feat: replace traversal_paths with access_paths

server/clip_server/executors/clip_onnx.py

@@ -23,14 +23,19 @@ def __init__(
         device: Optional[str] = None,
         num_worker_preprocess: int = 4,
         minibatch_size: int = 32,
-        traversal_paths: str = '@r',
+        access_paths: str = '@r',
         model_path: Optional[str] = None,
         **kwargs,
     ):
         super().__init__(**kwargs)
 
         self._minibatch_size = minibatch_size
-        self._traversal_paths = traversal_paths
+        self._access_paths = access_paths
+        if 'traversal_paths' in kwargs:
+            warnings.warn(
+                f'`traversal_paths` is deprecated. Use `access_paths` instead.'
+            )
+            self._access_paths = kwargs['traversal_paths']
 
         self._pool = ThreadPool(processes=num_worker_preprocess)
 
@@ -105,11 +110,16 @@ async def rank(self, docs: 'DocumentArray', parameters: Dict, **kwargs):
 
     @requests
     async def encode(self, docs: 'DocumentArray', parameters: Dict = {}, **kwargs):
-        traversal_paths = parameters.get('traversal_paths', self._traversal_paths)
+        access_paths = parameters.get('access_paths', self._access_paths)
+        if 'traversal_paths' in parameters:
+            warnings.warn(
+                f'`traversal_paths` is deprecated. Use `access_paths` instead.'
+            )
+            access_paths = parameters['traversal_paths']
 
         _img_da = DocumentArray()
         _txt_da = DocumentArray()
-        for d in docs[traversal_paths]:
+        for d in docs[access_paths]:
             split_img_txt_da(d, _img_da, _txt_da)
 
         # for image

server/clip_server/executors/clip_tensorrt.py

@@ -1,5 +1,6 @@
+import warnings
 from multiprocessing.pool import ThreadPool
-from typing import Dict
+from typing import Optional, Dict
 
 import numpy as np
 from clip_server.executors.helper import (
@@ -21,15 +22,20 @@ def __init__(
         device: str = 'cuda',
         num_worker_preprocess: int = 4,
         minibatch_size: int = 32,
-        traversal_paths: str = '@r',
+        access_paths: str = '@r',
         **kwargs,
     ):
         super().__init__(**kwargs)
 
         self._pool = ThreadPool(processes=num_worker_preprocess)
 
         self._minibatch_size = minibatch_size
-        self._traversal_paths = traversal_paths
+        self._access_paths = access_paths
+        if 'traversal_paths' in kwargs:
+            warnings.warn(
+                f'`traversal_paths` is deprecated. Use `access_paths` instead.'
+            )
+            self._access_paths = kwargs['traversal_paths']
 
         self._device = device
 
@@ -80,11 +86,16 @@ async def rank(self, docs: 'DocumentArray', parameters: Dict, **kwargs):
 
     @requests
     async def encode(self, docs: 'DocumentArray', parameters: Dict = {}, **kwargs):
-        traversal_paths = parameters.get('traversal_paths', self._traversal_paths)
+        access_paths = parameters.get('access_paths', self._access_paths)
+        if 'traversal_paths' in parameters:
+            warnings.warn(
+                f'`traversal_paths` is deprecated. Use `access_paths` instead.'
+            )
+            access_paths = parameters['traversal_paths']
 
         _img_da = DocumentArray()
         _txt_da = DocumentArray()
-        for d in docs[traversal_paths]:
+        for d in docs[access_paths]:
             split_img_txt_da(d, _img_da, _txt_da)
 
         # for image

server/clip_server/executors/clip_torch.py

@@ -25,13 +25,18 @@ def __init__(
         jit: bool = False,
         num_worker_preprocess: int = 4,
         minibatch_size: int = 32,
-        traversal_paths: str = '@r',
+        access_paths: str = '@r',
         **kwargs,
     ):
         super().__init__(**kwargs)
 
         self._minibatch_size = minibatch_size
-        self._traversal_paths = traversal_paths
+        self._access_paths = access_paths
+        if 'traversal_paths' in kwargs:
+            warnings.warn(
+                f'`traversal_paths` is deprecated. Use `access_paths` instead.'
+            )
+            self._access_paths = kwargs['traversal_paths']
 
         if not device:
             self._device = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -90,11 +95,16 @@ async def rank(self, docs: 'DocumentArray', parameters: Dict, **kwargs):
 
     @requests
     async def encode(self, docs: 'DocumentArray', parameters: Dict = {}, **kwargs):
-        traversal_paths = parameters.get('traversal_paths', self._traversal_paths)
+        access_paths = parameters.get('access_paths', self._access_paths)
+        if 'traversal_paths' in parameters:
+            warnings.warn(
+                f'`traversal_paths` is deprecated. Use `access_paths` instead.'
+            )
+            access_paths = parameters['traversal_paths']
 
         _img_da = DocumentArray()
         _txt_da = DocumentArray()
-        for d in docs[traversal_paths]:
+        for d in docs[access_paths]:
             split_img_txt_da(d, _img_da, _txt_da)
 
         with torch.inference_mode():

tests/test_simple.py

@@ -130,6 +130,9 @@ def test_docarray_traversal(make_flow, inputs, port_generator):
     from jina import Client as _Client
 
     c = _Client(host=f'grpc://0.0.0.0', port=make_flow.port)
-    r = c.post(on='/', inputs=da, parameters={'traversal_paths': '@c'})
-    assert r[0].chunks.embeddings.shape[0] == len(inputs)
-    assert '__created_by_CAS__' not in r[0].tags
+    r1 = c.post(on='/', inputs=da, parameters={'traversal_paths': '@c'})
+    r2 = c.post(on='/', inputs=da, parameters={'access_paths': '@c'})
+    assert r1[0].chunks.embeddings.shape[0] == len(inputs)
+    assert '__created_by_CAS__' not in r1[0].tags
+    assert r2[0].chunks.embeddings.shape[0] == len(inputs)
+    assert '__created_by_CAS__' not in r2[0].tags