Datasets:
Shravan2020
/
acea

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FROM python:3.11-slim
# Hugging Face Spaces compatibility
ENV HOME=/home/user
ENV PATH=/home/user/.local/bin:$PATH
WORKDIR /app
RUN apt-get update && apt-get install -y --no-install-recommends \
curl \
&& rm -rf /var/lib/apt/lists/*
COPY acea/requirements.txt ./requirements.txt
RUN pip install --no-cache-dir -r requirements.txt
COPY acea/ .
RUN touch backend/__init__.py env/__init__.py
EXPOSE 7860
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD curl -f http://localhost:7860/health || exit 1
CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"]
fastapi==0.115.0
uvicorn[standard]==0.30.6
pydantic==2.8.2
groq==0.9.0
httpx==0.27.2
python-multipart==0.0.12
name: autonomous-enterprise-chaosops-arena
version: "1.0.0"
description: >
AI training environment simulating an enterprise DevOps + Incident Response
engineer operating under dynamic, chaotic conditions. Agents must triage
incidents, diagnose failures, and stabilize production systems under pressure.
author: ACEA Team
license: MIT
environment:
class: ACEAEnvironment
module: env.environment
api_version: "openenv-1.0"
api:
reset:
description: Reset environment to initial state for given difficulty
returns: Observation
step:
description: Execute one action step
input: Action
returns:
observation: Observation
reward: float
done: bool
info: dict
state:
description: Return full internal environment state
returns: dict
observation_space:
type: structured
schema: env.models.Observation
fields:
alerts:
type: list[Alert]
description: Active system alerts with severity and service attribution
logs:
type: list[LogEntry]
description: Recent system logs (AWS/Kubernetes style)
tickets:
type: list[Ticket]
description: Customer support tickets including escalations
system_health:
type: SystemHealth
description: Real-time system metrics (CPU, memory, latency, uptime, error_rate)
active_incidents:
type: list[Incident]
description: Currently active incidents requiring resolution
risk_level:
type: enum[low, medium, high, critical]
description: Overall system risk classification
time_elapsed:
type: int
description: Seconds elapsed since episode start
chaos_events:
type: list[ChaosEvent]
description: Chaos events injected this step
step_count:
type: int
description: Current step index
action_space:
type: structured
schema: env.models.Action
fields:
type:
type: enum
values: