Welcome to the world of TT-NN and TT-Metalium, powerful tools for building neural networks and hardware programming in Python and C++. In this guide, we will explore how to set them up and start your journey into deep learning. Buckle up, and let’s dive in!

Step 1: Buying Hardware

Before you can start utilizing TT-NN and TT-Metalium, ensure you have the right hardware. You can purchase the recommended hardware from Tenstorrent.

Step 2: Installation

Follow the detailed instructions for installation in the documentation available at Installing TT-NN and TT-Metalium.

Step 3: Explore Model Demos

Once you’ve installed the libraries, familiarize yourself with the models through the demos available at Model Demos. This will help you understand the practical applications and capabilities of the libraries.

Step 4: Delve into the API Reference

The API reference documentation is your best friend in understanding the functions and capabilities of TT-NN and TT-Metalium. Access the API documentation for TT-NN at API Reference and for TT-Metalium at TT-Metalium API Reference.

Step 5: Start Building Models

Now that you have everything ready, start building your models using the defined layers and functions in both libraries. Refer to the Model Demos for inspiration and guidance on what’s possible.

Understanding Performance Metrics

The performance of models can be evaluated based on different metrics. Think of this as going to a fitness competition, where each contestant has specific attributes like speed, power, and endurance. For instance, the performance metrics you see in the documentation, like throughput and latency, are akin to a runner’s time and speed on the track:

• Throughput (tsu): How many tokens a model can generate in a certain time frame.
• Latency (ttft): The time it takes for the first token to be generated after input.

These measures allow you to compare the efficiency of various models and hardware setups, much like comparing athletes’ performances!

Troubleshooting

If you encounter issues during installation or usage, here are some troubleshooting tips:

• Ensure your hardware meets the specifications required for running TT-NN and TT-Metalium.
• Check your installation steps against the official documentation to confirm every item was correctly followed.
• Review the API documentation for updates or changes that may affect your implementation.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.