Notify users after training (either success or failure)

src/autotrain/trainers/clm/__main__.py

@@ -27,6 +27,7 @@
 from autotrain.trainers.clm import utils
 from autotrain.trainers.clm.callbacks import LoadBestPeftModelCallback, SavePeftModelCallback
 from autotrain.trainers.clm.params import LLMTrainingParams
+from autotrain.trainers.common import pause_space
 from autotrain.utils import monitor
 
 
@@ -494,16 +495,7 @@ def train(config):
             )
 
     if PartialState().process_index == 0:
-        if "SPACE_ID" in os.environ:
-            # shut down the space
-            logger.info("Pausing space...")
-            api = HfApi(token=config.token)
-            api.pause_space(repo_id=os.environ["SPACE_ID"])
-
-        if "ENDPOINT_ID" in os.environ:
-            # shut down the endpoint
-            logger.info("Pausing endpoint...")
-            utils.pause_endpoint(config)
+        pause_space(config)
 
 
 if __name__ == "__main__":

src/autotrain/trainers/common.py

@@ -3,11 +3,42 @@
 """
 import os
 
+import requests
+from huggingface_hub import HfApi
 from pydantic import BaseModel
 
 from autotrain import logger
 
 
+def pause_endpoint(params):
+    endpoint_id = os.environ["ENDPOINT_ID"]
+    username = endpoint_id.split("/")[0]
+    project_name = endpoint_id.split("/")[1]
+    api_url = f"https://api.endpoints.huggingface.cloud/v2/endpoint/{username}/{project_name}/pause"
+    headers = {"Authorization": f"Bearer {params.token}"}
+    r = requests.post(api_url, headers=headers, timeout=120)
+    return r.json()
+
+
+def pause_space(params):
+    if "SPACE_ID" in os.environ:
+        # shut down the space
+        logger.info("Pausing space...")
+        api = HfApi(token=params.token)
+        success_message = f"Your training run was successful! [Check out your trained model here](https://huggingface.co/{params.repo_id})"
+        api.create_discussion(
+            repo_id=os.environ["SPACE_ID"],
+            title="Your training has finished successfully ✅",
+            description=success_message,
+            repo_type="space",
+        )
+        api.pause_space(repo_id=os.environ["SPACE_ID"])
+    if "ENDPOINT_ID" in os.environ:
+        # shut down the endpoint
+        logger.info("Pausing endpoint...")
+        pause_endpoint(params)
+
+
 class AutoTrainParams(BaseModel):
     """
     Base class for all AutoTrain parameters.

src/autotrain/trainers/dreambooth/__main__.py

@@ -15,9 +15,9 @@
     SlicedAttnAddedKVProcessor,
 )
 from diffusers.models.lora import LoRALinearLayer
-from huggingface_hub import HfApi, snapshot_download
+from huggingface_hub import snapshot_download
 
-from autotrain import logger
+from autotrain.trainers.common import pause_space
 from autotrain.trainers.dreambooth import utils
 from autotrain.trainers.dreambooth.datasets import DreamBoothDataset, collate_fn
 from autotrain.trainers.dreambooth.params import DreamBoothTrainingParams
@@ -328,14 +328,7 @@ def load_model_hook(models, input_dir):
     if config.push_to_hub:
         trainer.push_to_hub()
 
-    if "SPACE_ID" in os.environ:
-        # remove config.image_path directory if it exists
-        if os.path.exists(config.image_path):
-            os.system(f"rm -rf {config.image_path}")
-        # shut down the space
-        logger.info("Pausing space...")
-        api = HfApi(token=config.token)
-        api.pause_space(repo_id=os.environ["SPACE_ID"])
+    pause_space(config)
 
 
 if __name__ == "__main__":

src/autotrain/trainers/generic/__main__.py

@@ -1,10 +1,8 @@
 import argparse
 import json
-import os
-
-from huggingface_hub import HfApi
 
 from autotrain import logger
+from autotrain.trainers.common import pause_space
 from autotrain.trainers.generic import utils
 from autotrain.trainers.generic.params import GenericParams
 from autotrain.utils import monitor
@@ -37,16 +35,7 @@ def run(config):
     logger.info("Running command...")
     utils.run_command(config)
 
-    if "SPACE_ID" in os.environ:
-        # shut down the space
-        logger.info("Pausing space...")
-        api = HfApi(token=config.token)
-        api.pause_space(repo_id=os.environ["SPACE_ID"])
-
-    if "ENDPOINT_ID" in os.environ:
-        # shut down the endpoint
-        logger.info("Pausing endpoint...")
-        utils.pause_endpoint(config)
+    pause_space(config)
 
 
 if __name__ == "__main__":

src/autotrain/trainers/image_classification/__main__.py

@@ -14,6 +14,7 @@
 )
 
 from autotrain import logger
+from autotrain.trainers.common import pause_space
 from autotrain.trainers.image_classification import utils
 from autotrain.trainers.image_classification.params import ImageClassificationParams
 
