def get_estimator(self):
from tensorflow.estimator import Estimator
from tensorflow.estimator import RunConfig
from tensorflow.estimator import EstimatorSpec
def model_fn(features, labels, mode, params):
with tf.gfile.GFile(self.graph_path, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
input_names = ['input_ids', 'input_mask', 'segment_ids']
output = tf.import_graph_def(
graph_def,
input_map={k + ':0': features[k]
for k in input_names},
return_elements=['final_encodes:0'])
return EstimatorSpec(mode=mode, predictions={'encodes': output[0]})
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = self.gpu_memory_fraction
config.log_device_placement = False
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
return Estimator(model_fn=model_fn,
config=RunConfig(session_config=config),
params={'batch_size': self.batch_size})
def __init__(self, args):
from tensorflow.estimator import RunConfig, Estimator
# load parameters
self.layer_indexes = args.layer_indexes
self.ckpt_name = args.ckpt_name
self.config_name = args.config_name
self.vocab_file = args.vocab_file
self.do_lower_case = args.do_lower_case
self.batch_size = args.batch_size
self.max_seq_len = args.max_seq_len
self.gpu_memory_fraction = args.gpu_memory_fraction
self.xla = args.xla
# load bert config & construct
tf.logging.info("load bert config & construct ...")
self.bert_config = modeling.BertConfig.from_json_file(self.config_name)
model_fn = model_fn_builder(bert_config=self.bert_config,
init_checkpoint=self.ckpt_name,
layer_indexes=self.layer_indexes)
# construct estimator
tf.logging.info("load estimator ...")
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = self.gpu_memory_fraction
config.log_device_placement = False
if self.xla:
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
self.estimator = Estimator(model_fn=model_fn,
config=RunConfig(session_config=config),
params={'batch_size': self.batch_size})
self.tokenizer = tokenization.FullTokenizer(
vocab_file=self.vocab_file, do_lower_case=self.do_lower_case)
tf.logging.info("initialization done.")
def fit(pattern_path, model_dir):
watermark_paths = pattern_path
image_paths = glob('./images/*')
number_for_train = -100
train_paths_images = image_paths[:number_for_train]
test_paths_images = image_paths[number_for_train:]
train_paths_watermark = watermark_paths[:]
test_paths_watermark = watermark_paths[:]
model_params = {"num_blocks": 4,
"num_filters": 8,
"batch_normalization": True,
"training": True}
params = {"model": make_unet,
"model_params": model_params,
"IOU_weight": 1,
"learning_rate": 0.001,
"lr_decay_steps": 1000,
"lr_decay_rate": 0.96,
"create_summary": True}
strategy = tf.contrib.distribute.OneDeviceStrategy(device='/gpu:0')
config = RunConfig(save_summary_steps=40,
train_distribute=strategy,
save_checkpoints_steps = 200,
keep_checkpoint_max = 60,
eval_distribute=strategy,
)
tf.logging.set_verbosity(tf.logging.INFO)
segmentation_model = tf.estimator.Estimator(
model_fn=make_unet_estimator,
model_dir=model_dir,
params=params,
config=config
)
train_params = {'image_paths': train_paths_images,
'watermark_paths': train_paths_watermark,
'batch_size': 10,
'num_epochs': 2
}
val_params = {'image_paths': test_paths_images,
'watermark_paths': test_paths_watermark,
'batch_size': 3,
'num_epochs': 1
}
train_spec = tf.estimator.TrainSpec(input_fn=lambda: input_fn(**train_params), max_steps = 75000)
eval_spec = tf.estimator.EvalSpec(input_fn=lambda: input_fn(**val_params), throttle_secs=100, steps=200)
tf.estimator.train_and_evaluate(segmentation_model, train_spec, eval_spec)
segmentation_model.train(input_fn=lambda: input_fn(**params)),
def get_config(self, ckpt_output_dir='./output', save_check_steps=1000):
if not self.config:
self.config = tf.ConfigProto(device_count={'GPU': 1})
self.config.gpu_options.allow_growth = True
self.config.gpu_options.per_process_gpu_memory_fraction = 0.5
run_config = RunConfig(
model_dir=ckpt_output_dir,
