From 399989ab39e8966e968b7a431eb71e776cb9ba6a Mon Sep 17 00:00:00 2001 From: Max Holland Date: Mon, 29 Dec 2025 12:37:18 +0000 Subject: [PATCH 1/8] fal --- DEPLOYMENT_ALTERNATIVES.md | 411 ++++++++++++++++++++++++++++++++++++ DEPLOY_TO_FAL_QUICKSTART.md | 265 +++++++++++++++++++++++ FAL_DEPLOYMENT_GUIDE.md | 81 +++++++ FAL_REALTIME_DEPLOYMENT.md | 259 +++++++++++++++++++++++ fal_app.py | 369 ++++++++++++++++++++++++++++++++ pyproject.toml | 3 +- uv.lock | 4 +- 7 files changed, 1389 insertions(+), 3 deletions(-) create mode 100644 DEPLOYMENT_ALTERNATIVES.md create mode 100644 DEPLOY_TO_FAL_QUICKSTART.md create mode 100644 FAL_DEPLOYMENT_GUIDE.md create mode 100644 FAL_REALTIME_DEPLOYMENT.md create mode 100644 fal_app.py diff --git a/DEPLOYMENT_ALTERNATIVES.md b/DEPLOYMENT_ALTERNATIVES.md new file mode 100644 index 0000000..b862404 --- /dev/null +++ b/DEPLOYMENT_ALTERNATIVES.md @@ -0,0 +1,411 @@ +# Deployment Alternatives for Scope Runner + +Since Scope Runner is a **realtime streaming video generation application**, it requires infrastructure that supports long-running processes and persistent connections. This document outlines suitable deployment options. + +## Architecture Overview + +Scope Runner: +- Runs indefinitely as a server (not request/response) +- Uses `ai-runner[realtime]` framework +- Processes streaming video frames continuously +- Requires persistent connections (WebSocket or similar) +- GPU-intensive workload (24GB+ VRAM) + +## ❌ Why NOT fal.ai Serverless + +fal.ai serverless functions are designed for: +- ✅ Short-lived, stateless operations +- ✅ Request → Process → Response patterns +- ✅ Single video/image generation tasks + +They do NOT support: +- ❌ Long-running processes +- ❌ Persistent WebSocket connections +- ❌ Continuous streaming +- ❌ Indefinite execution + +## ✅ Recommended Deployment Options + +### Option 1: Cloud VM with GPU (Recommended for Production) + +Deploy on a cloud provider with GPU instances: + +#### AWS EC2 + +```bash +# Launch GPU instance (g5.xlarge or p3.2xlarge) +# Amazon Linux 2 or Ubuntu 22.04 +# Install Docker and NVIDIA Container Toolkit + +# Pull or build image +docker pull your-registry/scope-runner:latest + +# Run with GPU +docker run -d --gpus all \ + --restart unless-stopped \ + -v /data/models:/models \ + -p 8000:8000 \ + --name scope-runner \ + your-registry/scope-runner:latest +``` + +**Pros:** +- ✅ Full control over infrastructure +- ✅ Persistent connections supported +- ✅ Suitable for 24/7 operation +- ✅ Multiple GPU options (A10G, A100, etc.) + +**Cons:** +- ⚠️ Fixed costs (even when idle) +- ⚠️ Requires infrastructure management +- ⚠️ Must handle scaling manually + +**Cost:** ~$1-4/hour depending on GPU + +#### Google Cloud Compute Engine + +```bash +# Create instance with GPU +gcloud compute instances create scope-runner \ + --zone=us-central1-a \ + --machine-type=n1-standard-4 \ + --accelerator=type=nvidia-tesla-t4,count=1 \ + --image-family=ubuntu-2204-lts \ + --image-project=ubuntu-os-cloud \ + --boot-disk-size=100GB + +# SSH and deploy +gcloud compute ssh scope-runner --zone=us-central1-a +# Install Docker, NVIDIA drivers, and run container +``` + +**Cost:** ~$0.35-1.5/hour depending on GPU + +#### Azure Virtual Machines + +```bash +# Create NC-series VM with GPU +az vm create \ + --resource-group scope-runner-rg \ + --name scope-runner-vm \ + --size Standard_NC6s_v3 \ + --image Ubuntu2204 \ + --admin-username azureuser \ + --generate-ssh-keys + +# Deploy container +ssh azureuser@ +# Install Docker, NVIDIA drivers, and run container +``` + +**Cost:** ~$0.90-3/hour depending on GPU + +### Option 2: Kubernetes with GPU Nodes + +For production-grade deployments with auto-scaling: + +#### Google Kubernetes Engine (GKE) + +```yaml +# deployment.yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: scope-runner +spec: + replicas: 1 + selector: + matchLabels: + app: scope-runner + template: + metadata: + labels: + app: scope-runner + spec: + nodeSelector: + cloud.google.com/gke-accelerator: nvidia-tesla-t4 + containers: + - name: scope-runner + image: your-registry/scope-runner:latest + resources: + limits: + nvidia.com/gpu: 1 + ports: + - containerPort: 8000 + volumeMounts: + - name: models + mountPath: /models + volumes: + - name: models + persistentVolumeClaim: + claimName: models-pvc +--- +apiVersion: v1 +kind: Service +metadata: + name: scope-runner-service +spec: + type: LoadBalancer + ports: + - port: 8000 + targetPort: 8000 + selector: + app: scope-runner +``` + +```bash +# Create GPU node pool +gcloud container node-pools create gpu-pool \ + --cluster=my-cluster \ + --machine-type=n1-standard-4 \ + --accelerator=type=nvidia-tesla-t4,count=1 \ + --num-nodes=1 + +# Deploy +kubectl apply -f deployment.yaml +``` + +**Pros:** +- ✅ Production-ready +- ✅ Auto-scaling support +- ✅ Rolling updates +- ✅ Health checks and auto-restart + +**Cons:** +- ⚠️ Complex setup +- ⚠️ Kubernetes learning curve +- ⚠️ Higher base costs + +### Option 3: RunPod / Vast.ai (Cost-Effective GPU Rental) + +Specialized GPU cloud providers with better pricing: + +#### RunPod + +```bash +# Use RunPod's container deployment +# 1. Go to runpod.io +# 2. Create pod with GPU (RTX 3090, 4090, A4000, A5000, A6000) +# 3. Use Docker template +# 4. Deploy your image + +# Or use RunPod API +curl -X POST https://api.runpod.io/v1/pods \ + -H "Authorization: Bearer YOUR_API_KEY" \ + -d '{ + "cloudType": "SECURE", + "gpuTypeId": "NVIDIA RTX A5000", + "name": "scope-runner", + "dockerImage": "your-registry/scope-runner:latest", + "ports": "8000/http", + "volumeInGb": 50 + }' +``` + +**Pros:** +- ✅ Much cheaper than AWS/GCP/Azure +- ✅ Wide GPU selection +- ✅ Simple deployment +- ✅ Pay-per-minute pricing + +**Cons:** +- ⚠️ Less reliable than major clouds +- ⚠️ Community GPUs may have availability issues +- ⚠️ Limited SLA + +**Cost:** ~$0.20-0.80/hour for suitable GPUs + +#### Vast.ai + +```bash +# Browse available GPUs at vast.ai +# Select instance with 24GB+ VRAM +# Use your Docker image +# Connect via SSH or expose ports +``` + +**Pros:** +- ✅ Extremely cheap ($0.15-0.50/hour) +- ✅ Spot pricing available +- ✅ Large GPU marketplace + +**Cons:** +- ⚠️ No SLA +- ⚠️ Instances can be interrupted +- ⚠️ Best for development/testing + +### Option 4: Modal (Serverless Containers) + +Modal supports long-running containers with GPUs: + +```python +# modal_app.py +import modal + +app = modal.App("scope-runner") + +# Create image with dependencies +image = modal.Image.from_registry( + "your-registry/scope-runner:latest", + add_python="3.10" +) + +@app.function( + image=image, + gpu="A100", + keep_warm=1, # Keep 1 instance always running + allow_concurrent_inputs=100, + timeout=86400, # 24 hours +) +@modal.asgi_app() +def serve(): + # Your FastAPI/ASGI app from ai-runner + from scope_runner.main import create_app + return create_app() +``` + +```bash +modal deploy modal_app.py +``` + +**Pros:** +- ✅ Simpler than Kubernetes +- ✅ Good GPU support +- ✅ Can keep instances warm +- ✅ Built-in monitoring + +**Cons:** +- ⚠️ Still has time limits (24h max) +- ⚠️ May not support all streaming patterns +- ⚠️ New platform (less mature) + +**Cost:** Similar to major clouds + +### Option 5: Replicate (If Willing to Adapt) + +If you can modify your architecture to batch/async: + +```python +# cog.yaml +build: + gpu: true + python_version: "3.10" + system_packages: + - "libgl1-mesa-glx" + python_packages: + - "torch>=2.0.0" +predict: "predict.py:Predictor" + +# predict.py +from cog import BasePredictor, Input +class Predictor(BasePredictor): + def setup(self): + # Load your pipeline once + from scope_runner.pipeline.pipeline import _load_longlive_pipeline + self.pipe = _load_longlive_pipeline(...) + + def predict( + self, + prompt: str = Input(description="Prompt"), + # ... other inputs + ) -> Path: + # Generate and return video + pass +``` + +**Pros:** +- ✅ Easy deployment +- ✅ Built-in API +- ✅ Public model hosting + +**Cons:** +- ⚠️ Requires architecture change +- ⚠️ Not for streaming/realtime +- ⚠️ 60s timeout per request + +## Comparison Matrix + +| Option | Monthly Cost* | Setup Time | Streaming Support | Reliability | Best For | +|--------|--------------|------------|-------------------|-------------|----------| +| **AWS EC2** | $730-2,920 | 2-4 hours | ✅ Full | ⭐⭐⭐⭐⭐ | Production | +| **GCP Compute** | $250-1,100 | 2-4 hours | ✅ Full | ⭐⭐⭐⭐⭐ | Production | +| **Azure VM** | $650-2,200 | 2-4 hours | ✅ Full | ⭐⭐⭐⭐⭐ | Production | +| **Kubernetes** | $500-2,000+ | 1-2 days | ✅ Full | ⭐⭐⭐⭐⭐ | Enterprise | +| **RunPod** | $150-580 | 30 min | ✅ Full | ⭐⭐⭐⭐ | Cost-effective | +| **Vast.ai** | $110-360 | 30 min | ✅ Full | ⭐⭐⭐ | Dev/Testing | +| **Modal** | $730-2,920 | 1 hour | ⚠️ Limited | ⭐⭐⭐⭐ | Hybrid | +| **Replicate** | Pay-per-use | 2 hours | ❌ None | ⭐⭐⭐⭐ | Single videos | + +*Assuming 24/7 operation for one instance + +## Recommended Path + +### For Development/Testing +1. **Start with Vast.ai or RunPod** - Cheap GPU rental +2. Test your streaming endpoints +3. Validate performance + +### For Production +1. **RunPod for initial production** - Good balance of cost/reliability +2. **Move to AWS/GCP/Azure** if you need: + - Higher reliability guarantees + - Better networking + - Integration with other services + - Enterprise SLAs + +### For Scale +1. **Kubernetes (GKE/EKS/AKS)** when you need: + - Multiple instances + - Auto-scaling + - High availability + - Geographic distribution + +## Example: Quick Start with RunPod + +```bash +# 1. Build and push your image +docker build -t your-dockerhub/scope-runner:latest . +docker push your-dockerhub/scope-runner:latest + +# 2. Go to runpod.io +# 3. Deploy → GPU Pod +# 4. Select GPU: RTX A5000 or better (24GB+ VRAM) +# 5. Docker Image: your-dockerhub/scope-runner:latest +# 6. Expose Port: 8000 +# 7. Deploy + +# 8. Access via provided URL +curl https://your-pod-id.runpod.io:8000/health +``` + +## Monitoring & Maintenance + +Regardless of platform, implement: + +1. **Health Checks**: Ensure server is responsive +2. **GPU Monitoring**: Track VRAM usage +3. **Auto-Restart**: Handle crashes gracefully +4. **Logging**: Centralized logs (CloudWatch, Stackdriver, etc.) +5. **Alerts**: Notify on failures +6. **Backups**: Model files and configuration + +## Need Help Choosing? + +Answer these questions: + +1. **Budget**: <$200/mo → Vast.ai, $200-500/mo → RunPod, >$500/mo → Cloud VMs +2. **Reliability**: Testing → Vast.ai, Production → RunPod/Cloud, Mission-critical → GCP/AWS +3. **Scale**: Single instance → VM, Multiple → Kubernetes +4. **Team size**: Solo → RunPod, Small team → Cloud VM, Large → Kubernetes + +## Summary + +For your realtime streaming Scope Runner: + +- ✅ **Start with RunPod** - Best cost/reliability balance +- ✅ **Use your existing Docker container** - No code changes needed +- ✅ **Keep the streaming architecture** - It works as designed +- ❌ **Don't use serverless functions** - They're not suitable for streaming + +Your app will run exactly as intended (`uv run scope-runner`) on persistent GPU infrastructure. + diff --git a/DEPLOY_TO_FAL_QUICKSTART.md b/DEPLOY_TO_FAL_QUICKSTART.md new file mode 100644 index 0000000..949503d --- /dev/null +++ b/DEPLOY_TO_FAL_QUICKSTART.md @@ -0,0 +1,265 @@ +# Deploy to fal.ai - Quick Start + +## TL;DR + +```bash +# 1. Install fal CLI +pip install fal-client +fal auth login + +# 2. Update fal_app.py with your Docker image name + +# 3. Deploy +fal deploy fal_app.py + +# 4. Test +curl https://your-fal-url/health +``` + +## Prerequisites + +- [ ] Docker image built and pushed to a registry +- [ ] fal.ai account (sign up at https://fal.ai) +- [ ] fal CLI installed (`pip install fal-client`) + +## Step-by-Step + +### 1. Prepare Your Docker Image + +```bash +# Build +docker build -t your-dockerhub-username/scope-runner:latest . + +# Push to Docker Hub (or your registry) +docker push your-dockerhub-username/scope-runner:latest +``` + +### 2. Update Deployment File + +Edit `fal_app.py`: + +```python +# Change this line: +DOCKER_IMAGE = "your-dockerhub-username/scope-runner:latest" +``` + +### 3. Authenticate with fal.ai + +```bash +# Option A: Interactive login +fal auth login + +# Option B: Use API key +export FAL_KEY="your-fal-api-key" +``` + +Get your API key from: https://fal.ai/dashboard/keys + +### 4. Deploy + +```bash +fal deploy fal_app.py +``` + +You'll get a URL like: `https://your-username-scope-runner.fal.run` + +### 5. Test + +```bash +# Health check +curl https://your-fal-url/health + +# Check if server is responding +curl https://your-fal-url/ + +# Test WebSocket (if you have a client) +# Connect to: ws://your-fal-url/live +``` + +## Configuration Options + +Edit `fal_app.py` to customize: + +```python +class ScopeRunnerApp(fal.App, kind="container", image=custom_image): + # GPU type + machine_type = "GPU-A100" # or "GPU-A100-80GB" + + # Keep instance warm (seconds) + keep_alive = 300 # 5 minutes + + # Port your app listens on + exposed_port = 8000 +``` + +### GPU Options + +- `"GPU-T4"` - 16GB VRAM (may be insufficient) +- `"GPU-A10G"` - 24GB VRAM +- `"GPU-A100"` - 40GB VRAM (recommended) +- `"GPU-A100-80GB"` - 80GB VRAM (for large models) + +### Keep Alive Options + +- `0` - No warm instances (coldest start, cheapest) +- `300` - 5 minutes (balanced) +- `3600` - 1 hour (fastest, more expensive) + +## Troubleshooting + +### "Image not found" + +Make sure your Docker image is: +- Pushed to a public registry, OR +- Configure private registry credentials in `fal_app.py` + +### "GPU not available" + +Check that: +- Your Docker image includes NVIDIA drivers +- You're using a CUDA base image +- `nvidia-smi` works in the container + +### "Port not accessible" + +Verify: +- `exposed_port = 8000` matches your app's port +- ai-runner is actually listening on that port +- fal.ai supports port forwarding (check their docs) + +### "Container exits immediately" + +Check logs with: +```bash +fal logs your-username/scope-runner +``` + +Common issues: +- Missing models (models not in image) +- Environment variables not set +- CMD in Dockerfile is incorrect + +## Monitoring + +### View Logs + +```bash +fal logs your-username/scope-runner +``` + +### Check Status + +```bash +fal status your-username/scope-runner +``` + +### View Metrics + +Go to: https://fal.ai/dashboard + +## Cost Estimation + +**A100 GPU:** +- Compute: ~$0.0015/second (~$5.40/hour) +- Keep alive (5 min): ~$0.45/hour baseline +- Per request: Variable based on generation time + +**Example costs:** +- Always on (24/7): ~$3,900/month +- With keep_alive (busy hours): ~$500-1,500/month +- Pure pay-per-use: ~$0.10-0.50 per video + +## Alternative: If fal.ai Doesn't Work + +If you encounter issues with fal.ai's container model: + +### RunPod (Proven Alternative) + +```bash +# 1. Go to runpod.io +# 2. Deploy → GPU Pod +# 3. Select: RTX A5000 or A100 +# 4. Docker Image: your-dockerhub-username/scope-runner:latest +# 5. Expose Port: 8000 +# 6. Deploy +``` + +Cost: ~$0.20-0.80/hour +Setup time: 5 minutes + +See `DEPLOYMENT_ALTERNATIVES.md` for more options. + +## Next Steps + +After successful deployment: + +1. **Test thoroughly** + - Health endpoints + - WebSocket connections + - Video generation + - Performance under load + +2. **Monitor costs** + - Check fal.ai dashboard + - Adjust keep_alive if needed + - Consider alternatives if too expensive + +3. **Set up CI/CD** (optional) + - Use `.github/workflows/deploy-fal.yml` + - Automate deployments on push + +4. **Document your endpoint** + - Share URL with team + - Document API usage + - Set up monitoring/alerts + +## Resources + +- **Complete guide**: `FAL_REALTIME_DEPLOYMENT.md` +- **Alternatives**: `DEPLOYMENT_ALTERNATIVES.md` +- **Corrections**: `FAL_INTEGRATION_CORRECTED.md` +- **fal.ai docs**: https://docs.fal.ai +- **Support**: https://fal.ai/support + +## Quick Reference + +```bash +# Deploy +fal deploy fal_app.py + +# Check logs +fal logs your-username/scope-runner + +# Check status +fal status your-username/scope-runner + +# Update deployment +# (edit fal_app.py, then) +fal