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docs: docs website + installation (#778)
* feat: WIP doc (vibe started and iterated) * install from scratch docs * caddyfile.example * gitignore * authentik script * authentik script * authentik script * llm doc * authentik ongoing * more daily setup logs * doc website * gpu self hosted setup guide (no-mistakes) * doc review round * doc review round * doc review round * update doc site sidebars * feat(docs): add mermaid diagram support * docs polishing * live pipeline doc * move pipeline dev docs to dev docs location * doc pr review iteration * dockerfile healthcheck * docs/pr-comments * remove jwt comment * llm suggestion * pr comments * pr comments * document auto migrations * cleanup docs --------- Co-authored-by: Mathieu Virbel <mat@meltingrocks.com> Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
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docs/docs/installation/modal-setup.md
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docs/docs/installation/modal-setup.md
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sidebar_position: 4
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title: Modal.com Setup
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---
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# Modal.com Setup
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This page covers Modal.com GPU setup in detail. For the complete deployment guide, see [Deployment Guide](./overview).
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Reflector uses [Modal.com](https://modal.com) for GPU-accelerated audio processing. This guide walks you through deploying the required GPU functions.
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## What is Modal.com?
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Modal is a serverless GPU platform. You deploy Python code that runs on their GPUs, and pay only for actual compute time. Reflector uses Modal for:
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- **Transcription**: Whisper model for speech-to-text
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- **Diarization**: Pyannote model for speaker identification
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## Prerequisites
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1. **Modal.com account** - Sign up at https://modal.com (free tier available)
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2. **HuggingFace account** - Required for Pyannote diarization models:
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- Create account at https://huggingface.co
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- Accept **both** Pyannote licenses:
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- https://huggingface.co/pyannote/speaker-diarization-3.1
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- https://huggingface.co/pyannote/segmentation-3.0
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- Generate access token at https://huggingface.co/settings/tokens
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## Deployment
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**Location: YOUR LOCAL COMPUTER (laptop/desktop)**
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Modal CLI requires browser authentication, so this must run on a machine with a browser - not on a headless server.
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### Install Modal CLI
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```bash
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uv tool install modal
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```
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### Authenticate with Modal
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```bash
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modal setup
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```
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This opens your browser for authentication. Complete the login flow.
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### Clone Repository and Deploy
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```bash
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git clone https://github.com/monadical-sas/reflector.git
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cd reflector/gpu/modal_deployments
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./deploy-all.sh --hf-token YOUR_HUGGINGFACE_TOKEN
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```
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Or run interactively (script will prompt for token):
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```bash
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./deploy-all.sh
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```
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### What the Script Does
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1. **Prompts for HuggingFace token** - Needed to download the Pyannote diarization model
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2. **Generates API key** - Creates a secure random key for authenticating requests to GPU functions
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3. **Creates Modal secrets**:
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- `hf_token` - Your HuggingFace token
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- `reflector-gpu` - The generated API key
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4. **Deploys GPU functions** - Transcriber (Whisper) and Diarizer (Pyannote)
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5. **Outputs configuration** - Prints URLs and API key to console
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### Example Output
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```
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==========================================
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Reflector GPU Functions Deployment
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==========================================
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Generating API key for GPU services...
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Creating Modal secrets...
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-> Creating secret: hf_token
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-> Creating secret: reflector-gpu
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Deploying transcriber (Whisper)...
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-> https://yourname--reflector-transcriber-web.modal.run
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Deploying diarizer (Pyannote)...
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-> https://yourname--reflector-diarizer-web.modal.run
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==========================================
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Deployment complete!
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==========================================
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Copy these values to your server's server/.env file:
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# --- Modal GPU Configuration ---
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TRANSCRIPT_BACKEND=modal
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TRANSCRIPT_URL=https://yourname--reflector-transcriber-web.modal.run
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TRANSCRIPT_MODAL_API_KEY=abc123...
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DIARIZATION_BACKEND=modal
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DIARIZATION_URL=https://yourname--reflector-diarizer-web.modal.run
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DIARIZATION_MODAL_API_KEY=abc123...
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# --- End Modal Configuration ---
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```
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Copy the output and paste it into your `server/.env` file on your server.
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## Costs
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Modal charges based on GPU compute time:
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- Functions scale to zero when not in use (no cost when idle)
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- You only pay for actual processing time
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- Free tier includes $30/month of credits
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Typical costs for audio processing:
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- Transcription: ~$0.01-0.05 per minute of audio
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- Diarization: ~$0.02-0.10 per minute of audio
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## Troubleshooting
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### "Modal CLI not installed"
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```bash
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uv tool install modal
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```
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### "Not authenticated with Modal"
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```bash
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modal setup
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# Complete browser authentication
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```
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### "Failed to create secret hf_token"
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- Verify your HuggingFace token is valid
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- Ensure you've accepted the Pyannote license
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- Token needs `read` permission
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### Deployment fails
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Check the Modal dashboard for detailed error logs:
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- Visit https://modal.com/apps
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- Click on the failed function
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- View build and runtime logs
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### Re-running deployment
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The script is safe to re-run. It will:
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- Update existing secrets if they exist
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- Redeploy functions with latest code
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- Output new configuration (API key stays the same if secret exists)
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## Manual Deployment (Advanced)
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If you prefer to deploy functions individually:
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```bash
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cd gpu/modal_deployments
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# Create secrets manually
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modal secret create hf_token HF_TOKEN=your-hf-token
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modal secret create reflector-gpu REFLECTOR_GPU_APIKEY=$(openssl rand -hex 32)
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# Deploy each function
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modal deploy reflector_transcriber.py
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modal deploy reflector_diarizer.py
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```
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## Monitoring
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View your deployed functions and their usage:
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- **Modal Dashboard**: https://modal.com/apps
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- **Function logs**: Click on any function to view logs
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- **Usage**: View compute time and costs in the dashboard
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