241 lines
10 KiB
Python
241 lines
10 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from unittest import mock
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from unittest.mock import MagicMock
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import pytest
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from pytorch_lightning import Trainer
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from pytorch_lightning.loggers import _MLFLOW_AVAILABLE, MLFlowLogger
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from pytorch_lightning.loggers.mlflow import MLFLOW_RUN_NAME, resolve_tags
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from tests.helpers import BoringModel
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def mock_mlflow_run_creation(logger, experiment_name=None, experiment_id=None, run_id=None):
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"""Helper function to simulate mlflow client creating a new (or existing) experiment."""
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run = MagicMock()
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run.info.run_id = run_id
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logger._mlflow_client.get_experiment_by_name = MagicMock(return_value=experiment_name)
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logger._mlflow_client.create_experiment = MagicMock(return_value=experiment_id)
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logger._mlflow_client.create_run = MagicMock(return_value=run)
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return logger
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@mock.patch("pytorch_lightning.loggers.mlflow.mlflow")
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@mock.patch("pytorch_lightning.loggers.mlflow.MlflowClient")
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def test_mlflow_logger_exists(client, mlflow, tmpdir):
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"""Test launching three independent loggers with either same or different experiment name."""
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run1 = MagicMock()
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run1.info.run_id = "run-id-1"
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run2 = MagicMock()
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run2.info.run_id = "run-id-2"
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run3 = MagicMock()
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run3.info.run_id = "run-id-3"
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# simulate non-existing experiment creation
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client.return_value.get_experiment_by_name = MagicMock(return_value=None)
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client.return_value.create_experiment = MagicMock(return_value="exp-id-1") # experiment_id
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client.return_value.create_run = MagicMock(return_value=run1)
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logger = MLFlowLogger("test", save_dir=tmpdir)
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assert logger._experiment_id is None
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assert logger._run_id is None
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_ = logger.experiment
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assert logger.experiment_id == "exp-id-1"
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assert logger.run_id == "run-id-1"
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assert logger.experiment.create_experiment.asset_called_once()
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client.reset_mock(return_value=True)
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# simulate existing experiment returns experiment id
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exp1 = MagicMock()
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exp1.experiment_id = "exp-id-1"
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client.return_value.get_experiment_by_name = MagicMock(return_value=exp1)
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client.return_value.create_run = MagicMock(return_value=run2)
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# same name leads to same experiment id, but different runs get recorded
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logger2 = MLFlowLogger("test", save_dir=tmpdir)
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assert logger2.experiment_id == logger.experiment_id
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assert logger2.run_id == "run-id-2"
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assert logger2.experiment.create_experiment.call_count == 0
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assert logger2.experiment.create_run.asset_called_once()
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client.reset_mock(return_value=True)
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# simulate a 3rd experiment with new name
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client.return_value.get_experiment_by_name = MagicMock(return_value=None)
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client.return_value.create_experiment = MagicMock(return_value="exp-id-3")
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client.return_value.create_run = MagicMock(return_value=run3)
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# logger with new experiment name causes new experiment id and new run id to be created
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logger3 = MLFlowLogger("new", save_dir=tmpdir)
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assert logger3.experiment_id == "exp-id-3" != logger.experiment_id
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assert logger3.run_id == "run-id-3"
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@mock.patch("pytorch_lightning.loggers.mlflow.mlflow")
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@mock.patch("pytorch_lightning.loggers.mlflow.MlflowClient")
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def test_mlflow_run_name_setting(client, mlflow, tmpdir):
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"""Test that the run_name argument makes the MLFLOW_RUN_NAME tag."""
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tags = resolve_tags({MLFLOW_RUN_NAME: "run-name-1"})
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# run_name is appended to tags
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logger = MLFlowLogger("test", run_name="run-name-1", save_dir=tmpdir)
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logger = mock_mlflow_run_creation(logger, experiment_id="exp-id")
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_ = logger.experiment
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client.return_value.create_run.assert_called_with(experiment_id="exp-id", tags=tags)
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# run_name overrides tags[MLFLOW_RUN_NAME]
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logger = MLFlowLogger("test", run_name="run-name-1", tags={MLFLOW_RUN_NAME: "run-name-2"}, save_dir=tmpdir)
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logger = mock_mlflow_run_creation(logger, experiment_id="exp-id")
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_ = logger.experiment
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client.return_value.create_run.assert_called_with(experiment_id="exp-id", tags=tags)
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# default run_name (= None) does not append new tag
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logger = MLFlowLogger("test", save_dir=tmpdir)
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logger = mock_mlflow_run_creation(logger, experiment_id="exp-id")
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_ = logger.experiment
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default_tags = resolve_tags(None)
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client.return_value.create_run.assert_called_with(experiment_id="exp-id", tags=default_tags)
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@mock.patch("pytorch_lightning.loggers.mlflow.mlflow")
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@mock.patch("pytorch_lightning.loggers.mlflow.MlflowClient")
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def test_mlflow_log_dir(client, mlflow, tmpdir):
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"""Test that the trainer saves checkpoints in the logger's save dir."""
