pytorch
Torchvision nightly not being published for Linux, Python 3.11. CUDA 11.8
🐛 Describe the bug
Everyday torchvision nightly package is published on torch_nightly. However, for the last 1-1.5 weeks, the Python 3.11 Linux CUDA 11.8 version is not getting published and all the other combinations seem fine to me. These are used in our pipelines and it would important to get this fixed.
This would fail from Python 3.11 environment:
python -m pip install --pre torchvision==0.19.0.dev20240602 -f https://download.pytorch.org/whl/nightly/cu118/torch_nightly.html
Versions
From an older version: Collecting environment information... PyTorch version: 2.4.0.dev20240514+cu118 Is debug build: False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 Clang version: Could not collect CMake version: version 3.26.0 Libc version: glibc-2.31
Python version: 3.11.9 (main, Apr 19 2024, 16:48:06) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.153.1-2.cm2-x86_64-with-glibc2.31 Is CUDA available: False CUDA runtime version: 11.8.89 CUDA_MODULE_LOADING set to: N/A GPU models and configuration: Nvidia driver version: Could not collect cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6 HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True
CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian Address sizes: 48 bits physical, 48 bits virtual CPU(s): 96 On-line CPU(s) list: 0-95 Thread(s) per core: 1 Core(s) per socket: 48 Socket(s): 2 NUMA node(s): 4 Vendor ID: AuthenticAMD CPU family: 23 Model: 49 Model name: AMD EPYC 7V12 64-Core Processor Stepping: 0 CPU MHz: 2445.436 BogoMIPS: 4890.87 Hypervisor vendor: Microsoft Virtualization type: full L1d cache: 3 MiB L1i cache: 3 MiB L2 cache: 48 MiB L3 cache: 384 MiB NUMA node0 CPU(s): 0-23 NUMA node1 CPU(s): 24-47 NUMA node2 CPU(s): 48-71 NUMA node3 CPU(s): 72-95 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Mitigation; untrained return thunk; SMT disabled Vulnerability Spec rstack overflow: Mitigation; safe RET, no microcode Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid
Versions of relevant libraries: [pip3] numpy==1.25.2 [pip3] onnx==1.16.0 [pip3] onnxruntime-training==1.19.0.dev20240513002 [pip3] pytorch-lightning==1.9.5 [pip3] pytorch-triton==3.0.0+a9bc1a3647 [pip3] torch==2.4.0.dev20240514+cu118 [pip3] torch-nebula==0.16.11 [pip3] torch-ort==1.18.0.dev20240206 [pip3] torchaudio==2.2.0.dev20240514+cu118 [pip3] torchmetrics==1.2.0 [pip3] torchsnapshot==0.1.0 [pip3] torchvision==0.19.0.dev20240514+cu118 [conda] magma-cuda118 2.6.1 1 pytorch [conda] mkl 2022.2.1 pypi_0 pypi [conda] mkl-include 2022.2.1 pypi_0 pypi [conda] numpy 1.25.2 pypi_0 pypi [conda] pytorch-lightning 1.9.5 pypi_0 pypi [conda] pytorch-triton 3.0.0+a9bc1a3647 pypi_0 pypi [conda] torch 2.4.0.dev20240514+cu118 pypi_0 pypi [conda] torch-nebula 0.16.11 pypi_0 pypi [conda] torch-ort 1.18.0.dev20240206 pypi_0 pypi [conda] torchaudio 2.2.0.dev20240514+cu118 pypi_0 pypi [conda] torchmetrics 1.2.0 pypi_0 pypi [conda] torchsnapshot 0.1.0 pypi_0 pypi [conda] torchvision 0.19.0.dev20240514+cu118 pypi_0 pypi
CC @atalman, did we stop release Python 3.11 / CUDA 11.8 nightlies recently?
hi @NicolasHug and @ajindal1 this is related to this issue: https://github.com/pytorch/vision/issues/8441
