Describe the bug
For PyTorch 2.11 + torchvision 0.26.0 when using the datasets library and setting the format to "torch", an ImportError occurs because the datasets library attempts to import VideoReader from torchvision.io, but this object is not found in the installed torchvision version.
Error message:
File "/usr/local/lib/python3.12/dist-packages/datasets/formatting/torch_formatter.py", line 80, in _tensorize
from torchvision.io import VideoReader
ImportError: cannot import name 'VideoReader' from 'torchvision.io' (/usr/local/lib/python3.12/dist-packages/torchvision/io/init.py)
Issue is related to pytorch/vision#9373 - torchvision is moving video_backend for internal-only use.
Steps to reproduce the bug
import torchvision
from datasets import Dataset
ds = Dataset.from_dict({"value": [1, 2, 3]})
ds.set_format("torch")
print(ds[0])
Expected behavior
The dataset element should be accessed and formatted as a PyTorch tensor without an import error.
Environment info
PyTorch version: 2.11.0+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version: Could not collect
CMake version: version 3.31.10
Libc version: glibc-2.35
Python version: 3.12.13 (main, Mar 4 2026, 09:23:07) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.6.113+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 2
On-line CPU(s) list: 0,1
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family: 6
Model: 79
Thread(s) per core: 2
Core(s) per socket: 1
Socket(s): 1
Stepping: 0
BogoMIPS: 4399.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 32 KiB (1 instance)
L1i cache: 32 KiB (1 instance)
L2 cache: 256 KiB (1 instance)
L3 cache: 55 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0,1
Vulnerability Gather data sampling: Not affected
Vulnerability Indirect target selection: Vulnerable
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable; SMT Host state unknown
Vulnerability Meltdown: Vulnerable
Vulnerability Mmio stale data: Vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Vulnerable
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2: Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Vulnerable
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Vulnerable
Vulnerability Vmscape: Not affected
Versions of relevant libraries:
[pip3] intel-cmplr-lib-ur==2025.3.2
[pip3] intel-openmp==2025.3.2
[pip3] mkl==2025.3.1
[pip3] numpy==2.0.2
[pip3] nvidia-nccl-cu12==2.29.7
[pip3] onemkl-license==2025.3.1
[pip3] optree==0.19.0
[pip3] tbb==2022.3.1
[pip3] tcmlib==1.4.1
[pip3] torch==2.11.0+cpu
[pip3] torchao==0.10.0
[pip3] torchaudio==2.10.0+cpu
[pip3] torchcodec==0.10.0
[pip3] torchdata==0.11.0
[pip3] torchsummary==1.5.1
[pip3] torchtune==0.6.1
[pip3] torchvision==0.26.0+cpu
[pip3] umf==1.0.3
[conda] Could not collect
Describe the bug
For PyTorch 2.11 + torchvision 0.26.0 when using the datasets library and setting the format to "torch", an ImportError occurs because the datasets library attempts to import VideoReader from torchvision.io, but this object is not found in the installed torchvision version.
Error message:
Issue is related to pytorch/vision#9373 - torchvision is moving video_backend for internal-only use.
Steps to reproduce the bug
import torchvision
from datasets import Dataset
ds = Dataset.from_dict({"value": [1, 2, 3]})
ds.set_format("torch")
print(ds[0])
Expected behavior
The dataset element should be accessed and formatted as a PyTorch tensor without an import error.
Environment info
PyTorch version: 2.11.0+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version: Could not collect
CMake version: version 3.31.10
Libc version: glibc-2.35
Python version: 3.12.13 (main, Mar 4 2026, 09:23:07) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.6.113+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 2
On-line CPU(s) list: 0,1
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family: 6
Model: 79
Thread(s) per core: 2
Core(s) per socket: 1
Socket(s): 1
Stepping: 0
BogoMIPS: 4399.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 32 KiB (1 instance)
L1i cache: 32 KiB (1 instance)
L2 cache: 256 KiB (1 instance)
L3 cache: 55 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0,1
Vulnerability Gather data sampling: Not affected
Vulnerability Indirect target selection: Vulnerable
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable; SMT Host state unknown
Vulnerability Meltdown: Vulnerable
Vulnerability Mmio stale data: Vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Vulnerable
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2: Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Vulnerable
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Vulnerable
Vulnerability Vmscape: Not affected
Versions of relevant libraries:
[pip3] intel-cmplr-lib-ur==2025.3.2
[pip3] intel-openmp==2025.3.2
[pip3] mkl==2025.3.1
[pip3] numpy==2.0.2
[pip3] nvidia-nccl-cu12==2.29.7
[pip3] onemkl-license==2025.3.1
[pip3] optree==0.19.0
[pip3] tbb==2022.3.1
[pip3] tcmlib==1.4.1
[pip3] torch==2.11.0+cpu
[pip3] torchao==0.10.0
[pip3] torchaudio==2.10.0+cpu
[pip3] torchcodec==0.10.0
[pip3] torchdata==0.11.0
[pip3] torchsummary==1.5.1
[pip3] torchtune==0.6.1
[pip3] torchvision==0.26.0+cpu
[pip3] umf==1.0.3
[conda] Could not collect