Merge 3784d25 into 65f6005

examples/Huggingface_Transformers/Transformer_handler_generalized.py

@@ -37,7 +37,7 @@ def initialize(self, ctx):
         is initialized here.
         Args:
             ctx (context): It is a JSON Object containing information
-            pertaining to the model artefacts parameters.
+            pertaining to the model artifacts parameters.
         """
         self.manifest = ctx.manifest
         properties = ctx.system_properties

examples/diffusers/stable_diffusion_handler.py

@@ -26,7 +26,7 @@ def initialize(self, ctx):
         initialized here.
         Args:
             ctx (context): It is a JSON Object containing information
-            pertaining to the model artefacts parameters.
+            pertaining to the model artifacts parameters.
         """
         self.manifest = ctx.manifest
         properties = ctx.system_properties

examples/large_models/Huggingface_accelerate/custom_handler.py

@@ -36,7 +36,7 @@ def initialize(self, ctx):
         is initialized here.
         Args:
             ctx (context): It is a JSON Object containing information
-            pertaining to the model artefacts parameters.
+            pertaining to the model artifacts parameters.
         """
         self.manifest = ctx.manifest
         properties = ctx.system_properties

examples/large_models/Huggingface_pippy/pippy_handler.py

@@ -35,7 +35,7 @@ def initialize(self, ctx):
         partitioned into multiple stages each on one device using PiPPy.
         Args:
             ctx (context): It is a JSON Object containing information
-            pertaining to the model artefacts parameters.
+            pertaining to the model artifacts parameters.
         """
         super().initialize(ctx)
         self.manifest = ctx.manifest

examples/large_models/deepspeed/custom_handler.py

@@ -30,7 +30,7 @@ def initialize(self, ctx: Context):
         partitioned using DeepSpeed.
         Args:
             ctx (context): It is a JSON Object containing information
-            pertaining to the model artefacts parameters.
+            pertaining to the model artifacts parameters.
         """
         super().initialize(ctx)
         model_dir = ctx.system_properties.get("model_dir")

examples/large_models/deepspeed_mii/DeepSpeed_mii_handler.py

@@ -27,7 +27,7 @@ def initialize(self, ctx):
         initialized here.
         Args:
             ctx (context): It is a JSON Object containing information
-            pertaining to the model artefacts parameters.
+            pertaining to the model artifacts parameters.
         """
         self.manifest = ctx.manifest
         properties = ctx.system_properties

examples/large_models/inferentia2/inf2_handler.py

@@ -28,7 +28,7 @@ def initialize(self, ctx):
         partitioned into multiple stages each on one device using PiPPy.
         Args:
             ctx (context): It is a JSON Object containing information
-            pertaining to the model artefacts parameters.
+            pertaining to the model artifacts parameters.
         """
 
         self.manifest = ctx.manifest

kubernetes/kserve/kserve_wrapper/TSModelRepository.py

@@ -26,7 +26,7 @@ def __init__(
         Args:
             inference_address (str): The Inference Address present in the kserve side.
             management_address (str): The Management Address present in the kserve side.
-            model_dir (str): the directory of the model artefacts in the kserve side.
+            model_dir (str): the directory of the model artifacts in the kserve side.
         """
         super().__init__(model_dir)
         logging.info("TSModelRepo is initialized")

ts/handler_utils/micro_batching.py

@@ -139,7 +139,7 @@ def __call__(self, data, context):
         Args:
             data (list): The input data that needs to be made a prediction request on.
             context (Context): It is a JSON Object containing information pertaining to
-                               the model artefacts parameters.
+                               the model artifacts parameters.
 
         Returns:
             list : Returns a list of dictionary with the predicted response.

ts/torch_handler/base_handler.py

@@ -233,7 +233,7 @@ def _load_pickled_model(self, model_dir, model_file, model_pt_path):
         Loads the pickle file from the given model path.
 
         Args:
-            model_dir (str): Points to the location of the model artefacts.
+            model_dir (str): Points to the location of the model artifacts.
             model_file (.py): the file which contains the model class.
             model_pt_path (str): points to the location of the model pickle file.
 
@@ -323,7 +323,7 @@ def handle(self, data, context):
         Args:
             data (list): The input data that needs to be made a prediction request on.
             context (Context): It is a JSON Object containing information pertaining to
-                               the model artefacts parameters.
+                               the model artifacts parameters.
 
         Returns:
             list : Returns a list of dictionary with the predicted response.