invalid filetypes: ' + str(args))
def main():
config = C.Config('config.yaml')
sample_to_device = lambda x: tuple(map(lambda t: t.to(config.device, non_blocking=True), x))
M = model.NNUE().to(config.device)
if (path.exists(config.model_save_path)):
print('Loading model ... ')
M.load_state_dict(torch.load(config.model_save_path))
data = nnue_bin_dataset.NNUEBinData(config)
data_loader = torch.utils.data.DataLoader(data,\
batch_size=config.batch_size,\
num_workers=config.num_workers,\
pin_memory=True,\
worker_init_fn=nnue_bin_dataset.worker_init_fn)
opt = optim.Adadelta(M.parameters(), lr=config.learning_rate)
scheduler = optim.lr_scheduler.StepLR(opt, 1, gamma=0.5)
loss_history = []
queue = []
for epoch in range(1, config.epochs + 1):
for i, sample in enumerate(data_loader):
# update visual data
if (i % config.test_rate) == 0 and i != 0:
loss_history.append(sum(queue) / len(queue))
plt.clf()
plt.plot(loss_history)
plt.savefig('{}/loss_graph.png'.format(config.visual_directory), bbox_inches='tight')
if (i % config.save_rate) == 0 and i != 0:
print('Saving model ...')
M.to_binary_file(config.bin_model_save_path)
torch.save(M.state_dict(), config.model_save_path)
train_step(M, sample_to_device(sample), opt, queue, max_queue_size=config.max_queue_size, lambda_=config.lambda_, report=(0 == i % config.report_rate))
scheduler.step()
def main():
parser = argparse.ArgumentParser(description="Trains the network.")
parser.add_argument("train", help="Training data (.bin or .binpack)")
parser.add_argument("val", help="Validation data (.bin or .binpack)")
parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
"--lambda",
default=1.0,
type=float,
dest='lambda_',
help=
"lambda=1.0 = train on evaluations, lambda=0.0 = train on game results, interpolates between (default=1.0)."
)
parser.add_argument(
"--num-workers",
default=1,
type=int,
dest='num_workers',
help=
"Number of worker threads to use for data loading. Currently only works well for binpack."
)
parser.add_argument(
"--batch-size",
default=-1,
type=int,
dest='batch_size',
help=
"Number of positions per batch / per iteration. Default on GPU = 8192 on CPU = 128."
)
parser.add_argument(
"--threads",
default=-1,
type=int,
dest='threads',
help="Number of torch threads to use. Default automatic (cores) .")
parser.add_argument("--seed",
default=42,
type=int,
dest='seed',
help="torch seed to use.")
parser.add_argument(
"--smart-fen-skipping",
action='store_true',
dest='smart_fen_skipping_deprecated',
help=
"If enabled positions that are bad training targets will be skipped during loading. Default: True, kept for backwards compatibility. This option is ignored"
)
parser.add_argument(
"--no-smart-fen-skipping",
action='store_true',
dest='no_smart_fen_skipping',
help=
"If used then no smart fen skipping will be done. By default smart fen skipping is done."
)
parser.add_argument(
"--random-fen-skipping",
default=3,
type=int,
dest='random_fen_skipping',
help=
"skip fens randomly on average random_fen_skipping before using one.")
