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improve partial Codecov (#1172)

Browse Source 
* ignore in setup

* show report

* abs imports

* abstract pass

* cover loggers

* doctest trains

* locals

* pass

* revert tensorboard

* use tensorboardX

* revert tensorboardX

* fix trains

* Add TrainsLogger.set_credentials (#1179)

* Add TrainsLogger.set_credentials to control trains server configuration and authentication from code. Sync trains package version.
Fix CI Trains tests

* Add global TrainsLogger set_bypass_mode (#1187)

* Add global TrainsLogger set_bypass_mode skips all external communication

Co-authored-by: bmartinn <>

* rm some no-cov

Co-authored-by: Martin.B <51887611+bmartinn@users.noreply.github.com>
This commit is contained in:
Jirka Borovec
2020-03-19 09:14:29 -04:00
committed by GitHub
co-authored by bmartinn Martin.B
parent 73a911890b
commit 22a7264e9a
34 changed files with 194 additions and 139 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.drone.yml
+1
View File
@@ -23,4 +23,5 @@ steps:
- pip list
- python -c "import torch ; print(' & '.join([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())]) if torch.cuda.is_available() else 'only CPU')"
- coverage run --source pytorch_lightning -m py.test pytorch_lightning tests pl_examples -v --doctest-modules # --flake8
- coverage report
- codecov --token $CODECOV_TOKEN # --pr $DRONE_PULL_REQUEST --build $DRONE_BUILD_NUMBER --branch $DRONE_BRANCH --commit $DRONE_COMMIT --tag $DRONE_TAG
.pep8speaks.yml
+1 -1
View File
@@ -5,7 +5,7 @@ scanner:
linter: pycodestyle # Other option is flake8
pycodestyle: # Same as scanner.linter value. Other option is flake8
max-line-length: 100 # Default is 79 in PEP 8
max-line-length: 110 # Default is 79 in PEP 8
ignore: # Errors and warnings to ignore
- W504 # line break after binary operator
- E402 # module level import not at top of file
pl_examples/basic_examples/lightning_module_template.py
+1 -1
View File
@@ -225,7 +225,7 @@ class LightningTemplateModel(LightningModule):
return self.__dataloader(train=False)
@staticmethod
def add_model_specific_args(parent_parser, root_dir): # pragma: no cover
def add_model_specific_args(parent_parser, root_dir): # pragma: no-cover
"""
Parameters you define here will be available to your model through self.hparams
:param parent_parser:
pl_examples/full_examples/imagenet/imagenet_example.py
+1 -1
View File
@@ -181,7 +181,7 @@ class ImageNetLightningModel(LightningModule):
return val_loader
@staticmethod
def add_model_specific_args(parent_parser): # pragma: no cover
def add_model_specific_args(parent_parser): # pragma: no-cover
parser = argparse.ArgumentParser(parents=[parent_parser])
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', choices=MODEL_NAMES,
help='model architecture: ' +
pytorch_lightning/__init__.py
+7 -8
View File
@@ -19,18 +19,17 @@ except NameError:
__LIGHTNING_SETUP__ = False
if __LIGHTNING_SETUP__:
import sys
sys.stderr.write('Partial import of `torchlightning` during the build process.\n')
# We are not importing the rest of the scikit during the build
# process, as it may not be compiled yet
import sys # pragma: no-cover
sys.stderr.write('Partial import of `torchlightning` during the build process.\n') # pragma: no-cover
# We are not importing the rest of the lightning during the build process, as it may not be compiled yet
else:
from logging import getLogger
_logger = getLogger("lightning")
from .core import LightningModule
from .trainer import Trainer
from .callbacks import Callback
from .core import data_loader
from pytorch_lightning.core import LightningModule
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.core import data_loader
__all__ = [
'Trainer',
pytorch_lightning/callbacks/__init__.py
+4 -4
View File
@@ -1,7 +1,7 @@
from .base import Callback
from .early_stopping import EarlyStopping
from .gradient_accumulation_scheduler import GradientAccumulationScheduler
from .model_checkpoint import ModelCheckpoint
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks.gradient_accumulation_scheduler import GradientAccumulationScheduler
