I’m running batch jobs for machine learning inference on Code Engine, and jobs consistently hang when processing large input files (>500MB). The job processes smaller files (<100MB) without issues, but with larger datasets, it just stops responding after about 15-20 minutes of execution with no error messages.
Job configuration:
memory: 4Gi
cpu: 2
timeout: 3600s
The inference code loads the entire input file into memory, runs predictions, and writes results. Job monitoring shows memory usage plateaus around 3.2GB, so we’re not hitting the memory limit. CPU utilization drops to near zero when it hangs. I suspect this is related to Code Engine resource limits or file handling, but I can’t pinpoint the exact cause. Are there hidden resource constraints I’m missing? What are the file streaming best practices for Code Engine batch jobs?
I’ve debugged similar issues. The problem is usually a combination of factors. Code Engine batch jobs don’t handle long-running network operations well. If you’re streaming data from COS and the connection stalls, the job hangs without failing. Implement retry logic and connection timeouts in your COS client configuration. Also, split your large files into smaller chunks before submitting to Code Engine - process multiple smaller jobs in parallel rather than one large job.
The timeout of 3600s might not be the issue here. Code Engine batch jobs have additional constraints beyond memory and CPU. Check if your job is hitting the ephemeral storage limit - by default, Code Engine provides 400Mi of ephemeral storage. If your inference process creates temporary files or the ML model itself is large, you could be hitting this limit silently.
Don’t forget about the Code Engine job resource allocation model. When you specify 4Gi memory, that’s the limit, but the actual allocated memory might be less initially and scale up during execution. This can cause performance issues with large file processing. Consider increasing memory to 8Gi to ensure sufficient headroom. Also, use the Code Engine CLI to check job logs in real-time during execution to identify the exact hang point.
Loading the entire 500MB file into memory is likely your problem, even though you’re not hitting the memory limit. Code Engine has network bandwidth constraints for downloading input data. If you’re pulling from Cloud Object Storage, large file downloads can timeout. Implement streaming file processing instead - read the file in chunks, process each chunk, and write results incrementally. This avoids memory spikes and network timeout issues.