GPU Memory, Temprature, Processes

GPU Metrics Generation

Create Prometheus Metrics for:
- Memory (Total, Used), Memory consumed by processes
- GPU Utilization
- Active Processes
- Temperature of GPU


# Updated GPUMonitor with Prometheus export
from prometheus_client import Gauge, start_http_server

class GPUMonitor:
    def __init__(self, gpu_ids):
        # Prometheus metrics
        self.gpu_util = Gauge('gpu_utilization', 'GPU utilization %', ['gpu_id'])
        self.gpu_mem = Gauge('gpu_memory', 'GPU memory used MB', ['gpu_id'])
        self.gpu_temp = Gauge('gpu_temp', 'GPU temperature C', ['gpu_id'])
        
        # Start metrics server
        start_http_server(8000)
        
    def collect_metrics(self):
        metrics = []
        for i, gpu in enumerate(self.gpus):
            # ... existing collection code ...
            
            # Export to Prometheus
            self.gpu_util.labels(gpu_id=i).set(util.gpu)
            self.gpu_mem.labels(gpu_id=i).set(mem.used / 1024**2)
            self.gpu_temp.labels(gpu_id=i).set(temp)

(GRAFANNA)Visualization & Dashboard

GPU Cluster Overview	Job Performance	Alert Dashboard
Heatmap of GPU utilization across nodes Memory usage trends Temperature distribution	Training loss/accuracy curves Batch processing times GPU memory vs. batch size	Active incidents Error rate over time Recovery success rate