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feat: remove GPU support and simplify to CPU-only architecture
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Developer
2026-02-19 12:41:13 +01:00
parent cff3eb0add
commit 706e6c431d
16 changed files with 116 additions and 323 deletions
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141
.gitea/workflows/build.yml
View File

@@ -1,141 +0,0 @@
name: Build and Push Docker Image
on:
push:
branches:
- main
- develop
tags:
- 'v*.*.*'
pull_request:
branches:
- main
workflow_dispatch:
inputs:
tag:
description: 'Custom tag for the image'
required: false
default: 'manual'
env:
REGISTRY: dev.pivoine.art
IMAGE_NAME: valknar/realesrgan-api
jobs:
build-gpu:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
submodules: recursive
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
platforms: linux/amd64
- name: Log in to Gitea Container Registry
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ gitea.actor }}
password: ${{ secrets.REGISTRY_TOKEN }}
- name: Extract metadata (GPU)
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=raw,value=latest,enable={{is_default_branch}}
type=ref,event=branch
type=ref,event=pr
type=semver,pattern={{version}}
type=semver,pattern={{major}}.{{minor}}
type=semver,pattern={{major}}
type=sha,prefix={{branch}}-
type=raw,value=${{ gitea.event.inputs.tag }},enable=${{ gitea.event_name == 'workflow_dispatch' }}
labels: |
org.opencontainers.image.title=realesrgan-api
org.opencontainers.image.description=REST API for Real-ESRGAN image upscaling (GPU)
org.opencontainers.image.vendor=valknar
- name: Build and push GPU image
uses: docker/build-push-action@v5
with:
context: .
platforms: linux/amd64
push: ${{ gitea.event_name != 'pull_request' }}
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
build-args: VARIANT=gpu
cache-from: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache-gpu
cache-to: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache-gpu,mode=max
- name: Summary
if: gitea.event_name != 'pull_request'
run: |
echo "### GPU Image Published" >> $GITEA_STEP_SUMMARY
echo "**Tags:**" >> $GITEA_STEP_SUMMARY
echo "\`\`\`" >> $GITEA_STEP_SUMMARY
echo "${{ steps.meta.outputs.tags }}" >> $GITEA_STEP_SUMMARY
echo "\`\`\`" >> $GITEA_STEP_SUMMARY
build-cpu:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
submodules: recursive
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
platforms: linux/amd64
- name: Log in to Gitea Container Registry
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ gitea.actor }}
password: ${{ secrets.REGISTRY_TOKEN }}
- name: Extract metadata (CPU)
id: meta-cpu
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
flavor: suffix=-cpu
tags: |
type=raw,value=latest-cpu,enable={{is_default_branch}}
type=ref,event=branch,suffix=-cpu
type=ref,event=pr,suffix=-cpu
type=semver,pattern={{version}},suffix=-cpu
type=sha,prefix={{branch}}-,suffix=-cpu
labels: |
org.opencontainers.image.title=realesrgan-api
org.opencontainers.image.description=REST API for Real-ESRGAN image upscaling (CPU)
org.opencontainers.image.vendor=valknar
- name: Build and push CPU image
uses: docker/build-push-action@v5
with:
context: .
platforms: linux/amd64
push: ${{ gitea.event_name != 'pull_request' }}
tags: ${{ steps.meta-cpu.outputs.tags }}
labels: ${{ steps.meta-cpu.outputs.labels }}
build-args: VARIANT=cpu
cache-from: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache-cpu
cache-to: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache-cpu,mode=max
- name: Summary
if: gitea.event_name != 'pull_request'
run: |
echo "### CPU Image Published" >> $GITEA_STEP_SUMMARY
echo "**Tags:**" >> $GITEA_STEP_SUMMARY
echo "\`\`\`" >> $GITEA_STEP_SUMMARY
echo "${{ steps.meta-cpu.outputs.tags }}" >> $GITEA_STEP_SUMMARY
echo "\`\`\`" >> $GITEA_STEP_SUMMARY