session_config=self.config,
keep_checkpoint_max=self.keep_checkpoint_max,
save_checkpoints_steps=save_check_steps)
return run_config
def main(args):
os.makedirs(args.model_dir, exist_ok=True)
estimator = tf.estimator.Estimator(
model_fn=model_fn,
model_dir=args.model_dir,
params=hparams,
config=RunConfig(
save_summary_steps=args.summary_interval,
save_checkpoints_steps=args.checkpoint_interval,
session_config=SESS_CFG,
# log_step_count_steps=100,
keep_checkpoint_max=2))
if args.mode == 'train':
os.makedirs(args.data_dir, exist_ok=True)
estimator.train(input_fn=lambda: train_input_fn(args.data_dir))
elif args.mode == 'predict':
assert len(args.texts), "No text to predict"
results = estimator.predict(
input_fn=lambda: predict_input_fn(args.texts))
for idx, wav in enumerate(results):
wav = inv_preemphasis(wav)
# wav = wav[:find_endpoint(wav)]
# sp.save('wav_{}.npy'.format(idx), wav, allow_pickle=False)
save_wav(wav, 'output_{}.wav'.format(idx))
# break
elif args.mode == 'export':
os.makedirs(args.export_dir, exist_ok=True)
estimator.export_saved_model(
args.export_dir,
tf.estimator.export.build_raw_serving_input_receiver_fn(
{
'inputs':
tf.placeholder(
dtype=tf.int32, shape=(None, None), name='inputs'),
'lengths':
tf.placeholder(
dtype=tf.int32, shape=(None, ), name='lengths'),
},
default_batch_size=None),
# assets_extra=None,
# as_text=False,
# checkpoint_path=None,
# experimental_mode=ModeKeys.PREDICT
)
else:
raise KeyError('Unknown Mode <{}>'.format(args.mode))
def __init__(self, params=None, aux_config=None, run_config=None):
self._comet_experiment = None
self._estimator = None
self.aux_config = aux_config or {}
self._hooks = (
[]
if not self.aux_config.get("debug")
else [tf_debug.LocalCLIDebugHook()]
if self.aux_config.get("debug") == "cli"
else [
tf_debug.TensorBoardDebugHook(
"localhost:{}".format(self.aux_config.get("debug"))
)
]
)
self.run_config = RunConfig(**(run_config or {}))
self.params = self.set_params()
if params:
self.params.update(params)
def train_and_evaluate(board_size, options):
train_input_fn = make_tfr_input_fn(options['train_data_pattern'],
options['train_batch_size'], board_size,
options)
eval_input_fn = make_tfr_input_fn(options['eval_data_pattern'],
options['eval_batch_size'], board_size,
options)
model_fn = make_model_fn(board_size, options)
serving_input_fn = make_serving_input_fn(board_size)
exporter = tf.estimator.LatestExporter('exporter', serving_input_fn)
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn,
max_steps=options['max_train_steps'])
eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn,
exporters=exporter,
steps=options['eval_steps'],
throttle_secs=options['throttle_secs'],
start_delay_secs=0)
strategy = MirroredStrategy() if options['distribute'] else None
config = RunConfig(
model_dir=options['model_dir'],
save_summary_steps=options['save_summary_steps'],
train_distribute=strategy,
save_checkpoints_steps=options['save_checkpoints_steps'],
log_step_count_steps=options['log_step_count_steps'])
estimator = tf.estimator.Estimator(config=config, model_fn=model_fn)
##################################################################
# Finally, train and evaluate the model
##################################################################
final_eval = tf.estimator.train_and_evaluate(estimator,
train_spec=train_spec,
eval_spec=eval_spec)
def get_run_config(strategy):
"""
Get Estimator run config
Returns:
Type: RunConfig
"""
strategy = get_strategy(strategy)
config = RunConfig(model_dir=None,
tf_random_seed=None,
save_summary_steps=10,
save_checkpoints_steps=20,
session_config=SESS_CONFIG,
keep_checkpoint_max=5,
log_step_count_steps=100,
train_distribute=strategy,
device_fn=None,
protocol=None,
eval_distribute=strategy,
experimental_distribute=None,
experimental_max_worker_delay_secs=None)
return config
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
"xnli": XnliProcessor,
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True."
