Setting Up Ollama with TensorFlow Serving

What is Ollama?

What is TensorFlow Serving?

Why Combine Ollama with TensorFlow Serving?

• Simplicity: Use Ollama to create & manage LLMs without diving deep into complex coding.
• High Performance: Leverage TensorFlow Serving’s efficient management for scalability & low latency.
• Integration: Seamless API connections that let you embed AI intuitively into your applications.

Prerequisites

• A machine running Ubuntu 22.04 or higher.
• Sufficient RAM (16GB recommended) and disk space (12GB for Ollama basics).
• NVIDIA or AMD GPUs for uplifting performance (NVIDIA drivers & CUDA, if applicable).

Using Docker (Recommended)

1. Pulling the TensorFlow Serving Image: First, you'll want to grab the TensorFlow Serving docker image to run your models easily:
   1 2 bash docker pull tensorflow/serving
2. Verifying Installation: Make sure that it's installed by checking the available images:
   1 2 bash docker images

Without Docker

Installation

1. Download Ollama: First, you need to get the Ollama CLI on your system by running:
   1 2 3 bash curl -L https://ollama.com/download/ollama-linux-amd64 -o /usr/bin/ollama chmod +x /usr/bin/ollama
2. Create Service: You need to create a service so that Ollama runs in the background. Set it up as follows:
   1 2 bash sudo useradd -r -s /bin/false -m -d /usr/share/ollama ollama
3. Load the Service: Create a service file located at
   1 /usr/lib/systemd/system/ollama.service
   :
   1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 bash sudo tee /usr/lib/systemd/system/ollama.service > /dev/null <<EOF [Unit] Description=Ollama Service After=network-online.target [Service] ExecStart=/usr/bin/ollama serve User=ollama Group=ollama Restart=always RestartSec=3 Environment="OLLAMA_HOST=0.0.0.0" Environment="OLLAMA_ORIGINS=*" [Install] WantedBy=default.target EOF
4. Start Ollama Service: Once you've set it up, activate the service with:
   1 2 3 4 bash sudo systemctl daemon-reload sudo systemctl enable ollama sudo systemctl start ollama

Step 3: Deploy Your Model with TensorFlow Serving

1. Prepare Your Model: Get your TensorFlow model exported in the
   1 SavedModel
   format. This can be done conveniently in your TensorFlow training script.
2. Serving the Model: Start the TensorFlow Serving container, allowing it to read your model:
   1 2 3 4 5 bash docker run -t --rm -p 8501:8501 \ -v "$TESTDATA/saved_model_half_plus_two_cpu:/models/half_plus_two" \ -e MODEL_NAME=half_plus_two \ tensorflow/serving &
3. Testing the Model: Verify that your model is running correctly by making an inference request:
   1 2 3 4 bash curl -d '{"instances": [1.0, 2.0, 5.0]}' \ -H 'Content-Type: application/json' \ -X POST http://localhost:8501/v1/models/half_plus_two:predict
   If everything is set up right, you should receive a response confirming the predictions.

Using the API

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import requests

url = 'http://127.0.0.1:11434/predict'
input_data = {'input': 'My Question Here'}
response = requests.post(url, json=input_data)
print(response.json())

Handling User Queries

Best Practices & Troubleshooting

1. Monitor Performance: Keep an eye on performance metrics to ensure your models are running efficiently. Use TensorBoard as needed.
2. Routine Maintenance: Regular updates for your models can help you keep performance high.
3. Common Issues: If you run into errors like
   1 could not select device driver
   , ensure your GPUs are aligned properly and drivers are updated.

Elevate Your Engagement with Arsturn

Conclusion