DeepSpeed cpu_adam Build Fails: 'cusolverDn.h: No such file or directory'
Building DeepSpeed's cpu_adam op fails with 'fatal error: cusolverDn.h: No such file or directory' and 'Error building extension cpu_adam'. PyTorch's conda package shipped its own nvcc that shadows the system CUDA toolkit, so CUDA headers aren't found. Point PATH/CUDA_HOME at a complete CUDA toolkit.
Building DeepSpeed's cpu_adam op fails with 'fatal error: cusolverDn.
What this failure is
DeepSpeed cpu_adam Build Fails: 'cusolverDn.h: No such file or directory' is a Environment failure seen during ML training runs. Building DeepSpeed's cpu_adam op fails with 'fatal error: cusolverDn.h: No such file or directory' and 'Error building extension cpu_adam'. PyTorch's conda package shipped its own nvcc that shadows the system CUDA toolkit, so CUDA headers aren't found. Point PATH/CUDA_HOME at a complete CUDA toolkit. Common tags: Deepspeed, Cuda, Cusolver, Nvcc.
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Why it happens (the mechanism)
PyTorch's conda package included its own nvcc, which shadowed the system CUDA toolkit and broke the include path. As a result the CUDA development headers (cusolverDn.h and others) are not found during compilation. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- DeepSpeed op (cpu_adam) JIT build fails
- nvcc cannot find CUDA headers like cusolverDn.h
- import deepspeed / op load errors out
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| fatal error: cusolverDn.h: No such file or directory | PyTorch's conda package included its own nvcc, which shadowed the system CUDA toolkit and broke the include path |
| RuntimeError: Error building extension 'cpu_adam' | As a result the CUDA development headers (cusolverDn.h and others) are not found during compilation |
| nvcc resolves to a conda-shipped binary | PyTorch's conda package included its own nvcc, which shadowed the system CUDA toolkit and broke the include path |
Which systems are affected
- DeepSpeed op JIT compilation (cpu_adam, fused_adam)
- Conda environments where PyTorch shipped nvcc
- Hosts without a full CUDA toolkit on PATH
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: fatal error: cusolverDn.h: No such file or directory
- ✓Verified signal present: RuntimeError: Error building extension 'cpu_adam'
- ✓Verified signal present: nvcc resolves to a conda-shipped binary
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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Install the free VS Code extensionDeepSpeed errors in context
DeepSpeed changes when parameters, gradients and optimizer state are created, partitioned, gathered and offloaded. The hub separates ZeRO, memory, checkpoint and pipeline failures by lifecycle phase.
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Root cause
- PyTorch's conda package included its own nvcc, which shadowed the system CUDA toolkit and broke the include path
- As a result the CUDA development headers (cusolverDn.h and others) are not found during compilation
The fix and how to prevent it
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