)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
dist_strategy = tf.contrib.distribute.MirroredStrategy(
num_gpus=FLAGS.n_gpus,
cross_device_ops=AllReduceCrossDeviceOps('nccl',
num_packs=FLAGS.n_gpus),
# cross_device_ops=AllReduceCrossDeviceOps('hierarchical_copy'),
)
log_every_n_steps = 8
run_config = RunConfig(
train_distribute=dist_strategy,
# eval_distribute=dist_strategy,
log_step_count_steps=log_every_n_steps,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps)
# run_config = tf.contrib.tpu.RunConfig(
# cluster=tpu_cluster_resolver,
# master=FLAGS.master,
# model_dir=FLAGS.output_dir,
# save_checkpoints_steps=FLAGS.save_checkpoints_steps,
# tpu_config=tf.contrib.tpu.TPUConfig(
# iterations_per_loop=FLAGS.iterations_per_loop,
# num_shards=FLAGS.num_tpu_cores,
# per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size *
FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = Estimator(model_fn=model_fn, params={}, config=run_config)
# estimator = tf.contrib.tpu.TPUEstimator(
# use_tpu=FLAGS.use_tpu,
# model_fn=model_fn,
# config=run_config,
# train_batch_size=FLAGS.train_batch_size,
# eval_batch_size=FLAGS.eval_batch_size,
# predict_batch_size=FLAGS.predict_batch_size)
if FLAGS.do_train:
train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
file_based_convert_examples_to_features(train_examples, label_list,
FLAGS.max_seq_length,
tokenizer, train_file)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
if FLAGS.do_eval:
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
num_actual_eval_examples = len(eval_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(PaddingInputExample())
eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
file_based_convert_examples_to_features(eval_examples, label_list,
FLAGS.max_seq_length,
tokenizer, eval_file)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(eval_examples), num_actual_eval_examples,
len(eval_examples) - num_actual_eval_examples)
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if FLAGS.use_tpu:
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if FLAGS.do_predict:
predict_examples = processor.get_test_examples(FLAGS.data_dir)
num_actual_predict_examples = len(predict_examples)
if FLAGS.use_tpu:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on.
while len(predict_examples) % FLAGS.predict_batch_size != 0:
predict_examples.append(PaddingInputExample())
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
file_based_convert_examples_to_features(predict_examples, label_list,
FLAGS.max_seq_length,
tokenizer, predict_file)
tf.logging.info("***** Running prediction*****")
tf.logging.info(" Num examples = %d (%d actual, %d padding)",
len(predict_examples), num_actual_predict_examples,
len(predict_examples) - num_actual_predict_examples)
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder)
result = estimator.predict(input_fn=predict_input_fn)
output_predict_file = os.path.join(FLAGS.output_dir,
"test_results.tsv")
with tf.gfile.GFile(output_predict_file, "w") as writer:
num_written_lines = 0
tf.logging.info("***** Predict results *****")
for (i, prediction) in enumerate(result):
probabilities = prediction["probabilities"]
if i >= num_actual_predict_examples:
break
output_line = "\t".join(
str(class_probability)
for class_probability in probabilities) + "\n"
writer.write(output_line)
num_written_lines += 1
assert num_written_lines == num_actual_predict_examples
"num_filters": 8,
"batch_normalization": True,
"training": True}
params = {"model": make_unet,
"model_params": model_params,
"IOU_weight": 1,
"learning_rate": 0.001,
"lr_decay_steps": 1000,
"lr_decay_rate": 0.96,
"create_summary": True}
strategy = tf.contrib.distribute.OneDeviceStrategy(device='/gpu:0')
config = RunConfig(save_summary_steps=40,
train_distribute=strategy,
save_checkpoints_steps = 200,
keep_checkpoint_max = 60,
eval_distribute=strategy,
)
tf.logging.set_verbosity(tf.logging.INFO)
segmentation_model = tf.estimator.Estimator(
model_fn=make_unet_estimator,
model_dir="/home/dokholyan/Projects/new_experiments/milk_blocks_4_fea_8_IOU_01_norm_Tr/",
params=params,
config=config
)
train_params = {'image_paths': train_paths_images,
'watermark_paths': train_paths_watermark,
'batch_size': 10,
'num_epochs': 500
}
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
tf.gfile.MakeDirs(FLAGS.output_dir)
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.gfile.Glob(input_pattern))
tf.logging.info("*** Input Files ***")
for input_file in input_files:
tf.logging.info(" %s" % input_file)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
dist_strategy = tf.contrib.distribute.MirroredStrategy(
num_gpus=FLAGS.n_gpus,
cross_device_ops=AllReduceCrossDeviceOps('nccl',
num_packs=FLAGS.n_gpus),
# cross_device_ops=AllReduceCrossDeviceOps('hierarchical_copy'),
)
log_every_n_steps = 8
run_config = RunConfig(
