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85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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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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from dataclasses import dataclass
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from typing import Any, Optional
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from hydra.core.config_store import ConfigStore
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__all__ = ['TrainerConfig']
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cs = ConfigStore.instance()
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@dataclass
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class TrainerConfig:
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"""
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Configuration of PyTorch Lightning Trainer.
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It is not derived from Config as it is not a NeMo object (and in particular it doesn't need a name).
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..warning:
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Picked just few params of the PTL trainer for now. This needs to be discussed.
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..note:
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For the details on the function/meanings of the arguments, please refer to:
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https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html
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"""
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logger: Any = True
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callbacks: Optional[Any] = None
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default_root_dir: Optional[str] = None
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gradient_clip_val: float = 0
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num_nodes: int = 1
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enable_progress_bar: bool = True
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overfit_batches: Any = 0.0
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check_val_every_n_epoch: int = 1
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fast_dev_run: bool = False
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accumulate_grad_batches: Any = 1
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max_epochs: int = 1000
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min_epochs: int = 1
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max_steps: Optional[int] = -1
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min_steps: Optional[int] = None
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limit_train_batches: Any = 1.0
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limit_val_batches: Any = 1.0
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limit_test_batches: Any = 1.0
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val_check_interval: Any = 1.0
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log_every_n_steps: int = 50
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accelerator: Optional[str] = 'auto'
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sync_batchnorm: bool = False
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precision: Any = 32
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num_sanity_val_steps: int = 2
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profiler: Optional[Any] = None
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benchmark: bool = False
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deterministic: bool = False
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use_distributed_sampler: bool = True
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detect_anomaly: bool = False
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plugins: Optional[Any] = None # Optional[Union[str, list]]
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limit_predict_batches: float = 1.0
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gradient_clip_algorithm: str = 'norm'
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max_time: Optional[Any] = None # can be one of Union[str, timedelta, Dict[str, int], None]
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reload_dataloaders_every_n_epochs: int = 0
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devices: Any = 'auto'
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strategy: Any = 'auto'
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enable_checkpointing: bool = False
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enable_model_summary: bool = True
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inference_mode: bool = True
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barebones: bool = False
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# Register the trainer config.
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cs.store(
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group="trainer",
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name="trainer",
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node=TrainerConfig,
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)
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