Add support for config command (#98)

* Add support for ai.config command

* Sort postprocessing methods

* Fix some warnings

Author: alonre24

redisai/client.py

@@ -145,9 +145,44 @@ def loadbackend(self, identifier: AnyStr, path: AnyStr) -> str:
         'OK'
         """
         args = builder.loadbackend(identifier, path)
-        res = self.execute_command(*args)
+        res = self.execute_command(args)
         return res if not self.enable_postprocess else processor.loadbackend(res)
 
+    def config(self, name: str, value: Union[str, int, None] = None) -> str:
+        """
+        Get/Set configuration item. Current available configurations are: BACKENDSPATH and MODEL_CHUNK_SIZE.
+        For more details, see: https://oss.redis.com/redisai/master/commands/#aiconfig.
+        If value is given - the configuration under name will be overriten.
+
+        Parameters
+        ----------
+        name: str
+            RedisAI config item to retreive/override (BACKENDSPATH / MODEL_CHUNK_SIZE).
+        value: Union[str, int]
+            Value to set the config item with (if given).
+
+        Returns
+        -------
+            The current configuration value if value is None,
+            'OK' if value was given and configuration overitten succeeded,
+            raise an exception otherwise
+
+
+        Example
+        -------
+        >>> con.config('MODEL_CHUNK_SIZE', 128 * 1024)
+        'OK'
+        >>> con.config('BACKENDSPATH', '/my/backends/path')
+        'OK'
+        >>> con.config('BACKENDSPATH')
+        '/my/backends/path'
+        >>> con.config('MODEL_CHUNK_SIZE')
+        '131072'
+        """
+        args = builder.config(name, value)
+        res = self.execute_command(args)
+        return res if not self.enable_postprocess or not isinstance(res, bytes) else processor.config(res)
+
     def modelstore(
         self,
         key: AnyStr,
@@ -209,6 +244,7 @@ def modelstore(
         ...              inputs=['a', 'b'], outputs=['mul'], tag='v1.0')
         'OK'
         """
+        chunk_size = self.config('MODEL_CHUNK_SIZE')
         args = builder.modelstore(
             key,
             backend,
@@ -220,6 +256,7 @@ def modelstore(
             tag,
             inputs,
             outputs,
+            chunk_size=chunk_size
         )
         res = self.execute_command(*args)
         return res if not self.enable_postprocess else processor.modelstore(res)

redisai/command_builder.py

@@ -8,7 +8,13 @@
 
 
 def loadbackend(identifier: AnyStr, path: AnyStr) -> Sequence:
-    return "AI.CONFIG LOADBACKEND", identifier, path
+    return f'AI.CONFIG LOADBACKEND {identifier} {path}'
+
+
+def config(name: str, value: Union[str, int, None] = None) -> Sequence:
+    if value is not None:
+        return f'AI.CONFIG {name} {value}'
+    return f'AI.CONFIG GET {name}'
 
 
 def modelstore(
@@ -22,6 +28,7 @@ def modelstore(
     tag: AnyStr,
     inputs: Union[AnyStr, List[AnyStr]],
     outputs: Union[AnyStr, List[AnyStr]],
+    chunk_size: int = 500 * 1024 * 1024
 ) -> Sequence:
     if name is None:
         raise ValueError("Model name was not given")
@@ -66,9 +73,7 @@ def modelstore(
         raise ValueError(
             "Inputs and outputs keywords should not be specified for this backend"
         )
-    chunk_size = 500 * 1024 * 1024  # TODO: this should be configurable.
     data_chunks = [data[i: i + chunk_size] for i in range(0, len(data), chunk_size)]
-    # TODO: need a test case for this
     args += ["BLOB", *data_chunks]
     return args
 

redisai/postprocessor.py

@@ -27,28 +27,28 @@ def tensorget(res, as_numpy, as_numpy_mutable, meta_only):
         rai_result = utils.list2dict(res)
         if meta_only is True:
             return rai_result
-        elif as_numpy_mutable is True:
+        if as_numpy_mutable is True:
             return utils.blob2numpy(
                 rai_result["blob"],
                 rai_result["shape"],
                 rai_result["dtype"],
                 mutable=True,
             )
-        elif as_numpy is True:
+        if as_numpy is True:
             return utils.blob2numpy(
                 rai_result["blob"],
                 rai_result["shape"],
                 rai_result["dtype"],
                 mutable=False,
             )
+
+        if rai_result["dtype"] == "STRING":
+            def target(b):
+                return b.decode()
         else:
-            if rai_result["dtype"] == "STRING":
-                def target(b):
-                    return b.decode()
-            else:
-                target = float if rai_result["dtype"] in ("FLOAT", "DOUBLE") else int
-            utils.recursive_bytetransform(rai_result["values"], target)
-            return rai_result
+            target = float if rai_result["dtype"] in ("FLOAT", "DOUBLE") else int
+        utils.recursive_bytetransform(rai_result["values"], target)
+        return rai_result
 
     @staticmethod
     def scriptget(res):
@@ -66,19 +66,20 @@ def infoget(res):
 # These functions are only doing decoding on the output from redis
 decoder = staticmethod(decoder)
 decoding_functions = (
+    "config",
+    "inforeset",
     "loadbackend",
-    "modelstore",
-    "modelset",
     "modeldel",
     "modelexecute",
     "modelrun",
-    "tensorset",
-    "scriptset",
-    "scriptstore",
+    "modelset",
+    "modelstore",
     "scriptdel",
-    "scriptrun",
     "scriptexecute",
-    "inforeset",
+    "scriptrun",
+    "scriptset",
+    "scriptstore",
+    "tensorset",
 )
 for fn in decoding_functions:
     setattr(Processor, fn, decoder)