Rename some field, and fix some bugs

docs/src/user/client-upload-pretrained-models.rst

@@ -12,7 +12,7 @@ Example:
                         task='IMAGE_CLASSIFICATION',
                         model_file_path='./examples/models/image_classification/TfFeedForward.py',
                         model_class='TfFeedForward',
-                        model_pretrained_params_id="b42cde03-0bc3-4b15-a276-4d95f6c88fa8.model",
+                        model_preload_file_path="b42cde03-0bc3-4b15-a276-4d95f6c88fa8.model",
                         dependencies={ModelDependency.TENSORFLOW: '1.12.0'}
                     )
 
@@ -53,7 +53,7 @@ FoodLg model upload
             task='IMAGE_CLASSIFICATION',
             model_file_path='./examples/models/image_object_detection/food_darknet_xception1.py',
             model_class='FoodDetection',
-            model_pretrained_params_id="model231.zip",
+            model_preload_file_path="model231.zip",
             dependencies={"keras": "2.2.4", "tensorflow": "1.12.0"}
         )
 

examples/models/image_object_detection/food_darknet_xception.py

@@ -93,15 +93,15 @@ def load_parameters(self, params):
 
         self.class_dict = {v: k for k, v in np.load(self.npy_index)[()].items()}
 
-        h5_models_base64 = params['h5_model_base64']
+        zip_file_base64 = params['zip_file_base64']
 
         self.xception_model = self._build_model(classes=self.classes, image_size=self.image_size)
 
         with tempfile.NamedTemporaryFile() as tmp:
             # Convert back to bytes & write to temp file
-            h5_models_bytes = base64.b64decode(h5_models_base64.encode('utf-8'))
+            zip_file_base64 = base64.b64decode(zip_file_base64.encode('utf-8'))
             with open(tmp.name, 'wb') as f:
-                f.write(h5_models_bytes)
+                f.write(zip_file_base64)
             with tempfile.TemporaryDirectory() as d:
                 dataset_zipfile = zipfile.ZipFile(tmp.name, 'r')
                 dataset_zipfile.extractall(path=d)

examples/models/image_object_detection/food_darknet_xception1.py

@@ -93,15 +93,15 @@ def load_parameters(self, params):
 
         self.class_dict = {v: k for k, v in np.load(self.npy_index)[()].items()}
 
-        h5_models_base64 = params['h5_model_base64']
+        zip_file_base64 = params['zip_file_base64']
 
         self.xception_model = self._build_model(classes=self.classes, image_size=self.image_size)
 
         with tempfile.NamedTemporaryFile() as tmp:
             # Convert back to bytes & write to temp file
-            h5_models_bytes = base64.b64decode(h5_models_base64.encode('utf-8'))
+            zip_file_base64 = base64.b64decode(zip_file_base64.encode('utf-8'))
             with open(tmp.name, 'wb') as f:
-                f.write(h5_models_bytes)
+                f.write(zip_file_base64)
             with tempfile.TemporaryDirectory() as d:
                 dataset_zipfile = zipfile.ZipFile(tmp.name, 'r')
                 dataset_zipfile.extractall(path=d)

examples/models/image_object_detection/food_darknet_xception2.py

@@ -93,15 +93,15 @@ def load_parameters(self, params):
 
         self.class_dict = {v: k for k, v in np.load(self.npy_index)[()].items()}
 
-        h5_models_base64 = params['h5_model_base64']
+        zip_file_base64 = params['zip_file_base64']
 
         self.xception_model = self._build_model(classes=self.classes, image_size=self.image_size)
 
         with tempfile.NamedTemporaryFile() as tmp:
             # Convert back to bytes & write to temp file
-            h5_models_bytes = base64.b64decode(h5_models_base64.encode('utf-8'))
+            zip_file_base64 = base64.b64decode(zip_file_base64.encode('utf-8'))
             with open(tmp.name, 'wb') as f:
-                f.write(h5_models_bytes)
+                f.write(zip_file_base64)
             with tempfile.TemporaryDirectory() as d:
                 dataset_zipfile = zipfile.ZipFile(tmp.name, 'r')
                 dataset_zipfile.extractall(path=d)

scripts/docker_swarm/build_images.sh

@@ -31,6 +31,7 @@ title "Using docker swarm"
 
 echo "using $APP_MODE docker files"
 if [[ $APP_MODE = "DEV" ]]
+then
     title "Building SINGA-Auto Admin's image..."
     docker build -t $SINGA_AUTO_IMAGE_ADMIN:$SINGA_AUTO_VERSION -f ./dockerfiles/dev_dockerfiles/admin.Dockerfile \
         --build-arg DOCKER_WORKDIR_PATH=$DOCKER_WORKDIR_PATH \

singa_auto/admin/view/model.py

@@ -61,12 +61,14 @@ def create_model(auth, params):
     if 'checkpoint_id' in params and params['checkpoint_id'] is not None:
 
