For preferred networks cupy infrastructure and platform teams

Preferred Networks CuPy: package conflict, runtime selection or array memory?

Connect the first useful exception with the relevant runtime evidence. Does the CuPy package match the selected CUDA runtime?

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Why this workflow may fit Preferred Networks CuPy

CuPy provides GPU array computation with backend-specific package and runtime requirements. Its install guide distinguishes CUDA packages, optional libraries and source builds.

Read the official Preferred Networks CuPy workflow reference Preferred Networks CuPy additional implementation context

Evaluation scope: This covers the public CuPy workflow associated with Preferred Networks, not proprietary systems. Denpex does not certify numerical correctness or modify host drivers. Use approved redacted evidence. No native integration, automatic repair or current outage is implied. The CUDA package comparison is not AMD ROCm hardware diagnosis.

Prepared from public materials. This is a proposed evaluation, not a report of an incident at Preferred Networks CuPy or an endorsement.

Selectable demo logs are shared examples, not logs from Preferred Networks CuPy. Upload a shareable incident from your own workflow to evaluate the fit.

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Replay a resolved Preferred Networks CuPy workflow incident

Upload your log file. Review the selected evidence and masking, then run diagnosis. Keep the original log for any follow-up questions.

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Up to 50 diagnoses a day for 30 days. Verify your work email to activate an eligible company evaluation, then replay a resolved incident and compare the evidence and next steps with your team's confirmed cause.

What a useful evaluation should establish

Compare one incident your team already resolved with the first failure, cited evidence and remaining questions. Does the CuPy package match the selected CUDA runtime? Only assign a rank, device or safe recovery state when the submitted evidence establishes it.

Does the CuPy package match the selected CUDA runtime?

Evidence to compare: Installed package variants, selected runtime and first import traceback.

Conflicting package variants or a mismatched runtime can fail before array computation. Establish the package boundary rather than interpreting every import failure as broken hardware.

Did host memory or device memory run out?

Evidence to compare: Container or scheduler host-memory limit, process kill evidence and first allocation exception.

Host memory and device memory are different resources. A host-memory kill needs different evidence from a GPU allocator failure; identify the failed operation before changing a limit.

Does the executable match the allocated architecture?

Evidence to compare: Actual CPU and GPU architecture, image identity and the first loader error.

A binary built for another architecture can fail before useful computation. Confirm the allocated hardware and software combination before recommending changes to the device.

Use Denpex alongside your existing telemetry tools to investigate submitted incident evidence. With monitoring configured on a supported plan, detected failures can trigger alerts and an automatic diagnosis from the captured evidence.

Inside Denpex

See the evidence, not just the claim

Follow the causal chain, inspect the quoted evidence and prepare the next handoff. These actual product captures use founder-account examples and the VS Code extension, with the source identified under each image. Open a capture to inspect it at full size.