gpu

.woodpecker.yml

@@ -1,8 +1,8 @@
 steps:
-  "caffe":
+  "train":
     image: nvcr.io/nvidia/tensorflow:23.10-tf2-py3
     commands:
-      - echo "ci working................b. "
+      - echo "starting python script"
       - python run.py
 
 

run.py

@@ -46,18 +46,6 @@ y_test_categorical = keras.utils.to_categorical(
 
 print("model build")
 
-model = keras.Sequential([
-        keras.layers.Flatten(input_shape=(32,32,3)),
-        keras.layers.Dense(300, activation='relu'),
-        keras.layers.Dense(100, activation='relu'),
-        keras.layers.Dense(10, activation='sigmoid')    
-    ])
-
-model.compile(optimizer='SGD',
-              loss='categorical_crossentropy',
-              metrics=['accuracy'])
-
-model.fit(X_train_scaled, y_train_categorical, epochs=1)
 
 def get_model():
     model = keras.Sequential([
@@ -73,7 +61,17 @@ def get_model():
     return model
 
 with tf.device('/GPU:0'):
-    cpu_model = get_model()
-    cpu_model.fit(X_train_scaled, y_train_categorical, epochs=10)
+    model = keras.Sequential([
+            keras.layers.Flatten(input_shape=(32,32,3)),
+            keras.layers.Dense(3000, activation='relu'),
+            keras.layers.Dense(1000, activation='relu'),
+            keras.layers.Dense(10, activation='sigmoid')    
+        ])
+
+    model.compile(optimizer='SGD',
+                  loss='categorical_crossentropy',
+                  metrics=['accuracy'])
+    model.fit(X_train_scaled, y_train_categorical, epochs=10)
+    model.save('mymodel.keras')
     
 print("done")