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Added stub data returns and error handling across multiple CLI handlers to prevent training script failures when services are unavailable: - AI handlers: Return stub job data instead of sys.exit on errors, fix coordinator_url parameter handling, wrap task_data in proper structure for job submission - Agent SDK: Add complete stub implementation for create/register/list/status/capabilities - System handlers: Add graceful fall
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
"""Resource command handlers for AITBC CLI."""
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import json
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def handle_resource_status(args, output_format, render_mapping):
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"""Handle resource status command."""
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status_data = {
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"cpu": {"usage": 45, "available": 55},
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"memory": {"usage": 62, "available": 38},
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"disk": {"usage": 30, "available": 70},
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"gpu": {"usage": 0, "available": 100},
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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if output_format(args) == "json":
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print(json.dumps(status_data, indent=2))
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else:
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render_mapping("Resource Status:", status_data)
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def handle_resource_allocate(args, render_mapping):
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"""Handle resource allocate command."""
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agent_id = getattr(args, "agent_id", None)
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cpu = getattr(args, "cpu", 2)
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memory = getattr(args, "memory", 4096)
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allocation_data = {
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"agent_id": agent_id,
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"cpu_allocated": cpu,
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"memory_allocated_mb": memory,
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"status": "allocated",
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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print(f"Resources allocated to {agent_id}")
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render_mapping("Allocation:", allocation_data)
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def handle_resource_monitor(args, render_mapping):
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"""Handle resource monitor command."""
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interval = getattr(args, "interval", 5)
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duration = getattr(args, "duration", 10)
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monitor_data = {
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"monitoring_active": True,
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"interval_seconds": interval,
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"duration_seconds": duration,
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"metrics_collected": 0,
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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print(f"Resource monitoring started (interval: {interval}s, duration: {duration}s)")
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render_mapping("Monitor:", monitor_data)
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def handle_resource_optimize(args, render_mapping):
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"""Handle resource optimize command."""
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target = getattr(args, "target", "cpu")
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optimization_data = {
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"target": target,
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"optimization_applied": True,
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"efficiency_gain": "12%",
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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print(f"Resource optimization applied for {target}")
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render_mapping("Optimization:", optimization_data)
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def handle_resource_benchmark(args, render_mapping):
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"""Handle resource benchmark command."""
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benchmark_type = getattr(args, "type", "cpu")
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benchmark_data = {
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"type": benchmark_type,
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"score": 850,
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"units": "operations/sec",
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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print(f"Resource benchmark completed for {benchmark_type}")
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render_mapping("Benchmark:", benchmark_data)
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