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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
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
"""Performance command handlers for AITBC CLI."""
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import json
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def handle_performance_benchmark(args, output_format, render_mapping):
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"""Handle performance benchmark command."""
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benchmark_data = {
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"tps": 1250,
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"latency_ms": 45,
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"throughput_mbps": 850,
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"cpu_usage": 65,
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"memory_usage": 72,
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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(benchmark_data, indent=2))
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else:
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print("Performance Benchmark:")
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print(f" TPS: {benchmark_data['tps']}")
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print(f" Latency: {benchmark_data['latency_ms']}ms")
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print(f" Throughput: {benchmark_data['throughput_mbps']}Mbps")
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print(f" CPU Usage: {benchmark_data['cpu_usage']}%")
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print(f" Memory Usage: {benchmark_data['memory_usage']}%")
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def handle_performance_optimize(args, render_mapping):
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"""Handle performance optimize command."""
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target = getattr(args, "target", "general")
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optimization_data = {
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"target": target,
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"optimization_applied": True,
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"improvement": "15-20%",
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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print(f"Performance optimization applied for {target}")
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render_mapping("Optimization:", optimization_data)
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def handle_performance_tune(args, render_mapping):
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"""Handle performance tune command."""
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parameters = getattr(args, "parameters", False)
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aggressive = getattr(args, "aggressive", False)
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tune_data = {
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"parameters_tuned": parameters,
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"aggressive_mode": aggressive,
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"tuning_applied": True,
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"timestamp": __import__('datetime').datetime.now().isoformat()
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}
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print("Performance tuning applied")
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render_mapping("Tuning:", tune_data)
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