Client (#801)

setup.py

@@ -33,8 +33,11 @@
 
 TESTS_REQUIRE = ["pytest"]
 
+CLIENT_REQUIRES = ["requests", "loguru"]
+
 
 EXTRAS_REQUIRE = {
+    "base": INSTALL_REQUIRES,
     "dev": INSTALL_REQUIRES + QUALITY_REQUIRE + TESTS_REQUIRE,
     "quality": INSTALL_REQUIRES + QUALITY_REQUIRE,
     "docs": INSTALL_REQUIRES
@@ -45,6 +48,7 @@
         "sphinx-rtd-theme==0.4.3",
         "sphinx-copybutton",
     ],
+    "client": CLIENT_REQUIRES,
 }
 
 setup(

src/autotrain/__init__.py

@@ -25,12 +25,16 @@
 
 import warnings
 
-import torch._dynamo
 
-from autotrain.logging import Logger
+try:
+    import torch._dynamo
+
+    torch._dynamo.config.suppress_errors = True
+except ImportError:
+    pass
 
+from autotrain.logging import Logger
 
-torch._dynamo.config.suppress_errors = True
 
 warnings.filterwarnings("ignore", category=UserWarning, module="tensorflow")
 warnings.filterwarnings("ignore", category=UserWarning, module="transformers")

src/autotrain/app/api_routes.py

@@ -3,7 +3,8 @@
 
 from fastapi import APIRouter, Depends, HTTPException, Request, status
 from fastapi.responses import JSONResponse
-from huggingface_hub import HfApi
+from huggingface_hub import HfApi, constants
+from huggingface_hub.utils import build_hf_headers, get_session, hf_raise_for_status
 from pydantic import BaseModel, create_model, model_validator
 
 from autotrain import __version__, logger
@@ -569,6 +570,10 @@ def validate_params(cls, values):
         return values
 
 
+class JobIDModel(BaseModel):
+    jid: str
+
+
 api_router = APIRouter()
 
 
@@ -690,33 +695,16 @@ async def api_version():
     return {"version": __version__}
 
 
-@api_router.get("/logs", response_class=JSONResponse)
-async def api_logs(job_id: str, token: bool = Depends(api_auth)):
-    """
-    Fetch logs for a specific job.
-
-    Args:
-        job_id (str): The ID of the job for which logs are to be fetched.
-        token (bool, optional): Authentication token, defaults to the result of api_auth dependency.
-
-    Returns:
-        dict: A dictionary containing the logs, success status, and a message.
-    """
-    # project = AutoTrainProject(job_id=job_id, token=token)
-    # logs = project.get_logs()
-    return {"logs": "Not implemented yet", "success": False, "message": "Not implemented yet"}
-
-
-@api_router.get("/stop_training", response_class=JSONResponse)
-async def api_stop_training(job_id: str, token: bool = Depends(api_auth)):
+@api_router.post("/stop_training", response_class=JSONResponse)
+async def api_stop_training(job: JobIDModel, token: bool = Depends(api_auth)):
     """
     Stops the training job with the given job ID.
 
     This asynchronous function pauses the training job identified by the provided job ID.
     It uses the Hugging Face API to pause the space associated with the job.
 
     Args:
-        job_id (str): The ID of the job to stop.
+        job (JobIDModel): The job model containing the job ID.
         token (bool, optional): The authentication token, provided by dependency injection.
 
     Returns:
@@ -728,9 +716,59 @@ async def api_stop_training(job_id: str, token: bool = Depends(api_auth)):
         Exception: If there is an error while attempting to stop the training job.
     """
     hf_api = HfApi(token=token)
+    job_id = job.jid
     try:
         hf_api.pause_space(repo_id=job_id)
     except Exception as e:
         logger.error(f"Failed to stop training: {e}")
         return {"message": f"Failed to stop training for {job_id}: {e}", "success": False}
     return {"message": f"Training stopped for {job_id}", "success": True}
+
+
+@api_router.post("/logs", response_class=JSONResponse)
+async def api_logs(job: JobIDModel, token: bool = Depends(api_auth)):
+    """
+    Fetch logs for a given job.
+
+    This endpoint retrieves logs for a specified job by its job ID. It first obtains a JWT token
+    to authenticate the request and then fetches the logs from the Hugging Face API.
+
+    Args:
+        job (JobIDModel): The job model containing the job ID.
+        token (bool, optional): Dependency injection for API authentication. Defaults to Depends(api_auth).
+
+    Returns:
+        JSONResponse: A JSON response containing the logs, success status, and a message.
+
+    Raises:
+        Exception: If there is an error fetching the logs, the exception message is returned in the response.
+    """
+    job_id = job.jid
+    jwt_url = f"{constants.ENDPOINT}/api/spaces/{job_id}/jwt"
+    response = get_session().get(jwt_url, headers=build_hf_headers())
+    hf_raise_for_status(response)
+    jwt_token = response.json()["token"]  # works for 24h (see "exp" field)
+
+    # fetch the logs
+    logs_url = f"https://api.hf.space/v1/{job_id}/logs/run"
+
+    _logs = []
+    try:
+        with get_session().get(logs_url, headers=build_hf_headers(token=jwt_token), stream=True) as response:
+            hf_raise_for_status(response)
+            for line in response.iter_lines():
+                if not line.startswith(b"data: "):
+                    continue
+                line_data = line[len(b"data: ") :]
+                try:
+                    event = json.loads(line_data.decode())
+                except json.JSONDecodeError:
+                    continue  # ignore (for example, empty lines or `b': keep-alive'`)
+                _logs.append((event["timestamp"], event["data"]))
+
+        # convert logs to a string
+        _logs = "\n".join([f"{timestamp}: {data}" for timestamp, data in _logs])
+
+        return {"logs": _logs, "success": True, "message": "Logs fetched successfully"}
+    except Exception as e:
+        return {"logs": str(e), "success": False, "message": "Failed to fetch logs"}

