RuntimeError: RuntimeErrorNCCL error

Description

I’m trying to train I3D on my dataset. As soon as I run the training command, I get the RuntimeError: RuntimeErrorNCCL error.

Reproduction

1. I’m using the following command to train the I3D model:

bash tools/dist_train.sh  configs/recognition/i3d/my_config.py 8

1. My config file is given below:

# model settings
model = dict(
    type='Recognizer3D',
    backbone=dict(
        type='ResNet3d',
        pretrained2d=True,
        pretrained='torchvision://resnet50',
        depth=50,
        conv1_kernel=(5, 7, 7),
        conv1_stride_t=2,
        pool1_stride_t=2,
        conv_cfg=dict(type='Conv3d'),
        norm_eval=False,
        inflate=((1, 1, 1), (1, 0, 1, 0), (1, 0, 1, 0, 1, 0), (0, 1, 0)),
        zero_init_residual=False),
    cls_head=dict(
        type='I3DHead',
        num_classes=40,
        in_channels=2048,
        spatial_type='avg',
        dropout_ratio=0.5,
        init_std=0.01),
    # model training and testing settings
    train_cfg=None,
    test_cfg=dict(average_clips='prob'))


# dataset settings
dataset_type = 'VideoDataset'
data_root = 'data/videos_train'
data_root_val = 'data/videos_val'
ann_file_train = 'data/train_list.txt'
ann_file_val = 'data/val_list.txt'
ann_file_test = 'data/val_list.txt'

###################################

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)
train_pipeline = [
    dict(type='DecordInit'),
    dict(type='SampleFrames', clip_len=32, frame_interval=2, num_clips=1),
    dict(type='DecordDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(
        type='MultiScaleCrop',
        input_size=224,
        scales=(1, 0.8),
        random_crop=False,
        max_wh_scale_gap=0),
    dict(type='Resize', scale=(224, 224), keep_ratio=False),
    dict(type='Flip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs', 'label'])
]
val_pipeline = [
    dict(type='DecordInit'),
    dict(
        type='SampleFrames',
        clip_len=32,
        frame_interval=2,
        num_clips=1,
        test_mode=True),
    dict(type='DecordDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='CenterCrop', crop_size=224),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]
test_pipeline = [
    dict(type='DecordInit'),
    dict(
        type='SampleFrames',
        clip_len=32,
        frame_interval=2,
        num_clips=10,
        test_mode=True),
    dict(type='DecordDecode'),
    dict(type='Resize', scale=(-1, 256)),
    dict(type='ThreeCrop', crop_size=256),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='FormatShape', input_format='NCTHW'),
    dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
    dict(type='ToTensor', keys=['imgs'])
]

data = dict(
    videos_per_gpu=8,
    workers_per_gpu=2,
    test_dataloader=dict(videos_per_gpu=1),
    train=dict(
        type=dataset_type,
        ann_file=ann_file_train,
        data_prefix=data_root,
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=ann_file_val,
        data_prefix=data_root_val,
        pipeline=val_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=ann_file_val,
        data_prefix=data_root_val,
        pipeline=test_pipeline))

# runtime settings
work_dir = './work_dirs/my_i3d/'

Environment

1. I’m working on Google Colab.
2. The necessary environment information is given below:

sys.platform: linux
Python: 3.7.10 | packaged by conda-forge | (default, Feb 19 2021, 16:07:37) [GCC 9.3.0]
CUDA available: True
GPU 0: Tesla T4
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.1, V11.1.105
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.12.0
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2022.0-Product Build 20211112 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.3
  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
  - CuDNN 8.3.2  (built against CUDA 11.5)
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, 

TorchVision: 0.13.0
OpenCV: 4.6.0
MMCV: 1.6.0
MMCV Compiler: n/a
MMCV CUDA Compiler: n/a
MMAction2: 0.24.0+12f16c1

1. I used the following command to install PyTorch and CUDA:

conda install pytorch torchvision cudatoolkit=11.3 -c pytorch

Error traceback

RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1656352464346/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1191, invalid usage, NCCL version 2.10.3
ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc).
    work = default_pg.broadcast([tensor], opts)
RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1656352464346/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1191, invalid usage, NCCL version 2.10.3
ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc).
        work = default_pg.broadcast([tensor], opts)work = default_pg.broadcast([tensor], opts)

RuntimeError: RuntimeErrorNCCL error in: /opt/conda/conda-bld/pytorch_1656352464346/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1191, invalid usage, NCCL version 2.10.3
ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc).: 
NCCL error in: /opt/conda/conda-bld/pytorch_1656352464346/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1191, invalid usage, NCCL version 2.10.3
ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc).
    work = default_pg.broadcast([tensor], opts)
RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1656352464346/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1191, invalid usage, NCCL version 2.10.3
ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc).
    work = default_pg.broadcast([tensor], opts)
RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1656352464346/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1191, invalid usage, NCCL version 2.10.3
ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc).
        work = default_pg.broadcast([tensor], opts)
RuntimeErrorwork = default_pg.broadcast([tensor], opts): NCCL error in: /opt/conda/conda-bld/pytorch_1656352464346/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1191, invalid usage, NCCL version 2.10.3
ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc).

RuntimeError: NCCL error in: /opt/conda/conda-bld/pytorch_1656352464346/work/torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1191, invalid usage, NCCL version 2.10.3
ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc).
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 3176) of binary: /usr/local/bin/python
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/local/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/usr/local/lib/python3.7/site-packages/torch/distributed/launch.py", line 193, in <module>
    main()
  File "/usr/local/lib/python3.7/site-packages/torch/distributed/launch.py", line 189, in main
    launch(args)
  File "/usr/local/lib/python3.7/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/usr/local/lib/python3.7/site-packages/torch/distributed/run.py", line 755, in run
    )(*cmd_args)
  File "/usr/local/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/usr/local/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 247, in launch_agent
    failures=result.failures,
torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
============================================================
tools/train.py FAILED
------------------------------------------------------------

[hukkai]

@hmhamza The labels should begin at 0 and end at num_classes - 1.

[hukkai]

@hmhamza How many GPUs does the Google Colab env have? When you are using this cmd to run the model:

bash tools/dist_train.sh  configs/recognition/i3d/my_config.py 8

The last number 8 means using 8 GPUs