         # if the checkpoint is not .model file, serialize it first
-        if params['checkpoint_id'].filename.split(".")[-1] != 'model':
-            h5_model_bytes = params['checkpoint_id'].read()
-            checkpoint_id = FileParamStore().save({'h5_model_base64': base64.b64encode(h5_model_bytes).decode('utf-8')})
+        if params['checkpoint_id'].filename.split(".")[-1] == 'zip':
+            zip_file_base64 = params['checkpoint_id'].read()
+            checkpoint_id = FileParamStore().save({'zip_file_base64': base64.b64encode(zip_file_base64).decode('utf-8')})
             feed_params['checkpoint_id'] = checkpoint_id
-        # if the model is trained with singa_auto, copy it to params files
-        else:
+
+        # if the model is trained with singa_auto (the model name is ended with 'model'), copy it to params files
+        # no need to encode it with b54 as it is already encoded in singa-auto after training
+        elif params['checkpoint_id'].filename.split(".")[-1] == 'model':
             with tempfile.NamedTemporaryFile() as f:
                 file_storage = params['checkpoint_id']
                 file_storage.save(f.name)
@@ -77,6 +79,12 @@ def create_model(auth, params):
                                               checkpoint_id)
                 shutil.copyfile(f.name, dest_file_path)
             feed_params['checkpoint_id'] = checkpoint_id
+        else:
+
+            # if the checkpoint name is not zip or model, return errormessage
+            return jsonify({'ErrorMsg': 'model preload file should be ended with "zip" or "model", '
+                                        'if it is a "*.model" file,'
+                                        'it should be the model_file saved after training by using singa-auto'}), 400
     with admin:
         return jsonify(admin.create_model(**feed_params))
 

singa_auto/client/client.py

@@ -230,7 +230,7 @@ def create_model(self,
                      task: str,
                      model_file_path: str,
                      model_class: str,
-                     model_pretrained_params_id: str = None,
+                     model_preload_file_path: str = None,
                      dependencies: ModelDependencies = None,
                      access_right: ModelAccessRight = ModelAccessRight.PRIVATE,
                      docker_image: str = None) -> Dict[str, Any]:
@@ -245,7 +245,7 @@ def create_model(self,
         :param model_class: The name of the model class inside the Python file. This class should implement :class:`singa_auto.model.BaseModel`
         :param dependencies: List of Python dependencies & their versions
         :param access_right: Model access right
-        :param model_pretrained_params_id: pretrained mdoel file
+        :param model_preload_file_path: pretrained mdoel file
         :param docker_image: A custom Docker image that extends ``singa_auto/singa_auto_worker``, publicly available on Docker Hub.
         :returns: Created model as dictionary
 
@@ -270,10 +270,10 @@ def create_model(self,
         }
         pretrained_files = {}
 
-        if model_pretrained_params_id is not None:
+        if model_preload_file_path is not None:
             pretrained_files = {'checkpoint_id':  (
-                                                    model_pretrained_params_id,
-                                                    open(model_pretrained_params_id, 'rb'),
+                                                    model_preload_file_path,
+                                                    open(model_preload_file_path, 'rb'),
                                                     'application/octet-stream')}
 
         files = {**model_files, **pretrained_files}

singa_auto/predictor/app.py

@@ -23,6 +23,8 @@
 from .predictor import Predictor
 from singa_auto.model import utils
 import traceback
+import json
+
 service_id = os.environ['SINGA_AUTO_SERVICE_ID']
 
 logger = logging.getLogger(__name__)
@@ -56,19 +58,32 @@ def predict():
             img for img in [img_store.read() for img_store in img_stores] if img
         ]
         print("img_stores", img_stores)
-        print("img_bytes", img_bytes)
         if not img_bytes:
             return jsonify({'ErrorMsg': 'No image provided'}), 400
+        print("img_bytes_first 10 bytes", img_bytes[0][:10])
+        queries = utils.dataset.load_images_from_bytes(img_bytes).tolist()
+        print("queries_sizes", len(queries))
+    elif request.get_json():
+        data = request.get_json()
+        queries = [data]
+    elif request.data:
+        data = json.loads(request.data)
+        print(data)
+        queries = [data]
     else:
-        return jsonify({'ErrorMsg': 'No image provided'}), 400
+        return jsonify({'ErrorMsg': 'data should be either at files or json payload'}), 400
     try:
         predictor = get_predictor()
-        queries = utils.dataset.load_images_from_bytes(img_bytes).tolist()
+        # this queries is type of List[Any]
         predictions = predictor.predict(queries)
-
+        print(type(predictions))
         if isinstance(predictions[0], list):
             # this is only for pandavgg demo as the frontend only accept the dictionary.
             return jsonify(predictions[0][0]), 200
+        elif isinstance(predictions, list) and isinstance(predictions[0], str):
+            # this is only match qa model,
+            print("this is only match qa model")
+            return predictions[0], 200
         else:
             return jsonify(predictions), 200
     except:

