# services/maintenance/node-ops/titan-22-gpu-canary-job.yaml
apiVersion: batch/v1
kind: Job
metadata:
  name: titan-22-gpu-canary-20260822
  namespace: maintenance
spec:
  backoffLimit: 1
  template:
    spec:
      restartPolicy: Never
      nodeSelector:
        kubernetes.io/hostname: titan-22
      containers:
        - name: cuda-canary
          image: docker.io/nvidia/cuda@sha256:da6791294b0b04d7e65d87b7451d6f2390b4d36225ab0701ee7dfec5769829f5
          imagePullPolicy: IfNotPresent
          command:
            - /bin/bash
            - -ceu
            - |
              cat >/tmp/titan_22_gpu_canary.cu <<'CUDA'
              #include <cuda_runtime.h>
              #include <stdio.h>

              __global__ void fill(float *values, int count) {
                int index = blockIdx.x * blockDim.x + threadIdx.x;
                if (index < count) {
                  values[index] = 22.0f;
                }
              }

              int main(void) {
                int devices = 0;
                cudaError_t result = cudaGetDeviceCount(&devices);
                if (result != cudaSuccess || devices < 1) {
                  fprintf(stderr, "cudaGetDeviceCount failed: %s\n", cudaGetErrorString(result));
                  return 1;
                }

                const int count = 16 * 1024 * 1024;
                float *values = NULL;
                result = cudaMalloc(&values, count * sizeof(float));
                if (result != cudaSuccess) {
                  fprintf(stderr, "cudaMalloc failed: %s\n", cudaGetErrorString(result));
                  return 1;
                }

                fill<<<(count + 255) / 256, 256>>>(values, count);
                result = cudaDeviceSynchronize();
                if (result != cudaSuccess) {
                  fprintf(stderr, "GPU kernel failed: %s\n", cudaGetErrorString(result));
                  cudaFree(values);
                  return 1;
                }

                float last = 0.0f;
                result = cudaMemcpy(&last, values + count - 1, sizeof(float), cudaMemcpyDeviceToHost);
                cudaFree(values);
                if (result != cudaSuccess || last != 22.0f) {
                  fprintf(stderr, "GPU verification failed: %s value=%f\n", cudaGetErrorString(result), last);
                  return 1;
                }

                printf("CUDA canary passed: devices=%d value=%.1f\n", devices, last);
                return 0;
              }
              CUDA
              nvcc -O2 /tmp/titan_22_gpu_canary.cu -o /tmp/titan_22_gpu_canary
              /tmp/titan_22_gpu_canary
          resources:
            limits:
              nvidia.com/gpu.shared: "1"
            requests:
              nvidia.com/gpu.shared: "1"