mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-18 12:50:58 +08:00
Nic Eggert
614cb3c03b
* Create underlying loggers lazily This avoids creating duplicate experiments or run in multi-node DDP. * Save hyperparameters automatically * Update docs for snapshotting hyperparams * Fix test tube * Fix test tube pickling
115 lines
2.7 KiB
Python
115 lines
2.7 KiB
Python
import os.path
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import pickle
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import shutil
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import numpy as np
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from pytorch_lightning import Trainer
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from pytorch_lightning.testing import LightningTestModel
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from .test_models import get_hparams, get_test_tube_logger, init_save_dir, clear_save_dir
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def test_testtube_logger():
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"""verify that basic functionality of test tube logger works"""
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hparams = get_hparams()
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model = LightningTestModel(hparams)
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save_dir = init_save_dir()
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logger = get_test_tube_logger(False)
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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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result = trainer.fit(model)
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assert result == 1, "Training failed"
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clear_save_dir()
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def test_testtube_pickle():
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"""Verify that pickling a trainer containing a test tube logger works"""
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hparams = get_hparams()
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model = LightningTestModel(hparams)
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save_dir = init_save_dir()
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logger = get_test_tube_logger(False)
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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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def test_mlflow_logger():
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"""verify that basic functionality of mlflow logger works"""
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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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result = trainer.fit(model)
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assert result == 1, "Training failed"
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n = np.random.randint(0, 10000000, 1)[0]
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shutil.move(mlflow_dir, mlflow_dir + f'_{n}')
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def test_mlflow_pickle():
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"""verify that pickling trainer with mlflow logger works"""
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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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