82
.gitea/workflows/docker-build-push.yml Normal file
View File

@@ -0,0 +1,82 @@
name: Build and Push Docker Image
on:
push:
branches:
- main
- develop
tags:
- 'v*.*.*'
pull_request:
branches:
- main
workflow_dispatch:
inputs:
tag:
description: 'Custom tag for the image'
required: false
default: 'manual'
env:
REGISTRY: dev.pivoine.art
IMAGE_NAME: valknar/realesrgan-api
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
submodules: recursive
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
with:
platforms: linux/amd64
- name: Log in to Gitea Container Registry
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ gitea.actor }}
password: ${{ secrets.REGISTRY_TOKEN }}
- name: Extract metadata
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=raw,value=latest,enable={{is_default_branch}}
type=ref,event=branch
type=ref,event=pr
type=semver,pattern={{version}}
type=semver,pattern={{major}}.{{minor}}
type=semver,pattern={{major}}
type=sha,prefix={{branch}}-
type=raw,value=${{ gitea.event.inputs.tag }},enable=${{ gitea.event_name == 'workflow_dispatch' }}
labels: |
org.opencontainers.image.title=realesrgan-api
org.opencontainers.image.description=REST API for Real-ESRGAN image upscaling
org.opencontainers.image.vendor=valknar
- name: Build and push image
uses: docker/build-push-action@v5
with:
context: .
platforms: linux/amd64
push: ${{ gitea.event_name != 'pull_request' }}
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
cache-from: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache
cache-to: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache,mode=max
- name: Summary
if: gitea.event_name != 'pull_request'
run: |
echo "### Image Published" >> $GITEA_STEP_SUMMARY
echo "**Tags:**" >> $GITEA_STEP_SUMMARY
echo "\`\`\`" >> $GITEA_STEP_SUMMARY
echo "${{ steps.meta.outputs.tags }}" >> $GITEA_STEP_SUMMARY
echo "\`\`\`" >> $GITEA_STEP_SUMMARY

5
API_USAGE.md
View File

@@ -405,10 +405,7 @@ Detailed system information and resource usage.
"cpu_usage_percent": 25.3, "cpu_usage_percent": 25.3,
"memory_usage_percent": 42.1, "memory_usage_percent": 42.1,
"disk_usage_percent": 15.2, "disk_usage_percent": 15.2,
"gpu_available": true, "execution_providers": ["cpu"],
"gpu_memory_mb": 8192,
"gpu_memory_used_mb": 2048,
"execution_providers": ["cuda"],
"models_dir_size_mb": 268.4, "models_dir_size_mb": 268.4,
"jobs_queue_length": 3 "jobs_queue_length": 3
} }

