update parallel flow
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@ -73,6 +73,7 @@ From there, it's easy to implement popular design patterns like ([Multi-](https:
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| [Multi-Agent](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-multi-agent) | ★☆☆ <br> *Beginner* | A Taboo word game for asynchronous communication between two agents |
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| [Supervisor](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-supervisor) | ★☆☆ <br> *Beginner* | Research agent is getting unreliable... Let's build a supervision process|
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| [Parallel](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-parallel-batch) | ★☆☆ <br> *Beginner* | A parallel execution demo that shows 3x speedup |
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| [Parallel Flow](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-parallel-batch-flow) | ★☆☆ <br> *Beginner* | A parallel image processing demo showing 8x speedup with multiple filters |
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| [Thinking](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-thinking) | ★☆☆ <br> *Beginner* | Solve complex reasoning problems through Chain-of-Thought |
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| [Memory](https://github.com/The-Pocket/PocketFlow/tree/main/cookbook/pocketflow-chat-memory) | ★☆☆ <br> *Beginner* | A chat bot with short-term and long-term memory |
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@ -1,75 +1,61 @@
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# Parallel Image Processor (AsyncParallelBatchFlow Example)
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# Parallel Image Processor
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This example demonstrates how to use `AsyncParallelBatchFlow` to process multiple images with multiple filters in parallel.
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Demonstrates how AsyncParallelBatchFlow processes multiple images with multiple filters >8x faster than sequential processing.
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## How it Works
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## Features
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1. **Image Generation**: Creates sample images (gradient, checkerboard, circles)
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2. **Filter Application**: Applies different filters (grayscale, blur, sepia) to each image
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3. **Parallel Processing**: Processes all image-filter combinations concurrently
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```mermaid
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graph TD
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subgraph AsyncParallelBatchFlow[Image Processing Flow]
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subgraph AsyncFlow[Per Image-Filter Flow]
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A[Load Image] --> B[Apply Filter]
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B --> C[Save Image]
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end
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end
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```
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### Flow Structure
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- Processes images with multiple filters in parallel
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- Applies three different filters (grayscale, blur, sepia)
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- Shows significant speed improvement over sequential processing
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- Manages system resources with semaphores
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```mermaid
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graph TD
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subgraph AsyncParallelBatchFlow[Image Processing Flow]
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subgraph AsyncFlow[Per Image-Filter Flow]
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A[Load Image] --> B[Apply Filter]
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B --> C[Save Image]
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end
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end
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```
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## Run It
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### Key Components
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1. **LoadImage (AsyncNode)**
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- Loads an image from file
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- Uses PIL for image handling
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2. **ApplyFilter (AsyncNode)**
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- Applies the specified filter
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- Supports grayscale, blur, and sepia
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3. **SaveImage (AsyncNode)**
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- Saves the processed image
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- Creates output directory if needed
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4. **ImageBatchFlow (AsyncParallelBatchFlow)**
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- Manages parallel processing of all image-filter combinations
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- Returns parameters for each sub-flow
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## Running the Example
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1. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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2. Run the example:
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```bash
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python main.py
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```
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## Sample Output
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## Output
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The example will:
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1. Create 3 sample images: `cat.jpg`, `dog.jpg`, `bird.jpg`
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2. Apply 3 filters to each image
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3. Save results in `output/` directory (9 total images)
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```=== Processing Images in Parallel ===
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Parallel Image Processor
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------------------------------
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Found 3 images:
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- images/bird.jpg
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- images/cat.jpg
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- images/dog.jpg
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Example output structure:
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```
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output/
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├── cat_grayscale.jpg
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├── cat_blur.jpg
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├── cat_sepia.jpg
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├── dog_grayscale.jpg
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Running sequential batch flow...
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Processing 3 images with 3 filters...
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Total combinations: 9
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Loading image: images/bird.jpg
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Applying grayscale filter...
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Saved: output/bird_grayscale.jpg
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...etc
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Timing Results:
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Sequential batch processing: 13.76 seconds
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Parallel batch processing: 1.71 seconds
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Speedup: 8.04x
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Processing complete! Check the output/ directory for results.
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```
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## Key Concepts
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## Key Points
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1. **Parallel Flow Execution**: Each image-filter combination runs as a separate flow in parallel
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2. **Parameter Management**: The batch flow generates parameters for each sub-flow
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3. **Resource Management**: Uses semaphores to limit concurrent image processing
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4. **Error Handling**: Gracefully handles failures in individual flows
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- **Sequential**: Total time = sum of all item times
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- Good for: Rate-limited APIs, maintaining order
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- **Parallel**: Total time ≈ longest single item time
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- Good for: I/O-bound tasks, independent operations
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@ -1,6 +1,6 @@
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"""Flow definitions for parallel image processing."""
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from pocketflow import AsyncFlow, AsyncParallelBatchFlow
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from pocketflow import AsyncFlow, AsyncParallelBatchFlow, AsyncBatchFlow
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from nodes import LoadImage, ApplyFilter, SaveImage, NoOp
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def create_base_flow():
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@ -17,9 +17,31 @@ def create_base_flow():
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save - "default" >> noop
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# Create flow
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return AsyncFlow(start=load)
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return load
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class ImageBatchFlow(AsyncParallelBatchFlow):
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class ImageBatchFlow(AsyncBatchFlow):
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"""Flow that processes multiple images with multiple filters in batch."""
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async def prep_async(self, shared):
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"""Generate parameters for each image-filter combination."""
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# Get list of images and filters
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images = shared.get("images", [])
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filters = ["grayscale", "blur", "sepia"]
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# Create parameter combinations
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params = []
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for image_path in images:
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for filter_type in filters:
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params.append({
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"image_path": image_path,
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"filter": filter_type
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})
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print(f"Processing {len(images)} images with {len(filters)} filters...")
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print(f"Total combinations: {len(params)}")
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return params
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class ImageParallelBatchFlow(AsyncParallelBatchFlow):
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"""Flow that processes multiple images with multiple filters in parallel."""
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async def prep_async(self, shared):
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@ -37,14 +59,14 @@ class ImageBatchFlow(AsyncParallelBatchFlow):
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"filter": filter_type
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})
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print(f"\nProcessing {len(images)} images with {len(filters)} filters...")
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print(f"Processing {len(images)} images with {len(filters)} filters...")
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print(f"Total combinations: {len(params)}")
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return params
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def create_flow():
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def create_flows():
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"""Create the complete parallel processing flow."""
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# Create base flow for single image processing
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base_flow = create_base_flow()
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# Wrap in parallel batch flow
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return ImageBatchFlow(start=base_flow)
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return ImageBatchFlow(start=base_flow), ImageParallelBatchFlow(start=base_flow)
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@ -1,8 +1,7 @@
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import os
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import asyncio
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import numpy as np
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from PIL import Image
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from flow import create_flow
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import time
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from flow import create_flows
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def get_image_paths():
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"""Get paths of existing images in the images directory."""
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@ -19,7 +18,7 @@ def get_image_paths():
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if not image_paths:
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raise ValueError(f"No images found in '{images_dir}' directory!")
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print(f"\nFound {len(image_paths)} images:")
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print(f"Found {len(image_paths)} images:")
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for path in image_paths:
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print(f"- {path}")
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@ -27,7 +26,7 @@ def get_image_paths():
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async def main():
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"""Run the parallel image processing example."""
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print("\nParallel Image Processor")
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print("Parallel Image Processor")
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print("-" * 30)
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# Get existing image paths
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@ -36,10 +35,26 @@ async def main():
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# Create shared store with image paths
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shared = {"images": image_paths}
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# Create and run flow
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flow = create_flow()
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# Create both flows
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batch_flow, parallel_batch_flow = create_flows()
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await flow.run_async(shared)
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# Run and time batch flow
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start_time = time.time()
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print("\nRunning sequential batch flow...")
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await batch_flow.run_async(shared)
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batch_time = time.time() - start_time
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# Run and time parallel batch flow
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start_time = time.time()
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print("\nRunning parallel batch flow...")
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await parallel_batch_flow.run_async(shared)
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parallel_time = time.time() - start_time
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# Print timing results
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print("\nTiming Results:")
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print(f"Sequential batch processing: {batch_time:.2f} seconds")
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print(f"Parallel batch processing: {parallel_time:.2f} seconds")
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print(f"Speedup: {batch_time/parallel_time:.2f}x")
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print("\nProcessing complete! Check the output/ directory for results.")
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"""AsyncNode implementations for image processing."""
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import os
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import asyncio
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from PIL import Image, ImageFilter
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import numpy as np
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from pocketflow import AsyncNode
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class NoOp(AsyncNode):
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"""Node that does nothing, used as a terminal node."""
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async def prep_async(self, shared):
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"""No preparation needed."""
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return None
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async def exec_async(self, prep_res):
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"""No execution needed."""
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return None
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async def post_async(self, shared, prep_res, exec_res):
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"""No post-processing needed."""
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return None
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class LoadImage(AsyncNode):
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"""Node that loads an image from file."""
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async def prep_async(self, shared):
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"""Get image path from parameters."""
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image_path = self.params["image_path"]
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print(f"\nLoading image: {image_path}")
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print(f"Loading image: {image_path}")
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return image_path
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async def exec_async(self, image_path):
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"""Load image using PIL."""
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# Simulate I/O delay
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await asyncio.sleep(0.1)
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await asyncio.sleep(0.5)
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return Image.open(image_path)
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async def post_async(self, shared, prep_res, exec_res):
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class ApplyFilter(AsyncNode):
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"""Node that applies a filter to an image."""
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async def prep_async(self, shared):
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"""Get image and filter type."""
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image = shared["image"]
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class SaveImage(AsyncNode):
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"""Node that saves the processed image."""
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async def prep_async(self, shared):
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"""Prepare output path."""
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image = shared["filtered_image"]
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image, output_path = inputs
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# Simulate I/O delay
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await asyncio.sleep(0.1)
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await asyncio.sleep(0.5)
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image.save(output_path)
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return output_path
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@ -29,7 +29,7 @@ We model the LLM workflow as a **Graph + Shared Store**:
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- [(Advanced) Parallel](./core_abstraction/parallel.md) nodes/flows handle I/O-bound tasks.
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<div align="center">
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<img src="https://github.com/the-pocket/.github/raw/main/assets/abstraction.png" width="900"/>
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<img src="https://github.com/the-pocket/.github/raw/main/assets/abstraction.png" width="700"/>
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</div>
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## Design Pattern
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@ -44,7 +44,7 @@ From there, it’s easy to implement popular design patterns:
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- [(Advanced) Multi-Agents](./design_pattern/multi_agent.md) coordinate multiple agents.
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<div align="center">
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<img src="https://github.com/the-pocket/.github/raw/main/assets/design.png" width="900"/>
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<img src="https://github.com/the-pocket/.github/raw/main/assets/design.png" width="700"/>
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</div>
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## Utility Function
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