diff --git a/run.sh b/run.sh
index 3143f52..cbd7eb9 100755
--- a/run.sh
+++ b/run.sh
@@ -16,7 +16,8 @@ source "$VENV_DIR/bin/activate"
 # 3. Install dependencies (lightweight, safe to re-run)
 echo "Installing dependencies..."
 pip install --upgrade pip
-pip install pandas numpy matplotlib seaborn pillow scikit-learn tensorflow
+pip install pandas numpy matplotlib seaborn pillow scikit-learn
+pip install install "tensorflow[and-cuda]"
 pip install --upgrade kagglehub[pandas-datasets,hf-datasets]
 
 python3 skin_cancer_classification.py
diff --git a/skin_cancer_classification.py b/skin_cancer_classification.py
index 3c41f77..d0cacd5 100644
--- a/skin_cancer_classification.py
+++ b/skin_cancer_classification.py
@@ -271,9 +271,9 @@ x = Dense(512, activation='relu')(x) # Added another Dense layer
 x = Dense(256, activation='relu')(x) # Existing Dense layer
 predictions = Dense(1, activation='sigmoid')(x) # Output layer for binary classification
 
-# with strategy.scope(): # Use all gpus
-model = Model(inputs=base_model.input, outputs=predictions)
-model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
+with strategy.scope(): # Use all gpus
+    model = Model(inputs=base_model.input, outputs=predictions)
+    model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
 
 """## 4. Data Generators