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
__all__ = [
'Callback',
pytorch_lightning/callbacks/early_stopping.py
+1 -1
View File
@@ -9,8 +9,8 @@ import warnings
import numpy as np
from .base import Callback
from pytorch_lightning import _logger as log
from pytorch_lightning.callbacks.base import Callback
class EarlyStopping(Callback):
pytorch_lightning/callbacks/gradient_accumulation_scheduler.py
+1 -1
View File
@@ -6,7 +6,7 @@ Change gradient accumulation factor according to scheduling.
import warnings
from .base import Callback
from pytorch_lightning.callbacks.base import Callback
class GradientAccumulationScheduler(Callback):
pytorch_lightning/core/__init__.py
+2 -2
View File
@@ -335,8 +335,8 @@ LightningModule Class
"""
from .decorators import data_loader
from .lightning import LightningModule
from pytorch_lightning.core.decorators import data_loader
from pytorch_lightning.core.lightning import LightningModule
__all__ = ['LightningModule', 'data_loader']
# __call__ = __all__
pytorch_lightning/core/memory.py
+5 -5
View File
@@ -72,12 +72,12 @@ class ModelSummary(object):
with torch.no_grad():
for _, m in mods:
if isinstance(input_, (list, tuple)): # pragma: no cover
if isinstance(input_, (list, tuple)): # pragma: no-cover
out = m(*input_)
else:
out = m(input_)
if isinstance(input_, (list, tuple)): # pragma: no cover
if isinstance(input_, (list, tuple)): # pragma: no-cover
in_size = []
for x in input_:
if isinstance(x, list):
@@ -89,7 +89,7 @@ class ModelSummary(object):
in_sizes.append(in_size)
if isinstance(out, (list, tuple)): # pragma: no cover
if isinstance(out, (list, tuple)): # pragma: no-cover
out_size = np.asarray([x.size() for x in out])
else:
out_size = np.array(out.size())
@@ -206,7 +206,7 @@ def _format_summary_table(*cols) -> str:
return summary
def print_mem_stack() -> None: # pragma: no cover
def print_mem_stack() -> None: # pragma: no-cover
for obj in gc.get_objects():
try:
if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
@@ -215,7 +215,7 @@ def print_mem_stack() -> None: # pragma: no cover
pass
def count_mem_items() -> Tuple[int, int]: # pragma: no cover
def count_mem_items() -> Tuple[int, int]: # pragma: no-cover
num_params = 0
num_tensors = 0
for obj in gc.get_objects():
pytorch_lightning/loggers/__init__.py
+26 -20
View File
@@ -82,8 +82,8 @@ Supported Loggers
"""
from os import environ
from .base import LightningLoggerBase, LoggerCollection, rank_zero_only
from .tensorboard import TensorBoardLogger
from pytorch_lightning.loggers.base import LightningLoggerBase, LoggerCollection, rank_zero_only
from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
__all__ = ['TensorBoardLogger']
@@ -91,37 +91,43 @@ try:
# needed to prevent ImportError and duplicated logs.
environ["COMET_DISABLE_AUTO_LOGGING"] = "1"
from .comet import CometLogger
from pytorch_lightning.loggers.comet import CometLogger
except ImportError: # pragma: no-cover
del environ["COMET_DISABLE_AUTO_LOGGING"] # pragma: no-cover
else:
__all__.append('CometLogger')
except ImportError:
del environ["COMET_DISABLE_AUTO_LOGGING"]
try:
from .mlflow import MLFlowLogger
from pytorch_lightning.loggers.mlflow import MLFlowLogger
except ImportError: # pragma: no-cover
pass # pragma: no-cover
else:
__all__.append('MLFlowLogger')
except ImportError:
pass
try:
from .neptune import NeptuneLogger
from pytorch_lightning.loggers.neptune import NeptuneLogger
except ImportError: # pragma: no-cover
pass # pragma: no-cover
else:
__all__.append('NeptuneLogger')
except ImportError:
pass
try:
from .test_tube import TestTubeLogger
from pytorch_lightning.loggers.test_tube import TestTubeLogger
except ImportError: # pragma: no-cover
pass # pragma: no-cover
else:
__all__.append('TestTubeLogger')
except ImportError:
pass
try:
from .wandb import WandbLogger
from pytorch_lightning.loggers.wandb import WandbLogger
except ImportError: # pragma: no-cover
pass # pragma: no-cover
else:
__all__.append('WandbLogger')
except ImportError:
pass
try:
from .trains import TrainsLogger
from pytorch_lightning.loggers.trains import TrainsLogger
except ImportError: # pragma: no-cover
pass # pragma: no-cover
else:
__all__.append('TrainsLogger')
except ImportError:
pass
pytorch_lightning/loggers/base.py
-5
View File
@@ -32,7 +32,6 @@ class LightningLoggerBase(ABC):
@abstractmethod
def experiment(self) -> Any:
"""Return the experiment object associated with this logger"""
pass
@abstractmethod
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None):
@@ -42,7 +41,6 @@ class LightningLoggerBase(ABC):
metrics: Dictionary with metric names as keys and measured quantities as values
step: Step number at which the metrics should be recorded
"""
pass
@staticmethod
def _convert_params(params: Union[Dict[str, Any], Namespace]) -> Dict[str, Any]:
@@ -85,7 +83,6 @@ class LightningLoggerBase(ABC):
Args:
params: argparse.Namespace containing the hyperparameters
"""
pass
def save(self) -> None:
"""Save log data."""
@@ -117,13 +114,11 @@ class LightningLoggerBase(ABC):
@abstractmethod
def name(self) -> str:
"""Return the experiment name."""
pass
@property
@abstractmethod
def version(self) -> Union[int, str]:
"""Return the experiment version."""
pass
class LoggerCollection(LightningLoggerBase):
pytorch_lightning/loggers/comet.py
+4 -4
View File
@@ -16,11 +16,11 @@ try:
from comet_ml import BaseExperiment as CometBaseExperiment
try:
from comet_ml.api import API
except ImportError:
except ImportError: # pragma: no-cover
# For more information, see: https://www.comet.ml/docs/python-sdk/releases/#release-300
from comet_ml.papi import API
except ImportError:
raise ImportError('You want to use `comet_ml` logger which is not installed yet,'
from comet_ml.papi import API # pragma: no-cover
except ImportError: # pragma: no-cover
raise ImportError('You want to use `comet_ml` logger which is not installed yet,' # pragma: no-cover
' install it with `pip install comet-ml`.')
import torch
pytorch_lightning/loggers/mlflow.py
+5 -4
View File
@@ -29,8 +29,9 @@ from typing import Optional, Dict, Any, Union
try:
import mlflow
except ImportError:
raise ImportError('You want to use `mlflow` logger which is not installed yet,'
from mlflow.tracking import MlflowClient
except ImportError: # pragma: no-cover
raise ImportError('You want to use `mlflow` logger which is not installed yet,' # pragma: no-cover
' install it with `pip install mlflow`.')
from pytorch_lightning import _logger as log
@@ -50,13 +51,13 @@ class MLFlowLogger(LightningLoggerBase):
tags (dict): todo this param
"""
super().__init__()
self._mlflow_client = mlflow.tracking.MlflowClient(tracking_uri)
self._mlflow_client = MlflowClient(tracking_uri)
self.experiment_name = experiment_name
self._run_id = None
self.tags = tags
@property
def experiment(self) -> mlflow.tracking.MlflowClient:
def experiment(self) -> MlflowClient:
r"""
Actual mlflow object. To use mlflow features do the following.
pytorch_lightning/loggers/neptune.py
+2 -2
View File
@@ -12,8 +12,8 @@ from typing import Optional, List, Dict, Any, Union, Iterable
try:
import neptune
from neptune.experiments import Experiment
except ImportError:
raise ImportError('You want to use `neptune` logger which is not installed yet,'
except ImportError: # pragma: no-cover
raise ImportError('You want to use `neptune` logger which is not installed yet,' # pragma: no-cover
' install it with `pip install neptune-client`.')
import torch
pytorch_lightning/loggers/tensorboard.py
+6 -8
View File
@@ -8,7 +8,7 @@ import torch
from pkg_resources import parse_version
from torch.utils.tensorboard import SummaryWriter
from .base import LightningLoggerBase, rank_zero_only
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
class TensorBoardLogger(LightningLoggerBase):
@@ -21,14 +21,12 @@ class TensorBoardLogger(LightningLoggerBase):
.. _tf-logger:
Example
------------------
Example:
.. code-block:: python
.. code-block:: python
logger = TensorBoardLogger("tb_logs", name="my_model")
trainer = Trainer(logger=logger)
trainer.train(model)
logger = TensorBoardLogger("tb_logs", name="my_model")