40
CLAUDE.md
View File

@@ -4,7 +4,7 @@ This file provides guidance to Claude Code when working with this repository.
## Overview ## Overview
This is the Real-ESRGAN API project - a sophisticated, full-featured REST API for image upscaling using Real-ESRGAN. The API supports both synchronous and asynchronous (job-based) processing with Docker containerization for CPU and GPU deployments. This is the Real-ESRGAN API project - a sophisticated, full-featured REST API for image upscaling using Real-ESRGAN. The API supports both synchronous and asynchronous (job-based) processing with Docker containerization for CPU deployments.
## Architecture ## Architecture
@@ -32,11 +32,11 @@ This is the Real-ESRGAN API project - a sophisticated, full-featured REST API fo
## Development Workflow ## Development Workflow
### Local Setup (CPU) ### Local Setup
```bash ```bash
# Install dependencies # Install dependencies
pip install -r requirements.txt -r requirements-cpu.txt pip install -r requirements.txt
# Run development server # Run development server
python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000 python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
@@ -49,7 +49,7 @@ python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
### Docker Development ### Docker Development
```bash ```bash
# Build CPU image # Build image
docker compose build docker compose build
# Run container # Run container
@@ -62,19 +62,6 @@ docker compose logs -f api
docker compose down docker compose down
``` ```
### GPU Development
```bash
# Build GPU image
docker compose -f docker-compose.gpu.yml build
# Run with GPU
docker compose -f docker-compose.gpu.yml up -d
# Check GPU usage
docker compose -f docker-compose.gpu.yml exec api nvidia-smi
```
## Configuration ## Configuration
### Environment Variables (prefix: RSR_) ### Environment Variables (prefix: RSR_)
@@ -85,7 +72,7 @@ All settings from `app/config.py` can be configured via environment:
RSR_UPLOAD_DIR=/data/uploads RSR_UPLOAD_DIR=/data/uploads
RSR_OUTPUT_DIR=/data/outputs RSR_OUTPUT_DIR=/data/outputs
RSR_MODELS_DIR=/data/models RSR_MODELS_DIR=/data/models
RSR_EXECUTION_PROVIDERS=["cpu"] # or ["cuda"] for GPU RSR_EXECUTION_PROVIDERS=["cpu"]
RSR_TILE_SIZE=400 # Tile size for large images RSR_TILE_SIZE=400 # Tile size for large images
RSR_MAX_UPLOAD_SIZE_MB=500 RSR_MAX_UPLOAD_SIZE_MB=500
RSR_SYNC_TIMEOUT_SECONDS=300 RSR_SYNC_TIMEOUT_SECONDS=300
@@ -93,7 +80,7 @@ RSR_SYNC_TIMEOUT_SECONDS=300
### Docker Compose Environment ### Docker Compose Environment
Set in `docker-compose.yml` or `docker-compose.gpu.yml` `environment` section. Set in `docker-compose.yml` `environment` section.
## API Endpoints ## API Endpoints
@@ -152,7 +139,7 @@ This project follows similar patterns to facefusion-api:
- **File Management**: Same `file_manager.py` utilities - **File Management**: Same `file_manager.py` utilities
- **Worker Queue**: Similar async job processing architecture - **Worker Queue**: Similar async job processing architecture
- **Docker Setup**: Multi-variant CPU/GPU builds - **Docker Setup**: CPU-only builds
- **Configuration**: Environment-based settings with pydantic - **Configuration**: Environment-based settings with pydantic
- **Gitea CI/CD**: Automatic Docker image building - **Gitea CI/CD**: Automatic Docker image building
- **API Structure**: Organized routers and services - **API Structure**: Organized routers and services
@@ -199,16 +186,6 @@ curl -X POST http://localhost:8000/api/v1/models/download \
-d '{"models": ["RealESRGAN_x4plus"]}' -d '{"models": ["RealESRGAN_x4plus"]}'
``` ```
### GPU Not Detected
```bash
# Check GPU availability
docker compose -f docker-compose.gpu.yml exec api python -c "import torch; print(torch.cuda.is_available())"
# Check system GPU
nvidia-smi
```
### Permission Issues with Volumes ### Permission Issues with Volumes
```bash ```bash
@@ -231,14 +208,13 @@ git push gitea main
``` ```
Gitea workflows automatically: Gitea workflows automatically:
- Build Docker images (CPU and GPU) - Build Docker image
- Run tests - Run tests
- Publish to Container Registry - Publish to Container Registry
## Important Notes ## Important Notes
- **Model Weights**: Downloaded from GitHub releases (~100MB each) - **Model Weights**: Downloaded from GitHub releases (~100MB each)
- **GPU Support**: Requires NVIDIA Docker runtime
- **Async Processing**: Uses thread pool (configurable workers) - **Async Processing**: Uses thread pool (configurable workers)
- **Tile Processing**: Handles large images by splitting into tiles - **Tile Processing**: Handles large images by splitting into tiles
- **Data Persistence**: Volumes recommended for production - **Data Persistence**: Volumes recommended for production

