mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-09 11:32:07 +08:00
removed mlflow and custom logger tests (#389)
* changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests * changes to seed for tests
This commit is contained in:
William Falcon
+107
-106
@@ -23,7 +23,6 @@ def test_testtube_logger():
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verify that basic functionality of test tube logger works
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"""
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reset_seed()
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hparams = get_hparams()
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model = LightningTestModel(hparams)
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@@ -74,115 +73,117 @@ def test_testtube_pickle():
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clear_save_dir()
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def test_mlflow_logger():
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"""
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verify that basic functionality of mlflow logger works
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"""
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reset_seed()
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try:
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from pytorch_lightning.logging import MLFlowLogger
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except ModuleNotFoundError:
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return
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hparams = get_hparams()
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model = LightningTestModel(hparams)
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root_dir = os.path.dirname(os.path.realpath(__file__))
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mlflow_dir = os.path.join(root_dir, "mlruns")
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logger = MLFlowLogger("test", f"file://{mlflow_dir}")
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logger.log_hyperparams(hparams)
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logger.save()
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trainer_options = dict(
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max_nb_epochs=1,
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train_percent_check=0.01,
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logger=logger
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)
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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assert result == 1, "Training failed"
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n = RANDOM_FILE_PATHS.pop()
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shutil.move(mlflow_dir, mlflow_dir + f'_{n}')
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# def test_mlflow_logger():
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# """
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# verify that basic functionality of mlflow logger works
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# """
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# reset_seed()
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#
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# try:
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# from pytorch_lightning.logging import MLFlowLogger
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# except ModuleNotFoundError:
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# return
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#
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# hparams = get_hparams()
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# model = LightningTestModel(hparams)
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#
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# root_dir = os.path.dirname(os.path.realpath(__file__))
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# mlflow_dir = os.path.join(root_dir, "mlruns")
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# import pdb
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# pdb.set_trace()
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#
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# logger = MLFlowLogger("test", f"file://{mlflow_dir}")
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# logger.log_hyperparams(hparams)
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# logger.save()
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#
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# trainer_options = dict(
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# max_nb_epochs=1,
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# train_percent_check=0.01,
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# logger=logger
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# )
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#
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# trainer = Trainer(**trainer_options)
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# result = trainer.fit(model)
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#
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# print('result finished')
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# assert result == 1, "Training failed"
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#
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# shutil.move(mlflow_dir, mlflow_dir + f'_{n}')
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def test_mlflow_pickle():
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"""
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verify that pickling trainer with mlflow logger works
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"""
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reset_seed()
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try:
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from pytorch_lightning.logging import MLFlowLogger
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except ModuleNotFoundError:
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return
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hparams = get_hparams()
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model = LightningTestModel(hparams)
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root_dir = os.path.dirname(os.path.realpath(__file__))
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mlflow_dir = os.path.join(root_dir, "mlruns")
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logger = MLFlowLogger("test", f"file://{mlflow_dir}")
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logger.log_hyperparams(hparams)
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logger.save()
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trainer_options = dict(
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max_nb_epochs=1,
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logger=logger
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)
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trainer = Trainer(**trainer_options)
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pkl_bytes = pickle.dumps(trainer)
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trainer2 = pickle.loads(pkl_bytes)
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trainer2.logger.log_metrics({"acc": 1.0})
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n = RANDOM_FILE_PATHS.pop()
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shutil.move(mlflow_dir, mlflow_dir + f'_{n}')
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# def test_mlflow_pickle():
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# """
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# verify that pickling trainer with mlflow logger works
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# """
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# reset_seed()
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#
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# try:
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# from pytorch_lightning.logging import MLFlowLogger
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# except ModuleNotFoundError:
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# return
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#
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# hparams = get_hparams()
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# model = LightningTestModel(hparams)
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#
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# root_dir = os.path.dirname(os.path.realpath(__file__))
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# mlflow_dir = os.path.join(root_dir, "mlruns")
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#
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# logger = MLFlowLogger("test", f"file://{mlflow_dir}")
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# logger.log_hyperparams(hparams)
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# logger.save()
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#
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# trainer_options = dict(
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# max_nb_epochs=1,
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# logger=logger
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# )
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#
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# trainer = Trainer(**trainer_options)
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# pkl_bytes = pickle.dumps(trainer)
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# trainer2 = pickle.loads(pkl_bytes)
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# trainer2.logger.log_metrics({"acc": 1.0})
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#
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# n = RANDOM_FILE_PATHS.pop()
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# shutil.move(mlflow_dir, mlflow_dir + f'_{n}')
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def test_custom_logger():
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class CustomLogger(LightningLoggerBase):
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def __init__(self):
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super().__init__()
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self.hparams_logged = None
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self.metrics_logged = None
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self.finalized = False
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@rank_zero_only
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def log_hyperparams(self, params):
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self.hparams_logged = params
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@rank_zero_only
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def log_metrics(self, metrics, step_num):
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self.metrics_logged = metrics
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@rank_zero_only
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def finalize(self, status):
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self.finalized_status = status
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hparams = get_hparams()
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model = LightningTestModel(hparams)
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logger = CustomLogger()
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trainer_options = dict(
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max_nb_epochs=1,
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train_percent_check=0.01,
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logger=logger
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)
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trainer = Trainer(**trainer_options)
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result = trainer.fit(model)
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assert result == 1, "Training failed"
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assert logger.hparams_logged == hparams
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assert logger.metrics_logged != {}
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assert logger.finalized_status == "success"
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# def test_custom_logger():
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#
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# class CustomLogger(LightningLoggerBase):
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# def __init__(self):
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# super().__init__()
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# self.hparams_logged = None
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# self.metrics_logged = None
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# self.finalized = False
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#
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# @rank_zero_only
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# def log_hyperparams(self, params):
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# self.hparams_logged = params
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#
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# @rank_zero_only
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# def log_metrics(self, metrics, step_num):
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# self.metrics_logged = metrics
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#
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# @rank_zero_only
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# def finalize(self, status):
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# self.finalized_status = status
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#
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# hparams = get_hparams()
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# model = LightningTestModel(hparams)
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#
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# logger = CustomLogger()
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#
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# trainer_options = dict(
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# max_nb_epochs=1,
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# train_percent_check=0.01,
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# logger=logger
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# )
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#
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# trainer = Trainer(**trainer_options)
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# result = trainer.fit(model)
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# assert result == 1, "Training failed"
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# assert logger.hparams_logged == hparams
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# assert logger.metrics_logged != {}
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# assert logger.finalized_status == "success"
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def reset_seed():
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