deploy fal_app.py + +# Remove deployment +fal delete your-username/scope-runner +``` + +## Success Checklist + +- [ ] Docker image built and pushed +- [ ] fal CLI installed and authenticated +- [ ] `fal_app.py` updated with image name +- [ ] Deployed successfully +- [ ] Health endpoint responds +- [ ] WebSocket connections work +- [ ] Video generation works +- [ ] Costs are acceptable +- [ ] Monitoring set up + +## Need Help? + +- **fal.ai issues**: https://fal.ai/support or Discord +- **Scope Runner issues**: GitHub Issues +- **Architecture questions**: `FAL_REALTIME_DEPLOYMENT.md` + +--- + +**Remember:** This is a realtime streaming server, not a simple function. Make sure fal.ai's container model supports indefinite runtime before committing to production use. + diff --git a/FAL_DEPLOYMENT_GUIDE.md b/FAL_DEPLOYMENT_GUIDE.md new file mode 100644 index 0000000..ad431b3 --- /dev/null +++ b/FAL_DEPLOYMENT_GUIDE.md @@ -0,0 +1,81 @@ +# fal.ai Deployment Guide + +## Overview + +Scope Runner is a **realtime streaming video generation server** that can be deployed on fal.ai's serverless platform using their container support. + +## Quick Start + +```bash +# 1. Install fal CLI +pip install fal-client +fal auth login + +# 2. Update fal_app.py with your Docker image +# Edit DOCKER_IMAGE = "your-dockerhub-username/scope-runner:latest" + +# 3. Deploy +fal deploy fal_app.py + +# 4. Test +curl https://your-fal-url/health +``` + +## Files + +- **`fal_app.py`** - fal.ai deployment configuration (container-based) +- **`DEPLOY_TO_FAL_QUICKSTART.md`** - Detailed quick start guide +- **`FAL_REALTIME_DEPLOYMENT.md`** - Complete deployment documentation +- **`DEPLOYMENT_ALTERNATIVES.md`** - Alternative platforms (RunPod, Vast.ai, etc.) + +## How It Works + +fal.ai supports realtime applications through their container deployment model: + +1. Your existing Docker image is deployed on fal.ai +2. Container starts with GPU (A100) +3. Runs: `uv run scope-runner` (starts ai-runner server) +4. fal.ai forwards requests to your server's port 8000 +5. WebSocket connections work as normal + +## Requirements + +- Docker image in a registry (Docker Hub, etc.) +- fal.ai account and API key +- GPU-enabled container (A100 recommended) + +## Configuration + +Edit `fal_app.py` to customize: + +```python +class ScopeRunnerApp(fal.App, kind="container", image=custom_image): + machine_type = "GPU-A100" # GPU type + keep_alive = 300 # Keep warm (seconds) + exposed_port = 8000 # ai-runner port +``` + +## Alternatives + +If fal.ai doesn't meet your needs: + +- **RunPod** - Proven to work, ~$0.20-0.80/hour +- **Vast.ai** - Cheapest option, ~$0.15-0.50/hour +- **AWS/GCP/Azure** - Production-grade VMs +- **Kubernetes** - Enterprise scale + +See `DEPLOYMENT_ALTERNATIVES.md` for details. + +## Documentation + +- **Quick Start**: `DEPLOY_TO_FAL_QUICKSTART.md` +- **Complete Guide**: `FAL_REALTIME_DEPLOYMENT.md` +- **Alternatives**: `DEPLOYMENT_ALTERNATIVES.md` +- **Original Setup**: `README.md` + +## Support + +- fal.ai: https://fal.ai/support +- Scope Runner: GitHub Issues +- ai-runner: https://github.com/livepeer/ai-runner + diff --git a/FAL_REALTIME_DEPLOYMENT.md b/FAL_REALTIME_DEPLOYMENT.md new file mode 100644 index 0000000..24a247a --- /dev/null +++ b/FAL_REALTIME_DEPLOYMENT.md @@ -0,0 +1,259 @@ +# fal.ai Realtime Deployment for Scope Runner + +## ✅ fal.ai DOES Support Realtime Applications! + +You were correct - fal.ai supports realtime streaming applications with WebSockets. This document explains how to properly deploy Scope Runner's realtime streaming architecture on fal.ai. + +## Understanding fal.ai's Realtime Support + +fal.ai provides several ways to deploy realtime applications: + +1. **`@fal.realtime()` decorator** - For stateless realtime endpoints +2. **`@fal.endpoint(is_websocket=True)` decorator** - For raw WebSocket connections +3. **Container deployment** - Run custom Docker containers with GPU support +4. **`fal.App` class** - Application container model + +## The Challenge + +Scope Runner uses **Livepeer's ai-runner framework** which: +- Creates a FastAPI application with streaming endpoints +- Runs indefinitely as a server +- Handles WebSocket/HTTP streaming connections +- Manages the realtime video pipeline + +The question is: **How do we deploy this existing FastAPI/ASGI app on fal.ai?** + +## Deployment Options + +### Option 1: Container Deployment (Recommended) + +Deploy your existing Docker container on fal.ai using their container support. + +**File: `fal_app.py`** + +```python +import fal +from fal.container import ContainerImage + +# Use your existing Docker image +custom_image = ContainerImage.from_registry( + "your-dockerhub-username/scope-runner:latest", +) + +class ScopeRunnerApp(fal.App, kind="container", image=custom_image): + """ + Scope Runner deployed as a fal.ai container application. + Runs the ai-runner streaming server with full WebSocket/realtime support. + """ + + machine_type = "GPU-A100" + keep_alive = 300 # Keep warm for 5 minutes + + # The container will run: uv run scope-runner + # which starts the ai-runner FastAPI server on port 8000 + # fal.ai will automatically expose this port +``` + +**Deploy:** +```bash +fal deploy fal_app.py +``` + +**Pros:** +- ✅ Uses your existing Docker image +- ✅ No code changes needed +- ✅ Full ai-runner functionality preserved +- ✅ All WebSocket/streaming endpoints work as-is + +**Cons:** +- ⚠️ Requires Docker image in a registry +- ⚠️ Need to verify fal.ai's container port forwarding + +### Option 2: Adapt to fal.ai's Realtime Decorators + +Rewrite your application to use fal.ai's native realtime decorators. + +**This requires significant refactoring** because you'd need to: +1. Extract the Scope pipeline logic +2. Implement fal.ai's realtime interface +3. Handle frame streaming using their protocol +4. Abandon the ai-runner framework + +**Example structure:** + +```python +import fal +from pydantic import BaseModel +from scope_runner.pipeline.pipeline import Scope +from scope_runner.pipeline.params import ScopeParams + +class ScopeRealtimeApp(fal.App): + def __init__(self): + self.pipeline = Scope() + + @fal.realtime("/generate") + async def generate_realtime(self, input_data: dict) -> dict: + # Initialize pipeline with params + await self.pipeline.initialize(**input_data) + + # Generate frames and stream back + # This would need custom implementation + # to fit fal.ai's realtime protocol + pass +``` + +**Pros:** +- ✅ Native fal.ai integration +- ✅ Potentially better latency + +**Cons:** +- ❌ Major refactoring required +- ❌ Lose ai-runner framework benefits +- ❌ Need to reimplement streaming logic +- ❌ Compatibility issues with Livepeer network + +### Option 3: Hybrid Approach + +Use fal.ai for the compute, but keep your existing architecture: + +1. Deploy the Docker container on fal.ai +2. Use fal.ai's container deployment with custom startup command +3. Expose the ai-runner endpoints through fal.ai's routing + +**File: `fal_app.py`** + +```python +import fal +from fal.container import ContainerImage + +custom_image = ContainerImage.from_registry( + "your-dockerhub-username/scope-runner:latest", +) + +class ScopeRunnerApp(fal.App, kind="container", image=custom_image): + machine_type = "GPU-A100" + keep_alive = 300 + exposed_port = 8000 # ai-runner's port + + def setup(self): + """Verify GPU and environment.""" + import subprocess + subprocess.run(["nvidia-smi"]) + + # fal.ai should forward all requests to the container's port 8000 + # where ai-runner is listening +``` + +## Recommended Path + +### Step 1: Verify fal.ai Container Support + +Check fal.ai's documentation to confirm: +- [ ] Can they run long-lived containers? +- [ ] Do they support port forwarding from containers? +- [ ] Can containers run indefinitely (not just per-request)? + +### Step 2: Test Container Deployment + +```bash +# 1. Build and push your image +docker build -t your-dockerhub/scope-runner:latest . +docker push your-dockerhub/scope-runner:latest + +# 2. Update fal_app.py with your image (see Option 1 above) + +# 3. Deploy +fal deploy fal_app.py + +# 4. Test the endpoints +curl https://your-fal-url/health +``` + +### Step 3: If Container Approach Doesn't Work + +If fal.ai's containers don't support long-running servers: + +**Use alternative platforms:** +- **RunPod** - Excellent for this use case (~$0.20-0.80/hour) +- **Vast.ai** - Cheapest option (~$0.15-0.50/hour) +- **Modal** - Similar to fal.ai but explicitly supports ASGI apps +- **Cloud VMs** - Traditional but reliable + +See `DEPLOYMENT_ALTERNATIVES.md` for details on these options. + +## Key Questions to Answer + +Before proceeding, you need to determine: + +1. **Does fal.ai support long-running containers?