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77222b8017
Introduce Dirty Arbiters - a separate process pool for executing long-running, blocking operations (AI model loading, heavy computation) without blocking HTTP workers. Inspired by Erlang's dirty schedulers. Key features: - Completely separate from HTTP workers - can be killed/restarted independently - Stateful - loaded resources persist in dirty worker memory - Message-passing IPC via Unix sockets with JSON serialization - Explicit execute() API from HTTP workers - Asyncio-based for clean concurrent handling Architecture: - DirtyArbiter: manages the dirty worker pool, routes requests - DirtyWorker: executes functions, maintains state, handles requests - DirtyClient: sync/async API for HTTP workers to call dirty apps - DirtyProtocol: length-prefixed JSON messages over Unix sockets - DirtyApp: base class for dirty applications Configuration options: - dirty_apps: list of import paths for dirty applications - dirty_workers: number of dirty workers (default: 0) - dirty_timeout: task timeout in seconds (default: 300) - dirty_graceful_timeout: shutdown timeout (default: 30) Lifecycle hooks: - on_dirty_starting(arbiter) - dirty_post_fork(arbiter, worker) - dirty_worker_init(worker) - dirty_worker_exit(arbiter, worker) Includes comprehensive test suite with 164 tests covering: - Protocol encoding/decoding - Worker and arbiter lifecycle - Client sync/async APIs - Signal handling - Error handling and timeouts - Integration tests
161 lines
3.8 KiB
Python
161 lines
3.8 KiB
Python
#!/usr/bin/env python
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"""
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Test script to demonstrate Dirty App functionality directly.
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This tests the dirty app without running the full gunicorn server.
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Run with:
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python examples/dirty_example/test_dirty_app.py
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"""
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import sys
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import os
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# Add parent directory to path
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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from examples.dirty_example.dirty_app import MLApp, ComputeApp
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def test_ml_app():
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"""Test the MLApp dirty application."""
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print("=" * 60)
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print("Testing MLApp")
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print("=" * 60)
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# Create and initialize the app
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app = MLApp()
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print("\n1. Initializing app (loads default model)...")
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app.init()
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# List models
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print("\n2. Listing models...")
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result = app("list_models")
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print(f" Models: {result}")
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# Load another model
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print("\n3. Loading 'gpt-4' model...")
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result = app("load_model", "gpt-4")
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print(f" Result: {result}")
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# List models again
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print("\n4. Listing models again...")
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result = app("list_models")
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print(f" Models: {result}")
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# Run inference
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print("\n5. Running inference on 'default' model...")
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result = app("inference", "default", "Hello, world!")
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print(f" Result: {result}")
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# Run more inferences
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print("\n6. Running more inferences...")
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for i in range(3):
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result = app("inference", "gpt-4", f"Input data {i}")
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print(f" Inference {i+1}: {result['prediction']}")
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# Unload a model
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print("\n7. Unloading 'gpt-4' model...")
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result = app("unload_model", "gpt-4")
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print(f" Result: {result}")
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# Final stats
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print("\n8. Final stats...")
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result = app("list_models")
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print(f" {result}")
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# Close
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print("\n9. Closing app...")
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app.close()
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print("\n" + "=" * 60)
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print("MLApp test complete!")
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print("=" * 60)
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def test_compute_app():
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"""Test the ComputeApp dirty application."""
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print("\n" + "=" * 60)
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print("Testing ComputeApp")
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print("=" * 60)
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# Create and initialize
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app = ComputeApp()
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app.init()
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# Fibonacci
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print("\n1. Computing Fibonacci numbers...")
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for n in [10, 20, 30, 40]:
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result = app("fibonacci", n)
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print(f" fib({n}) = {result['result']}")
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# Prime checks
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print("\n2. Checking prime numbers...")
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for n in [17, 100, 997, 1000]:
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result = app("prime_check", n)
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status = "is prime" if result['is_prime'] else "is NOT prime"
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print(f" {n} {status}")
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# Stats
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print("\n3. Stats...")
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result = app("stats")
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print(f" {result}")
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# Close
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app.close()
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print("\n" + "=" * 60)
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print("ComputeApp test complete!")
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print("=" * 60)
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def test_error_handling():
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"""Test error handling in dirty apps."""
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print("\n" + "=" * 60)
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print("Testing Error Handling")
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print("=" * 60)
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app = MLApp()
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app.init()
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# Try to run inference on non-existent model
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print("\n1. Trying inference on non-existent model...")
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try:
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app("inference", "nonexistent", "data")
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except ValueError as e:
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print(f" Caught expected error: {e}")
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# Try unknown action
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print("\n2. Trying unknown action...")
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try:
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app("unknown_action")
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except ValueError as e:
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print(f" Caught expected error: {e}")
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# Try private method
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print("\n3. Trying private method...")
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try:
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app("_load_model", "test")
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except ValueError as e:
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print(f" Caught expected error: {e}")
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app.close()
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print("\n" + "=" * 60)
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print("Error handling test complete!")
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print("=" * 60)
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if __name__ == "__main__":
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print("\n" + "#" * 60)
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print("# Dirty App Demonstration")
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print("#" * 60)
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test_ml_app()
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test_compute_app()
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test_error_handling()
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print("\n" + "#" * 60)
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print("# All tests passed!")
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print("#" * 60 + "\n")
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