parser.add_argument(
"--resume-from-model",
dest='resume_from_model',
help="Initializes training using the weights from the given .pt model")
features.add_argparse_args(parser)
args = parser.parse_args()
if not os.path.exists(args.train):
raise Exception('{0} does not exist'.format(args.train))
if not os.path.exists(args.val):
raise Exception('{0} does not exist'.format(args.val))
feature_set = features.get_feature_set_from_name(args.features)
if args.resume_from_model is None:
nnue = M.NNUE(feature_set=feature_set, lambda_=args.lambda_)
nnue.cuda()
else:
nnue = torch.load(args.resume_from_model)
nnue.set_feature_set(feature_set)
nnue.lambda_ = args.lambda_
nnue.cuda()
print("Feature set: {}".format(feature_set.name))
print("Num real features: {}".format(feature_set.num_real_features))
print("Num virtual features: {}".format(feature_set.num_virtual_features))
print("Num features: {}".format(feature_set.num_features))
print("Training with {} validating with {}".format(args.train, args.val))
pl.seed_everything(args.seed)
print("Seed {}".format(args.seed))
batch_size = args.batch_size
if batch_size <= 0:
batch_size = 16384
print('Using batch size {}'.format(batch_size))
print('Smart fen skipping: {}'.format(not args.no_smart_fen_skipping))
print('Random fen skipping: {}'.format(args.random_fen_skipping))
if args.threads > 0:
print('limiting torch to {} threads.'.format(args.threads))
t_set_num_threads(args.threads)
logdir = args.default_root_dir if args.default_root_dir else 'logs/'
print('Using log dir {}'.format(logdir), flush=True)
tb_logger = pl_loggers.TensorBoardLogger(logdir)
checkpoint_callback = pl.callbacks.ModelCheckpoint(save_last=True,
period=20,
save_top_k=-1)
trainer = pl.Trainer.from_argparse_args(args,
callbacks=[checkpoint_callback],
logger=tb_logger)
main_device = trainer.root_device if trainer.root_gpu is None else 'cuda:' + str(
trainer.root_gpu)
print('Using c++ data loader')
train, val = make_data_loaders(args.train, args.val, feature_set,
args.num_workers, batch_size,
not args.no_smart_fen_skipping,
args.random_fen_skipping, main_device)
trainer.fit(nnue, train, val)
from os import path
import torch
import ataxx
import config as C
import util
import model
config = C.Config('config.yaml')
M = model.NNUE().to(config.device)
if (path.exists(config.model_save_path)):
print('Loading model ... ')
M.load_state_dict(torch.load(config.model_save_path, map_location=config.device))
num_parameters = sum(map(lambda x: torch.numel(x), M.parameters()))
print(num_parameters)
M.cpu()
while True:
bd = ataxx.Board(input("fen: "))
w, b = util.to_tensors(bd)
white, black = util.to_tensors(bd)
val = M(torch.tensor([bd.turn]).float(), white.unsqueeze(0).float(), black.unsqueeze(0).float())
print(val)
def main(args):
# Select which device to use
if torch.cuda.is_available():
main_device = 'cuda:0'
else:
main_device = 'cpu'
# Create directories to store data and logs in
output_path = prepare_output_directory()
log_path = prepare_log_directory()
# Print configuration info
print(f'Device: {main_device}')
print(f'Training set: {args.train}')
print(f'Validation set: {args.val}')
print(f'Batch size: {args.batch_size}')
print(f'Using factorizer: {args.use_factorizer}')
print(f'Lambda: {args.lambda_}')
print(f'Validation check interval: {args.val_check_interval}')
print(f'Logs written to: {log_path}')
print(f'Data written to: {output_path}')
print('')
# Create log writer
writer = SummaryWriter(log_path)
# Create data loaders
train_data_loader, val_data_loader = create_data_loaders(
args.train, args.val, args.train_size, args.val_size, args.batch_size,
args.use_factorizer, main_device)
# Create model
nnue = M.NNUE(args.use_factorizer,
feature_set=halfkp.Features()).to(main_device)
# Configure optimizer
optimizer = ranger.Ranger(nnue.parameters(), lr=1e-3)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=0.1,
patience=1,
verbose=True,
min_lr=1e-6)
# Main training loop
num_batches = len(train_data_loader)
epoch = 0
running_train_loss = 0.0
while True:
best_val_loss = 1000000.0
for k, sample in enumerate(train_data_loader):
train_loss = train_step(nnue, sample, optimizer, args.lambda_,
epoch, k, num_batches)
running_train_loss += train_loss.item()
if k % args.val_check_interval == (args.val_check_interval - 1):
val_loss = calculate_validation_loss(nnue, val_data_loader,
args.lambda_)
new_best = False
if (val_loss < best_val_loss):
new_best = True
best_val_loss = val_loss
save_model(nnue, output_path, epoch, k, val_loss, new_best,
False)
writer.add_scalar('training loss',
running_train_loss / args.val_check_interval,
epoch * num_batches + k)
writer.add_scalar('validation loss', val_loss,
epoch * num_batches + k)
running_train_loss = 0.0
val_loss = calculate_validation_loss(nnue, val_data_loader,
args.lambda_)
new_best = False
if (val_loss < best_val_loss):
new_best = True
best_val_loss = val_loss
save_model(nnue, output_path, epoch, num_batches - 1, val_loss,
new_best, True)
print('')
scheduler.step(val_loss)
epoch += 1
def main():
t_set_printoptions(profile="full")
parser = argparse.ArgumentParser(description="Trains the network.")