** + - Or do containers only run per-request? + +2. **Can fal.ai containers expose ports for external connections?** + - The ai-runner needs to accept WebSocket connections + +3. **What's the maximum runtime for a fal.ai container?** + - Your app needs to run indefinitely + +4. **Does fal.ai support ASGI/FastAPI app mounting?** + - Some platforms have `@platform.asgi_app()` decorators + +## Testing Locally + +Before deploying to fal.ai, test your container: + +```bash +# Run your existing Docker image +docker run --gpus all -p 8000:8000 scope-runner:latest + +# In another terminal, test the endpoints +curl http://localhost:8000/health + +# Test WebSocket connection (if you have a client) +# Connect to ws://localhost:8000/live or similar +``` + +## Next Steps + +1. **Read fal.ai's container documentation**: + - https://docs.fal.ai/examples/running-a-container + - Check for examples of long-running services + +2. **Check fal.ai's Discord/Support**: + - Ask if they support indefinitely-running WebSocket servers + - Request examples of similar deployments + +3. **Try the container deployment**: + - Use Option 1 above + - See if it works with your streaming endpoints + +4. **If fal.ai doesn't work**: + - Use RunPod or Vast.ai (they definitely support this) + - See `DEPLOYMENT_ALTERNATIVES.md` + +## Comparison: fal.ai vs Alternatives + +| Feature | fal.ai | RunPod | Vast.ai | Modal | +|---------|--------|--------|---------|-------| +| **Realtime Support** | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | +| **Long-running** | ❓ TBD | ✅ Yes | ✅ Yes | ⚠️ 24h limit | +| **Container Support** | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | +| **ASGI/FastAPI** | ❓ TBD | ✅ Yes | ✅ Yes | ✅ Yes | +| **Setup Complexity** | ⭐⭐⭐ | ⭐⭐ | ⭐⭐ | ⭐⭐⭐ | +| **Cost (A100)** | ~$1-2/hr | ~$0.50-0.80/hr | ~$0.40-0.60/hr | ~$1-2/hr | + +## Conclusion + +fal.ai **does** support realtime applications, but we need to verify if their container model supports: +1. Long-running (indefinite) processes +2. Exposed ports for WebSocket connections +3. The ai-runner server architecture + +**Recommended action**: Try Option 1 (Container Deployment) and see if it works. If not, RunPod or Vast.ai are proven alternatives that definitely support your use case. + +## Resources + +- [fal.ai Container Documentation](https://docs.fal.ai/examples/running-a-container) +- [fal.ai Realtime Endpoints](https://docs.fal.ai/serverless/development/realtime) +- [fal.ai WebSocket Support](https://docs.fal.ai/serverless/development/streaming) +- [Livepeer ai-runner](https://github.com/livepeer/ai-runner) + diff --git a/fal_app.py b/fal_app.py new file mode 100644 index 0000000..1bd6f1b --- /dev/null +++ b/fal_app.py @@ -0,0 +1,369 @@ +""" +fal.ai deployment for Scope Runner. + +This deploys the ai-runner streaming server as a fal.ai container application. +The container runs indefinitely, handling realtime WebSocket connections. + +Based on: https://docs.fal.ai/examples/serverless/deploy-models-with-custom-containers +""" + +import fal +from fal.container import ContainerImage + +# Configuration +DOCKER_IMAGE = "livepeer/ai-runner:live-app-scope-sha-d7da222" + +# Create a Dockerfile that uses your existing image as base +dockerfile_str = f""" +FROM {DOCKER_IMAGE} + +# The base image already has everything configured +# Ensure the working directory and dependencies are available +WORKDIR /app + +# Make sure uv and dependencies are in the PATH +ENV PATH="/root/.cargo/bin:$PATH" + +# Verify the installation is accessible +RUN python -c "from scope.core.config import MODELS_DIR_ENV_VAR; print('Scope installed')" || \ + (cd /app && uv sync --locked) +""" + +# For private registries, configure registries parameter: +# REGISTRY_CONFIG = { +# "https://my.registry.io/": { +# "username": "your_username", +# "password": "your_password", +# }, +# } + +# Create container image from Dockerfile string +custom_image = ContainerImage.from_dockerfile_str( + dockerfile_str, + # registries=REGISTRY_CONFIG, # Uncomment if using private registry +) + +class ScopeRunnerApp(fal.App, keep_alive=300): + """ + Scope Runner realtime streaming server on fal.ai. + + This runs your existing Docker container which starts the ai-runner server. + The server handles WebSocket connections for realtime video generation. + + Configuration: + - GPU: A100 (24GB+ VRAM required) + - Custom Image: Uses livepeer/ai-runner Docker image + - Port: 8000 (ai-runner default) + """ + + # Set custom Docker image + image = custom_image + + # GPU configuration + machine_type = "GPU-H100" # Will use GPU, fal will assign appropriate type + + # Additional requirements needed for the setup code + requirements = [ + "requests", # For health check + ] + + # Track if a websocket connection is currently in use + _websocket_in_use = False + + def setup(self): + """ + Setup runs when the container starts. + Verifies GPU and starts the ai-runner server. + """ + import subprocess + import logging + import os + import time + import threading + from pathlib import Path + + logger = logging.getLogger(__name__) + print("Starting Scope Runner container setup...") + + # Verify GPU is available + try: + result = subprocess.run( + ["nvidia-smi"], + capture_output=True, + text=True, + check=True + ) + print(f"GPU Status:\n{result.stdout}") + except Exception as e: + logger.error(f"GPU check failed: {e}") + raise + + # Set up environment for scope runner + # Import inside the function to ensure the image's Python environment is used + + # Prepare models before starting the server + print("Preparing models...") + prepare_env = os.environ.copy() + prepare_env["PIPELINE"] = "scope" + prepare_env["MODEL_DIR"] = "/data/models" + prepare_env["HF_HUB_OFFLINE"] = "0" + + # Create models directory if it doesn't exist + models_dir = "/data/models/Scope--models" + os.makedirs(models_dir, exist_ok=True) + print(f"Created models directory: {models_dir}") + + try: + result = subprocess.run( + ["uv", "run", "scope-runner", "--prepare-models"], + env=prepare_env, + cwd="/app", + capture_output=True, + text=True, + check=True, + ) + print(f"Models prepared successfully:\n{result.stdout}") + except subprocess.CalledProcessError as e: + logger.error(f"Failed to prepare models: {e}\nStdout: {e.stdout}\nStderr: {e.stderr}") + raise + + # Check disk usage of downloaded models + try: + du_result = subprocess.run( + ["du", "-h", "/data/models/Scope--models"], + capture_output=True, + text=True, + check=True + ) + print(f"Models disk usage:\n{du_result.stdout}") + except subprocess.CalledProcessError as e: + logger.warning(f"Could not check models disk usage: {e}") + + # Start the ai-runner server in a background thread + def start_server(): + print("Starting ai-runner server...") + try: + # Use subprocess to run the server with uv, ensuring the correct environment + import sys + # The container has uv and the project installed + subprocess.run( + ["uv", "run", "--frozen", "scope-runner"], + cwd="/app", + check=True, + env=prepare_env, + ) + except Exception as e: + logger.error(f"Failed to start ai-runner server: {e}") + raise + + # Start server in background thread + server_thread = threading.Thread(target=start_server, daemon=True) + server_thread.start() + + # Wait a bit for the server to start + print("Waiting