@@ -163,6 +164,9 @@ def train(config):
             api.create_repo(repo_id=config.repo_id, repo_type="model")
             api.upload_folder(folder_path=config.project_name, repo_id=config.repo_id, repo_type="model")
 
+    if PartialState().process_index == 0:
+        pause_space(config)
+
 
 if __name__ == "__main__":
     args = parse_args()

src/autotrain/trainers/seq2seq/__main__.py

@@ -23,6 +23,7 @@
 )
 
 from autotrain import logger
+from autotrain.trainers.common import pause_space
 from autotrain.trainers.seq2seq import utils
 from autotrain.trainers.seq2seq.dataset import Seq2SeqDataset
 from autotrain.trainers.seq2seq.params import Seq2SeqParams
@@ -238,16 +239,7 @@ def train(config):
             )
 
     if PartialState().process_index == 0:
-        if "SPACE_ID" in os.environ:
-            # shut down the space
-            logger.info("Pausing space...")
-            api = HfApi(token=config.token)
-            api.pause_space(repo_id=os.environ["SPACE_ID"])
-
-        if "ENDPOINT_ID" in os.environ:
-            # shut down the endpoint
-            logger.info("Pausing endpoint...")
-            utils.pause_endpoint(config)
+        pause_space(config)
 
 
 if __name__ == "__main__":

src/autotrain/trainers/tabular/__main__.py

@@ -13,6 +13,7 @@
 from sklearn.compose import ColumnTransformer
 
 from autotrain import logger
+from autotrain.trainers.common import pause_space
 from autotrain.trainers.tabular import utils
 from autotrain.trainers.tabular.params import TabularParams
 from autotrain.utils import monitor
@@ -329,16 +330,7 @@ def train(config):
         api.create_repo(repo_id=config.repo_id, repo_type="model", private=True)
         api.upload_folder(folder_path=config.project_name, repo_id=config.repo_id, repo_type="model")
 
-    if "SPACE_ID" in os.environ:
-        # shut down the space
-        logger.info("Pausing space...")
-        api = HfApi(token=config.token)
-        api.pause_space(repo_id=os.environ["SPACE_ID"])
-
-    if "ENDPOINT_ID" in os.environ:
-        # shut down the endpoint
-        logger.info("Pausing endpoint...")
-        utils.pause_endpoint(config)
+    pause_space(config)
 
 
 if __name__ == "__main__":

src/autotrain/trainers/text_classification/__main__.py

@@ -16,6 +16,7 @@
 )
 
 from autotrain import logger
+from autotrain.trainers.common import pause_space
 from autotrain.trainers.text_classification import utils
 from autotrain.trainers.text_classification.dataset import TextClassificationDataset
 from autotrain.trainers.text_classification.params import TextClassificationParams
@@ -192,19 +193,14 @@ def train(config):
             logger.info("Pushing model to hub...")
             api = HfApi(token=config.token)
             api.create_repo(repo_id=config.repo_id, repo_type="model", private=True)
-            api.upload_folder(folder_path=config.project_name, repo_id=config.repo_id, repo_type="model")
+            api.upload_folder(
+                folder_path=config.project_name,
+                repo_id=config.repo_id,
+                repo_type="model",
+            )
 
     if PartialState().process_index == 0:
-        if "SPACE_ID" in os.environ:
-            # shut down the space
-            logger.info("Pausing space...")
-            api = HfApi(token=config.token)
-            api.pause_space(repo_id=os.environ["SPACE_ID"])
-
-        if "ENDPOINT_ID" in os.environ:
-            # shut down the endpoint
-            logger.info("Pausing endpoint...")
-            utils.pause_endpoint(config)
+        pause_space(config)
 
 
 if __name__ == "__main__":

src/autotrain/utils.py

@@ -280,12 +280,18 @@ def wrapper(*args, **kwargs):
             return func(*args, **kwargs)
         except Exception:
             if PartialState().process_index == 0:
-                logger.error(f"{func.__name__} has failed due to an exception:")
-                logger.error(traceback.format_exc())
+                error_message = f"""{func.__name__} has failed due to an exception: {traceback.format_exc()}"""
+                logger.error(error_message)
                 if "SPACE_ID" in os.environ:
                     # shut down the space
                     logger.info("Pausing space...")
                     api = HfApi(token=os.environ["HF_TOKEN"])
+                    api.create_discussion(
+                        repo_id=os.environ["SPACE_ID"],
+                        title="Your training has failed ❌",
+                        description=error_message,
+                        repo_type="space",
+                    )
                     api.pause_space(repo_id=os.environ["SPACE_ID"])
 
     return wrapper