simplify imports Omegaconf

docs/source/conf.py

@@ -311,6 +311,7 @@ def package_list_from_file(file):
     'PyYAML': 'yaml',
     'comet-ml': 'comet_ml',
     'neptune-client': 'neptune',
+    'hydra-core': 'hydra',
 }
 MOCK_PACKAGES = []
 if SPHINX_MOCK_REQUIREMENTS:

pytorch_lightning/accelerators/ddp2_accelerator.py

@@ -21,18 +21,14 @@
 from pytorch_lightning.distributed.dist import LightningDistributed
 from pytorch_lightning import _logger as log
 from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
-from pytorch_lightning.utilities import AMPType
+from pytorch_lightning.utilities import AMPType, HYDRA_AVAILABLE
 from pytorch_lightning.utilities.distributed import rank_zero_only, sync_ddp_if_available
 from torch.nn.parallel import DistributedDataParallel
 from typing import List, Optional, Union, Any
 
-try:
+if HYDRA_AVAILABLE:
     from hydra.utils import to_absolute_path, get_original_cwd
     from hydra.core.hydra_config import HydraConfig
-except ImportError:
-    HYDRA_AVAILABLE = False
-else:
-    HYDRA_AVAILABLE = True
 
 
 class DDP2Accelerator(Accelerator):

pytorch_lightning/accelerators/ddp_accelerator.py

@@ -26,7 +26,7 @@
 from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.distributed.dist import LightningDistributed
-from pytorch_lightning.utilities import AMPType
+from pytorch_lightning.utilities import AMPType, HYDRA_AVAILABLE
 from pytorch_lightning.utilities.distributed import find_free_network_port
 from pytorch_lightning.utilities.distributed import rank_zero_only
 from pytorch_lightning.utilities.distributed import sync_ddp_if_available
@@ -35,13 +35,9 @@
 from torch.nn.parallel import DistributedDataParallel
 
 
-try:
+if HYDRA_AVAILABLE:
     from hydra.utils import to_absolute_path, get_original_cwd
     from hydra.core.hydra_config import HydraConfig
-except ImportError:
-    HYDRA_AVAILABLE = False
-else:
-    HYDRA_AVAILABLE = True
 
 
 class DDPAccelerator(Accelerator):

pytorch_lightning/accelerators/ddp_cpu_hpc_accelerator.py

@@ -12,15 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License
 from pytorch_lightning.accelerators.ddp_hpc_accelerator import DDPHPCAccelerator
+from pytorch_lightning.utilities import HYDRA_AVAILABLE
 
-
-try:
+if HYDRA_AVAILABLE:
     from hydra.utils import to_absolute_path, get_original_cwd
     from hydra.core.hydra_config import HydraConfig
-except ImportError:
-    HYDRA_AVAILABLE = False
-else:
-    HYDRA_AVAILABLE = True
 
 
 class DDPCPUHPCAccelerator(DDPHPCAccelerator):

pytorch_lightning/accelerators/ddp_cpu_spawn_accelerator.py

@@ -23,18 +23,14 @@
 from pytorch_lightning import _logger as log
 from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
 from pytorch_lightning.core.lightning import LightningModule
-from pytorch_lightning.utilities import AMPType
+from pytorch_lightning.utilities import AMPType, HYDRA_AVAILABLE
 from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn
 from pytorch_lightning.utilities.distributed import find_free_network_port, sync_ddp_if_available
 from pytorch_lightning.distributed.dist import LightningDistributed
 
-try:
+if HYDRA_AVAILABLE:
     from hydra.core.hydra_config import HydraConfig
     from hydra.utils import get_original_cwd, to_absolute_path
-except ImportError:
-    HYDRA_AVAILABLE = False
-else:
-    HYDRA_AVAILABLE = True
 
 
 class DDPCPUSpawnAccelerator(Accelerator):

pytorch_lightning/accelerators/ddp_hpc_accelerator.py

@@ -23,17 +23,13 @@
 from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.distributed.dist import LightningDistributed
-from pytorch_lightning.utilities import AMPType
+from pytorch_lightning.utilities import AMPType, HYDRA_AVAILABLE
 from pytorch_lightning.utilities.distributed import rank_zero_only, sync_ddp_if_available
 
 
-try:
+if HYDRA_AVAILABLE:
     from hydra.utils import to_absolute_path, get_original_cwd
     from hydra.core.hydra_config import HydraConfig
-except ImportError:
-    HYDRA_AVAILABLE = False
-else:
-    HYDRA_AVAILABLE = True
 
 
 class DDPHPCAccelerator(Accelerator):

pytorch_lightning/accelerators/ddp_spawn_accelerator.py

@@ -24,20 +24,16 @@
 from pytorch_lightning import _logger as log
 from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
 from pytorch_lightning.core.lightning import LightningModule
-from pytorch_lightning.utilities import AMPType
+from pytorch_lightning.utilities import AMPType, HYDRA_AVAILABLE
 from pytorch_lightning.utilities.cloud_io import atomic_save, load as pl_load
 from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn, find_free_network_port
 from pytorch_lightning.utilities.distributed import sync_ddp_if_available
 from pytorch_lightning.utilities.seed import seed_everything
 from pytorch_lightning.distributed.dist import LightningDistributed
 
