Do not force mlflow usage

dan/manager/training.py

@@ -8,7 +8,6 @@ import sys
 from datetime import date
 from time import time
 
-import mlflow
 import numpy as np
 import torch
 import torch.distributed as dist
@@ -22,10 +21,16 @@ from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
 from dan.manager.metrics import MetricManager
-from dan.mlflow import logging_metrics, logging_tags_metrics
 from dan.ocr.utils import LM_ind_to_str
 from dan.schedulers import DropoutScheduler
 
+try:
+    import mlflow
+
+    from dan.mlflow import logging_metrics, logging_tags_metrics
+except ImportError:
+    pass
+
 
 class GenericTrainingManager:
     def __init__(self, params):

dan/ocr/document/train.py

@@ -5,19 +5,28 @@ import random
 from copy import deepcopy
 from pathlib import Path
 
-import mlflow
 import numpy as np
 import torch
 import torch.multiprocessing as mp
 from torch.optim import Adam
 
+from dan import logger
 from dan.decoder import GlobalHTADecoder
 from dan.manager.ocr import OCRDataset, OCRDatasetManager
 from dan.manager.training import Manager
-from dan.mlflow import start_mlflow_run
 from dan.models import FCN_Encoder
 from dan.schedulers import exponential_dropout_scheduler
 from dan.transforms import aug_config
+from dan.utils import MLflowNotInstalled
+
+try:
+    import mlflow
+
+    MLFLOW = True
+    logger.info("MLflow Logging available.")
+    from dan.mlflow import start_mlflow_run
+except ImportError:
+    MLFLOW = False
 
 logger = logging.getLogger(__name__)
 
@@ -62,9 +71,9 @@ def get_config():
     Retrieve model configuration
     """
     dataset_name = "esposalles"
-    dataset_level = "record"
-    dataset_variant = "_debug"
-    dataset_path = ""
+    dataset_level = "page"
+    dataset_variant = ""
+    dataset_path = "/home/training_data/ATR_paragraph/Esposalles"
     params = {
         "mlflow": {
             "dataset_name": dataset_name,
@@ -218,7 +227,7 @@ def get_config():
         },
     }
 
-    return params
+    return params, dataset_name
 
 
 def serialize_config(config):
@@ -269,16 +278,14 @@ def run():
     Main program, training a new model, using a valid configuration
     """
 
-    config = get_config()
-    config_artifact = serialize_config(config)
-    labels_artifact = ""
-    dataset_name = config["mlflow"]["dataset_name"]
-    labels_path = (
-        Path(config_artifact["dataset_params"]["datasets"][dataset_name])
-        / "labels.json"
-    )
+    config, dataset_name = get_config()
 
-    if config["mlflow"]:
+    if MLFLOW and "mlflow" in config:
+        config_artifact = serialize_config(config)
+        labels_path = (
+            Path(config_artifact["dataset_params"]["datasets"][dataset_name])
+            / "labels.json"
+        )
         with start_mlflow_run(config["mlflow"]) as run:
             logger.info(f"Set tags to MLflow on {config['mlflow']['run_name']}")
             mlflow.set_tags({"Dataset": config["mlflow"]["dataset_name"]})
@@ -303,6 +310,11 @@ def run():
                 )
             else:
                 train_and_test(0, config, True)
+    elif "mlflow" in config:
+        logger.error(
+            "Cannot log to MLflow as the `mlflow` module was not found in your environment."
+        )
+        raise MLflowNotInstalled()
     else:
         if (
             config["training_params"]["use_ddp"]

dan/utils.py

@@ -18,6 +18,12 @@ SEM_MATCHING_TOKENS_STR = {
 SEM_MATCHING_TOKENS = {"ⓘ": "Ⓘ", "ⓓ": "Ⓓ", "ⓢ": "Ⓢ", "ⓒ": "Ⓒ", "ⓟ": "Ⓟ", "ⓐ": "Ⓐ"}
 
 
+class MLflowNotInstalled(Exception):
+    """
+    Raised when MLflow logging was requested but the module was not installed
+    """
+
+
 def randint(low, high):
     """
     call torch.randint to preserve random among dataloader workers