src/autotrain/app/params.py

@@ -250,7 +250,8 @@ def _munge_common_params(self):
     def _munge_params_sent_transformers(self):
         _params = self._munge_common_params()
         _params["model"] = self.base_model
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
         if not self.using_hub_dataset:
             _params["sentence1_column"] = "autotrain_sentence1"
             _params["sentence2_column"] = "autotrain_sentence2"
@@ -291,7 +292,8 @@ def _munge_params_llm(self):
                 "rejected_text" if not self.api else "rejected_text_column", "rejected_text"
             )
             _params["train_split"] = self.train_split
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
 
         trainer = self.task.split(":")[1]
         if trainer != "generic":
@@ -321,7 +323,8 @@ def _munge_params_vlm(self):
             )
             _params["train_split"] = self.train_split
             _params["valid_split"] = self.valid_split
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
 
         trainer = self.task.split(":")[1]
         _params["trainer"] = trainer.lower()
@@ -335,7 +338,8 @@ def _munge_params_vlm(self):
     def _munge_params_text_clf(self):
         _params = self._munge_common_params()
         _params["model"] = self.base_model
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
         if not self.using_hub_dataset:
             _params["text_column"] = "autotrain_text"
             _params["target_column"] = "autotrain_label"
@@ -350,7 +354,8 @@ def _munge_params_text_clf(self):
     def _munge_params_extractive_qa(self):
         _params = self._munge_common_params()
         _params["model"] = self.base_model
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
         if not self.using_hub_dataset:
             _params["text_column"] = "autotrain_text"
             _params["question_column"] = "autotrain_question"
@@ -369,7 +374,8 @@ def _munge_params_extractive_qa(self):
     def _munge_params_text_reg(self):
         _params = self._munge_common_params()
         _params["model"] = self.base_model
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
         if not self.using_hub_dataset:
             _params["text_column"] = "autotrain_text"
             _params["target_column"] = "autotrain_label"
@@ -384,7 +390,8 @@ def _munge_params_text_reg(self):
     def _munge_params_token_clf(self):
         _params = self._munge_common_params()
         _params["model"] = self.base_model
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
         if not self.using_hub_dataset:
             _params["tokens_column"] = "autotrain_text"
             _params["tags_column"] = "autotrain_label"
@@ -400,7 +407,8 @@ def _munge_params_token_clf(self):
     def _munge_params_seq2seq(self):
         _params = self._munge_common_params()
         _params["model"] = self.base_model
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
         if not self.using_hub_dataset:
             _params["text_column"] = "autotrain_text"
             _params["target_column"] = "autotrain_label"
@@ -416,7 +424,8 @@ def _munge_params_seq2seq(self):
     def _munge_params_img_clf(self):
         _params = self._munge_common_params()
         _params["model"] = self.base_model
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
         if not self.using_hub_dataset:
             _params["image_column"] = "autotrain_image"
             _params["target_column"] = "autotrain_label"
@@ -432,7 +441,8 @@ def _munge_params_img_clf(self):
     def _munge_params_img_reg(self):
         _params = self._munge_common_params()
         _params["model"] = self.base_model
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
         if not self.using_hub_dataset:
             _params["image_column"] = "autotrain_image"
             _params["target_column"] = "autotrain_label"
@@ -448,7 +458,8 @@ def _munge_params_img_reg(self):
     def _munge_params_img_obj_det(self):
         _params = self._munge_common_params()
         _params["model"] = self.base_model
-        _params["log"] = "tensorboard"
+        if "log" not in _params:
+            _params["log"] = "tensorboard"
         if not self.using_hub_dataset:
             _params["image_column"] = "autotrain_image"
             _params["objects_column"] = "autotrain_objects"

src/autotrain/backends/spaces.py

@@ -41,6 +41,8 @@ def _create_readme(self):
         _readme += "colorTo: indigo\n"
         _readme += "sdk: docker\n"
         _readme += "pinned: false\n"
+        _readme += "tags:\n"
+        _readme += "- autotrain\n"
         _readme += "duplicated_from: autotrain-projects/autotrain-advanced\n"
         _readme += "---\n"
         _readme = io.BytesIO(_readme.encode())