trainer = Trainer(logger=logger)
trainer.train(model)
Args:
save_dir (str): Save directory
pytorch_lightning/loggers/test_tube.py
+3 -3
View File
@@ -3,11 +3,11 @@ from typing import Optional, Dict, Any, Union
try:
from test_tube import Experiment
except ImportError:
raise ImportError('You want to use `test_tube` logger which is not installed yet,'
except ImportError: # pragma: no-cover
raise ImportError('You want to use `test_tube` logger which is not installed yet,' # pragma: no-cover
' install it with `pip install test-tube`.')
from .base import LightningLoggerBase, rank_zero_only
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
class TestTubeLogger(LightningLoggerBase):
pytorch_lightning/loggers/trains.py
+85 -30
View File
@@ -33,8 +33,9 @@ import torch
try:
import trains
except ImportError:
raise ImportError('You want to use `TRAINS` logger which is not installed yet,'
from trains import Task
except ImportError: # pragma: no-cover
raise ImportError('You want to use `TRAINS` logger which is not installed yet,' # pragma: no-cover
' install it with `pip install trains`.')
from pytorch_lightning import _logger as log
@@ -55,24 +56,49 @@ class TrainsLogger(LightningLoggerBase):
auto_connect_frameworks: If True, automatically patch to trains backend. Defaults to True.
auto_resource_monitoring: If true, machine vitals will be
sent along side the task scalars. Defaults to True.
Examples:
>>> logger = TrainsLogger("lightning_log", "my-test", output_uri=".") # doctest: +ELLIPSIS
TRAINS Task: ...
TRAINS results page: https://demoapp.trains.allegro.ai/.../log
>>> logger.log_metrics({"val_loss": 1.23}, step=0)
>>> logger.log_text("sample test")
sample test
>>> import numpy as np
>>> logger.log_artifact("confusion matrix", np.ones((2, 3)))
>>> logger.log_image("passed", "Image 1", np.random.randint(0, 255, (200, 150, 3), dtype=np.uint8))
"""
_bypass = False
def __init__(
self, project_name: Optional[str] = None, task_name: Optional[str] = None,
task_type: str = 'training', reuse_last_task_id: bool = True,
output_uri: Optional[str] = None, auto_connect_arg_parser: bool = True,
auto_connect_frameworks: bool = True, auto_resource_monitoring: bool = True) -> None:
self,
project_name: Optional[str] = None,
task_name: Optional[str] = None,
task_type: str = 'training',
reuse_last_task_id: bool = True,
output_uri: Optional[str] = None,
auto_connect_arg_parser: bool = True,
auto_connect_frameworks: bool = True,
auto_resource_monitoring: bool = True
) -> None:
super().__init__()
self._trains = trains.Task.init(
project_name=project_name, task_name=task_name, task_type=task_type,
reuse_last_task_id=reuse_last_task_id, output_uri=output_uri,
auto_connect_arg_parser=auto_connect_arg_parser,
auto_connect_frameworks=auto_connect_frameworks,
auto_resource_monitoring=auto_resource_monitoring
)
if self._bypass:
self._trains = None
else:
self._trains = Task.init(
project_name=project_name,
task_name=task_name,
task_type=task_type,
reuse_last_task_id=reuse_last_task_id,
output_uri=output_uri,
auto_connect_arg_parser=auto_connect_arg_parser,
auto_connect_frameworks=auto_connect_frameworks,
auto_resource_monitoring=auto_resource_monitoring
)
@property
def experiment(self) -> trains.Task:
def experiment(self) -> Task:
r"""Actual TRAINS object. To use TRAINS features do the following.
Example:
@@ -88,7 +114,7 @@ class TrainsLogger(LightningLoggerBase):
"""
ID is a uuid (string) representing this specific experiment in the entire system.
"""
if not self._trains:
if self._bypass or not self._trains:
return None
return self._trains.id
@@ -100,7 +126,7 @@ class TrainsLogger(LightningLoggerBase):
params:
The hyperparameters that passed through the model.
"""
if not self._trains:
if self._bypass or not self._trains:
return None
if not params:
return
@@ -121,7 +147,7 @@ class TrainsLogger(LightningLoggerBase):
then the elements will be logged as "title" and "series" respectively.
step: Step number at which the metrics should be recorded. Defaults to None.
"""
if not self._trains:
