RTX5090d:ImportError: cannot import name 'EPOCH_OUTPUT' from 'pytorch_lightning.utilities.types'

[paomian001]

Description & Motivation

RTX5090d can only install the nightly versions of CUDA12.8 and torch2.8. Is there a corresponding PyTorch_lightning version for CUDA12.8 and torch2.8 that can be installed？

Pitch

Alternatives

No response

Additional context

Versions

PyTorch version: 2.8.0.dev20250416+cu128
Is debug build: False
CUDA used to build PyTorch: 12.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35

Python version: 3.9.21 (main, Dec 11 2024, 16:24:11) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-57-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 5090 D
Nvidia driver version: 570.133.07
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
架构： x86_64
CPU 运行模式： 32-bit, 64-bit
Address sizes: 39 bits physical, 48 bits virtual
字节序： Little Endian
CPU: 32
在线 CPU 列表： 0-31
厂商 ID： GenuineIntel
型号名称： Intel(R) Core(TM) i9-14900KF
CPU 系列： 6
型号： 183
每个核的线程数： 2
每个座的核数： 24
座： 1
步进： 1
CPU 最大 MHz： 6000.0000
CPU 最小 MHz： 800.0000
BogoMIPS： 6374.40
标记： 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_tart arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities
虚拟化： VT-x
L1d 缓存： 896 KiB (24 instances)
L1i 缓存： 1.3 MiB (24 instances)
L2 缓存： 32 MiB (12 instances)
L3 缓存： 36 MiB (1 instance)
NUMA 节点： 1
NUMA 节点0 CPU： 0-31
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 Reg file data sampling: Mitigation; Clear Register File
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: 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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.8.3.14
[pip3] nvidia-cuda-cupti-cu12==12.8.57
[pip3] nvidia-cuda-nvrtc-cu12==12.8.61
[pip3] nvidia-cuda-runtime-cu12==12.8.57
[pip3] nvidia-cudnn-cu12==9.8.0.87
[pip3] nvidia-cufft-cu12==11.3.3.41
[pip3] nvidia-curand-cu12==10.3.9.55
[pip3] nvidia-cusolver-cu12==11.7.2.55
[pip3] nvidia-cusparse-cu12==12.5.7.53
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.8.61
[pip3] nvidia-nvtx-cu12==12.8.55
[pip3] pytorch-lightning==2.5.1
[pip3] pytorch-msssim==1.0.0
[pip3] pytorch-triton==3.3.0+git96316ce5
[pip3] torch==2.8.0.dev20250416+cu128
[pip3] torch-geometric==2.6.1
[pip3] torchaudio==2.6.0.dev20250416+cu128
[pip3] torchmetrics==1.7.1
[pip3] torchvision==0.22.0.dev20250416+cu128
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.8.3.14 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.8.57 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.8.61 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.8.57 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.8.0.87 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.3.41 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.9.55 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.2.55 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.7.53 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.8.61 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.8.55 pypi_0 pypi
[conda] pytorch-lightning 2.5.1 pypi_0 pypi
[conda] pytorch-msssim 1.0.0 pypi_0 pypi
[conda] pytorch-triton 3.3.0+git96316ce5 pypi_0 pypi
[conda] torch 2.8.0.dev20250416+cu128 pypi_0 pypi
[conda] torch-geometric 2.6.1 pypi_0 pypi
[conda] torchaudio 2.6.0.dev20250416+cu128 pypi_0 pypi
[conda] torchmetrics 1.7.1 pypi_0 pypi
[conda] torchvision 0.22.0.dev20250416+cu128 pypi_0 pypi

cc @lantiga @Borda

[Zyriix]

Upvoting this
We really need support for torch 2.8 and cuda 12.8

[paomian001]

UpUpUp

[Borda]

hi, could you pls elaborate why or when in past this import worked?

[paomian001]

hi, could you pls elaborate why or when in past this import worked?

Maybe the POCH_OUTPUT variable was included in the previous types file？

[paomian001]

hi, could you pls elaborate why or when in past this import worked?

pytorch_lighting is still broken for Torch 2.7 and CUDA 12.8 - we need it for RTX 5000 series. @Borda

[paomian001]

Upvoting this We really need support for torch 2.8 and cuda 12.8

May I ask if you have resolved the issue of pytorch_lightning adapting to 50 series GTX?