diff --git a/skin_cancer_classification.py b/skin_cancer_classification.py
index fbf7d51..61b4c54 100644
--- a/skin_cancer_classification.py
+++ b/skin_cancer_classification.py
@@ -29,7 +29,7 @@ Original file is located at
 import sys
 IN_COLAB = 'google.colab' in sys.modules
 
-# f IN_COLAB:
+# if IN_COLAB:
 #  !pip install pandas numpy matplotlib seaborn pillow scikit-learn tensorflow
 #  !pip install --upgrade kagglehub[pandas-datasets,hf-datasets]
 
@@ -241,6 +241,12 @@ from tensorflow.keras.applications import DenseNet121
 from tensorflow.keras.applications.densenet import preprocess_input
 from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
 from tensorflow.keras.models import Model
+from tensorflow.keras.optimizers import Adam
+
+gpus = tf.config.list_physical_devices('GPU')
+print("GPUs:", gpus)
+
+strategy = tf.distribute.MirroredStrategy()
 
 data_augmentation = tf.keras.Sequential([
     tf.keras.layers.RandomFlip("horizontal"),
@@ -265,7 +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
 
-model = Model(inputs=base_model.input, outputs=predictions)
+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
 
@@ -313,21 +321,7 @@ val_generator = val_datagen.flow_from_dataframe(
     seed=42
 )
 
-"""## 5. Compile the Model
-
-I will compile the model using the Adam optimizer, binary cross-entropy loss (suitable for binary classification), and track accuracy as a metric.
-"""
-
-
-
-from tensorflow.keras.optimizers import Adam
-
-model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
-
-"""## 6. Train the Model
-
-I will now train the model using the prepared data generators. I'll also add callbacks for early stopping to prevent overfitting and to save the best model.
-"""
+"""## 6. Train the Model"""
 
 from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
 
@@ -354,7 +348,7 @@ history = model.fit(
     class_weight=class_weights # Use class weights to handle imbalance
 )
 
-"""## X. Evaluation
+"""## 7. Evaluation
 
 ### Load best model
 """