def test_trains_pickle(tmpdir):
"""Verify that pickling trainer with TRAINS logger works."""
tutils.reset_seed()
# hparams = tutils.get_default_hparams()
# model = LightningTestModel(hparams)
TrainsLogger.set_bypass_mode(True)
TrainsLogger.set_credentials(
api_host='http://integration.trains.allegro.ai:8008',
files_host='http://integration.trains.allegro.ai:8081',
web_host='http://integration.trains.allegro.ai:8080',
)
logger = TrainsLogger(project_name="lightning_log",
task_name="pytorch lightning test")
trainer_options = dict(default_root_dir=tmpdir,
max_epochs=1,
logger=logger)
trainer = Trainer(**trainer_options)
pkl_bytes = pickle.dumps(trainer)
trainer2 = pickle.loads(pkl_bytes)
trainer2.logger.log_metrics({"acc": 1.0})
trainer2.logger.finalize()
logger.finalize()
def test_trains_logger(tmpdir):
"""Verify that basic functionality of TRAINS logger works."""
tutils.reset_seed()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
TrainsLogger.set_bypass_mode(True)
TrainsLogger.set_credentials(
api_host='http://integration.trains.allegro.ai:8008',
files_host='http://integration.trains.allegro.ai:8081',
web_host='http://integration.trains.allegro.ai:8080',
)
logger = TrainsLogger(project_name="lightning_log",
task_name="pytorch lightning test")
trainer_options = dict(default_root_dir=tmpdir,
max_epochs=1,
train_percent_check=0.05,
logger=logger)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
print('result finished')
logger.finalize()
assert result == 1, "Training failed"
def test_trains_logger(tmpdir):
"""Verify that basic functionality of TRAINS logger works."""
model = EvalModelTemplate()
TrainsLogger.set_bypass_mode(True)
TrainsLogger.set_credentials(api_host='http://integration.trains.allegro.ai:8008',
files_host='http://integration.trains.allegro.ai:8081',
web_host='http://integration.trains.allegro.ai:8080', )
logger = TrainsLogger(project_name="lightning_log", task_name="pytorch lightning test")
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
train_percent_check=0.05,
logger=logger
)
result = trainer.fit(model)
print('result finished')
logger.finalize()
assert result == 1, "Training failed"