def __init__(
self,
optimization: Optional[Literal["trace", "script", "onnx", "tensorrt"]] = None,
server: Literal["fastapi", "ml_server", "torchserve", "sagemaker"] = "fastapi",
host: str = "127.0.0.1",
port: int = 8080,
timeout: int = 10,
exit_on_failure: bool = True,
):
super().__init__()
fastapi_installed = _RequirementAvailable("fastapi")
if not fastapi_installed:
raise ModuleNotFoundError(fastapi_installed.message)
uvicorn_installed = _RequirementAvailable("uvicorn")
if not uvicorn_installed:
raise ModuleNotFoundError(uvicorn_installed.message)
# TODO: Add support for the other options
if optimization is not None:
raise NotImplementedError(f"The optimization {optimization} is currently not supported.")
# TODO: Add support for testing with those server services
if server != "fastapi":
raise NotImplementedError("Only the fastapi server is currently supported.")
self.optimization = optimization
self.host = host
self.port = port
self.server = server
self.timeout = timeout
self.exit_on_failure = exit_on_failure
self.resp: Optional[requests.Response] = None
get_default_process_group_backend_for_device,
log,
)
from pytorch_lightning.utilities.enums import AMPType, PrecisionType
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _RequirementAvailable
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.optimizer import optimizers_to_device
from pytorch_lightning.utilities.rank_zero import rank_zero_info
from pytorch_lightning.utilities.seed import reset_seed
from pytorch_lightning.utilities.types import _PATH, LRSchedulerConfig, LRSchedulerTypeUnion, STEP_OUTPUT
from pytorch_lightning.utilities.warnings import rank_zero_warn, WarningCache
warning_cache = WarningCache()
_DEEPSPEED_AVAILABLE: bool = _RequirementAvailable("deepspeed")
if _DEEPSPEED_AVAILABLE:
import deepspeed
def remove_module_hooks(model: torch.nn.Module) -> None:
# todo (tchaton) awaiting this feature to move upstream to DeepSpeed
for module in model.modules():
module._backward_hooks = OrderedDict()
module._is_full_backward_hook = None
module._forward_hooks = OrderedDict()
module._forward_pre_hooks = OrderedDict()
module._state_dict_hooks = OrderedDict()
module._load_state_dict_pre_hooks = OrderedDict()
from functools import partial, update_wrapper
from types import MethodType
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
import torch
from torch.optim import Optimizer
import pytorch_lightning as pl
from pytorch_lightning import Callback, LightningDataModule, LightningModule, seed_everything, Trainer
from pytorch_lightning.utilities.cloud_io import get_filesystem
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _RequirementAvailable
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.rank_zero import _warn, rank_zero_deprecation, rank_zero_warn
_JSONARGPARSE_SIGNATURES_AVAILABLE = _RequirementAvailable(
"jsonargparse[signatures]>=4.12.0")
if _JSONARGPARSE_SIGNATURES_AVAILABLE:
import docstring_parser
from jsonargparse import (
ActionConfigFile,
ArgumentParser,
class_from_function,
Namespace,
register_unresolvable_import_paths,
set_config_read_mode,
)
register_unresolvable_import_paths(
torch
) # Required until fix https://github.com/pytorch/pytorch/issues/74483
from pytorch_lightning.callbacks import Checkpoint
from pytorch_lightning.loggers.logger import Logger, rank_zero_experiment
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _RequirementAvailable
from pytorch_lightning.utilities.logger import _add_prefix, _convert_params, _flatten_dict, _sanitize_callable_params
from pytorch_lightning.utilities.rank_zero import rank_zero_only, rank_zero_warn
try:
import wandb
from wandb.sdk.lib import RunDisabled
from wandb.wandb_run import Run
except ModuleNotFoundError:
# needed for test mocks, these tests shall be updated
wandb, Run, RunDisabled = None, None, None # type: ignore
_WANDB_AVAILABLE = _RequirementAvailable("wandb")
_WANDB_GREATER_EQUAL_0_10_22 = _RequirementAvailable("wandb>=0.10.22")
_WANDB_GREATER_EQUAL_0_12_10 = _RequirementAvailable("wandb>=0.12.10")
class WandbLogger(Logger):
r"""
Log using `Weights and Biases <https://docs.wandb.ai/integrations/lightning>`_.
