From 35416aececcfcdf46371e7eb8f03416cb22ea113 Mon Sep 17 00:00:00 2001 From: gachiemchiep Date: Wed, 22 Mar 2023 10:00:00 +0900 Subject: [PATCH] Integrating mlflow --- doc/doc_en/config_en.md | 10 +++- doc/doc_en/logging_en.md | 41 +++++++++++++ doc/imgs_en/mlflow_metrics.png | Bin 0 -> 78574 bytes doc/imgs_en/mlflow_models.png | Bin 0 -> 89672 bytes ppocr/utils/loggers/__init__.py | 1 + ppocr/utils/loggers/loggers.py | 2 +- ppocr/utils/loggers/mlflow_logger.py | 84 +++++++++++++++++++++++++++ tools/program.py | 12 +++- 8 files changed, 147 insertions(+), 3 deletions(-) create mode 100644 doc/imgs_en/mlflow_metrics.png create mode 100644 doc/imgs_en/mlflow_models.png create mode 100644 ppocr/utils/loggers/mlflow_logger.py diff --git a/doc/doc_en/config_en.md b/doc/doc_en/config_en.md index d467a7f918..3651b1c870 100644 --- a/doc/doc_en/config_en.md +++ b/doc/doc_en/config_en.md @@ -37,6 +37,7 @@ Take rec_chinese_lite_train_v2.0.yml as an example | checkpoints | set model parameter path | None | Used to load parameters after interruption to continue training| | use_visualdl | Set whether to enable visualdl for visual log display | False | [Tutorial](https://www.paddlepaddle.org.cn/paddle/visualdl) | | use_wandb | Set whether to enable W&B for visual log display | False | [Documentation](https://docs.wandb.ai/) +| use_mlflow | Set whether to enable MLFlow for visual log display | False | [Documentation](https://mlflow.org/docs/latest) | infer_img | Set inference image path or folder path | ./infer_img | \|| | character_dict_path | Set dictionary path | ./ppocr/utils/ppocr_keys_v1.txt | If the character_dict_path is None, model can only recognize number and lower letters | | max_text_length | Set the maximum length of text | 25 | \ | @@ -141,6 +142,13 @@ In PaddleOCR, the network is divided into four stages: Transform, Backbone, Neck | save_dir | local directory in which all the models and other data is saved | wandb | \ | config | model configuration | None | \ +### mlflow ([mlflow](../../ppocr/utils/loggers/mlflow_logger.py)) +| Parameter | Use | Defaults | Note | +| :---------------------: | :---------------------: | :--------------: | :--------------------: | +| tracking_uri | path of tracking_uri or local directory path | if not set, all logs will be saved inside local directory save_model_dir/mlflow | \ +| exp_name | Name of the project | Randomly generated by mlflow | \ +| run_name | Name of the run | Randomly generated by mlflow | \ + @@ -245,4 +253,4 @@ For more supported languages, please refer to : [Multi-language model](https://g The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods. * [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA),Extraction code:frgi. -* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view) \ No newline at end of file +* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view) diff --git a/doc/doc_en/logging_en.md b/doc/doc_en/logging_en.md index d00ab8bd56..132a00b12c 100644 --- a/doc/doc_en/logging_en.md +++ b/doc/doc_en/logging_en.md @@ -57,5 +57,46 @@ For more advanced usage to log images, audios, videos or any other form of data, To view the dashboard, the link to the dashboard is printed to the console at the beginning and end of every training job and you can also access it by logging into your W&B account on your browser. +### mlflow +mlflow is a MLOps tool that can be used for experiment tracking, dataset/model versioning, visualizing results and collaborating with colleagues. A mlflow logger is integrated directly into PaddleOCR and to use it, first you need to install the mlflow package. + +```shell +# Install MLflow +pip install mlflow + +# Install MLflow with extra ML libraries and 3rd-party tools +pip install mlflow[extras] + +# Start a local server +mlflow server --backend-store-uri /datadrive/mlflow --default-artifact-root /datadrive/mlflow --host 0.0.0.0 +``` + +To visualize and track your model training add the following flag to your config yaml file under the `Global` section - + +``` +Global: + use_mlflow: True +``` + +To add more arguments to the `MlflowLogger` listed [here](./config_en.md) add the header `mlflow` to the yaml file and add the arguments under it - + +``` +mlflow: + tracking_uri: http://localhost:5000 + exp_name: paddleocr + run_name: icdar2015.det_mv3_db +``` + + + +These config variables from the yaml file are used to instantiate the `MlflowLogger` object with the project name. During the training process, the `log_metrics` function is called to log training and evaluation metrics at the training and evaluation steps respectively from the rank 0 process only. +At every model saving step, the MlflowLogger, logs the model using the `log_model` function along with relavant metadata and tags showing the epoch in which the model is saved, the model is best or not and so on. + +All the logging mentioned above is integrated into the `program.train` function and will generate dashboards like this - + +![mlflow Dashboard](../imgs_en/mlflow_metrics.png) + +![mlflow Models](../imgs_en/mlflow_models.png) + ### Using Multiple Loggers Both