train_distribute=dist_strategy,
# eval_distribute=dist_strategy,
log_step_count_steps=log_every_n_steps,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps)
model_fn = model_fn_builder(bert_config=bert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=FLAGS.num_train_steps,
num_warmup_steps=FLAGS.num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = Estimator(model_fn=model_fn, params={}, config=run_config)
if FLAGS.do_train:
tf.logging.info("***** Running training *****")
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
train_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=True)
estimator.train(input_fn=train_input_fn,
max_steps=FLAGS.num_train_steps)
if FLAGS.do_eval:
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
eval_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=False)
result = estimator.evaluate(input_fn=eval_input_fn,
steps=FLAGS.max_eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
Steps to run the training job for. If --num-epochs is not specified,
this must be. Otherwise the training job will run indefinitely.\
""",
type=int,
required=True)
parser.add_argument(
'--eval-steps',
help='Number of steps to run evalution for at each checkpoint',
default=100,
type=int)
parser.add_argument('--trainer-type',
help='Which trainer to use (spam or component)',
choices=['spam', 'component'],
required=True)
args = parser.parse_args()
logger = logging.getLogger()
logger.setLevel(getattr(logging, args.verbosity))
if not args.num_epochs:
args.num_epochs = args.train_steps
# Set C++ Graph Execution level verbosity.
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(
getattr(logging, args.verbosity) / 10)
# Run the training job.
train_and_evaluate_model(config=RunConfig(model_dir=args.job_dir),
hparams=vars(args))
def train_and_evaluate(options):
import tensorflow as tf
from tensorflow.estimator import RunConfig
from tensorflow.contrib.distribute import MirroredStrategy
import mlflow
from train.make_model_fn import make_model_fn
from train.make_tft_serving_input_fn import make_tft_serving_input_fn
from train.create_feature_columns import create_feature_columns
from train.make_tfr_input_fn import make_tfr_input_fn
from train.make_hypotheses import make_hypotheses
from train.make_input_fns import make_input_fns
with mlflow.start_run():
log_params = [
'base_dir', 'file_format', 'train_batch_size', 'max_train_steps',
'reader_num_threads', 'parser_num_threads', 'prefetch_buffer_size'
]
for key in log_params:
mlflow.log_param(key, options[key])
##################################################################
# Train and Eval Input Functions
##################################################################
make_input_fn = make_input_fns()[options['file_format']]
train_input_fn = make_input_fn(options['train_data_pattern'],
options['train_batch_size'], options)
eval_input_fn = make_input_fn(options['eval_data_pattern'],
options['eval_batch_size'], options)
##################################################################
# Create the hypothesis and the model_fn
##################################################################
hypothesis = make_hypotheses()[options['hypothesis']]
feature_columns = create_feature_columns()
model_fn = make_model_fn(feature_columns, options, hypothesis)
##################################################################
# Train and Eval Spec
##################################################################
serving_input_fn = make_tft_serving_input_fn(options['metadata_dir'])
exporter = tf.estimator.LatestExporter('exporter', serving_input_fn)
train_spec = tf.estimator.TrainSpec(
input_fn=train_input_fn, max_steps=options['max_train_steps'])
eval_spec = tf.estimator.EvalSpec(
input_fn=eval_input_fn,
exporters=exporter,
steps=options['eval_steps'],
throttle_secs=options['throttle_secs'],
start_delay_secs=0)
##################################################################
# Create and configure the estimator
##################################################################
strategy = MirroredStrategy() if options['distribute'] else None
config = RunConfig(
model_dir=options['model_dir'],
save_summary_steps=options['save_summary_steps'],
train_distribute=strategy,
save_checkpoints_steps=options['save_checkpoints_steps'],
log_step_count_steps=options['log_step_count_steps'])
estimator = tf.estimator.Estimator(config=config, model_fn=model_fn)
##################################################################
# Finally, train and evaluate the model
##################################################################
final_eval = tf.estimator.train_and_evaluate(estimator,
train_spec=train_spec,
eval_spec=eval_spec)
if final_eval[0] is not None:
mlflow.log_metric('loss', final_eval[0]['loss'])
mlflow.log_metric('mean_error', final_eval[0]['mean_error'])
return final_eval