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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from argparse import Namespace
from dataclasses import dataclass
from pathlib import Path
from typing import Any, List, Literal, Mapping, Optional, Union
import pandas as pd
from lightning_utilities.core.apply_func import apply_to_collection
from omegaconf import DictConfig, ListConfig, OmegaConf
from pytorch_lightning.callbacks import Checkpoint
from pytorch_lightning.loggers import Logger
from pytorch_lightning.utilities.parsing import AttributeDict
from torch import Tensor
from nemo.utils import logging
try:
from clearml import OutputModel, Task
HAVE_CLEARML_LOGGER = True
except (ImportError, ModuleNotFoundError):
HAVE_CLEARML_LOGGER = False
@dataclass
class ClearMLParams:
project: Optional[str] = None
task: Optional[str] = None
connect_pytorch: Optional[bool] = False
model_name: Optional[str] = None
tags: Optional[List[str]] = None
log_model: Optional[bool] = False
log_cfg: Optional[bool] = False
log_metrics: Optional[bool] = False
class ClearMLLogger(Logger):
@property
def name(self) -> str:
return self.clearml_task.name
@property
def version(self) -> str:
return self.clearml_task.id
def __init__(
self, clearml_cfg: DictConfig, log_dir: str, prefix: str, save_best_model: bool, postfix: str = ".nemo"
) -> None:
if not HAVE_CLEARML_LOGGER:
raise ImportError(
"Found create_clearml_logger is True."
"But ClearML not found. Please see the README for installation instructions:"
"https://github.com/allegroai/clearml"
)
self.clearml_task = None
self.clearml_model = None
self.clearml_cfg = clearml_cfg
self.path_nemo_model = os.path.abspath(
os.path.expanduser(os.path.join(log_dir, "checkpoints", prefix + postfix))
)
self.save_best_model = save_best_model
self.prefix = prefix
self.previos_best_model_path = None
self.last_metrics = None
self.save_blocked = True
self.project_name = os.getenv("CLEARML_PROJECT", clearml_cfg.project if clearml_cfg.project else "NeMo")
self.task_name = os.getenv("CLEARML_TASK", clearml_cfg.task if clearml_cfg.task else f"Trainer {self.prefix}")
tags = ["NeMo"]
if clearml_cfg.tags:
tags.extend(clearml_cfg.tags)
self.clearml_task: Task = Task.init(
project_name=self.project_name,
task_name=self.task_name,
auto_connect_frameworks={"pytorch": clearml_cfg.connect_pytorch},
output_uri=True,
tags=tags,
)
if clearml_cfg.model_name:
model_name = clearml_cfg.model_name
elif self.prefix:
model_name = self.prefix
else:
model_name = self.task_name
if clearml_cfg.log_model:
self.clearml_model: OutputModel = OutputModel(
name=model_name, task=self.clearml_task, tags=tags, framework="NeMo"
)
def log_hyperparams(self, params, *args, **kwargs) -> None:
if self.clearml_model and self.clearml_cfg.log_cfg:
if isinstance(params, Namespace):
params = vars(params)
elif isinstance(params, AttributeDict):
params = dict(params)
params = apply_to_collection(params, (DictConfig, ListConfig), OmegaConf.to_container, resolve=True)
params = apply_to_collection(params, Path, str)
params = OmegaConf.to_yaml(params)
self.clearml_model.update_design(config_text=params)
def log_metrics(self, metrics: Mapping[str, float], step: Optional[int] = None) -> None:
if self.clearml_model and self.clearml_cfg.log_metrics:
metrics = {
k: {
"value": str(v.item() if type(v) == Tensor else v),
"type": str(type(v.item() if type(v) == Tensor else v)),
}
for k, v in metrics.items()
}
self.last_metrics = metrics
def log_table(
self,
key: str,
columns: List[str] = None,
data: List[List[Any]] = None,
dataframe: Any = None,
step: Optional[int] = None,
) -> None:
table: Optional[Union[pd.DataFrame, List[List[Any]]]] = None
if dataframe is not None:
table = dataframe
if columns is not None:
table.columns = columns
if data is not None:
table = data
assert len(columns) == len(table[0]), "number of column names should match the total number of columns"
table.insert(0, columns)
if table is not None:
self.clearml_task.logger.report_table(title=key, series=key, iteration=step, table_plot=table)
def after_save_checkpoint(self, checkpoint_callback: Checkpoint) -> None:
if self.clearml_model:
if self.save_best_model:
if self.save_blocked:
self.save_blocked = False
return None
if not os.path.exists(checkpoint_callback.best_model_path):
return None
if self.previos_best_model_path == checkpoint_callback.best_model_path:
return None
self.previos_best_model_path = checkpoint_callback.best_model_path
self._log_model(self.path_nemo_model)
def finalize(self, status: Literal["success", "failed", "aborted"] = "success") -> None:
if status == "success":
self.clearml_task.mark_completed()
elif status == "failed":
self.clearml_task.mark_failed()
elif status == "aborted":
self.clearml_task.mark_stopped()
def _log_model(self, save_path: str) -> None:
if self.clearml_model:
if os.path.exists(save_path):
self.clearml_model.update_weights(
weights_filename=save_path,
upload_uri=self.clearml_task.storage_uri or self.clearml_task._get_default_report_storage_uri(),
auto_delete_file=False,
is_package=True,
)
if self.clearml_cfg.log_metrics and self.last_metrics:
self.clearml_model.set_all_metadata(self.last_metrics)
self.save_blocked = True
else:
logging.warning((f"Logging model enabled, but cant find .nemo file!" f" Path: {save_path}"))
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