Fix comet_ml import and add ensure availability (#7933)

* Fix comet_ml import and add ensure availability

* Make isort happy

* Make flake8 happy

* Don't show comet_ml warn if COMET_MODE=DISABLED

* Make isort happy

src/transformers/integrations.py

@@ -2,16 +2,24 @@
 import math
 import os
 
+from .utils import logging
+
+
+logger = logging.get_logger(__name__)
+
 
-# Import 3rd-party integrations first:
+# Import 3rd-party integrations before ML frameworks:
 
 try:
     # Comet needs to be imported before any ML frameworks
     import comet_ml  # noqa: F401
 
-    # XXX: there should be comet_ml.ensure_configured(), like `wandb`, for now emulate it
-    comet_ml.Experiment(project_name="ensure_configured")
+    if comet_ml.config.get_config("comet.api_key"):
         _has_comet = True
+    else:
+        if os.getenv("COMET_MODE", "").upper() != "DISABLED":
+            logger.warning("comet_ml is installed but `COMET_API_KEY` is not set.")
+        _has_comet = False
 except (ImportError, ValueError):
     _has_comet = False
 
@@ -63,13 +71,9 @@ except ImportError:
 
 # No transformer imports above this point
 
-from .file_utils import is_torch_tpu_available
-from .trainer_callback import TrainerCallback
-from .trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun
-from .utils import logging
-
-
-logger = logging.get_logger(__name__)
+from .file_utils import is_torch_tpu_available  # noqa: E402
+from .trainer_callback import TrainerCallback  # noqa: E402
+from .trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun  # noqa: E402
 
 
 # Integration functions:

src/transformers/trainer.py

@@ -26,18 +26,9 @@ import warnings
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
-import numpy as np
-import torch
-from packaging import version
-from torch import nn
-from torch.utils.data.dataloader import DataLoader
-from torch.utils.data.dataset import Dataset
-from torch.utils.data.distributed import DistributedSampler
-from torch.utils.data.sampler import RandomSampler, SequentialSampler
 
-from .data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
-from .file_utils import WEIGHTS_NAME, is_datasets_available, is_in_notebook, is_torch_tpu_available
-from .integrations import (
+# Integrations must be imported before ML frameworks:
+from .integrations import (  # isort: split
     default_hp_search_backend,
     hp_params,
     is_comet_available,
@@ -49,6 +40,18 @@ from .integrations import (
     run_hp_search_optuna,
     run_hp_search_ray,
 )
+
+import numpy as np
+import torch
+from packaging import version
+from torch import nn
+from torch.utils.data.dataloader import DataLoader
+from torch.utils.data.dataset import Dataset
+from torch.utils.data.distributed import DistributedSampler
+from torch.utils.data.sampler import RandomSampler, SequentialSampler
+
+from .data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
+from .file_utils import WEIGHTS_NAME, is_datasets_available, is_in_notebook, is_torch_tpu_available
 from .modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
 from .modeling_utils import PreTrainedModel
 from .optimization import AdamW, get_linear_schedule_with_warmup

src/transformers/trainer_tf.py

@@ -6,12 +6,18 @@ import os
 import warnings
 from typing import Callable, Dict, Optional, Tuple
 
+
+# Integrations must be imported before ML frameworks:
+from .integrations import (  # isort: split
+    is_comet_available,
+    is_wandb_available,
+)
+
 import numpy as np
 import tensorflow as tf
 from packaging.version import parse
 from tensorflow.python.distribute.values import PerReplica
 
-from .integrations import is_comet_available, is_wandb_available
 from .modeling_tf_utils import TFPreTrainedModel
 from .optimization_tf import GradientAccumulator, create_optimizer
 from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, PredictionOutput, set_seed