pytorch
torchvision 0.21.0+cu126 RuntimeError: operator torchvision::nms does not exist
🐛 Describe the bug
micromamba create -n torchvision python=3.12
micromamba activate torchvision
pip install torchvision
import torchvision
throw error:
Python 3.12.9 | packaged by conda-forge | (main, Mar 4 2025, 22:48:41) [GCC 13.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torchvision
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/andi/micromamba/envs/torchvision/lib/python3.12/site-packages/torchvision/__init__.py", line 10, in <module>
from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils # usort:skip
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/andi/micromamba/envs/torchvision/lib/python3.12/site-packages/torchvision/_meta_registrations.py", line 163, in <module>
@torch.library.register_fake("torchvision::nms")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/andi/micromamba/envs/torchvision/lib/python3.12/site-packages/torch/library.py", line 828, in register
use_lib._register_fake(op_name, func, _stacklevel=stacklevel + 1)
File "/home/andi/micromamba/envs/torchvision/lib/python3.12/site-packages/torch/library.py", line 198, in _register_fake
handle = entry.fake_impl.register(func_to_register, source)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/andi/micromamba/envs/torchvision/lib/python3.12/site-packages/torch/_library/fake_impl.py", line 31, in register
if torch._C._dispatch_has_kernel_for_dispatch_key(self.qualname, "Meta"):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: operator torchvision::nms does not exist
Versions
Collecting environment information... PyTorch version: 2.6.0+cu124 Is debug build: False CUDA used to build PyTorch: 12.4 ROCM used to build PyTorch: N/A
OS: CentOS Linux release 7.9.2009 (Core) (x86_64) GCC version: (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) Clang version: Could not collect CMake version: version 3.31.0 Libc version: glibc-2.17
Python version: 3.12.9 | packaged by conda-forge | (main, Mar 4 2025, 22:48:41) [GCC 13.3.0] (64-bit runtime) Python platform: Linux-3.10.0-1160.31.1.el7.x86_64-x86_64-with-glibc2.17 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: Tesla V100S-PCIE-32GB GPU 1: Tesla V100S-PCIE-32GB
Nvidia driver version: 550.54.15 cuDNN version: Probably one of the following: /opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn.so.8.0.5 /opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.0.5 /opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.0.5 /opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.0.5 /opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.0.5 /opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.0.5 /opt/cuda_11.1.0_455.23.05_linux/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.0.5 /usr/local/cuda-10.1/targets/x86_64-linux/lib/libcudnn.so.7.6.5 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 CPU(s): 96 On-line CPU(s) list: 0-95 Thread(s) per core: 1 Core(s) per socket: 24 Socket(s): 4 NUMA node(s): 4 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Platinum 8268 CPU @ 2.90GHz Stepping: 5 CPU MHz: 3499.859 CPU max MHz: 3900.0000 CPU min MHz: 1000.0000 BogoMIPS: 5800.00 Virtualization: VT-x L1d cache: 32K L1i cache: 32K L2 cache: 1024K L3 cache: 33792K NUMA node0 CPU(s): 0-23 NUMA node1 CPU(s): 24-47 NUMA node2 CPU(s): 48-71 NUMA node3 CPU(s): 72-95 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch epb cat_l3 cdp_l3 invpcid_single intel_ppin intel_pt ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_pkg_req pku ospke spec_ctrl intel_stibp flush_l1d arch_capabilities
Versions of relevant libraries: [pip3] numpy==2.2.4 [pip3] nvidia-cublas-cu12==12.4.5.8 [pip3] nvidia-cuda-cupti-cu12==12.4.127 [pip3] nvidia-cuda-nvrtc-cu12==12.4.127 [pip3] nvidia-cuda-runtime-cu12==12.4.127 [pip3] nvidia-cudnn-cu12==9.1.0.70 [pip3] nvidia-cufft-cu12==11.2.1.3 [pip3] nvidia-curand-cu12==10.3.5.147 [pip3] nvidia-cusolver-cu12==11.6.1.9 [pip3] nvidia-cusparse-cu12==12.3.1.170 [pip3] nvidia-cusparselt-cu12==0.6.2 [pip3] nvidia-nccl-cu12==2.21.5 [pip3] nvidia-nvjitlink-cu12==12.4.127 [pip3] nvidia-nvtx-cu12==12.4.127 [pip3] torch==2.6.0+cu124 [pip3] torchvision==0.21.0+cu126 [pip3] triton==3.2.0 [conda] Could not collect
Hi @AndrewTsao , I'm not sure this is the reason but:
[pip3] torch==2.6.0+cu124 [pip3] torchvision==0.21.0+cu126
try to make the cuda toolkit version match?