singa_auto/predictor/ensemble.py

@@ -52,7 +52,6 @@ def ensemble_probabilities(predictions: List[Any]) -> Any:
 def ensemble(predictions: List[Any]) -> Any:
     if len(predictions) == 0:
         return None
-    print("predictions is (in ensemble)", predictions)
     # Return some worker's predictions
     index = 0
     prediction = predictions[index]

singa_auto/predictor/predictor.py

@@ -68,6 +68,7 @@ def start(self):
 
     def predict(self, queries):
         worker_predictions_list = self._get_predictions_from_workers(queries)
+        print("Getting prediction list")
         predictions = self._combine_worker_predictions(worker_predictions_list)
         return predictions
 
@@ -124,8 +125,11 @@ def _get_predictions_from_workers(
 
         # Wait for at least 1 free worker
         worker_ids = []
+
         while len(worker_ids) == 0:
+            print("Getting free worker from redis...")
             worker_ids = self._redis_cache.get_workers()
+            time.sleep(0.5)
 
         # For each worker, send queries to worker
         pending_queries = set()  # {(query_id, worker_id)}
@@ -150,7 +154,7 @@ def _get_predictions_from_workers(
                 # Record prediction & mark as not pending
                 query_id_to_predictions[query_id].append(prediction)
                 pending_queries.remove((query_id, worker_id))
-
+            print("Getting prediction result from kafka...")
             time.sleep(PREDICT_LOOP_SLEEP_SECS)
 
         # Reorganize predictions

singa_auto/utils/service.py

@@ -31,13 +31,12 @@
 curr_time = datetime.now().strftime("%Y-%m-%d_%I.%M.%S.%p")
 
 
-
 def run_worker(meta_store, start_worker, stop_worker):
     service_id = os.environ['SINGA_AUTO_SERVICE_ID']
     service_type = os.environ['SINGA_AUTO_SERVICE_TYPE']
     container_id = os.environ.get('HOSTNAME', 'localhost')
-    configure_logging('{}-SvcID-{}'
-        .format(curr_time, service_id))
+    configure_logging('{}-SvcID-{}-ContainerID-{}'
+        .format(curr_time, service_id,container_id))
 
     def _sigterm_handler(_signo, _stack_frame):
         logger.warn("Terminal signal received: %s, %s" % (_signo, _stack_frame))

singa_auto/worker/inference.py

@@ -124,25 +124,35 @@ def _pull_job_info(self):
 
             inference_job = self._meta_store.get_inference_job(
                 worker.inference_job_id)
+
             if inference_job is None:
                 raise InvalidWorkerError(
                     'No such inference job with ID "{}"'.format(
                         worker.inference_job_id))
-            if inference_job.model_id:
-                model = self._meta_store.get_model(inference_job.model_id)
-                logger.info(f'Using checkpoint of the model "{model.name}"...')
-
-                self._proposal = Proposal.from_jsonable({
-                    "trial_no": 1,
-                    "knobs": {}
-                })
-                self._store_params_id = model.checkpoint_id
-            else:
 
-                trial = self._meta_store.get_trial(worker.trial_id)
-                if trial is None or trial.store_params_id is None:  # Must have model saved
+            trial = self._meta_store.get_trial(worker.trial_id)
+
+            # check if there are trained model saved
+            if trial is None or trial.store_params_id is None:
+
+                # if there are no train job, then check if there is checkpoint uplaoded
+                if inference_job.model_id:
+                    model = self._meta_store.get_model(inference_job.model_id)
+                    logger.info(f'Using checkpoint of the model "{model.name}"...')
+
+                    self._proposal = Proposal.from_jsonable({
+                        "trial_no": 1,
+                        "knobs": {}
+                    })
+                    self._store_params_id = model.checkpoint_id
+                else:
+
+                    # if there is no checkpoint id and no trained model saved
                     raise InvalidTrialError(
-                        'No saved trial with ID "{}"'.format(worker.trial_id))
+                        'No saved trial with ID "{}" and no checkpoint uploaded'.format(worker.trial_id))
+            else:
+
+                # create inference with trained parameters first
                 logger.info(f'Using trial "{trial.id}"...')
 
                 model = self._meta_store.get_model(trial.model_id)
@@ -183,6 +193,7 @@ def _predict(self, queries: List[Query]) -> List[Prediction]:
         try:
             predictions = self._model_inst.predict([x.query for x in queries])
         except:
+            print('Error while making predictions:')
             logger.error('Error while making predictions:')
             logger.error(traceback.format_exc())
             predictions = [None for x in range(len(queries))]