**Installation and set-up**
Install with pip:
.. code-block:: bash
pip install wandb
from pytorch_lightning.core.optimizer import _init_optimizers_and_lr_schedulers, _set_scheduler_opt_idx
from pytorch_lightning.loggers.logger import DummyLogger
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _RequirementAvailable
from pytorch_lightning.utilities.parsing import lightning_hasattr, lightning_setattr
from pytorch_lightning.utilities.rank_zero import rank_zero_warn
from pytorch_lightning.utilities.types import LRSchedulerConfig, STEP_OUTPUT
# check if ipywidgets is installed before importing tqdm.auto
# to ensure it won't fail and a progress bar is displayed
if importlib.util.find_spec("ipywidgets") is not None:
from tqdm.auto import tqdm
else:
from tqdm import tqdm
_MATPLOTLIB_AVAILABLE = _RequirementAvailable("matplotlib")
if _MATPLOTLIB_AVAILABLE and TYPE_CHECKING:
import matplotlib.pyplot as plt
log = logging.getLogger(__name__)
def _determine_lr_attr_name(trainer: "pl.Trainer", model: "pl.LightningModule") -> str:
if isinstance(trainer.auto_lr_find, str):
if not lightning_hasattr(model, trainer.auto_lr_find):
raise MisconfigurationException(
f"`auto_lr_find` was set to {trainer.auto_lr_find}, however"
" could not find this as a field in `model` or `model.hparams`."
)
return trainer.auto_lr_find
from functools import reduce
from typing import Any, Callable, Dict, Generator, Mapping, Optional, Sequence, Set, Union
from weakref import ReferenceType
import torch
from torch import Tensor
from pytorch_lightning import __version__
from pytorch_lightning.callbacks import Checkpoint
from pytorch_lightning.loggers.logger import Logger, rank_zero_experiment
from pytorch_lightning.utilities.imports import _RequirementAvailable
from pytorch_lightning.utilities.logger import _add_prefix, _convert_params, _sanitize_callable_params
from pytorch_lightning.utilities.model_summary import ModelSummary
from pytorch_lightning.utilities.rank_zero import rank_zero_only
_NEPTUNE_AVAILABLE = _RequirementAvailable("neptune")
_NEPTUNE_GREATER_EQUAL_0_9 = _RequirementAvailable("neptune>=0.9.0")
if _NEPTUNE_AVAILABLE and _NEPTUNE_GREATER_EQUAL_0_9:
try:
from neptune import new as neptune
from neptune.new.exceptions import NeptuneLegacyProjectException, NeptuneOfflineModeFetchException
from neptune.new.run import Run
from neptune.new.types import File as NeptuneFile
except ModuleNotFoundError:
import neptune
from neptune.exceptions import NeptuneLegacyProjectException, NeptuneOfflineModeFetchException
from neptune.run import Run
from neptune.types import File as NeptuneFile
else:
def test_requirement_avaliable():
assert _RequirementAvailable(f"torch>={torch.__version__}")
assert not _RequirementAvailable(f"torch<{torch.__version__}")
assert "Requirement '-' not met" in str(_RequirementAvailable("-"))
from typing import Any, Callable, Optional, TYPE_CHECKING, Union
from torch import Tensor
from torch.nn import Module
from torch.optim import LBFGS, Optimizer
import pytorch_lightning as pl
from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
from pytorch_lightning.utilities import GradClipAlgorithmType
from pytorch_lightning.utilities.enums import PrecisionType
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _RequirementAvailable
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.warnings import WarningCache
_DEEPSPEED_GREATER_EQUAL_0_6 = _RequirementAvailable("deepspeed>=0.6.0")
if TYPE_CHECKING:
if pl.strategies.deepspeed._DEEPSPEED_AVAILABLE:
import deepspeed
warning_cache = WarningCache()
class DeepSpeedPrecisionPlugin(PrecisionPlugin):
"""Precision plugin for DeepSpeed integration.
Args:
precision: Double precision (64), full precision (32), half precision (16) or bfloat16 precision (bf16).
amp_type: The mixed precision backend to use ("native" or "apex").
amp_level: The optimization level to use (O1, O2, etc...). By default it will be set to "O2"
if ``amp_type`` is set to "apex".