-try:
+if HYDRA_AVAILABLE:
     from hydra.core.hydra_config import HydraConfig
     from hydra.utils import get_original_cwd, to_absolute_path
-except ImportError:
-    HYDRA_AVAILABLE = False
-else:
-    HYDRA_AVAILABLE = True
 
 
 class DDPSpawnAccelerator(Accelerator):

pytorch_lightning/core/saving.py

@@ -20,22 +20,20 @@
 from typing import Union, Dict, Any, Optional, Callable, MutableMapping, IO
 from warnings import warn
 
-import fsspec
 import torch
 import yaml
 
 from pytorch_lightning import _logger as log
-from pytorch_lightning.utilities import rank_zero_warn, AttributeDict
+from pytorch_lightning.utilities import rank_zero_warn, AttributeDict, OMEGACONF_AVAILABLE
 from pytorch_lightning.utilities.cloud_io import load as pl_load
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.utilities.parsing import parse_class_init_keys
 
 PRIMITIVE_TYPES = (bool, int, float, str)
 ALLOWED_CONFIG_TYPES = (AttributeDict, MutableMapping, Namespace)
-try:
+
+if OMEGACONF_AVAILABLE:
     from omegaconf import OmegaConf
-except ImportError:
-    OmegaConf = None
 
 # the older shall be on the top
 CHECKPOINT_PAST_HPARAMS_KEYS = (
@@ -362,7 +360,7 @@ def save_hparams_to_yaml(config_yaml, hparams: Union[dict, Namespace]) -> None:
         hparams = dict(hparams)
 
     # saving with OmegaConf objects
-    if OmegaConf is not None:
+    if OMEGACONF_AVAILABLE:
         if OmegaConf.is_config(hparams):
             with fs.open(config_yaml, "w", encoding="utf-8") as fp:
                 OmegaConf.save(hparams, fp, resolve=True)

pytorch_lightning/loggers/tensorboard.py

@@ -20,7 +20,6 @@
 import os
 from argparse import Namespace
 from typing import Any, Dict, Optional, Union
-from warnings import warn
 
 import torch
 from torch.utils.tensorboard import SummaryWriter
@@ -30,16 +29,11 @@
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.core.saving import save_hparams_to_yaml
 from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
-from pytorch_lightning.utilities import rank_zero_only, rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_only, rank_zero_warn, OMEGACONF_AVAILABLE
 from pytorch_lightning.utilities.cloud_io import get_filesystem
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
-try:
+if OMEGACONF_AVAILABLE:
     from omegaconf import Container, OmegaConf
-except ImportError:
-    OMEGACONF_AVAILABLE = False
-else:
-    OMEGACONF_AVAILABLE = True
 
 
 class TensorBoardLogger(LightningLoggerBase):

pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -12,20 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import io
 import os
 import re
-import signal
-from abc import ABC
-from subprocess import call
 
 import torch
-import torch.distributed as torch_distrib
 
 import pytorch_lightning
 from pytorch_lightning import _logger as log
 from pytorch_lightning.core.lightning import LightningModule
-from pytorch_lightning.utilities import AMPType, rank_zero_warn, APEX_AVAILABLE
+from pytorch_lightning.utilities import APEX_AVAILABLE, AMPType, OMEGACONF_AVAILABLE, rank_zero_warn
 from pytorch_lightning.utilities.cloud_io import atomic_save, get_filesystem
 from pytorch_lightning.utilities.cloud_io import load as pl_load
 from pytorch_lightning.utilities.upgrade_checkpoint import KEYS_MAPPING as DEPRECATED_CHECKPOINT_KEYS
@@ -34,12 +29,8 @@
 if APEX_AVAILABLE:
     from apex import amp
 
-try:
+if OMEGACONF_AVAILABLE:
     from omegaconf import Container
-except ImportError:
-    OMEGACONF_AVAILABLE = False
-else:
-    OMEGACONF_AVAILABLE = True
 
 
 class CheckpointConnector:

pytorch_lightning/utilities/__init__.py

@@ -44,6 +44,8 @@ def _module_available(module_path: str) -> bool:
 
 APEX_AVAILABLE = _module_available("apex.amp")
 NATIVE_AMP_AVAILABLE = _module_available("torch.cuda.amp") and hasattr(torch.cuda.amp, "autocast")
+OMEGACONF_AVAILABLE = _module_available("omegaconf")
+HYDRA_AVAILABLE = _module_available("hydra")
 
 TPU_AVAILABLE = XLADeviceUtils.tpu_device_exists()
 

requirements/extra.txt

@@ -8,3 +8,4 @@ scikit-learn>=0.22.2
 torchtext>=0.3.1, <0.7  # TODO: temporary fix fix for compatibility
 onnx>=1.7.0
 onnxruntime>=1.3.0
+hydra-core>=1.0