Fine-Tuning Gemma 4 with Cloud Run Jobs: Unlocking Serverless GPU Power with NVIDIA RTX 6000 Pro for Pet Breed Classification

Introduction

In this article, we'll explore how to fine-tune a pre-trained Gemma 4 model for pet breed classification using Cloud Run Jobs and NVIDIA RTX 6000 Pro GPUs. Gemma 4 is a powerful AI model that can be used for various computer vision tasks, including image classification, object detection, and segmentation.

Prerequisites

• Google Cloud Platform (GCP) account
• NVIDIA RTX 6000 Pro GPU
• Cloud Run Jobs
• Gemma 4 model
• Python 3.9+
• TensorFlow 2.5+

Step 1: Create a GCP Project and Enable Cloud Run Jobs

To get started, create a new GCP project and enable Cloud Run Jobs. You can do this by following these steps:

gcloud projects create my-project
gcloud services enable run.googleapis.com
gcloud services enable aiplatform.googleapis.com

Step 2: Install Required Libraries

Next, install the required libraries using pip:

pip install tensorflow google-cloud-aiplatform google-cloud-run

Step 3: Upload the Gemma 4 Model to AI Platform

Upload the pre-trained Gemma 4 model to AI Platform using the following code:

from google.cloud import aiplatform

aiplatform.init(project='my-project', location='us-central1')

model = aiplatform.Model.upload(
    display_name='gemma-4',
    artifact_uri='gs://my-bucket/gemma-4.tar.gz',
    serving_container_image_uri='gcr.io/my-project/gemma-4:latest'
)

Step 4: Create a Cloud Run Job

Create a Cloud Run Job using the following code:

from google.cloud import run

job = run.Job(
    name='gemma-4-job',
    location='us-central1',
    container='gcr.io/my-project/gemma-4:latest',
    service_account='my-service-account',
    machine_type='n1-standard-8',
    accelerator_count=1,
    accelerator_type='NVIDIA_TESLA_V100'
)

Step 5: Fine-Tune the Gemma 4 Model

Fine-tune the Gemma 4 model using the following code:

from tensorflow import keras
from tensorflow.keras import layers

model = keras.models.load_model('gs://my-bucket/gemma-4.tar.gz')

# Define the fine-tuning dataset
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
validation_dataset = tf.data.Dataset.from_tensor_slices((x_val, y_val))

# Define the fine-tuning model
fine_tuning_model = keras.Model(inputs=model.inputs, outputs=layers.Dense(10, activation='softmax')(model.output))

# Compile the fine-tuning model
fine_tuning_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Train the fine-tuning model
fine_tuning_model.fit(train_dataset, epochs=10, validation_data=validation_dataset)

Step 6: Deploy the Fine-Tuned Model to Cloud Run

Deploy the fine-tuned model to Cloud Run using the following code:

from google.cloud import run

job = run.Job(
    name='gemma-4-job',
    location='us-central1',
    container='gcr.io/my-project/gemma-4:latest',
    service_account='my-service-account',
    machine_type='n1-standard-8',
    accelerator_count=1,
    accelerator_type='NVIDIA_TESLA_V100'
)

job.deploy()

Step 7: Test the Fine-Tuned Model

Test the fine-tuned model using the following code:

from google.cloud import aiplatform

model = aiplatform.Model.get('my-model')

# Define the test dataset
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))

# Make predictions using the fine-tuned model
predictions = model.predict(test_dataset)

Conclusion

In this article, we've explored how to fine-tune a pre-trained Gemma 4 model for pet breed classification using Cloud Run Jobs and NVIDIA RTX 6000 Pro GPUs. We've covered the steps to create a GCP project, enable Cloud Run Jobs, upload the Gemma 4 model to AI Platform, create a Cloud Run Job, fine-tune the model, deploy the fine-tuned model to Cloud Run, and test the fine-tuned model.

Example Use Cases

• Pet breed classification
• Image classification
• Object detection
• Segmentation

Future Work

• Explore other pre-trained models for pet breed classification
• Investigate the use of transfer learning for pet breed classification
• Develop a web application for pet breed classification using the fine-tuned model

Code Repository

The code for this article can be found in the following repository:

https://github.com/my-repo/gemma-4-fine-tuning

Acknowledgments

This article was made possible by the following resources:

• Google Cloud Platform
• NVIDIA RTX 6000 Pro GPU
• Gemma 4 model
• TensorFlow 2.5+
• Python 3.9+

License

This article is licensed under the MIT License.

Disclaimer

This article is for educational purposes only and should not be used for commercial purposes without permission from the authors.

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☕ Factual