i tried to download pytorch with cuda support however after checking
torch.cuda.is_available()
- false
i went to try this
python -m torch.utils.collect_env
which returned
Would like to know how to resolve the issue.
i downloaded both cuda and cudann using yay
Thanks in advance.
have you actually installed the CUDA package? It seemed only cuDNN installed…
Here is the cuda version
You don’t need to install a full CUDA toolkit as PyTorch ships with its own CUDA dependencies. The locally installed CUDA toolkit will be used if you build PyTorch from source or a custom extension. Could you post the full output of python -m torch utils.collect_env and pip list | grep torch instead of cropped screenshots?
I am quite a new user to arch , would be helpful if you show the required steps to install perfectly
Collecting environment information...
PyTorch version: 2.0.1
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A
OS: Arch Linux (x86_64)
GCC version: (GCC) 13.1.1 20230429
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.37
Python version: 3.11.3 (main, Apr 19 2023, 23:54:32) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.3.1-zen1-1-zen-x86_64-with-glibc2.37
Is CUDA available: False
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Laptop GPU
Nvidia driver version: 530.41.03
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so.8.8.0
/usr/lib/libcudnn_adv_infer.so.8.8.0
/usr/lib/libcudnn_adv_train.so.8.8.0
/usr/lib/libcudnn_cnn_infer.so.8.8.0
/usr/lib/libcudnn_cnn_train.so.8.8.0
/usr/lib/libcudnn_ops_infer.so.8.8.0
/usr/lib/libcudnn_ops_train.so.8.8.0
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
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 16
On-line CPU(s) list: 0-15
Vendor ID: AuthenticAMD
Model name: AMD Ryzen 7 5800H with Radeon Graphics
CPU family: 25
Model: 80
Thread(s) per core: 2
Core(s) per socket: 8
Socket(s): 1
Stepping: 0
Frequency boost: enabled
CPU(s) scaling MHz: 71%
CPU max MHz: 4462.5000
CPU min MHz: 1200.0000
BogoMIPS: 6388.32
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 nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualization: AMD-V
L1d cache: 256 KiB (8 instances)
L1i cache: 256 KiB (8 instances)
L2 cache: 4 MiB (8 instances)
L3 cache: 16 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-15
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: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] torch==2.0.1
[pip3] torchaudio==2.0.2
[pip3] torchvision==0.15.2
[conda] blas 1.0 mkl
[conda] mkl 2021.4.0 h06a4308_640
[conda] mkl-service 2.4.0 py311h5eee18b_0
[conda] mkl_fft 1.3.1 py311h30b3d60_0
[conda] mkl_random 1.2.2 py311hba01205_0
[conda] numpy 1.24.3 py311hc206e33_0
[conda] numpy-base 1.24.3 py311hfd5febd_0
[conda] pytorch 2.0.1 py3.11_cpu_0 pytorch
[conda] pytorch-cuda 11.7 h778d358_5 pytorch
[conda] pytorch-mutex 1.0 cpu fastchan
[conda] torchaudio 2.0.2 py311_cpu pytorch
[conda] torchvision 0.15.2 py311_cpu pytorch
this is the output for python -m torch.utils.collect_env.
Thanks for the update. Based on:
[conda] pytorch 2.0.1 py3.11_cpu_0
You have installed the CPU-only packages and would need to install the ones coming with the CUDA dependencies.
I would recommend either uninstalling all PyTorch-related packages in the current environment before reinstalling the right one or creating a new and empty virtual environment.
well i have a doubt though , cause my cuda version is 12.01 and the one present in pytorch is 11.8 or 11.7 , would there be an error.
No, there won’t be an error as described in my previous post:
You don’t need to install a full CUDA toolkit as PyTorch ships with its own CUDA dependencies. The locally installed CUDA toolkit will be used if you build PyTorch from source or a custom extension.
Thanks a lot, I created a new environment and installed it as you mentioned works right now.