VisualDL and W&B can also be used simultaneously by just setting both the aforementioned flags to True. \ No newline at end of file diff --git a/doc/imgs_en/mlflow_metrics.png b/doc/imgs_en/mlflow_metrics.png new file mode 100644 index 0000000000000000000000000000000000000000..8844c020ec8e8106b0bf4bc20d705736aa003504 GIT binary patch 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zscvp+_W7l)_eBfKe_3QdvlxF~{I@90e-gM!fM|yL{u}Y}+J*f8(AiJ4f71P5)A&!w z{vUDazsL69a{E7x1ONZ&WI~*NaL%u3p|g#TxBS=2)@chN{luhPTr#m1zZSqZhdJ4~ z2geSZMvmnm;$u&Kc(T7$D$Z5+JUh(UwBy%*xn@yGKjSH!N({*EuL&sjFgKLo`+yZ# RICwz7@Rr%%)%y3J{$H4^#ee_+ literal 0 HcmV?d00001 diff --git a/ppocr/utils/loggers/__init__.py b/ppocr/utils/loggers/__init__.py index b1e92f734e..fd1845c093 100644 --- a/ppocr/utils/loggers/__init__.py +++ b/ppocr/utils/loggers/__init__.py @@ -1,3 +1,4 @@ from .vdl_logger import VDLLogger from .wandb_logger import WandbLogger +from .mlflow_logger import MlflowLogger from .loggers import Loggers diff --git a/ppocr/utils/loggers/loggers.py b/ppocr/utils/loggers/loggers.py index 2601466208..381d860a6a 100644 --- a/ppocr/utils/loggers/loggers.py +++ b/ppocr/utils/loggers/loggers.py @@ -1,5 +1,5 @@ from .wandb_logger import WandbLogger - +from .mlflow_logger import MlflowLogger class Loggers(object): def __init__(self, loggers): super().__init__() diff --git a/ppocr/utils/loggers/mlflow_logger.py b/ppocr/utils/loggers/mlflow_logger.py new file mode 100644 index 0000000000..fd5f4c7138 --- /dev/null +++ b/ppocr/utils/loggers/mlflow_logger.py @@ -0,0 +1,84 @@ +import os +from .base_logger import BaseLogger +from typing import Dict, Optional + +class MlflowLogger(BaseLogger): + def __init__(self, + save_dir: str, + exp_name: Optional[str] = None, + run_name: Optional[str] = None, + tags: Optional[dict] = None, + params: Optional[dict] = None, + tracking_uri: Optional[str] = None, + **kwargs): + + self.import_mlflow() + self.save_dir = save_dir + self.exp_name = exp_name + self.run_name = run_name + self.tags = tags + self.params = params + self.tracking_uri = tracking_uri + + if self.tracking_uri is not None: + self.mlflow.set_tracking_uri(self.tracking_uri) + else: + mlflow_writer_path = "{}/mlflow".format(self.save_dir) + file_url = os.path.abspath(mlflow_writer_path) + self.mlflow.set_tracking_uri(file_url) + + if self.mlflow.get_experiment_by_name(self.exp_name) is None: + self.mlflow.create_experiment(self.exp_name) + + self.mlflow.set_experiment(self.exp_name) + + self._run = None + _ = self.run + + if self.run_name is not None: + self._run = self.mlflow.start_run(run_name=self.run_name) + self.mlflow.set_tag('mlflow.runName', self.run_name) + if self.tags is not None: + self.mlflow.set_tags(self.tags) + if self.params is not None: + self.mlflow.log_params(self.params) + + + def import_mlflow(self) -> None: + try: + import mlflow + import mlflow.paddle as mlflow_paddle + except ImportError: + raise ImportError( + 'Please run "pip install mlflow" to install mlflow') + self.mlflow = mlflow + self.mlflow_paddle = mlflow_paddle + + @property + def run(self): + return + + def log_metrics(self, metrics, prefix=None, step=None): + if not prefix: + prefix = "" + updated_metrics = {prefix.lower() + "/" + k: v for k, v in metrics.items()} + self.mlflow.log_metrics(updated_metrics, step=step) + + def log_model(self, is_best, prefix, metadata=None): + model_path = os.path.join(self.save_dir, prefix + '.pdparams') + config_path = os.path.join(self.save_dir, "config.yml") + if is_best: + artifact_path = '{}'.format(prefix) + else: + artifact_path = '{}'.format(prefix) + + self.mlflow.log_artifact(model_path, artifact_path=artifact_path) + self.mlflow.log_artifact(config_path, artifact_path=artifact_path) + + if metadata is not None: + metadata_path = os.path.join(artifact_path, "epoch_" + str(metadata["best_epoch"]) + ".json") + self.mlflow.log_dict(metadata, artifact_file=metadata_path) + + def close(self): + self.mlflow.end_run() + return diff --git a/tools/program.py b/tools/program.py index afb8a47254..74c5288266 100755 --- a/tools/program.py +++ b/tools/program.py @@ -33,7 +33,7 @@ from ppocr.utils.save_load import save_model from ppocr.utils.utility import print_dict, AverageMeter from ppocr.utils.logging import get_logger -from ppocr.utils.loggers import VDLLogger, WandbLogger, Loggers +from ppocr.utils.loggers import VDLLogger, WandbLogger, Loggers, MlflowLogger from ppocr.utils import profiler from ppocr.data import build_dataloader @@ -679,6 +679,16 @@ def preprocess(is_train=False): wandb_params.update({'save_dir': save_model_dir}) log_writer = WandbLogger(**wandb_params, config=config) loggers.append(log_writer) + if ('use_mlflow' in config['Global'] and + config['Global']['use_mlflow']) or 'mlflow' in config: + save_model_dir = config['Global']['save_model_dir'] + if "mlflow" in config: + mlflow_params = config['mlflow'] + else: + mlflow_params = dict() + mlflow_params.update({'save_dir': save_model_dir}) + log_writer = MlflowLogger(**mlflow_params, config=config) + loggers.append(log_writer) else: log_writer = None print_dict(config, logger)