if self._bypass or not self._trains:
return None
if not step:
@@ -153,7 +179,7 @@ class TrainsLogger(LightningLoggerBase):
value: The value to log.
step: Step number at which the metrics should be recorded. Defaults to None.
"""
if not self._trains:
if self._bypass or not self._trains:
return None
if not step:
@@ -171,7 +197,7 @@ class TrainsLogger(LightningLoggerBase):
Args:
text: The value of the log (data-point).
"""
if not self._trains:
if self._bypass or not self._trains:
return None
self._trains.get_logger().report_text(text)
@@ -196,7 +222,7 @@ class TrainsLogger(LightningLoggerBase):
step:
Step number at which the metrics should be recorded. Defaults to None.
"""
if not self._trains:
if self._bypass or not self._trains:
return None
if not step:
@@ -220,7 +246,7 @@ class TrainsLogger(LightningLoggerBase):
metadata: Optional[Dict[str, Any]] = None, delete_after_upload: bool = False) -> None:
"""Save an artifact (file/object) in TRAINS experiment storage.
Args:
Arguments:
name: Artifact name. Notice! it will override previous artifact
if name already exists
artifact: Artifact object to upload. Currently supports:
@@ -237,7 +263,7 @@ class TrainsLogger(LightningLoggerBase):
If True local artifact will be deleted (only applies if artifact_object is a
local file). Defaults to False.
"""
if not self._trains:
if self._bypass or not self._trains:
return None
self._trains.upload_artifact(
@@ -249,8 +275,8 @@ class TrainsLogger(LightningLoggerBase):
pass
@rank_zero_only
def finalize(self, status: str) -> None:
if not self._trains:
def finalize(self, status: str = None) -> None:
if self._bypass or not self._trains:
return None
self._trains.close()
self._trains = None
@@ -260,23 +286,52 @@ class TrainsLogger(LightningLoggerBase):
"""
Name is a human readable non-unique name (str) of the experiment.
"""
if not self._trains:
return None
if self._bypass or not self._trains:
return ''
return self._trains.name
@property
def version(self) -> Union[str, None]:
if not self._trains:
if self._bypass or not self._trains:
return None
return self._trains.id
@classmethod
def set_credentials(cls, api_host: str = None, web_host: str = None, files_host: str = None,
key: str = None, secret: str = None) -> None:
"""
Set new default TRAINS-server host and credentials
These configurations could be overridden by either OS environment variables
or trains.conf configuration file
Notice! credentials needs to be set *prior* to Logger initialization
:param api_host: Trains API server url, example: host='http://localhost:8008'
:param web_host: Trains WEB server url, example: host='http://localhost:8080'
:param files_host: Trains Files server url, example: host='http://localhost:8081'
:param key: user key/secret pair, example: key='thisisakey123'
:param secret: user key/secret pair, example: secret='thisisseceret123'
"""
Task.set_credentials(api_host=api_host, web_host=web_host, files_host=files_host,
key=key, secret=secret)
@classmethod
def set_bypass_mode(cls, bypass: bool) -> None:
"""
set_bypass_mode will bypass all outside communication, and will drop all logs.
Should only be used in "standalone mode", when there is no access to the *trains-server*
:param bypass: If True, all outside communication is skipped
"""
cls._bypass = bypass
def __getstate__(self) -> Union[str, None]:
if not self._trains:
return None
if self._bypass or not self._trains:
return ''
return self._trains.id
def __setstate__(self, state: str) -> None:
self._rank = 0
self._trains = None
if state:
self._trains = trains.Task.get_task(task_id=state)
self._trains = Task.get_task(task_id=state)
pytorch_lightning/loggers/wandb.py
+3 -3
View File
@@ -14,11 +14,11 @@ import torch.nn as nn
try:
import wandb
from wandb.wandb_run import Run
except ImportError:
raise ImportError('You want to use `wandb` logger which is not installed yet,'
except ImportError: # pragma: no-cover
raise ImportError('You want to use `wandb` logger which is not installed yet,' # pragma: no-cover
' install it with `pip install wandb`.')
from .base import LightningLoggerBase, rank_zero_only
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
class WandbLogger(LightningLoggerBase):
pytorch_lightning/overrides/data_parallel.py
+4 -4
View File
@@ -8,7 +8,7 @@ from torch.nn import DataParallel
from torch.nn.parallel import DistributedDataParallel
def _find_tensors(obj): # pragma: no cover
def _find_tensors(obj): # pragma: no-cover
r"""
Recursively find all tensors contained in the specified object.
"""
@@ -21,7 +21,7 @@ def _find_tensors(obj): # pragma: no cover
return []
def get_a_var(obj): # pragma: no cover
def get_a_var(obj): # pragma: no-cover
if isinstance(obj, torch.Tensor):
return obj
@@ -77,7 +77,7 @@ class LightningDistributedDataParallel(DistributedDataParallel):
def parallel_apply(self, replicas, inputs, kwargs):
return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
def forward(self, *inputs, **kwargs): # pragma: no cover
def forward(self, *inputs, **kwargs): # pragma: no-cover
self._sync_params()
if self.device_ids:
inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
@@ -114,7 +114,7 @@ class LightningDistributedDataParallel(DistributedDataParallel):
return output
def parallel_apply(modules, inputs, kwargs_tup=None, devices=None): # pragma: no cover
def parallel_apply(modules, inputs, kwargs_tup=None, devices=None): # pragma: no-cover
r"""Applies each `module` in :attr:`modules` in parallel on arguments
contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword)
on each of :attr:`devices`.
pytorch_lightning/profiler/__init__.py
+1 -1
View File
@@ -113,7 +113,7 @@ to track and the profiler will record performance for code executed within this
"""
from .profiler import Profiler, AdvancedProfiler, PassThroughProfiler
from pytorch_lightning.profiler.profiler import Profiler, AdvancedProfiler, PassThroughProfiler
__all__ = [
'Profiler',
pytorch_lightning/profiler/profiler.py
+5 -13
View File
@@ -18,17 +18,11 @@ class BaseProfiler(ABC):
@abstractmethod
def start(self, action_name):
"""
Defines how to start recording an action.
"""
pass
"""Defines how to start recording an action."""
@abstractmethod
def stop(self, action_name):
"""
Defines how to record the duration once an action is complete.
"""
pass
"""Defines how to record the duration once an action is complete."""
@contextmanager
def profile(self, action_name):
@@ -62,9 +56,7 @@ class BaseProfiler(ABC):
break
def describe(self):
"""
Logs a profile report after the conclusion of the training run.
"""
"""Logs a profile report after the conclusion of the training run."""
pass
@@ -104,7 +96,7 @@ class Profiler(BaseProfiler):
def stop(self, action_name):
end_time = time.monotonic()
if action_name not in self.current_actions:
raise ValueError(
raise ValueError( # pragma: no-cover
f"Attempting to stop recording an action ({action_name}) which was never started."
)
start_time = self.current_actions.pop(action_name)
@@ -154,7 +146,7 @@ class AdvancedProfiler(BaseProfiler):
def stop(self, action_name):
pr = self.profiled_actions.get(action_name)
if pr is None:
raise ValueError(
raise ValueError( # pragma: no-cover
f"Attempting to stop recording an action ({action_name}) which was never started."
)
pr.disable()
pytorch_lightning/trainer/__init__.py
+1 -1
View File
@@ -878,6 +878,6 @@ Trainer class
"""
from .trainer import Trainer
from pytorch_lightning.trainer.trainer import Trainer
__all__ = ['Trainer']
pytorch_lightning/trainer/auto_mix_precision.py
+1 -1
View File
@@ -21,7 +21,7 @@ class TrainerAMPMixin(ABC):
if self.use_amp:
log.info('Using 16bit precision.')
if use_amp and not APEX_AVAILABLE: # pragma: no cover
if use_amp and not APEX_AVAILABLE: # pragma: no-cover
msg = """
You set `use_amp=True` but do not have apex installed.
Install apex first using this guide and rerun with use_amp=True:
pytorch_lightning/trainer/distrib_data_parallel.py
+1 -1
View File
@@ -208,7 +208,7 @@ class TrainerDDPMixin(ABC):
self.use_ddp2 = False
# throw error to force user ddp or ddp2 choice
if num_gpu_nodes > 1 and not (self.use_ddp2 or self.use_ddp): # pragma: no cover
