Introduction

The future of AI development belongs not necessarily to those with the most powerful infrastructure but to those who can extract maximum value from available resources. This tutorial emphasizes CPU-based fine-tuning demonstrating that with intelligent resource management through NativeLink, impressive results can be achieved without expensive GPU or TPU infrastructure. As compute becomes increasingly costly, competitive advantage will shift toward teams that optimize resource efficiency rather than those deploying state-of-the-art hardware.

This guide demonstrates how to establish an optimized AI development pipeline by integrating several key technologies:

• Bazel Build System: For efficient repository management. A repository managed by Bazel allows your team to work in a unified codebase while maintaining clean separation of concerns.
• NativeLink: A remote execution system hosted in your cloud. With NativeLink's remote execution capabilities, you can leverage your cloud resources optimally without wasteful duplication of work.
• Hugging Face Transformers: For integrating with the rich ecosystem of open-source models that you can run locally or deploy anywhere. The transformers library also provides a sophisticated caching mechanism for optimizing loading model weights.

Setting Up Your Repository with Bazel

Bazel is a build system designed for repositories that allows you to organize code into logical components while maintaining dependency relationships. For AI workloads, this is particularly valuable as it lets you separate model definitions, data processing pipelines, training code, and inference services.

Prerequisites

1. A recent version of Bazel (installation instructions)
2. NativeLink Cloud Account (it’s free to get started and secure) or NativeLink 0.6.0 (Apache-licensed, open source is hard-mode)

Initial Setup

First, let's download all the files. From the folder where you want to download the files, run the following commands:

# Clone the entire repository
git clone <https://github.com/TraceMachina/nativelink-blogs.git>

# Navigate to the subdirectory
cd nativelink-blogs/finetuning_on_cpu

Here's a description of some of the files:

1. README.md - Instructions on how to connect/use NativeLink Cloud and how to run the code locally as well as remotely
2. requirements.lock - Ensures consistent Python dependencies across all environments
3. .bazelrc - Main Bazel configuration file setting global options for hermetic builds and remote execution
4. MODULE.bazel - Configures the project as a Bazel module, tells Bazel we'll need Python, pip and CPU-only PyTorch, and manages external dependencies