feat: LoRAX On-Premise Deployment Playbook & DX Enhancements

[minhkhoango]

Problem: Addressing "Frozen Pain" in On-Premise LoRAX Adoption

This Pull Request introduces a comprehensive LoRAX Deployment Playbook designed to drastically improve the on-premise adoption experience, directly addressing documented pain points related to installation, configuration, and initial deployment.

My research into community feedback and internal discussions (e.g., 'Documentation / DX' as a problem category, complex VPC setups) highlighted significant friction for users attempting to deploy LoRAX on their own GPU infrastructure. Common barriers included:

• Fragmented or unclear documentation.
• Obscure infrastructure and dependency management issues.
• Pervasive user experience friction points (permissions, build-time OOMs, undocumented model compatibility).

Solution: The "Impossible-to-Fail" Deployment Playbook

This MVP is a meticulously battle-tested playbook (lorax_deployment_playbook.md) that transforms a multi-day, error-prone setup into a demonstrably more "impossible-to-fail" process. It serves as a comprehensive guide for first-time operators, covering:

1. Bulletproof Host Setup: Verified steps for NVIDIA drivers, Docker, nvidia-container-toolkit, and user permissions.
2. Streamlined LoRAX Deployment: Clear guidance for both pre-built image (e.g., ghcr.io/predibase/lorax:main) and building from source (with resolved build-time complexities).
3. Expanded Model Compatibility: Validated support for mistralai/Mistral-7B-Instruct-v0.1, meta-llama/Meta-Llama-3-8B-Instruct, and meta-llama/Llama-3.2-3B-Instruct on both pre-built and source-compiled images.
4. Comprehensive Troubleshooting: Context-specific fixes for common pitfalls like disk space issues, CUDA OOMs, and complex vLLM/transformers compatibility quirks (including gpt2, Qwen, and Bigcode model-specific challenges).
5. Optimized Build Process: Insights into MAX_JOBS tuning for faster kernel compilation and managing build resource spikes.

Key Impact & Value Proposition

• Reduced Time-to-Value: New users can deploy LoRAX and run their first LLM inference in minutes, not days.
• Lower Support Burden: Proactive problem prevention and clear troubleshooting reduce recurring installation-related support tickets.
• Enhanced Developer Experience (DX): Provides a truly frictionless onboarding path, increasing user adoption and satisfaction.
• Directly Addresses "Frozen Pain": A surgical fix for a high-frequency, high-pain problem that impacts every on-premise user.

Demonstration & Code

A brief video demonstration of the playbook in action is available here:
[YouTube Demo Video]

Note on Source: This playbook was developed and tested using a feature branch (feat/deployment-playbook-enhancements) of my personal LoRAX fork. This branch includes specific Dockerfile and dependency fixes (e.g., for msgspec in requirements.txt, punica_kernels submodule handling, MAX_JOBS consistency) that ensure the "build from source" path functions reliably for the documented models. My testing indicates some of these fixes may not yet be present on the main upstream branch, contributing to the very "frozen pain" this playbook addresses. I am happy to discuss these specific points during review.

Review Request

I welcome your feedback on this playbook. My aim is to make it merge-ready to immediately benefit the LoRAX community and align with Predibase's commitment to frictionless on-premise adoption.

[minhkhoango]

Hey @brightsparc @Narsil @alexsherstinsky, could one of you please 📦 Approve workflows on this PR so CI can run?