add fts_superglue pl_example graceful exit and horovod test condition

Author: speediedan

pl_examples/basic_examples/fts_superglue.py

@@ -38,15 +38,18 @@
 from datetime import datetime
 from typing import Any, Dict, List, Optional
 
-import datasets
 import torch
 from torch.utils.data import DataLoader
-from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
 
 import pytorch_lightning as pl
+from pl_examples import _HF_AVAILABLE
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.cli import instantiate_class, LightningCLI
 
+if _HF_AVAILABLE:
+    import datasets
+    from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
+
 TASK_NUM_LABELS = {"boolq": 2, "rte": 2}
 DEFAULT_TASK = "rte"
 
@@ -286,6 +289,9 @@ def add_arguments_to_parser(self, parser):
 
 
 def cli_main() -> None:
+    if not _HF_AVAILABLE:  # pragma: no cover
+        print("Running the fts_superglue example requires the `transformers` and `datasets` packages from Hugging Face")
+        return
     # every configuration of this example depends upon a shared set of defaults.
     default_config_file = os.path.join(os.path.dirname(__file__), "config", "fts", "fts_defaults.yaml")
     _ = CustLightningCLI(

tests/callbacks/test_finetuning_scheduler_callback.py

@@ -414,7 +414,7 @@ def test_finetuningscheduling_misconfiguration(tmpdir, callbacks: List[Callback]
     [
         pytest.param("ddp2", 2, None, marks=RunIf(min_gpus=2)),
         pytest.param("ddp_fully_sharded", 2, None, marks=RunIf(min_gpus=2)),
-        ("horovod", None, None),
+        pytest.param("horovod", None, None, marks=RunIf(min_gpus=2)),
         pytest.param("ddp", 2, "deepspeed_stage_2", marks=RunIf(deepspeed=True, min_gpus=2)),
     ],
 )