Repository: pytorch
Issue: some official docker images has mismatch nccl version with pytorch
Labels: module: binaries,triaged,module: docker

### 🐛 Describe the bug

docker pull pytorch/pytorch:2.2.1-cuda11.8-cudnn8-devel

export | grep NCCL 
```
declare -x NCCL_VERSION="2.15.5-1"                        
declare -x NV_LIBNCCL_DEV_PACKAGE="libnccl-dev=2.15.5-1+cuda11.8"
declare -x NV_LIBNCCL_DEV_PACKAGE_NAME="libnccl-dev"
declare -x NV_LIBNCCL_DEV_PACKAGE_VERSION="2.15.5-1"
declare -x NV_LIBNCCL_PACKAGE="libnccl2=2.15.5-1+cuda11.8"
declare -x NV_LIBNCCL_PACKAGE_NAME="libnccl2"                
declare -x NV_LIBNCCL_PACKAGE_VERSION="2.15.5-1"  
```

- print(torch.cuda.nccl.version())
```
(2, 19, 3)
```

- related issue https://github.com/pytorch/pytorch/issues/113245 
```
torch.distributed.DistBackendError: NCCL error in: 
/opt/conda/conda-bld/pytorch_1708025847130/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1691
internal error - please report this issue to the NCCL developers, NCCL version 2.19.3
```
- related issue https://github.com/pytorch/pytorch/issues/122301
```
[rank21]: [E ProcessGroupNCCL.cpp:537] Some NCCL operations have failed or time out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data
NCCL version 2.19.3
ncclRemoteERror: A call failed possibly due to a network error or a remote process exting prematurely
NET/IB : Got completion from peer 192.168.xxx.xxx<40846> with error 5, opcode 0, len 5, vendor err 249 (Recv) localGid
```

### Versions

Collecting environment information...
PyTorch version: 2.2.1
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.26.4
Libc version: glibc-2.35

Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.15.0-29-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090
GPU 2: NVIDIA GeForce RTX 4090
GPU 3: NVIDIA GeForce RTX 4090
GPU 4: NVIDIA GeForce RTX 4090
GPU 5: NVIDIA GeForce RTX 4090
GPU 6: NVIDIA GeForce RTX 4090
GPU 7: NVIDIA GeForce RTX 4090

Nvidia driver version: 535.54.03
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
Address sizes:                   52 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          96
On-line CPU(s) list:             0-95
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) Gold 5318Y CPU @ 2.10GHz
CPU family:                      6
Model:                           106
Thread(s) per core:              2
Stepping:                        6
Frequency boost:                 enabled
CPU max MHz:                     2101.0000
CPU min MHz:                     800.0000
BogoMIPS:                        4200.00
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 cpuid aperf
mperf 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 cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp tpr
_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mb
m_total cqm_mbm_local dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid pconfig arch_capabilities
Virtualization:                  VT-x
L1d cache:                       2.3 MiB (48 instances)
L1i cache:                       1.5 MiB (48 instances)
L2 cache:                        60 MiB (48 instances)
L3 cache:                        72 MiB (2 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-23,48-71
NUMA node1 CPU(s):               24-47,72-95
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Full generic retpoline, IBPB, IBRS_FW

Versions of relevant libraries:
[pip3] numpy==1.26.3
[pip3] optree==0.10.0
[pip3] torch==2.2.1
[pip3] torchaudio==2.2.1
[pip3] torchelastic==0.2.2
[pip3] torchvision==0.17.1
[pip3] triton==2.2.0
[conda] blas                      1.0                         mkl  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-service               2.4.0           py310h5eee18b_1  
[conda] mkl_fft                   1.3.8           py310h5eee18b_0  
[conda] mkl_random                1.2.4           py310hdb19cb5_0  
[conda] numpy                     1.26.3          py310h5f9d8c6_0  
[conda] numpy-base                1.26.3          py310hb5e798b_0  
[conda] optree                    0.10.0                   pypi_0    pypi
[conda] pytorch                   2.2.1           py3.10_cuda11.8_cudnn8.7.0_0    pytorch
[conda] pytorch-cuda              11.8                 h7e8668a_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.2.1               py310_cu118    pytorch
[conda] torchelastic              0.2.2                    pypi_0    pypi
[conda] torchtriton               2.2.0                     py310    pytorch
[conda] torchvision               0.17.1              py310_cu118    pytorch

cc @seemethere @malfet @osalpekar @atalman