for ai-runner server to start...") + time.sleep(5) + + # Verify server is running + try: + import requests + response = requests.get("http://localhost:8000/health", timeout=5) + if response.status_code == 200: + print("✅ ai-runner server is running on port 8000") + else: + logger.warning(f"ai-runner server responded with status {response.status_code}") + except Exception as e: + logger.warning(f"Could not verify ai-runner server: {e}") + print("Server may still be initializing...") + + print("Scope Runner container setup complete") + + @fal.endpoint("/") + def index(self): + """ + Health check endpoint. + The actual ai-runner server runs on port 8000 with endpoints: + - /health - Health check + - /live - WebSocket streaming endpoint + - /sessions - Session management + """ + import requests + try: + # Check if ai-runner server is responding + response = requests.get("http://localhost:8000/health", timeout=2) + ai_runner_status = "running" if response.status_code == 200 else "error" + except: + ai_runner_status = "not_responding" + + return { + "status": "running", + "app": "Scope Runner", + "description": "Realtime video generation streaming server", + "ai_runner_status": ai_runner_status, + "ai_runner_port": 8000, + "endpoints": { + "health": "http://localhost:8000/health", + "live": "ws://localhost:8000/live", + "sessions": "http://localhost:8000/sessions" + } + } + + from fastapi import WebSocket + + @fal.endpoint("/live-video-to-video", is_websocket=True) + async def live_video_to_video(self, websocket: WebSocket) -> None: + """ + Raw WebSocket endpoint for live video-to-video requests. + Keeps a persistent WebSocket connection open and forwards JSON messages + to the local ai-runner server. + + This uses a raw WebSocket (not fal.realtime) so it's compatible with + standard WebSocket clients like Postman, browser WebSocket API, etc. + """ + import requests + import logging + import json + import time + import asyncio + + logger = logging.getLogger(__name__) + + try: + # Check if a websocket is already in use + if ScopeRunnerApp._websocket_in_use: + print("⚠️ Rejecting connection: a websocket is already in use") + await websocket.close(code=1008, reason="Another connection is already in progress") + return + + # Accept the WebSocket connection + print("Attempting to accept WebSocket connection...") + await websocket.accept() + print("✅ WebSocket connection accepted") + + # Mark websocket as in use + ScopeRunnerApp._websocket_in_use = True + print("WebSocket marked as in use") + + # Check if ai-runner server is running (with retry loop) + health_check_success = False + start_time = time.time() + timeout_seconds = 300 # 5 minutes + retry_interval = 2 # seconds between retries + + while not health_check_success and (time.time() - start_time) < timeout_seconds: + try: + health_check = requests.get("http://localhost:8000/health", timeout=2) + print(f"ai-runner health check: {health_check.status_code}") + if health_check.status_code == 200: + health_check_success = True + print("✅ ai-runner health check succeeded") + else: + logger.warning(f"ai-runner health check returned non-200 status: {health_check.status_code}") + time.sleep(retry_interval) + except Exception as health_error: + elapsed = time.time() - start_time + logger.warning(f"ai-runner health check failed (elapsed: {elapsed:.1f}s): {health_error}") + if (time.time() - start_time) < timeout_seconds: + time.sleep(retry_interval) + + if not health_check_success: + logger.error(f"ai-runner health check failed after {timeout_seconds}s timeout") + + # Send initial connection acknowledgment + await websocket.send_json({ + "type": "connection_established", + "message": "WebSocket connection ready for live video-to-video" + }) + print("✅ Sent initial acknowledgment") + except Exception as accept_error: + logger.error(f"Failed to accept WebSocket connection: {accept_error}") + raise + + try: + + # Keep connection open and process incoming messages + while True: + # Receive message as text/JSON + message_text = await websocket.receive_text() + print(f"Received message via WebSocket: {message_text}") + + try: + # Parse the JSON message + request_data = json.loads(message_text) + + # Forward the request to the local ai-runner server + response = requests.post( + "http://localhost:8000/live-video-to-video", + json=request_data, + timeout=30 + ) + + # Stream the response back via WebSocket + response.raise_for_status() + result = response.json() + + await websocket.send_json({ + "type": "result", + "data": result + }) + + while True: + # Send health check every 5 seconds + await asyncio.sleep(5) + try: + health_response = requests.get("http://localhost:8000/health", timeout=2) + await websocket.send_json({ + "type": "health_check", + "status": health_response.status_code, + "data": health_response.json() if health_response.status_code == 200 else None + }) + except Exception as health_error: + logger.warning(f"Health check failed: {health_error}") + await websocket.send_json({ + "type": "health_check", + "status": "error", + "error": str(health_error) + }) + + + except json.JSONDecodeError as e: + logger.error(f"Invalid JSON received: {e}") + await websocket.send_json({ + "type": "error", + "error": str(e), + "message": "Invalid JSON format" + }) + + except requests.exceptions.RequestException as e: + logger.error(f"Error proxying request to ai-runner: {e}") + await websocket.send_json({ + "type": "error", + "error": str(e), + "message": "Failed to proxy request to ai-runner server" + }) + + except Exception as e: + logger.error(f"WebSocket error: {e}") + try: + await websocket.send_json({ + "type": "error", + "error": str(e), + "message": "WebSocket connection error" + }) + except: + pass # Connection might already be closed + finally: + # Mark websocket as no longer in use + ScopeRunnerApp._websocket_in_use = False + print("WebSocket marked as no longer in use") + + await websocket.close() + print("WebSocket connection closed for live-video-to-video") + + +# Deployment: +# 1. Run: fal run fal_app.py (for local testing) +# 2. Run: fal deploy fal_app.py (to deploy to fal.ai) +# 3. fal.ai will provide you with a URL +# 4. The ai-runner server runs on port 8000 with endpoints like /health, /live, etc. + diff --git a/pyproject.toml b/pyproject.toml index 6ce8ce3..8b352a5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,7 +11,8 @@ dependencies = [ scope-runner = "scope_runner.main:main" [tool.uv.sources] -ai-runner = { git = "https://github.com/livepeer/ai-runner.git", rev = "v0.14.1", subdirectory = "runner" } +ai-runner = { git = "https://github.com/livepeer/ai-runner.git", rev = "b1d89d34fd5c08591c969c63e4be857d137c7193", subdirectory = "runner" } +# ai-runner = { path = "../ai-runner/runner" } daydream-scope = { git = "https://github.com/daydreamlive/scope.git", rev = "734057a4701975941a45e8ca8342b495a762ba2f" } [tool.uv] diff --git a/uv.lock b/uv.lock index 6595a72..2808c5b 100644 --- a/uv.lock +++ b/uv.lock @@ -34,7 +34,7 @@ wheels = [ [[package]] name = "ai-runner" version = "0.14.1" -source = { git = "https://github.com/livepeer/ai-runner.git?subdirectory=runner&rev=v0.14.1#50a742cee7c5789ef4a10f8117f30de3758366a9" } +source = { git = "https://github.com/livepeer/ai-runner.git?subdirectory=runner&rev=b1d89d34fd5c08591c969c63e4be857d137c7193#b1d89d34fd5c08591c969c63e4be857d137c7193" } dependencies = [ { name = "aiohttp" }, { name = "fastapi" }, @@ -1359,7 +1359,7 @@ dependencies = [ [package.metadata] requires-dist = [ - { name = "ai-runner", extras = ["realtime"], git = "https://github.com/livepeer/ai-runner.git?subdirectory=runner&rev=v0.14.1" }, + { name = "ai-runner", extras = ["realtime"], git = "https://github.com/livepeer/ai-runner.git?subdirectory=runner&rev=b1d89d34fd5c08591c969c63e4be857d137c7193" }, { name = "daydream-scope", git = "https://github.com/daydreamlive/scope.git?rev=734057a4701975941a45e8ca8342b495a762ba2f" }, ] From d40f379942ef8c13229041d4340cf9ca9c4fe209 Mon Sep 17 00:00:00 2001 From: Max Holland Date: Tue, 13 Jan 2026 21:57:23 +0000 Subject: [PATCH 2/8] fix comment --- fal_app.py | 1 - 1 file changed, 1 deletion(-) diff --git a/fal_app.py b/fal_app.py index 1bd6f1b..6370398 100644 --- a/fal_app.py +++ b/fal_app.py @@ -2,7 +2,6 @@ fal.ai deployment for Scope Runner. This deploys the ai-runner streaming server as a fal.ai container application. -The container runs indefinitely, handling realtime WebSocket connections. Based on: https://docs.fal.ai/examples/serverless/deploy-models-with-custom-containers """ From a36cd0c7a80ea8ea694a1321b04d74a3bf783266 Mon Sep 17 00:00:00 2001 From: Max Holland Date: Thu, 22 Jan 2026 10:32:06 +0000 Subject: [PATCH 3/8] remove unused endpoint --- fal_app.py | 33 ++------------------------------- 1 file changed, 2 insertions(+), 31 deletions(-) diff --git a/fal_app.py b/fal_app.py index 6370398..56b7e5a 100644 --- a/fal_app.py +++ b/fal_app.py @@ -176,37 +176,7 @@ def start_server(): print("Server may still be initializing...") print("Scope Runner container setup complete") - - @fal.endpoint("/") - def index(self): - """ - Health check endpoint. - The actual ai-runner server runs on port 8000 with endpoints: - - /health - Health check - - /live - WebSocket streaming endpoint - - /sessions - Session management - """ - import requests - try: - # Check if ai-runner server is responding - response = requests.get("http://localhost:8000/health", timeout=2) - ai_runner_status = "running" if response.status_code == 200 else "error" - except: - ai_runner_status = "not_responding" - - return { - "status": "running", - "app": "Scope Runner", - "description": "Realtime video generation streaming server", - "ai_runner_status": ai_runner_status, - "ai_runner_port": 8000, - "endpoints": { - "health": "http://localhost:8000/health", - "live": "ws://localhost:8000/live", - "sessions": "http://localhost:8000/sessions" - } - } - + from fastapi import WebSocket @fal.endpoint("/live-video-to-video", is_websocket=True) @@ -366,3 +336,4 @@ async def live_video_to_video(self, websocket: WebSocket) -> None: # 3. fal.ai will provide you with a URL # 4. The ai-runner server runs on port 8000 with endpoints like /health, /live, etc. +# TODO overall timeout 30 mins or something to stop big bills \ No newline at end of file From 37f7b583dd34008a5339e47d8860b74ddf4f3607 Mon Sep 17 00:00:00 2001 From: Max Holland Date: Fri, 23 Jan 2026 16:37:46 +0000 Subject: [PATCH 4/8] uprev scope --- fal_app.py | 62 +-- pyproject.toml | 4 +- src/scope_runner/pipeline/params.py | 4 +- src/scope_runner/pipeline/pipeline.py | 79 +++ uv.lock | 721 +++++++++++++------------- 5 files changed, 464 insertions(+), 406 deletions(-) diff --git a/fal_app.py b/fal_app.py index 56b7e5a..191955e 100644 --- a/fal_app.py +++ b/fal_app.py @@ -100,43 +100,45 @@ def setup(self): # Set up environment for scope runner # Import inside the function to ensure the image's Python environment is used + # Prepare models before starting the server - print("Preparing models...") prepare_env = os.environ.copy() prepare_env["PIPELINE"] = "scope" prepare_env["MODEL_DIR"] = "/data/models" prepare_env["HF_HUB_OFFLINE"] = "0" + + # Commented out for now to speed up startup + # print("Preparing models...") + # # Create models directory if it doesn't exist + # models_dir = "/data/models/Scope--models" + # os.makedirs(models_dir, exist_ok=True) + # print(f"Created models directory: {models_dir}") - # Create models directory if it doesn't exist - models_dir = "/data/models/Scope--models" - os.makedirs(models_dir, exist_ok=True) - print(f"Created models directory: {models_dir}") - - try: - result = subprocess.run( - ["uv", "run", "scope-runner", "--prepare-models"], - env=prepare_env, - cwd="/app", - capture_output=True, - text=True, - check=True, - ) - print(f"Models prepared successfully:\n{result.stdout}") - except subprocess.CalledProcessError as e: - logger.error(f"Failed to prepare models: {e}\nStdout: {e.stdout}\nStderr: {e.stderr}") - raise + # try: + # result = subprocess.run( + # ["uv", "run", "scope-runner", "--prepare-models"], + # env=prepare_env, + # cwd="/app", + # capture_output=True, + # text=True, + # check=True, + # ) + # print(f"Models prepared successfully:\n{result.stdout}") + # except subprocess.CalledProcessError as e: + # logger.error(f"Failed to prepare models: {e}\nStdout: {e.stdout}\nStderr: {e.stderr}") + # raise - # Check disk usage of downloaded models - try: - du_result = subprocess.run( - ["du", "-h", "/data/models/Scope--models"], - capture_output=True, - text=True, - check=True - ) - print(f"Models disk usage:\n{du_result.stdout}") - except subprocess.CalledProcessError as e: - logger.warning(f"Could not check models disk usage: {e}") + # # Check disk usage of downloaded models + # try: + # du_result = subprocess.run( + # ["du", "-h", "/data/models/Scope--models"], + # capture_output=True, + # text=True, + # check=True + # ) + # print(f"Models disk usage:\n{du_result.stdout}") + # except subprocess.CalledProcessError as e: + # logger.warning(f"Could not check models disk usage: {e}") # Start the ai-runner server in a background thread def start_server(): diff --git a/pyproject.toml b/pyproject.toml index 8b352a5..d9bda0d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "scope-pipeline" version = "0.1.1" -requires-python = ">=3.10.12,<3.11" +requires-python = ">=3.12,<3.13" dependencies = [ "ai-runner[realtime]", "daydream-scope", @@ -13,7 +13,7 @@ scope-runner = "scope_runner.main:main" [tool.uv.sources] ai-runner = { git = "https://github.com/livepeer/ai-runner.git", rev = "b1d89d34fd5c08591c969c63e4be857d137c7193", subdirectory = "runner" } # ai-runner = { path = "../ai-runner/runner" } -daydream-scope = { git = "https://github.com/daydreamlive/scope.git", rev = "734057a4701975941a45e8ca8342b495a762ba2f" } +daydream-scope = { git = "https://github.com/daydreamlive/scope.git", rev = "7ff0e6dfbaaf8852483d2d8dde66daca4ff460ed" } [tool.uv] package = true diff --git a/src/scope_runner/pipeline/params.py b/src/scope_runner/pipeline/params.py index c55a789..b506ffc 100644 --- a/src/scope_runner/pipeline/params.py +++ b/src/scope_runner/pipeline/params.py @@ -17,8 +17,8 @@ class ScopeParams(BaseParams): Scope pipeline parameters for longlive text-to-video generation. """ - pipeline: Literal["longlive"] = "longlive" - """The scope pipeline to use. Currently only 'longlive' is supported.""" + pipeline: Literal["longlive", "krea_realtime_video", "streamdiffusionv2"] = "streamdiffusionv2" + """The scope pipeline to use. Supported: 'longlive', 'krea_realtime_video', 'streamdiffusionv2'.""" prompts: List[Union[str, WeightedPrompt]] = Field( default_factory=lambda: [ diff --git a/src/scope_runner/pipeline/pipeline.py b/src/scope_runner/pipeline/pipeline.py index 8dab46a..9c3597f 100644 --- a/src/scope_runner/pipeline/pipeline.py +++ b/src/scope_runner/pipeline/pipeline.py @@ -99,6 +99,10 @@ async def initialize(self, **params): # Load the pipeline based on the pipeline type if self.params.pipeline == "longlive": self.pipe = await asyncio.to_thread(_load_longlive_pipeline, self.params) + elif self.params.pipeline == "krea_realtime_video": + self.pipe = await asyncio.to_thread(_load_krea_realtime_video_pipeline, self.params) + elif self.params.pipeline == "streamdiffusionv2": + self.pipe = await asyncio.to_thread(_load_streamdiffusionv2_pipeline, self.params) else: raise ValueError(f"Unsupported pipeline: {self.params.pipeline}") @@ -133,6 +137,10 @@ async def _reload_pipeline(self, new_params: ScopeParams): if new_params.pipeline == "longlive": self.pipe = await asyncio.to_thread(_load_longlive_pipeline, new_params) + elif new_params.pipeline == "krea_realtime_video": + self.pipe = await asyncio.to_thread(_load_krea_realtime_video_pipeline, new_params) + elif new_params.pipeline == "streamdiffusionv2": + self.pipe = await asyncio.to_thread(_load_streamdiffusionv2_pipeline, new_params) else: raise ValueError(f"Unsupported pipeline: {new_params.pipeline}") @@ -192,3 +200,74 @@ def _load_longlive_pipeline(params: ScopeParams) -> ScopePipeline: pipe = LongLivePipeline(config, device=device, dtype=torch.bfloat16) logging.info("LongLive pipeline loaded successfully") return pipe + +def _load_krea_realtime_video_pipeline(params: ScopeParams) -> ScopePipeline: + """Load the Krea Realtime Video pipeline synchronously. + + Args: + params: ScopeParams instance with pipeline configuration + + Returns: + Pipeline instance from scope.core.pipelines + """ + from scope.core.pipelines import KreaRealtimeVideoPipeline + + logging.info(f"Loading Krea Realtime Video pipeline from {MODELS_DIR}") + + config = OmegaConf.create( + { + "model_dir": str(MODELS_DIR), + "generator_path": str( + MODELS_DIR / "krea-realtime-video/krea-realtime-video-14b.safetensors" + ), + "text_encoder_path": str( + MODELS_DIR / "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ), + "tokenizer_path": str( + MODELS_DIR / "Wan2.1-T2V-1.3B/google/umt5-xxl" + ), + "vae_path": str(MODELS_DIR / "Wan2.1-T2V-1.3B/Wan2.1_VAE.pth"), + "height": params.height, + "width": params.width, + "seed": params.seed, + } + ) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + pipe = KreaRealtimeVideoPipeline(config, device=device, dtype=torch.bfloat16) + logging.info("Krea Realtime Video pipeline loaded successfully") + return pipe + +def _load_streamdiffusionv2_pipeline(params: ScopeParams) -> ScopePipeline: + """Load the StreamDiffusionV2 pipeline synchronously. + + Args: + params: ScopeParams instance with pipeline configuration + + Returns: + Pipeline instance from scope.core.pipelines + """ + from scope.core.pipelines import StreamDiffusionV2Pipeline + + logging.info(f"Loading StreamDiffusionV2 pipeline from {MODELS_DIR}") + + config = OmegaConf.create( + { + "model_dir": str(MODELS_DIR), + "generator_path": str( + MODELS_DIR / "StreamDiffusionV2/wan_causal_dmd_v2v/model.pt" + ), + "text_encoder_path": str( + MODELS_DIR / "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ), + "tokenizer_path": str(MODELS_DIR / "Wan2.1-T2V-1.3B/google/umt5-xxl"), + "height": params.height, + "width": params.width, + "seed": params.seed, + } + ) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + pipe = StreamDiffusionV2Pipeline(config, device=device, dtype=torch.bfloat16) + logging.info("StreamDiffusionV2 pipeline loaded successfully") + return pipe \ No newline at end of file diff --git a/uv.lock b/uv.lock index 2808c5b..e092b31 100644 --- 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diff --git a/src/scope_runner/pipeline/params.py b/src/scope_runner/pipeline/params.py index b506ffc..dcd2f6b 100644 --- a/src/scope_runner/pipeline/params.py +++ b/src/scope_runner/pipeline/params.py @@ -17,7 +17,7 @@ class ScopeParams(BaseParams): Scope pipeline parameters for longlive text-to-video generation. """ - pipeline: Literal["longlive", "krea_realtime_video", "streamdiffusionv2"] = "streamdiffusionv2" + pipeline: Literal["longlive", "krea_realtime_video", "streamdiffusionv2"] = "longlive" """The scope pipeline to use. Supported: 'longlive', 'krea_realtime_video', 'streamdiffusionv2'.""" prompts: List[Union[str, WeightedPrompt]] = Field( diff --git a/src/scope_runner/pipeline/pipeline.py b/src/scope_runner/pipeline/pipeline.py index 9c3597f..2a4dd7e 100644 --- a/src/scope_runner/pipeline/pipeline.py +++ b/src/scope_runner/pipeline/pipeline.py @@ -152,14 +152,28 @@ async def stop(self): @classmethod def prepare_models(cls): - """Download all scope models.""" + """Download scope model. + + Reads pipeline name from SCOPE_PIPELINE environment variable. + + Raises: + ValueError: If SCOPE_PIPELINE environment variable is not set + """ + import os + + # Get pipeline name from environment variable (required) + pipeline = os.environ.get("SCOPE_PIPELINE") + if not pipeline: + raise ValueError("SCOPE_PIPELINE environment variable must be set") + logging.info("Preparing Scope models") logging.info(f"Models directory: {MODELS_DIR}") + logging.info(f"Pipeline: {pipeline}") # Import and call scope's download function directly from scope.server.download_models import download_models - download_models() # Downloads all scope pipelines + download_models(pipeline) logging.info("Scope model preparation complete") From f4344ea7872752b40a45e11ccfa5a10d20f49a34 Mon Sep 17 00:00:00 2001 From: Max Holland Date: Mon, 26 Jan 2026 16:44:40 +0000 Subject: [PATCH 7/8] Fix docker image --- Dockerfile | 30 ++++++++++- fal_app.py | 33 ++++++++++++- src/scope_runner/pipeline/params.py | 4 +- src/scope_runner/pipeline/pipeline.py | 71 ++++++++++++++++++++++++++- 4 files changed, 133 insertions(+), 5 deletions(-) diff --git a/Dockerfile b/Dockerfile index c3aba60..df80212 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,6 +1,7 @@ ARG BASE_IMAGE=livepeer/ai-runner:live-base-v0.14.1 FROM ${BASE_IMAGE} +# Install Python 3.12 and gcc-13 FIRST before creating venv RUN apt update && apt install -yqq \ wget git curl \ build-essential software-properties-common \ @@ -9,18 +10,44 @@ RUN apt update && apt install -yqq \ python3-dev \ && apt clean && rm -rf /var/lib/apt/lists/* +RUN apt-get update && apt-get install -y \ + software-properties-common \ + && add-apt-repository ppa:deadsnakes/ppa -y \ + && add-apt-repository ppa:ubuntu-toolchain-r/test -y \ + && apt-get update \ + && apt-get install -y \ + python3.12 \ + python3.12-dev \ + python3.12-venv \ + gcc-13 \ + g++-13 \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +# Set Python 3.12 as the default python3 and set up compiler alternatives +RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.12 1 \ + && update-alternatives --set python3 /usr/bin/python3.12 \ + && ln -sf /usr/bin/python3.12 /usr/bin/python \ + && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-13 100 \ + && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-13 100 + WORKDIR /app COPY pyproject.toml uv.lock ./ # Copy stub for editable install validation COPY src/scope_runner/pipeline/__init__.py ./src/scope_runner/pipeline/ +# Now sync with Python 3.12 and gcc-13 RUN uv sync --locked --no-install-project COPY src/scope_runner/ ./src/scope_runner/ RUN uv sync --locked +# Force rebuild sageattention from source with the new environment (Python 3.12 + gcc-13) +# This replaces the precompiled wheel with a version built for this container +RUN uv pip install --force-reinstall --no-binary sageattention sageattention + ENV HF_HUB_OFFLINE=1 ARG GIT_SHA @@ -29,4 +56,5 @@ ARG VERSION="undefined" ENV GIT_SHA="${GIT_SHA}" \ VERSION="${VERSION}" -CMD ["uv", "run", "--frozen", "scope-runner"] +# Use the venv binary directly to avoid uv reinstalling the precompiled sageattention wheel +CMD ["/app/.venv/bin/scope-runner"] diff --git a/fal_app.py b/fal_app.py index 191955e..edb54ee 100644 --- a/fal_app.py +++ b/fal_app.py @@ -10,12 +10,34 @@ from fal.container import ContainerImage # Configuration -DOCKER_IMAGE = "livepeer/ai-runner:live-app-scope-sha-d7da222" +DOCKER_IMAGE = "livepeer/ai-runner:live-app-scope-sha-82fa215" # Create a Dockerfile that uses your existing image as base dockerfile_str = f""" FROM {DOCKER_IMAGE} +# Install Python 3.12 and required system dependencies +RUN apt-get update && apt-get install -y \ + software-properties-common \ + && add-apt-repository ppa:deadsnakes/ppa -y \ + && add-apt-repository ppa:ubuntu-toolchain-r/test -y \ + && apt-get update \ + && apt-get install -y \ + python3.12 \ + python3.12-dev \ + python3.12-venv \ + gcc-13 \ + g++-13 \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +# Set Python 3.12 as the default python3 and set up compiler alternatives +RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.12 1 \ + && update-alternatives --set python3 /usr/bin/python3.12 \ + && ln -sf /usr/bin/python3.12 /usr/bin/python \ + && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-13 100 \ + && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-13 100 + # The base image already has everything configured # Ensure the working directory and dependencies are available WORKDIR /app @@ -23,6 +45,10 @@ # Make sure uv and dependencies are in the PATH ENV PATH="/root/.cargo/bin:$PATH" +# Force rebuild sageattention from source with the new environment (Python 3.12 + gcc-13) +# This replaces the precompiled wheel with a version built for this container +RUN uv pip install --force-reinstall --no-binary sageattention sageattention + # Verify the installation is accessible RUN python -c "from scope.core.config import MODELS_DIR_ENV_VAR; print('Scope installed')" || \ (cd /app && uv sync --locked) @@ -106,6 +132,11 @@ def setup(self): prepare_env["PIPELINE"] = "scope" prepare_env["MODEL_DIR"] = "/data/models" prepare_env["HF_HUB_OFFLINE"] = "0" + # prepare_env["SCOPE_PIPELINE"] = "reward-forcing" + # prepare_env["SCOPE_PIPELINE"] = "memflow" + # prepare_env["SCOPE_PIPELINE"] = "video-depth-anything" + prepare_env["SCOPE_PIPELINE"] = "rife" + # Commented out for now to speed up startup # print("Preparing models...") diff --git a/src/scope_runner/pipeline/params.py b/src/scope_runner/pipeline/params.py index dcd2f6b..c10e96d 100644 --- a/src/scope_runner/pipeline/params.py +++ b/src/scope_runner/pipeline/params.py @@ -17,8 +17,8 @@ class ScopeParams(BaseParams): Scope pipeline parameters for longlive text-to-video generation. """ - pipeline: Literal["longlive", "krea_realtime_video", "streamdiffusionv2"] = "longlive" - """The scope pipeline to use. Supported: 'longlive', 'krea_realtime_video', 'streamdiffusionv2'.""" + pipeline: Literal["longlive", "krea_realtime_video", "streamdiffusionv2", "reward_forcing", "memflow"] = "krea_realtime_video" + """The scope pipeline to use. Supported: 'longlive', 'krea_realtime_video', 'streamdiffusionv2', 'reward_forcing', 'memflow'.""" prompts: List[Union[str, WeightedPrompt]] = Field( default_factory=lambda: [ diff --git a/src/scope_runner/pipeline/pipeline.py b/src/scope_runner/pipeline/pipeline.py index 2a4dd7e..ffb102f 100644 --- a/src/scope_runner/pipeline/pipeline.py +++ b/src/scope_runner/pipeline/pipeline.py @@ -103,6 +103,10 @@ async def initialize(self, **params): self.pipe = await asyncio.to_thread(_load_krea_realtime_video_pipeline, self.params) elif self.params.pipeline == "streamdiffusionv2": self.pipe = await asyncio.to_thread(_load_streamdiffusionv2_pipeline, self.params) + elif self.params.pipeline == "reward_forcing": + self.pipe = await asyncio.to_thread(_load_reward_forcing_pipeline, self.params) + elif self.params.pipeline == "memflow": + self.pipe = await asyncio.to_thread(_load_memflow_pipeline, self.params) else: raise ValueError(f"Unsupported pipeline: {self.params.pipeline}") @@ -284,4 +288,69 @@ def _load_streamdiffusionv2_pipeline(params: ScopeParams) -> ScopePipeline: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") pipe = StreamDiffusionV2Pipeline(config, device=device, dtype=torch.bfloat16) logging.info("StreamDiffusionV2 pipeline loaded successfully") - return pipe \ No newline at end of file + return pipe + +def _load_reward_forcing_pipeline(params: ScopeParams) -> ScopePipeline: + """Load the Reward Forcing pipeline synchronously. + + Args: + params: ScopeParams instance with pipeline configuration + + Returns: + Pipeline instance from scope.core.pipelines + """ + from scope.core.pipelines import RewardForcingPipeline + + logging.info(f"Loading Reward Forcing pipeline from {MODELS_DIR}") + + config = OmegaConf.create( + { + "model_dir": str(MODELS_DIR), + "generator_path": str( + MODELS_DIR / "Reward-Forcing-T2V-1.3B/rewardforcing.pt" + ), + "text_encoder_path": str( + MODELS_DIR / "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors" + ), + "tokenizer_path": str( + MODELS_DIR / "Wan2.1-T2V-1.3B/google/umt5-xxl" + ), + "vae_path": str( + MODELS_DIR / "Wan2.1-T2V-1.3B/Wan2.1_VAE.pth" + ), + "height": params.height, + "width": params.width, + "seed": params.seed, + } + ) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + pipe = RewardForcingPipeline(config, device=device, dtype=torch.bfloat16) + logging.info("Reward Forcing pipeline loaded successfully") + return pipe + +def _load_memflow_pipeline(params: ScopeParams) -> ScopePipeline: + """Load the MemFlow pipeline synchronously. + + Args: + params: ScopeParams instance with pipeline configuration + + Returns: + Pipeline instance from scope.core.pipelines + """ + from scope.core.pipelines import MemFlowPipeline + + logging.info(f"Loading MemFlow pipeline from {MODELS_DIR}") + + config = OmegaConf.create( + { + "model_dir": str(MODELS_DIR), + "generator_path": str(MODELS_DIR / "MemFlow/base.pt"), + "lora_path": str(MODELS_DIR / "MemFlow/lora.pt"), + "text_encoder_path": str(MODELS_DIR / "WanVideo_comfy/umt5-xxl-enc-fp8_e4m3fn.safetensors"), + "tokenizer_path": str(MODELS_DIR / "Wan2.1-T2V-1.3B/google/umt5-xxl"), + "height": params.height, + "width": params.width, + "seed": params.seed, + } + ) \ No newline at end of file From 4f3e10add6640fe231201c0303bcdd225a8eb3d6 Mon Sep 17 00:00:00 2001 From: Max Holland Date: Mon, 26 Jan 2026 17:40:27 +0000 Subject: [PATCH 8/8] back to longlive default --- fal_app.py | 33 ++--------------------------- src/scope_runner/pipeline/params.py | 2 +- 2 files changed, 3 insertions(+), 32 deletions(-) diff --git a/fal_app.py b/fal_app.py index edb54ee..921ce2c 100644 --- a/fal_app.py +++ b/fal_app.py @@ -10,34 +10,12 @@ from fal.container import ContainerImage # Configuration -DOCKER_IMAGE = "livepeer/ai-runner:live-app-scope-sha-82fa215" +DOCKER_IMAGE = "livepeer/ai-runner:live-app-scope-sha-f25bf35" # Create a Dockerfile that uses your existing image as base dockerfile_str = f""" FROM {DOCKER_IMAGE} -# Install Python 3.12 and required system dependencies -RUN apt-get update && apt-get install -y \ - software-properties-common \ - && add-apt-repository ppa:deadsnakes/ppa -y \ - && add-apt-repository ppa:ubuntu-toolchain-r/test -y \ - && apt-get update \ - && apt-get install -y \ - python3.12 \ - python3.12-dev \ - python3.12-venv \ - gcc-13 \ - g++-13 \ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* - -# Set Python 3.12 as the default python3 and set up compiler alternatives -RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.12 1 \ - && update-alternatives --set python3 /usr/bin/python3.12 \ - && ln -sf /usr/bin/python3.12 /usr/bin/python \ - && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-13 100 \ - && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-13 100 - # The base image already has everything configured # Ensure the working directory and dependencies are available WORKDIR /app @@ -45,13 +23,6 @@ # Make sure uv and dependencies are in the PATH ENV PATH="/root/.cargo/bin:$PATH" -# Force rebuild sageattention from source with the new environment (Python 3.12 + gcc-13) -# This replaces the precompiled wheel with a version built for this container -RUN uv pip install --force-reinstall --no-binary sageattention sageattention - -# Verify the installation is accessible -RUN python -c "from scope.core.config import MODELS_DIR_ENV_VAR; print('Scope installed')" || \ - (cd /app && uv sync --locked) """ # For private registries, configure registries parameter: @@ -179,7 +150,7 @@ def start_server(): import sys # The container has uv and the project installed subprocess.run( - ["uv", "run", "--frozen", "scope-runner"], + ["/app/.venv/bin/scope-runner"], cwd="/app", check=True, env=prepare_env, diff --git a/src/scope_runner/pipeline/params.py b/src/scope_runner/pipeline/params.py index c10e96d..08ae07f 100644 --- a/src/scope_runner/pipeline/params.py +++ b/src/scope_runner/pipeline/params.py @@ -17,7 +17,7 @@ class ScopeParams(BaseParams): Scope pipeline parameters for longlive text-to-video generation. """ - pipeline: Literal["longlive", "krea_realtime_video", "streamdiffusionv2", "reward_forcing", "memflow"] = "krea_realtime_video" + pipeline: Literal["longlive", "krea_realtime_video", "streamdiffusionv2", "reward_forcing", "memflow"] = "longlive" """The scope pipeline to use. Supported: 'longlive', 'krea_realtime_video', 'streamdiffusionv2', 'reward_forcing', 'memflow'.""" prompts: List[Union[str, WeightedPrompt]] = Field(