38
Dockerfile
View File

@@ -1,42 +1,18 @@
ARG VARIANT=cpu # ---- Base Stage ----
FROM python:3.11-slim AS base
# ---- CPU base ----
FROM python:3.11-slim AS base-cpu
# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \ RUN apt-get update && apt-get install -y --no-install-recommends \
curl libgl1 libglib2.0-0 \ curl libgl1 libglib2.0-0 \
&& rm -rf /var/lib/apt/lists/* && rm -rf /var/lib/apt/lists/*
# Install Python dependencies
COPY requirements.txt /tmp/requirements.txt COPY requirements.txt /tmp/requirements.txt
COPY requirements-cpu.txt /tmp/requirements-cpu.txt RUN pip install --no-cache-dir -r /tmp/requirements.txt \
RUN pip install --no-cache-dir -r /tmp/requirements.txt -r /tmp/requirements-cpu.txt \
&& rm /tmp/requirements*.txt && rm /tmp/requirements*.txt
# ---- GPU base (CUDA 12.4) ---- # ---- Final Stage ----
FROM nvidia/cuda:12.4.1-cudnn-runtime-ubuntu22.04 AS base-gpu FROM base AS final
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y --no-install-recommends \
software-properties-common \
&& add-apt-repository ppa:deadsnakes/ppa \
&& apt-get update \
&& apt-get install -y --no-install-recommends \
python3.11 python3.11-venv python3.11-dev \
curl libgl1 libglib2.0-0 \
&& ln -sf /usr/bin/python3.11 /usr/bin/python3 \
&& ln -sf /usr/bin/python3 /usr/bin/python \
&& python3 -m ensurepip --upgrade \
&& python3 -m pip install --no-cache-dir --upgrade pip \
&& rm -rf /var/lib/apt/lists/*
COPY requirements.txt /tmp/requirements.txt
COPY requirements-gpu.txt /tmp/requirements-gpu.txt
RUN pip install --no-cache-dir -r /tmp/requirements.txt -r /tmp/requirements-gpu.txt \
&& rm /tmp/requirements*.txt
# ---- Final stage ----
FROM base-${VARIANT} AS final
WORKDIR /app WORKDIR /app

24
QUICKSTART.md
View File

@@ -1,6 +1,6 @@
# Quick Start Guide # Quick Start Guide
## 1. Local Development (CPU, ~2 minutes) ## 1. Local Development (~2 minutes)
```bash ```bash
# Clone or navigate to project # Clone or navigate to project
@@ -100,7 +100,7 @@ Try endpoints directly in Swagger UI!
# Check system health # Check system health
curl http://localhost:8000/api/v1/health curl http://localhost:8000/api/v1/health
# Get detailed system info (CPU, memory, GPU, etc.) # Get detailed system info (CPU, memory, disk, etc.)
curl http://localhost:8000/api/v1/system curl http://localhost:8000/api/v1/system
# Get API statistics # Get API statistics
@@ -110,25 +110,7 @@ curl http://localhost:8000/api/v1/stats
curl http://localhost:8000/api/v1/jobs curl http://localhost:8000/api/v1/jobs
``` ```
## 6. GPU Setup (Optional, requires NVIDIA) ## 6. Production Deployment
```bash
# Build with GPU support
docker compose -f docker-compose.gpu.yml build
# Run with GPU
docker compose -f docker-compose.gpu.yml up -d
# Verify GPU is accessible
docker compose -f docker-compose.gpu.yml exec api nvidia-smi
# Download models again on GPU
curl -X POST http://localhost:8000/api/v1/models/download \
-H 'Content-Type: application/json' \
-d '{"models": ["RealESRGAN_x4plus"]}'
```
## 7. Production Deployment
```bash ```bash
# Update docker-compose.prod.yml with your registry/domain # Update docker-compose.prod.yml with your registry/domain

25
README.md
View File

@@ -1,6 +1,6 @@
# Real-ESRGAN API # Real-ESRGAN API
REST API wrapping [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) for image upscaling. Features synchronous and asynchronous (job-based) processing with multi-target Docker builds (CPU + CUDA GPU support). REST API wrapping [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) for image upscaling. Features synchronous and asynchronous (job-based) processing with Docker containerization.
## Features ## Features
@@ -9,15 +9,15 @@ REST API wrapping [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) for imag
- **Multi-Model Support**: Multiple Real-ESRGAN models (2x, 3x, 4x upscaling) - **Multi-Model Support**: Multiple Real-ESRGAN models (2x, 3x, 4x upscaling)
- **Batch Processing**: Process up to 100 images per batch request - **Batch Processing**: Process up to 100 images per batch request
- **Model Management**: Download and manage upscaling models - **Model Management**: Download and manage upscaling models
- **Docker Deployment**: CPU and CUDA GPU support with multi-target builds - **Docker Deployment**: Simplified containerized deployment
- **Gitea CI/CD**: Automatic Docker image building and publishing - **Gitea CI/CD**: Automatic Docker image building and publishing
- **API Documentation**: Interactive Swagger UI and ReDoc - **API Documentation**: Interactive Swagger UI and ReDoc
- **Health Checks**: Kubernetes-ready liveness and readiness probes - **Health Checks**: Kubernetes-ready liveness and readiness probes
- **System Monitoring**: CPU, memory, disk, and GPU metrics - **System Monitoring**: CPU, memory, and disk metrics
## Quick Start ## Quick Start
### Local Development (CPU) ### Local Development
```bash ```bash
# Build and run # Build and run
@@ -47,14 +47,6 @@ curl http://localhost:8000/api/v1/jobs/{job_id}
curl http://localhost:8000/api/v1/jobs/{job_id}/result -o output.jpg curl http://localhost:8000/api/v1/jobs/{job_id}/result -o output.jpg
``` ```
### GPU Support
```bash
# Build with GPU support and run
docker compose -f docker-compose.gpu.yml build
docker compose -f docker-compose.gpu.yml up -d
```
## API Endpoints ## API Endpoints
### Upscaling ### Upscaling
@@ -122,14 +114,14 @@ RSR_AUTO_CLEANUP_HOURS=24 # Auto cleanup interval
## Docker Deployment ## Docker Deployment
### Development (CPU) ### Local/Development
```bash ```bash
docker compose build docker compose build
docker compose up -d docker compose up -d
``` ```
### Production (GPU) ### Production
```bash ```bash
docker compose -f docker-compose.prod.yml up -d docker compose -f docker-compose.prod.yml up -d
@@ -139,7 +131,7 @@ docker compose -f docker-compose.prod.yml up -d
The `.gitea/workflows/build.yml` automatically: The `.gitea/workflows/build.yml` automatically:
1. Builds Docker images for CPU and GPU variants 1. Builds Docker image
2. Publishes to Gitea Container Registry 2. Publishes to Gitea Container Registry
3. Tags with git commit SHA and latest 3. Tags with git commit SHA and latest
@@ -174,7 +166,6 @@ app/
- **Synchronous Upscaling**: Best for small images (< 4MP) - **Synchronous Upscaling**: Best for small images (< 4MP)
- **Asynchronous Jobs**: Recommended for large batches or high concurrency - **Asynchronous Jobs**: Recommended for large batches or high concurrency
- **GPU Performance**: 2-5x faster than CPU depending on model and image size
- **Tile Processing**: Efficiently handles images up to 8K resolution - **Tile Processing**: Efficiently handles images up to 8K resolution
## Development ## Development
@@ -182,7 +173,7 @@ app/
Install dependencies: Install dependencies:
```bash ```bash
pip install -r requirements.txt -r requirements-cpu.txt pip install -r requirements.txt
``` ```
Run locally: Run locally:

17
app/routers/health.py
View File

@@ -75,20 +75,6 @@ async def get_system_info() -> SystemInfo:
disk = psutil.disk_usage('/') disk = psutil.disk_usage('/')
disk_percent = disk.percent disk_percent = disk.percent
# GPU
gpu_available = False
gpu_memory_mb = None
gpu_memory_used_mb = None
try:
import torch
gpu_available = torch.cuda.is_available()
if gpu_available:
gpu_memory_mb = int(torch.cuda.get_device_properties(0).total_memory / (1024 * 1024))
gpu_memory_used_mb = int(torch.cuda.memory_allocated(0) / (1024 * 1024))
except Exception:
pass
# Models directory size # Models directory size
models_size = file_manager.get_directory_size_mb(settings.models_dir) models_size = file_manager.get_directory_size_mb(settings.models_dir)
@@ -103,9 +89,6 @@ async def get_system_info() -> SystemInfo:
cpu_usage_percent=cpu_percent, cpu_usage_percent=cpu_percent,
memory_usage_percent=memory_percent, memory_usage_percent=memory_percent,
disk_usage_percent=disk_percent, disk_usage_percent=disk_percent,
gpu_available=gpu_available,
gpu_memory_mb=gpu_memory_mb,
gpu_memory_used_mb=gpu_memory_used_mb,
execution_providers=settings.get_execution_providers(), execution_providers=settings.get_execution_providers(),
models_dir_size_mb=models_size, models_dir_size_mb=models_size,
jobs_queue_length=queue_length, jobs_queue_length=queue_length,

3
app/schemas/health.py
View File

@@ -20,9 +20,6 @@ class SystemInfo(BaseModel):
cpu_usage_percent: float cpu_usage_percent: float
memory_usage_percent: float memory_usage_percent: float
disk_usage_percent: float disk_usage_percent: float
gpu_available: bool
gpu_memory_mb: Optional[int] = None
gpu_memory_used_mb: Optional[int] = None
execution_providers: list execution_providers: list
models_dir_size_mb: float models_dir_size_mb: float
jobs_queue_length: int jobs_queue_length: int

13
app/services/realesrgan_bridge.py
View File

@@ -83,7 +83,7 @@ class RealESRGANBridge:
tile=settings.tile_size, tile=settings.tile_size,
tile_pad=settings.tile_pad, tile_pad=settings.tile_pad,
pre_pad=0, pre_pad=0,
half=('cuda' in settings.get_execution_providers()), half=False,
) )
self.current_model = model_name self.current_model = model_name
@@ -134,7 +134,7 @@ class RealESRGANBridge:
tile=settings.tile_size, tile=settings.tile_size,
tile_pad=settings.tile_pad, tile_pad=settings.tile_pad,
pre_pad=0, pre_pad=0,
half=('cuda' in settings.get_execution_providers()), half=False,
) )
self.current_model = model_name self.current_model = model_name
@@ -198,15 +198,6 @@ class RealESRGANBridge:
return self.upsampler.scale return self.upsampler.scale
return 4 return 4
def clear_memory(self) -> None:
"""Clear GPU memory if available."""
try:
import torch
torch.cuda.empty_cache()
logger.debug('GPU memory cleared')
except Exception:
pass
# Global instance # Global instance
_bridge: Optional[RealESRGANBridge] = None _bridge: Optional[RealESRGANBridge] = None

27
docker-compose.gpu.yml
View File

@@ -1,27 +0,0 @@
services:
api:
build:
context: .
args:
VARIANT: gpu
ports:
- "8000:8000"
volumes:
- ./data/uploads:/data/uploads
- ./data/outputs:/data/outputs
- ./data/models:/data/models
- ./data/temp:/data/temp
- ./data/jobs:/data/jobs
environment:
- RSR_EXECUTION_PROVIDERS=["cuda"]
- RSR_EXECUTION_THREAD_COUNT=8
- RSR_TILE_SIZE=400
- RSR_LOG_LEVEL=info
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
restart: unless-stopped

9
docker-compose.prod.yml
View File

@@ -10,17 +10,10 @@ services:
- temp:/data/temp - temp:/data/temp
- jobs:/data/jobs - jobs:/data/jobs
environment: environment:
- RSR_EXECUTION_PROVIDERS=["cuda"] - RSR_EXECUTION_PROVIDERS=["cpu"]
- RSR_EXECUTION_THREAD_COUNT=8 - RSR_EXECUTION_THREAD_COUNT=8
- RSR_TILE_SIZE=400 - RSR_TILE_SIZE=400
- RSR_LOG_LEVEL=info - RSR_LOG_LEVEL=info
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
restart: always restart: always
healthcheck: healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/api/v1/health"] test: ["CMD", "curl", "-f", "http://localhost:8000/api/v1/health"]

2
docker-compose.yml
View File

@@ -2,8 +2,6 @@ services:
api: api:
build: build:
context: . context: .
args:
VARIANT: cpu
ports: ports:
- "8000:8000" - "8000:8000"
volumes: volumes:

4
requirements-cpu.txt
View File

@@ -1,4 +0,0 @@
torch==2.0.1
torchvision==0.15.2
realesrgan>=0.2.5
basicsr>=1.4.1

5
requirements-gpu.txt
View File

@@ -1,5 +0,0 @@
--extra-index-url https://download.pytorch.org/whl/cu118
torch==2.0.1
torchvision==0.15.2
realesrgan>=0.2.5
basicsr>=1.4.1

4
requirements.txt
View File

@@ -10,3 +10,7 @@ opencv-python==4.8.1.78
numpy==1.24.3 numpy==1.24.3
scipy==1.11.4 scipy==1.11.4
tqdm==4.66.1 tqdm==4.66.1
torch==2.0.1
torchvision==0.15.2
realesrgan>=0.2.5
basicsr>=1.4.1