energy_optimization / client.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""He Demo Environment Client."""
from typing import Dict
from openenv.core import EnvClient
from openenv.core.client_types import StepResult
from openenv.core.env_server.types import State
from .models import EnergyOptimizationAction, EnergyOptimizationObservation, Task, TaskSummary
class EnergyOptimizationEnv(
EnvClient[EnergyOptimizationAction, EnergyOptimizationObservation, State]
):
"""
Client for the Energy & Memory RAM Optimization Environment.
This client maintains a persistent WebSocket connection to the environment server,
enabling efficient multi-step interactions with lower latency.
Each client instance has its own dedicated environment session on the server.
Example:
>>> # Connect to a running server
>>> with EnergyOptimizationEnv(base_url="http://localhost:8000") as client:
... result = client.reset()
... print(f"RAM: {result.observation.ram_usage:.1f}%, Energy: {result.observation.energy_consumption:.1f} kWh")
...
... result = client.step(EnergyOptimizationAction(action_type="reduce_ram", intensity=0.8))
... print(f"Task: {result.observation.current_task.name if result.observation.current_task else 'None'}")
Example with Docker:
>>> # Automatically start container and connect
>>> client = EnergyOptimizationEnv.from_docker_image("energy-optimization-env:latest")
>>> try:
... result = client.reset()
... result = client.step(EnergyOptimizationAction(action_type="balance_resources", intensity=0.6))
... finally:
... client.close()
"""
def _step_payload(self, action: EnergyOptimizationAction) -> Dict:
"""
Convert EnergyOptimizationAction to JSON payload for step message.
Args:
action: EnergyOptimizationAction instance
Returns:
Dictionary representation suitable for JSON encoding
"""
return {
"action_type": action.action_type,
"intensity": action.intensity,
}
def _parse_result(self, payload: Dict) -> StepResult[EnergyOptimizationObservation]:
"""
Parse server response into StepResult[EnergyOptimizationObservation].
Args:
payload: JSON response data from server
Returns:
StepResult with EnergyOptimizationObservation
"""
obs_data = payload.get("observation", {})
# Parse current task if present
current_task = None
if obs_data.get("current_task"):
task_data = obs_data["current_task"]
current_task = TaskSummary(
name=task_data.get("name", ""),
description=task_data.get("description", ""),
difficulty=task_data.get("difficulty", 1),
ram_target=task_data.get("ram_target", 100.0),
energy_target=task_data.get("energy_target", 10.0),
max_steps=task_data.get("max_steps", 10),
completed=task_data.get("completed", False),
remaining_steps=task_data.get("remaining_steps"),
progress=task_data.get("progress", 0.0)
)
observation = EnergyOptimizationObservation(
ram_usage=obs_data.get("ram_usage", 0.0),
energy_consumption=obs_data.get("energy_consumption", 0.0),
system_load=obs_data.get("system_load", 0.0),
current_task=current_task,
tasks_completed=obs_data.get("tasks_completed", []),
steps_taken=obs_data.get("steps_taken", 0),
task_progress=obs_data.get("task_progress", 0.0),
efficiency_score=obs_data.get("efficiency_score", 0.0),
done=payload.get("done", False),
reward=payload.get("reward"),
metadata=obs_data.get("metadata", {}),
)
return StepResult(
observation=observation,
reward=payload.get("reward"),
done=payload.get("done", False),
)
def _parse_state(self, payload: Dict) -> State:
"""
Parse server response into State object.
Args:
payload: JSON response from state request
Returns:
State object with episode_id and step_count
"""
return State(
episode_id=payload.get("episode_id"),
step_count=payload.get("step_count", 0),
)