This week’s focus on AI infrastructure and open source tooling reminded us that even the most robust systems can fail in subtle, unexpected ways. When it comes to GPU-dependent cron jobs, a simple try/except block is not enough to ensure reliability. We've seen too many cases where a job fails silently, or worse, consumes resources without producing value. That’s why we’ve built a two-stage preflight check for our GPU-dependent cron agents.
At Apex Grid, we run a suite of background jobs that leverage GPU acceleration for tasks like model training, inference, and data preprocessing. These jobs are typically scheduled via cron, but GPU contention is a real issue in shared environments. A job might start executing, only to find that the GPU is already locked by another process, leading to timeouts, wasted compute cycles, and, in the worst case, cascading failures.
To avoid this, we use a two-stage preflight check: first, we ensure that the GPU is accessible by querying /api/tags within a 3-second timeout. This endpoint is lightweight and serves as a health check for the GPU service. If it fails, we skip the job entirely. Second, we perform a warmup by calling /api/generate with a minimal payload. This step verifies that the GPU is not just available, but also responsive enough to handle a real task. If either of these checks fails, we log the event and move on without firing the job. This prevents the job from running in a state that will inevitably fail, which is a common pitfall in many systems.
Here’s what our gpu_ready() helper looks like in practice:
import requests
import time
def gpu_ready(timeout=3):
try:
# First check: GPU service is accessible
tags_response = requests.get("http://gpu-service/api/tags", timeout=timeout)
if tags_response.status_code != 200:
return False
# Second check: GPU is responsive for a warmup
generate_response = requests.post(
"http://gpu-service/api/generate",
json={"prompt": "test", "max_tokens": 1},
timeout=timeout
)
return generate_response.status_code == 200
except requests.exceptions.RequestException:
return False
This approach has several tradeoffs. The first is that it adds a small but measurable latency to the scheduling process. In our case, the overhead is negligible compared to the cost of running a job that will fail due to GPU contention. The second tradeoff is the need to maintain two endpoints (/api/tags and /api/generate) that are tightly coupled to the GPU service. This increases the surface area for potential failures, though we mitigate this with health checks and monitoring.
Looking ahead, we’re exploring ways to make these checks even more lightweight and resilient. One idea is to use a GPU-specific health check protocol, like querying the NVIDIA driver directly via a system call. This would eliminate the need for a separate API and reduce the number of moving parts. We’re also investigating the possibility of using hardware-level metrics, like GPU utilization or memory usage, to make more granular decisions about job scheduling.
What do you think about using system-level metrics for preflight checks? Have you encountered similar challenges with GPU contention in your own work?