parser.add_argument("--py-data",
action="store_true",
help="Use python data loader (default=False)")
parser.add_argument("train", help="Training data (.bin or .binpack)")
parser.add_argument("val", help="Validation data (.bin or .binpack)")
parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
"--lambda",
default=1.0,
type=float,
dest='lambda_',
help=
"lambda=1.0 = train on evaluations, lambda=0.0 = train on game results, interpolates between (default=1.0)."
)
parser.add_argument(
"--num-workers",
default=1,
type=int,
dest='num_workers',
help=
"Number of worker threads to use for data loading. Currently only works well for binpack."
)
parser.add_argument(
"--batch-size",
default=-1,
type=int,
dest='batch_size',
help=
"Number of positions per batch / per iteration. Default on GPU = 8192 on CPU = 128."
)
parser.add_argument(
"--threads",
default=-1,
type=int,
dest='threads',
help="Number of torch threads to use. Default automatic (cores) .")
parser.add_argument(
"--random-fen-skipping",
default=0,
type=int,
dest='random_fen_skipping',
help=
"skip fens randomly on average random_fen_skipping before using one.")
parser.add_argument(
"--smart-fen-skipping",
action='store_true',
dest='smart_fen_skipping',
help=
"If enabled positions that are bad training targets will be skipped during loading. Default: False"
)
args = parser.parse_args()
nnue = M.NNUE(lambda_=args.lambda_)
print("Training with {} validating with {}".format(args.train, args.val))
batch_size = args.batch_size
if batch_size <= 0:
batch_size = 1024 if args.gpus == 0 else 2048
print('Using batch size {}'.format(batch_size))
if args.threads > 0:
print('limiting torch to {} threads.'.format(args.threads))
t_set_num_threads(args.threads)
tb_logger = pl_loggers.TensorBoardLogger('logs/')
checkpoint_callback = pl.callbacks.ModelCheckpoint(save_last=True,
save_top_k=-1)
trainer = pl.Trainer.from_argparse_args(args,
callbacks=[checkpoint_callback],
logger=tb_logger,
profiler='advanced')
main_device = trainer.root_device if trainer.root_gpu is None else 'cuda:' + str(
trainer.root_gpu)
if args.py_data:
print('Using python data loader')
train, val = data_loader_py(args.train, args.val, args.num_workers,
batch_size)
else:
print('Using c++ data loader')
train, val = data_loader_cc(args.train, args.val, args.num_workers,
batch_size, args.smart_fen_skipping,
args.random_fen_skipping, main_device)
trainer.fit(nnue, train, val)
def main():
parser = argparse.ArgumentParser(description="Trains the network.")
parser.add_argument("train", help="Training data (.bin or .binpack)")
parser.add_argument("val", help="Validation data (.bin or .binpack)")
parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument("--py-data",
action="store_true",
help="Use python data loader (default=False)")
parser.add_argument(
"--lambda",
default=1.0,
type=float,
dest='lambda_',
help=
"lambda=1.0 = train on evaluations, lambda=0.0 = train on game results, interpolates between (default=1.0)."
