Adds a FlyteCallback (#23759)

* initial flyte callback

* lint

* logs should still be saved to Flyte even if pandas isn't install (unlikely)

* cr - flyte team

* add docs for Flytecallback

* fix doc string - cr sgugger

* Apply suggestions from code review

cr - sgugger fix doc strings

Co-authored-by: Sylvain Gugger <[email protected]>

---------

Co-authored-by: Sylvain Gugger <[email protected]>

docs/source/en/main_classes/callback.mdx

@@ -39,6 +39,7 @@ By default a [`Trainer`] will use the following callbacks:
   installed.
 - [`~integrations.ClearMLCallback`] if [clearml](https://github.com/allegroai/clearml) is installed.
 - [`~integrations.DagsHubCallback`] if [dagshub](https://dagshub.com/) is installed.
+- [`~integrations.FlyteCallback`] if [flyte](https://flyte.org/) is installed.
 
 The main class that implements callbacks is [`TrainerCallback`]. It gets the
 [`TrainingArguments`] used to instantiate the [`Trainer`], can access that
@@ -79,6 +80,8 @@ Here is the list of the available [`TrainerCallback`] in the library:
 
 [[autodoc]] integrations.DagsHubCallback
 
+[[autodoc]] integrations.FlyteCallback
+
 ## TrainerCallback
 
 [[autodoc]] TrainerCallback

src/transformers/integrations.py

@@ -30,7 +30,7 @@
 import numpy as np
 
 from . import __version__ as version
-from .utils import flatten_dict, is_datasets_available, is_torch_available, logging
+from .utils import flatten_dict, is_datasets_available, is_pandas_available, is_torch_available, logging
 from .utils.versions import importlib_metadata
 
 
@@ -146,6 +146,16 @@ def is_codecarbon_available():
     return importlib.util.find_spec("codecarbon") is not None
 
 
+def is_flytekit_available():
+    return importlib.util.find_spec("flytekit") is not None
+
+
+def is_flyte_deck_standard_available():
+    if not is_flytekit_available():
+        return False
+    return importlib.util.find_spec("flytekitplugins.deck") is not None
+
+
 def hp_params(trial):
     if is_optuna_available():
         import optuna
@@ -1537,6 +1547,69 @@ def on_save(self, args, state, control, **kwargs):
             self._clearml_task.update_output_model(artifact_path, iteration=state.global_step, auto_delete_file=False)
 
 
+class FlyteCallback(TrainerCallback):
+    """A [`TrainerCallback`] that sends the logs to [Flyte](https://flyte.org/).
+    NOTE: This callback only works within a Flyte task.
+
+    Args:
+        save_log_history (`bool`, *optional*, defaults to `True`):
+            When set to True, the training logs are saved as a Flyte Deck.
+
+        sync_checkpoints (`bool`, *optional*, defaults to `True`):
+            When set to True, checkpoints are synced with Flyte and can be used to resume training in the case of an
+            interruption.
+
+    Example:
+
+    ```python
+    # Note: This example skips over some setup steps for brevity.
+    from flytekit import current_context, task
+
+
+    @task
+    def train_hf_transformer():
+        cp = current_context().checkpoint
+        trainer = Trainer(..., callbacks=[FlyteCallback()])
+        output = trainer.train(resume_from_checkpoint=cp.restore())
+    ```
+    """
+
+    def __init__(self, save_log_history: bool = True, sync_checkpoints: bool = True):
+        super().__init__()
+        if not is_flytekit_available():
+            raise ImportError("FlyteCallback requires flytekit to be installed. Run `pip install flytekit`.")
+
+        if not is_flyte_deck_standard_available() or not is_pandas_available():
+            logger.warning(
+                "Syncing log history requires both flytekitplugins-deck-standard and pandas to be installed. "
+                "Run `pip install flytekitplugins-deck-standard pandas` to enable this feature."
+            )
+            save_log_history = False
+
+        from flytekit import current_context
+
+        self.cp = current_context().checkpoint
+        self.save_log_history = save_log_history
+        self.sync_checkpoints = sync_checkpoints
+
+    def on_save(self, args, state, control, **kwargs):
+        if self.sync_checkpoints and state.is_world_process_zero:
+            ckpt_dir = f"checkpoint-{state.global_step}"
+            artifact_path = os.path.join(args.output_dir, ckpt_dir)
+
+            logger.info(f"Syncing checkpoint in {ckpt_dir} to Flyte. This may take time.")
+            self.cp.save(artifact_path)
+
+    def on_train_end(self, args, state, control, **kwargs):
+        if self.save_log_history:
+            import pandas as pd
+            from flytekit import Deck
+            from flytekitplugins.deck.renderer import TableRenderer
+
+            log_history_df = pd.DataFrame(state.log_history)
+            Deck("Log History", TableRenderer().to_html(log_history_df))
+
+
 INTEGRATION_TO_CALLBACK = {
     "azure_ml": AzureMLCallback,
     "comet_ml": CometCallback,
@@ -1547,6 +1620,7 @@ def on_save(self, args, state, control, **kwargs):
     "codecarbon": CodeCarbonCallback,
     "clearml": ClearMLCallback,
     "dagshub": DagsHubCallback,
+    "flyte": FlyteCallback,
 }