Merge pull request #13 from RedisAI/cleanup

Removed model utlities

.gitignore

@@ -2,4 +2,5 @@
 .pydevproject
 *.pyc
 .venv/
-redisai.egg-info
+redisai.egg-info
+.idea

README.md

@@ -8,7 +8,7 @@
 # RedisAI Python Client
 
 
-## Installing
+## Installation
 
 1. Install Redis 5.0 or above
 
@@ -20,6 +20,49 @@
 $ pip install redisai
 ```
 
-[RedisAI example repo](https://github.com/RedisAI/redisai-examples) shows few examples made using redisai-py under `python_client` section.
+4. Install serialization-deserialization utility (optional)
+```sh
+$ pip install ml2rt
+```
+
+[RedisAI example repo](https://github.com/RedisAI/redisai-examples) shows few examples made using redisai-py under `python_client` section. Checkout [ml2rt](https://github.com/hhsecond/ml2rt) for convenient functions those might help in converting models (sparkml, sklearn, xgboost to ONNX), serializing models to disk, loading it back to redisai-py etc.
+
+For a quick walk through, checkout this example
+
+```python
+from redisai import Client
+from redisai import Tensor, BlobTensor, DType, Device, Backend
+import ml2rt
+
+client = Client()
+client.tensorset('x', Tensor(DType.float, [2], [2, 3]))
+t = client.tensorget('x')
+print(t.value)
+
+model = mlut.load_model('test/testdata/graph.pb')
+client.tensorset('a', Tensor.scalar(DType.float, 2, 3))
+client.tensorset('b', Tensor.scalar(DType.float, 12, 10))
+client.modelset('m', Backend.tf,
+                Device.cpu,
+                input=['a', 'b'],
+                output='mul',
+                data=model)
+client.modelrun('m', ['a', 'b'], ['mul'])
+print(client.tensorget('mul').value)
+
+# Try with a script
+script = mlut.load_script('test/testdata/script.txt')
+client.scriptset('ket', Device.cpu, script)
+client.scriptrun('ket', 'bar', input=['a', 'b'], output='c')
+
+b1 = client.tensorget('c', as_type=BlobTensor)
+b2 = client.tensorget('c', as_type=BlobTensor)
+
+client.tensorset('d', BlobTensor(DType.float, b1.shape, b1, b2))
+
+tnp = b1.to_numpy()
+print(tnp)
+
+```
 
 

example.py

@@ -1,16 +1,15 @@
-from __future__ import print_function
-from redisai import Client, Tensor, ScalarTensor, \
+from redisai import Client, Tensor, \
     BlobTensor, DType, Device, Backend
-from redisai import model as raimodel
+import mlut
 
 client = Client()
 client.tensorset('x', Tensor(DType.float, [2], [2, 3]))
 t = client.tensorget('x')
 print(t.value)
 
-model = raimodel.Model.load('../RedisAI/examples/models/graph.pb')
-client.tensorset('a', ScalarTensor(DType.float, 2, 3))
-client.tensorset('b', ScalarTensor(DType.float, 12, 10))
+model = mlut.load_model('test/testdata/graph.pb')
+client.tensorset('a', Tensor.scalar(DType.float, 2, 3))
+client.tensorset('b', Tensor.scalar(DType.float, 12, 10))
 client.modelset('m', Backend.tf,
                 Device.cpu,
                 input=['a', 'b'],
@@ -20,18 +19,14 @@
 print(client.tensorget('mul').value)
 
 # Try with a script
-script = raimodel.Model.load('../RedisAI/examples/models/script.txt')
+script = mlut.load_script('test/testdata/script.txt')
 client.scriptset('ket', Device.cpu, script)
 client.scriptrun('ket', 'bar', input=['a', 'b'], output='c')
 
-b1 = client.tensorget('c', astype=BlobTensor)
-b2 = client.tensorget('c', astype=BlobTensor)
-bt = BlobTensor(DType.float, b1.shape, b1, b2)
-
-print(len(bytes(bt.blob)))
-print(bt.shape)
+b1 = client.tensorget('c', as_type=BlobTensor)
+b2 = client.tensorget('c', as_type=BlobTensor)
 
 client.tensorset('d', BlobTensor(DType.float, b1.shape, b1, b2))
 
 tnp = b1.to_numpy()
-client.tensorset('e', tnp)
+client.tensorset('e', tnp)

redisai/__init__.py

@@ -1,22 +1,2 @@
-from .version import __version__
-from .client import (Client, Tensor, BlobTensor, DType, Device, Backend)
-
-
-def save_model(*args, **kwargs):
-    """
-    Importing inside to avoid loading the TF/PyTorch/ONNX
-    into the scope unnecessary. This function wraps the
-    internal save model utility to make it user friendly
-    """
-    from .model import Model
-    Model.save(*args, **kwargs)
-
-
-def load_model(*args, **kwargs):
-    """
-    Importing inside to avoid loading the TF/PyTorch/ONNX
-    into the scope unnecessary. This function wraps the
-    internal load model utility to make it user friendly
-    """
-    from .model import Model
-    return Model.load(*args, **kwargs)
+from .version import __version__  # noqa
+from .client import (Client, Tensor, BlobTensor, DType, Device, Backend)  # noqa

redisai/client.py

@@ -9,7 +9,7 @@
     np = None
 
 try:
-    from typing import Union, Any, AnyStr, ByteString, Collection, Type
+    from typing import Union, Any, AnyStr, ByteString, Collection, Type  # noqa
 except ImportError:
     pass
 
@@ -165,7 +165,7 @@ def _to_numpy_type(t):
         }
         if t in mm:
             return mm[t]
-        return t
+        return t.lower()
 
     @classmethod
     def from_resp(cls, dtype, shape, value):
@@ -225,7 +225,7 @@ def tensorset(self, key, tensor):
         return self.execute_command(*args)
 
     def tensorget(self, key, as_type=Tensor, meta_only=False):
-        # type: (AnyStr, Type[Tensor], bool) -> Tensor
+        # type: (AnyStr, Type[Tensor], bool) -> Union[Tensor, BlobTensor]
         """
         Retrieve the value of a tensor from the server
         :param key: the name of the tensor

test-requirements.txt

@@ -1,6 +1,2 @@
 numpy
-torch
-tensorflow
-onnx
-skl2onnx
-pandas
+mlut

test/test.py

@@ -2,7 +2,7 @@
 import numpy as np
 import os.path
 from redisai import Client, DType, Backend, Device, Tensor, BlobTensor
-from redisai import load_model
+from mlut import load_model
 from redis.exceptions import ResponseError