Local Courseware Deployment

This project builds a student-friendly local lab environment for the courseware with a small control surface:

• ./deploy-courseware.sh installs and configures the environment, then starts every managed service.
• ./destroy-courseware.sh stops the managed services, uninstalls courseware-managed Ollama, and removes the project-owned lab state.
• ./labctl provides day-two controls such as assets lab2, start, stop, status, urls, logs, and open kiln.

What It Installs

• Ollama
• llama.cpp
• TransformerLab, pinned to the classic single-user v0.28.2 release
• Open WebUI
• ChunkViz
• Embedding Atlas
• Promptfoo
• Unsloth Studio
• Kiln Desktop
• Course-specific support assets for lab 2 and lab 4

Supported Baselines

This build intentionally avoids the reference VM's hardware workarounds.

• macOS: Apple Silicon only, with at least 16 GB unified memory.
• Linux: Debian/Ubuntu-family only, with an NVIDIA GPU visible to nvidia-smi and at least 8 GB VRAM.
• WSL: treated as Linux, so the NVIDIA GPU must be exposed into WSL.

WSL Check

If you run this inside WSL, the launcher checks GPU readiness before Ansible starts.

If that check fails, fix WSL first:

• Install or update the NVIDIA Windows driver with WSL/CUDA support
• Run wsl --update in Windows PowerShell
• Run wsl --shutdown
• Reopen WSL and confirm nvidia-smi works

Important: nvidia-smi is only the driver check. Building CUDA-enabled llama.cpp also requires the Linux-side CUDA toolkit inside the distro.

On Linux and WSL, the first ./labctl up or ./labctl preflight run may prompt once for your sudo password so Ansible can install system packages.

On Ubuntu WSL x86_64, preflight now installs the Linux-side CUDA toolkit automatically if it is missing.

It first tries the distro package:

• sudo apt install -y nvidia-cuda-toolkit

If that package is unavailable or still does not expose nvcc, the installer falls back to NVIDIA's WSL-Ubuntu repository bootstrap for the toolkit only, not a Linux GPU driver.

If the automatic bootstrap still fails, verify:

• nvcc --version
• ls /usr/local/cuda/include/cuda_runtime.h

For non-Ubuntu WSL distros, install the CUDA toolkit manually before running the deploy script.

Standard Assumptions

• The host-side install path assumes modern local tooling, but TransformerLab itself is provisioned from a pinned classic single-user layout.
• TransformerLab is intentionally pinned to the older single-user v0.28.2 release because newer upstream releases changed the project structure and behavior in ways that break this courseware.
• This project does not rely on TransformerLab's upstream install.sh; the Ansible role provisions the pinned release directly so web assets, env layout, and runtime behavior stay reproducible.
• The courseware repairs installed TransformerLab Fastchat plugin manifests so Fastchat-gated features such as Model Architecture and Visualize Logprobs stay available on pinned installs.
• No Ollama models are pulled during ./labctl up; students pull models manually as part of the courseware.
• WhiteRabbitNeo assets are handled separately from ./labctl up and ./labctl preflight.
• Run ./labctl assets lab2 when you want to populate repo-local lab 2 assets in assets/lab2/ from Hugging Face.
• After base setup, run state/lab2/download_whiterabbitneo-gguf.sh to fetch only the Q4_K_M, Q8_0, and IQ2_M files from bartowski/WhiteRabbitNeo_WhiteRabbitNeo-V3-7B-GGUF and register local Ollama models WhiteRabbitNeo, WhiteRabbitNeo-Q4, WhiteRabbitNeo-Q8, and WhiteRabbitNeo-IQ2.
• TransformerLab and Unsloth homes are redirected into this project's state/ tree via symlinks.
• Managed web services bind for access from both Linux and the Windows side of WSL, while labctl urls still reports localhost-friendly URLs.
• The local Ansible bootstrap in .venv-ansible/ is machine-specific and will be recreated automatically if the folder is copied between hosts.

Lab URLs

After ./deploy-courseware.sh, run ./labctl urls.

Default endpoints:

• Ollama API: http://127.0.0.1:11434
• Open WebUI: http://127.0.0.1:8080
• TransformerLab: http://127.0.0.1:8338
• ChunkViz: http://127.0.0.1:3001
• Embedding Atlas: http://127.0.0.1:5055
• Unsloth Studio: http://127.0.0.1:8888
• Promptfoo UI: http://127.0.0.1:15500
• Wiki: http://127.0.0.1:80

Notes

• ./labctl up installs the environment and then starts every managed service.
• ./labctl versions shows the pinned TransformerLab and Ansible runtime versions used by this workspace.
• ./labctl assets lab2 is a separate manual step that clones the base WhiteRabbitNeo repo into assets/lab2/WhiteRabbitNeo-V3-7B and downloads the supported Q4_K_M, Q8_0, and IQ2_M GGUFs into assets/lab2/WhiteRabbitNeo_WhiteRabbitNeo-V3-7B-GGUF.
• TransformerLab is installed as a pinned single-user app and no default courseware-managed TransformerLab user is created automatically.
• ./labctl start core starts only ollama and open-webui.
• ./labctl start all starts every managed web service.
• ./labctl open kiln launches the Kiln desktop app installed into the project state.
• The scripted Promptfoo install drops a starter config at state/lab6/promptfoo.yaml.
• labctl start all now includes Promptfoo via promptfoo view and the cloned wiki app.
• Lab 2 includes state/lab2/download_whiterabbitneo-gguf.sh, which uses git + git lfs to pull only the supported WhiteRabbitNeo quants. Add --download-only if you want the files without Ollama registration.
• The wiki is cloned from https://git.zuccaro.me/bzuccaro/LLM-Labs.git into state/repos/LLM-Labs and started with npm.
• ./labctl down now uninstalls Ollama entirely when this project installed it, instead of only stopping the service.
• Unsloth Studio currently supports chat and data workflows on macOS; Linux/WSL remains the standard path for NVIDIA-backed training.