)
parser.add_argument("--alpha",
default=1.0,
type=float,
dest='alpha_',
help="random multiply factor (default=1.0).")
parser.add_argument(
"--beta",
default=6000,
type=int,
dest='beta_',
help=
"definite random step frequency - according to steps (default=6000).")
parser.add_argument(
"--gamma",
default=0.0005,
type=float,
dest='gamma_',
help="randomized random step frequency (default=0.0005).")
parser.add_argument(
"--num-workers",
default=1,
type=int,
dest='num_workers',
help=
"Number of worker threads to use for data loading. Currently only works well for binpack."
)
parser.add_argument(
"--batch-size",
default=-1,
type=int,
dest='batch_size',
help=
"Number of positions per batch / per iteration. Default on GPU = 8192 on CPU = 128."
)
parser.add_argument(
"--threads",
default=-1,
type=int,
dest='threads',
help="Number of torch threads to use. Default automatic (cores) .")
parser.add_argument("--seed",
default=42,
type=int,
dest='seed',
help="torch seed to use.")
parser.add_argument(
"--smart-fen-skipping",
action='store_true',
dest='smart_fen_skipping',
help=
"If enabled positions that are bad training targets will be skipped during loading. Default: False"
)
parser.add_argument(
"--random-fen-skipping",
default=0,
type=int,
dest='random_fen_skipping',
help=
"skip fens randomly on average random_fen_skipping before using one.")
parser.add_argument(
"--resume-from-model",
dest='resume_from_model',
help="Initializes training using the weights from the given .pt model")
features.add_argparse_args(parser)
args = parser.parse_args()
if not os.path.exists(args.train):
raise Exception('{0} does not exist'.format(args.train))
if not os.path.exists(args.val):
raise Exception('{0} does not exist'.format(args.val))
feature_set = features.get_feature_set_from_name(args.features)
if args.resume_from_model is None:
nnue = M.NNUE(feature_set=feature_set,
lambda_=args.lambda_,
alpha_=args.alpha_,
beta_=args.beta_,
gamma=args.gamma_)
else:
nnue = torch.load(args.resume_from_model)
nnue.set_feature_set(feature_set)
nnue.lambda_ = args.lambda_
nnue.alpha_ = args.alpha_
nnue.beta_ = args.beta_
nnue.gamma_ = args.gamma_
print("Feature set: {}".format(feature_set.name))
print("Num real features: {}".format(feature_set.num_real_features))
print("Num virtual features: {}".format(feature_set.num_virtual_features))
print("Num features: {}".format(feature_set.num_features))
print("Training with {} validating with {}".format(args.train, args.val))
pl.seed_everything(args.seed)
print("Seed {}".format(args.seed))
batch_size = args.batch_size
if batch_size <= 0:
batch_size = 128 if args.gpus == 0 else 8192
print('Using batch size {}'.format(batch_size))
print('Smart fen skipping: {}'.format(args.smart_fen_skipping))
print('Random fen skipping: {}'.format(args.random_fen_skipping))
if args.threads > 0:
print('limiting torch to {} threads.'.format(args.threads))
t_set_num_threads(args.threads)
logdir = args.default_root_dir if args.default_root_dir else 'logs/'
print('Using log dir {}'.format(logdir), flush=True)
wandb_logger = WandbLogger()
checkpoint_callback = pl.callbacks.ModelCheckpoint(save_last=True,
period=5,
save_top_k=-1)
trainer = pl.Trainer.from_argparse_args(args,
callbacks=[checkpoint_callback],
logger=wandb_logger)
main_device = trainer.root_device if trainer.root_gpu is None else 'cuda:' + str(
trainer.root_gpu)
if args.py_data:
print('Using python data loader')
train, val = data_loader_py(args.train, args.val, feature_set,
batch_size, main_device)
else:
print('Using c++ data loader')
train, val = data_loader_cc(args.train, args.val, feature_set,
args.num_workers, batch_size,
args.smart_fen_skipping,
args.random_fen_skipping, main_device)
trainer.fit(nnue, train, val)