if num_gpu_nodes > 1 and not (self.use_ddp2 or self.use_ddp):
w = 'DataParallel does not support num_nodes > 1. ' \
'Switching to DistributedDataParallel for you. ' \
'To silence this warning set distributed_backend=ddp' \
pytorch_lightning/trainer/distrib_parts.py
+1 -1
View File
@@ -511,7 +511,7 @@ class TrainerDPMixin(ABC):
# check for this bug (amp + dp + !01 doesn't work)
# https://github.com/NVIDIA/apex/issues/227
if self.use_dp and self.use_amp:
if self.amp_level == 'O2': # pragma: no cover
if self.amp_level == 'O2':
m = f"""
Amp level {self.amp_level} with DataParallel is not supported.
See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.
pytorch_lightning/trainer/evaluation_loop.py
+1 -1
View File
@@ -248,7 +248,7 @@ class TrainerEvaluationLoopMixin(ABC):
dataloader = dataloader.per_device_loader(device)
for batch_idx, batch in enumerate(dataloader):
if batch is None: # pragma: no cover
if batch is None:
continue
# stop short when on fast_dev_run (sets max_batch=1)
pytorch_lightning/trainer/trainer.py
+2 -2
View File
@@ -598,7 +598,7 @@ class Trainer(
elif self.single_gpu:
self.single_gpu_train(model)
elif self.use_tpu: # pragma: no cover
elif self.use_tpu: # pragma: no-cover
log.info(f'training on {self.num_tpu_cores} TPU cores')
# COLAB_GPU is an env var available by default in Colab environments.
@@ -855,7 +855,7 @@ class Trainer(
if model is not None:
self.model = model
self.fit(model)
elif self.use_ddp or self.use_tpu: # pragma: no cover
elif self.use_ddp or self.use_tpu: # pragma: no-cover
# attempt to load weights from a spawn
path = os.path.join(self.default_save_path, '__temp_weight_ddp_end.ckpt')
test_model = self.model
pytorch_lightning/trainer/training_io.py
+1 -1
View File
@@ -206,7 +206,7 @@ class TrainerIOMixin(ABC):
signal.signal(signal.SIGUSR1, self.sig_handler)
signal.signal(signal.SIGTERM, self.term_handler)
def sig_handler(self, signum, frame): # pragma: no cover
def sig_handler(self, signum, frame): # pragma: no-cover
if self.proc_rank == 0:
# save weights
log.info('handling SIGUSR1')
requirements-extra.txt
+1 -1
View File
@@ -3,4 +3,4 @@ comet-ml>=1.0.56
mlflow>=1.0.0
test_tube>=0.7.5
wandb>=0.8.21
trains>=0.13.3
trains>=0.14.1rc0
setup.cfg
+2 -2
View File
@@ -19,9 +19,9 @@ max-line-length = 120
[coverage:report]
exclude_lines =
pragma: no cover
def __repr__
pragma: no-cover
warnings
pass
[flake8]
# TODO: this should be 88 or 100 according PEP8
tests/loggers/test_tensorboard.py
+1 -3
View File
@@ -6,9 +6,7 @@ import torch
import tests.models.utils as tutils
from pytorch_lightning import Trainer
from pytorch_lightning.loggers import (
TensorBoardLogger
)
from pytorch_lightning.loggers import TensorBoardLogger
from tests.models import LightningTestModel
tests/loggers/test_trains.py
+13 -3
View File
@@ -12,7 +12,11 @@ def test_trains_logger(tmpdir):
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
logger = TrainsLogger(project_name="examples", task_name="pytorch lightning test")
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_save_path=tmpdir,
@@ -24,6 +28,7 @@ def test_trains_logger(tmpdir):
result = trainer.fit(model)
print('result finished')
logger.finalize()
assert result == 1, "Training failed"
@@ -33,8 +38,11 @@ def test_trains_pickle(tmpdir):
# hparams = tutils.get_hparams()
# model = LightningTestModel(hparams)
logger = TrainsLogger(project_name="examples", task_name="pytorch lightning test")
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_save_path=tmpdir,
@@ -46,3 +54,5 @@ def test_trains_pickle(tmpdir):
pkl_bytes = pickle.dumps(trainer)
trainer2 = pickle.loads(pkl_bytes)
trainer2.logger.log_metrics({"acc": 1.0})
trainer2.logger.finalize()
logger.finalize()
tests/models/base.py
+1 -1
View File
@@ -203,7 +203,7 @@ class TestModelBase(LightningModule):
return loader
@staticmethod
def add_model_specific_args(parent_parser, root_dir): # pragma: no cover
def add_model_specific_args(parent_parser, root_dir): # pragma: no-cover
"""
Parameters you define here will be available to your model through self.hparams
:param parent_parser: