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15c3238 8f2eab9 15c3238 8f2eab9 15c3238 782222a 15c3238 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | """
FastAPI application for the Neural Tuner Env Environment.
This module creates an HTTP server that exposes the NeuralTunerEnvironment
over HTTP and WebSocket endpoints, compatible with EnvClient.
Endpoints:
- POST /reset: Reset the environment
- POST /step: Execute an action
- GET /state: Get current environment state
- GET /schema: Get action/observation schemas
- WS /ws: WebSocket endpoint for persistent sessions
Usage:
# Development (with auto-reload):
uvicorn server.app:app --reload --host 0.0.0.0 --port 8000
# Production:
uvicorn server.app:app --host 0.0.0.0 --port 8000 --workers 4
# Or run directly:
python -m server.app
"""
from openenv.core.env_server.http_server import create_app
from models import NeuralTunerAction, NeuralTunerObservation
from server.neural_tuner_env_environment import NeuralTunerEnvironment
app = create_app(
NeuralTunerEnvironment,
NeuralTunerAction,
NeuralTunerObservation,
env_name="neural_tuner_env",
max_concurrent_envs=1,
)
def main(host: str = "0.0.0.0", port: int = 8000):
"""
Entry point for direct execution via uv run or python -m.
This function enables running the server without Docker:
uv run --project . server
uv run --project . server --port 8001
python -m neural_tuner_env.server.app
Args:
host: Host address to bind to (default: "0.0.0.0")
port: Port number to listen on (default: 8000)
For production deployments, consider using uvicorn directly with
multiple workers:
uvicorn neural_tuner_env.server.app:app --workers 4
"""
import uvicorn
uvicorn.run(app, host=host, port=port)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--port", type=int, default=8000)
args = parser.parse_args()
main(port=args.port)
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