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# simulate experiment creation with mlflow client mock
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run = MagicMock()
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run.info.run_id = "run-id"
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client.return_value.get_experiment_by_name = MagicMock(return_value=None)
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client.return_value.create_experiment = MagicMock(return_value="exp-id")
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client.return_value.create_run = MagicMock(return_value=run)
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# test construction of default log dir path
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logger = MLFlowLogger("test", save_dir=tmpdir)
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assert logger.save_dir == tmpdir
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assert logger.version == "run-id"
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assert logger.name == "exp-id"
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model = BoringModel()
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trainer = Trainer(default_root_dir=tmpdir, logger=logger, max_epochs=1, limit_train_batches=1, limit_val_batches=3)
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assert trainer.log_dir == logger.save_dir
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trainer.fit(model)
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assert trainer.checkpoint_callback.dirpath == (tmpdir / "exp-id" / "run-id" / "checkpoints")
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assert set(os.listdir(trainer.checkpoint_callback.dirpath)) == {"epoch=0-step=0.ckpt"}
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assert trainer.log_dir == logger.save_dir
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def test_mlflow_logger_dirs_creation(tmpdir):
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"""Test that the logger creates the folders and files in the right place."""
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if not _MLFLOW_AVAILABLE:
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pytest.xfail("test for explicit file creation requires mlflow dependency to be installed.")
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assert not os.listdir(tmpdir)
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logger = MLFlowLogger("test", save_dir=tmpdir)
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assert logger.save_dir == tmpdir
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assert set(os.listdir(tmpdir)) == {".trash"}
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run_id = logger.run_id
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exp_id = logger.experiment_id
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# multiple experiment calls should not lead to new experiment folders
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for i in range(2):
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_ = logger.experiment
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assert set(os.listdir(tmpdir)) == {".trash", exp_id}
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assert set(os.listdir(tmpdir / exp_id)) == {run_id, "meta.yaml"}
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class CustomModel(BoringModel):
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def training_epoch_end(self, *args, **kwargs):
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super().training_epoch_end(*args, **kwargs)
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self.log("epoch", self.current_epoch)
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model = CustomModel()
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limit_batches = 5
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trainer = Trainer(
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default_root_dir=tmpdir,
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logger=logger,
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max_epochs=1,
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limit_train_batches=limit_batches,
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limit_val_batches=limit_batches,
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)
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trainer.fit(model)
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assert set(os.listdir(tmpdir / exp_id)) == {run_id, "meta.yaml"}
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assert "epoch" in os.listdir(tmpdir / exp_id / run_id / "metrics")
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assert set(os.listdir(tmpdir / exp_id / run_id / "params")) == model.hparams.keys()
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assert trainer.checkpoint_callback.dirpath == (tmpdir / exp_id / run_id / "checkpoints")
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assert os.listdir(trainer.checkpoint_callback.dirpath) == [f"epoch=0-step={limit_batches - 1}.ckpt"]
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@mock.patch("pytorch_lightning.loggers.mlflow.mlflow")
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@mock.patch("pytorch_lightning.loggers.mlflow.MlflowClient")
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def test_mlflow_experiment_id_retrieved_once(client, mlflow, tmpdir):
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"""Test that the logger experiment_id retrieved only once."""
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logger = MLFlowLogger("test", save_dir=tmpdir)
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_ = logger.experiment
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_ = logger.experiment
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_ = logger.experiment
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assert logger.experiment.get_experiment_by_name.call_count == 1
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@mock.patch("pytorch_lightning.loggers.mlflow.mlflow")
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@mock.patch("pytorch_lightning.loggers.mlflow.MlflowClient")
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def test_mlflow_logger_with_unexpected_characters(client, mlflow, tmpdir):
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"""Test that the logger raises warning with special characters not accepted by MLFlow."""
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logger = MLFlowLogger("test", save_dir=tmpdir)
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metrics = {"[some_metric]": 10}
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with pytest.warns(RuntimeWarning, match="special characters in metric name"):
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logger.log_metrics(metrics)
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@mock.patch("pytorch_lightning.loggers.mlflow.mlflow")
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@mock.patch("pytorch_lightning.loggers.mlflow.MlflowClient")
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def test_mlflow_logger_with_long_param_value(client, mlflow, tmpdir):
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"""Test that the logger raises warning with special characters not accepted by MLFlow."""
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logger = MLFlowLogger("test", save_dir=tmpdir)
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value = "test" * 100
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key = "test_param"
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params = {key: value}
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with pytest.warns(RuntimeWarning, match=f"Discard {key}={value}"):
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logger.log_hyperparams(params)
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@mock.patch("pytorch_lightning.loggers.mlflow.time")
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@mock.patch("pytorch_lightning.loggers.mlflow.mlflow")
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@mock.patch("pytorch_lightning.loggers.mlflow.MlflowClient")
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def test_mlflow_logger_experiment_calls(client, mlflow, time, tmpdir):
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"""Test that the logger calls methods on the mlflow experiment correctly."""
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time.return_value = 1
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logger = MLFlowLogger("test", save_dir=tmpdir, artifact_location="my_artifact_location")
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logger._mlflow_client.get_experiment_by_name.return_value = None
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params = {"test": "test_param"}
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logger.log_hyperparams(params)
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logger.experiment.log_param.assert_called_once_with(logger.run_id, "test", "test_param")
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metrics = {"some_metric": 10}
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logger.log_metrics(metrics)
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logger.experiment.log_metric.assert_called_once_with(logger.run_id, "some_metric", 10, 1000, None)
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logger._mlflow_client.create_experiment.assert_called_once_with(
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name="test", artifact_location="my_artifact_location"
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)
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