updated with redis ai examples

Author: cfregly

mnist_model/pipeline_conda_environment.yaml

@@ -7,11 +7,11 @@ dependencies:
   - graphviz
   - pip:
     - grpcio>=1.0
-    - tensorflow==1.13.1
+    - tensorflow==1.12.0
     - pipeline-runtime==1.0.8
 # These are required
     - pandas
-    - mlflow==0.9.1
+    - mlflow
     - click==6.7
     - scikit-learn
     - pillow

mnist_model/pipeline_train.py

@@ -81,13 +81,6 @@ def on_train_end(self, logs={}):
         message = f'Best validation loss = {valid_loss:.4f} Training Loss = {train_loss:.2f} Validation accuracy = {100*valid_acc:.2f}%'
         self.report_stats(message)
 
-    def on_train_batch_begin(self, batch, logs={}):
-        pass
-
-    def on_train_batch_end(self, batch, logs={}):
-        pass
-
-
 class MLflowLogger(Callback):
     """
     Keras callback for logging metrics and final model with MLflow.
@@ -138,12 +131,6 @@ def on_train_end(self, *args, **kwargs):
         for name, value in zip(self._model.metrics_names, valid_res):
             mlflow.log_metric("valid_{}".format(name), value)
 
-    def on_train_batch_begin(self, batch, logs={}):
-        pass
-
-    def on_train_batch_end(self, batch, logs={}):
-        pass
-
 
 @click.command(help="Trains a Keras model mnist dataset."
                     "The model and its metrics are logged with mlflow.")

redis/RedisAI.ipynb

@@ -0,0 +1,9 @@
+def pre_process_3ch(image):
+    return image.float().div(255).unsqueeze(0)
+
+def pre_process_4ch(image):
+    return image.float().div(255)[:,:,:-1].contiguous().unsqueeze(0)
+
+def post_process(output):
+    # tf model has 1001 classes, hence negative 1
+    return output.max(1)[1] - 1

redis/RedisAI_PyTorch.ipynb

@@ -0,0 +1,9 @@
+def pre_process_3ch(image):
+    return image.float().div(255).unsqueeze(0)
+
+def pre_process_4ch(image):
+    return image.float().div(255)[:,:,:-1].contiguous().unsqueeze(0)
+
+def post_process(output):
+    # tf model has 1001 classes, hence negative 1
+    return output.max(1)[1] - 1

redis/RedisAI_TensorFlow.ipynb

@@ -0,0 +1,9 @@
+def pre_process_3ch(image):
+    return image.float().div(255).unsqueeze(0)
+
+def pre_process_4ch(image):
+    return image.float().div(255)[:,:,:-1].contiguous().unsqueeze(0)
+
+def post_process(output):
+    # tf model has 1001 classes, hence negative 1
+    return output.max(1)[1] - 1