{
    "version": "2.0.0",
    "tasks": [
        {
            "label": "Kill Stale Processes",
            "type": "shell",
            "command": "python",
            "args": [
                "-c",
                "import psutil; [p.kill() for p in psutil.process_iter() if any(x in p.name().lower() for x in [\"python\", \"tensorboard\"]) and any(x in \" \".join(p.cmdline()) for x in [\"scalping\", \"training\", \"tensorboard\"]) and p.pid != psutil.Process().pid]; print(\"Stale processes killed\")"
            ],
            "presentation": {
                "reveal": "silent",
                "panel": "shared"
            },
            "problemMatcher": []
        },
        {
            "label": "Start TensorBoard",
            "type": "shell",
            "command": "python",
            "args": [
                "run_tensorboard.py"
            ],
            "isBackground": true,
            "problemMatcher": {
                "pattern": {
                    "regexp": "^.*$",
                    "file": 1,
                    "location": 2,
                    "message": 3
                },
                "background": {
                    "activeOnStart": true,
                    "beginsPattern": ".*Starting TensorBoard.*",
                    "endsPattern": ".*TensorBoard.*available.*"
                }
            },
            "presentation": {
                "reveal": "always",
                "panel": "new",
                "group": "monitoring"
            },
            "runOptions": {
                "runOn": "folderOpen"
            }
        },
        {
            "label": "Monitor GPU Usage",
            "type": "shell",
            "command": "python",
            "args": [
                "-c",
                "import GPUtil; import time; [print(f'GPU {gpu.id}: {gpu.load*100:.1f}% load, {gpu.memoryUsed}/{gpu.memoryTotal}MB memory ({gpu.memoryUsed/gpu.memoryTotal*100:.1f}%)') or time.sleep(5) for _ in iter(int, 1) for gpu in GPUtil.getGPUs()]"
            ],
            "isBackground": true,
            "presentation": {
                "reveal": "always",
                "panel": "new",
                "group": "monitoring"
            },
            "problemMatcher": []
        },
        {
            "label": "Check CUDA Setup",
            "type": "shell",
            "command": "python",
            "args": [
                "-c",
                "import torch; print(f'PyTorch: {torch.__version__}'); print(f'CUDA Available: {torch.cuda.is_available()}'); print(f'CUDA Version: {torch.version.cuda}' if torch.cuda.is_available() else 'CUDA not available'); [print(f'GPU {i}: {torch.cuda.get_device_name(i)}') for i in range(torch.cuda.device_count())] if torch.cuda.is_available() else None"
            ],
            "presentation": {
                "reveal": "always",
                "panel": "shared"
            },
            "problemMatcher": []
        },
        {
            "label": "Setup Training Environment",
            "type": "shell",
            "command": "python",
            "args": [
                "-c",
                "import os; os.makedirs('models/rl', exist_ok=True); os.makedirs('models/cnn', exist_ok=True); os.makedirs('logs/overnight_training', exist_ok=True); os.makedirs('reports/overnight_training', exist_ok=True); os.makedirs('plots/overnight_training', exist_ok=True); print('Training directories created')"
            ],
            "presentation": {
                "reveal": "silent",
                "panel": "shared"
            },
            "problemMatcher": []
        },
        {
            "label": "Validate Model Parameters",
            "type": "shell",
            "command": "python",
            "args": [
                "model_parameter_audit.py"
            ],
            "presentation": {
                "reveal": "always",
                "panel": "shared"
            },
            "problemMatcher": []
        }
    ]
}