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build cuda 13.0.0 base image with 2.9.0 #3229

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d7f6db4
build cuda 13.0.0 base image with 2.9.0
winglian Oct 23, 2025
6a80917
upgrade causal-conv1d
winglian Oct 24, 2025
20a346a
1.5.4 not in pypi yet
winglian Oct 24, 2025
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14 changes: 14 additions & 0 deletions .github/workflows/base.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -60,6 +60,13 @@ jobs:
pytorch: 2.9.0
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
dockerfile: "Dockerfile-base"
- cuda: "130"
cuda_version: 13.0.0
cudnn_version: ""
python_version: "3.11"
pytorch: 2.9.0
torch_cuda_arch_list: "9.0+PTX"
dockerfile: "Dockerfile-base"
Comment on lines +63 to +69
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⚠️ Potential issue | 🔴 Critical

🧩 Analysis chain

Verify torch_cuda_arch_list is intentional for CUDA 13.0.0.

The new CUDA 13.0.0 entry restricts torch_cuda_arch_list to "9.0+PTX", while all existing CUDA 12.x entries support a broader range: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX". This significantly limits hardware compatibility—users with older GPUs (compute capability 7.0–8.9) won't have optimized binaries.

Confirm whether this reduction in supported architectures is:

  • An intentional limitation of CUDA 13.0.0 or PyTorch 2.9.0 with CUDA 13.0.0
  • An oversight that should match the broader architecture support of earlier entries

Also applies to: 153-159

🌐 Web query:

CUDA 13.0.0 supported GPU compute capabilities PyTorch 2.9.0

💡 Result:

Short answer:

  • CUDA 13.0 supports GPU compute capabilities (compute_xx / sm_xx) listed in NVIDIA's nvcc docs: compute_75, 80, 86, 87, 88, 89, 90, 90a, 100, 100f, 100a, 103, 103f, 103a, 110, 110f, 110a, 120, 120f, 120a, 121, 121f, 121a (and corresponding sm_75, sm_80, sm_86, sm_87, sm_88, sm_89, sm_90, sm_90a, sm_100, sm_100f, sm_100a, sm_103, sm_103f, sm_103a, sm_110, sm_110f, sm_110a, sm_120, sm_120f, sm_120a, sm_121, sm_121f, sm_121a). [1][2]

  • CUDA 13.0 supports NVIDIA architectures from Turing (SM 7.5) through Grace/Blackwell; Maxwell/Pascal/Volta (older than Turing) are dropped for several libraries. [3]

  • PyTorch 2.9.0 expands wheel support to include CUDA 13 (i.e., PyTorch 2.9 provides CUDA‑13 build variants). Confirm PyTorch package variant for your platform/installer before installing. [4]

Sources:
[1] NVIDIA nvcc GPU code generation / List of supported GPU codes (CUDA 13.0).
[2] CUDA Toolkit 13.0 release notes (architectures/support details).
[3] NVIDIA developer blog “What’s New in CUDA 13.0”.
[4] PyTorch 2.9 release blog (notes on CUDA 13 wheel support).

Correct torch_cuda_arch_list for CUDA 13.0.0 to match supported GPU architectures.

CUDA 13.0 supports GPU compute capabilities from Turing (SM 7.5) through Grace/Blackwell. The current entry restricts this to "9.0+PTX" only, which excludes Turing (7.5) and Ampere (8.0–8.9) GPUs that CUDA 13.0 officially supports.

Update line 66 (and 156) to:

torch_cuda_arch_list: "7.5 8.0 8.6 8.7 8.8 8.9 9.0+PTX"

Note: Unlike CUDA 12.x entries, omit "7.0" (Pascal) since Maxwell/Pascal/Volta are dropped in CUDA 13.0.

Also applies to: 153-159

🤖 Prompt for AI Agents 
In .github/workflows/base.yml around lines 63-69 (and also apply the same change
at lines 153-159), the torch_cuda_arch_list is incorrectly set to "9.0+PTX"
which excludes supported CUDA 13.0 architectures; update the
torch_cuda_arch_list value to "7.5 8.0 8.6 8.7 8.8 8.9 9.0+PTX" in both places
so Turing and Ampere families are included and Pascal (7.0) is omitted per CUDA
13.0 support.

# - cuda: "128"
# cuda_version: 12.8.1
# cudnn_version: ""
Expand Down Expand Up @@ -143,6 +150,13 @@ jobs:
pytorch: 2.9.0
torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
dockerfile: "Dockerfile-uv-base"
- cuda: "130"
cuda_version: 13.0.0
cudnn_version: ""
python_version: "3.11"
pytorch: 2.9.0
torch_cuda_arch_list: "9.0+PTX"
dockerfile: "Dockerfile-uv-base"
steps:
- name: Checkout
uses: actions/checkout@v4
Expand Down
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