/usr/local/lib/python3.9/dist-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional. warnings.warn( [2024-03-09 06:24:37,357] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-03-09 06:24:37,704] [INFO] [comm.py:637:init_distributed] cdb=None [2024-03-09 06:24:37,927] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-03-09 06:24:38,102] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-03-09 06:24:38,115] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-03-09 06:24:38,140] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-03-09 06:24:38,148] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-03-09 06:24:38,158] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-03-09 06:24:38,181] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect) [2024-03-09 06:24:38,238] [INFO] [comm.py:637:init_distributed] cdb=None [2024-03-09 06:24:38,458] [INFO] [comm.py:637:init_distributed] cdb=None [2024-03-09 06:24:38,458] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl [2024-03-09 06:24:38,470] [INFO] [comm.py:637:init_distributed] cdb=None You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. [2024-03-09 06:24:38,483] [INFO] [comm.py:637:init_distributed] cdb=None [2024-03-09 06:24:38,487] [INFO] [comm.py:637:init_distributed] cdb=None [2024-03-09 06:24:38,493] [INFO] [comm.py:637:init_distributed] cdb=None [2024-03-09 06:24:38,536] [INFO] [comm.py:637:init_distributed] cdb=None Loading checkpoint shards: 0%| | 0/4 [00:00 2024-03-09 06:25:01.984 n213-017-210:2252776:2252776 [0] NCCL INFO NET/Plugin : dlerror=libnccl-net.so: cannot open shared object file: No such file or directory No plugin found (libnccl-net.so), using internal implementation 2024-03-09 06:25:01.991 n213-017-210:2252776:2252776 [0] NCCL INFO cudaDriverVersion 12010 NCCL version 2.19.3+cuda12.1 2024-03-09 06:25:01.995 n213-017-210:2252781:2252781 [5] NCCL INFO cudaDriverVersion 12010 2024-03-09 06:25:01.996 n213-017-210:2252781:2252781 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth0 2024-03-09 06:25:01.996 n213-017-210:2252781:2252781 [5] NCCL INFO Bootstrap : Using eth0:10.213.17.210<0> 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n213-017-210:2252783:2253658 [7] NCCL INFO comm 0x754554d0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId c9000 commId 0x5adbfca4a74bfde - Init START 2024-03-09 06:25:04.863 n213-017-210:2252780:2253646 [4] NCCL INFO comm 0x77ae8fb0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 89000 commId 0x5adbfca4a74bfde - Init START 2024-03-09 06:25:04.863 n213-017-210:2252778:2253608 [2] NCCL INFO comm 0x7a3a2bd0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 4a000 commId 0x5adbfca4a74bfde - Init START 2024-03-09 06:25:04.863 n213-017-210:2252779:2253578 [3] NCCL INFO comm 0x799eb500 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 4e000 commId 0x5adbfca4a74bfde - Init START 2024-03-09 06:25:04.863 n213-017-210:2252777:2253640 [1] NCCL INFO comm 0x75bb64c0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 16000 commId 0x5adbfca4a74bfde - Init START 2024-03-09 06:25:04.863 n213-017-210:2252782:2253624 [6] NCCL INFO comm 0x91380c80 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId c5000 commId 0x5adbfca4a74bfde - Init START 2024-03-09 06:25:04.863 n213-017-210:2252776:2253572 [0] NCCL INFO comm 0x18afec00 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 10000 commId 0x5adbfca4a74bfde - Init START 2024-03-09 06:25:04.863 n213-017-210:2252781:2253577 [5] NCCL INFO comm 0xa08bc940 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 8e000 commId 0x5adbfca4a74bfde - Init START 2024-03-09 06:25:06.929 n213-017-210:2252783:2253658 [7] NCCL INFO Setting affinity for GPU 7 to ffffffff,00000000,ffffffff,00000000 2024-03-09 06:25:06.929 n213-017-210:2252783:2253658 [7] NCCL INFO NVLS multicast support is not available on dev 7 2024-03-09 06:25:06.930 n213-017-210:2252780:2253646 [4] NCCL INFO Setting affinity for GPU 4 to ffffffff,00000000,ffffffff,00000000 2024-03-09 06:25:06.930 n213-017-210:2252780:2253646 [4] NCCL INFO NVLS multicast support is not available on dev 4 2024-03-09 06:25:06.930 n213-017-210:2252782:2253624 [6] NCCL INFO Setting affinity for GPU 6 to ffffffff,00000000,ffffffff,00000000 2024-03-09 06:25:06.930 n213-017-210:2252782:2253624 [6] NCCL INFO NVLS multicast support is not available on dev 6 2024-03-09 06:25:06.931 n213-017-210:2252777:2253640 [1] NCCL INFO Setting affinity for GPU 1 to ffffffff,00000000,ffffffff 2024-03-09 06:25:06.931 n213-017-210:2252777:2253640 [1] NCCL INFO NVLS multicast support is not available on dev 1 2024-03-09 06:25:06.936 n213-017-210:2252778:2253608 [2] NCCL INFO Setting affinity for GPU 2 to ffffffff,00000000,ffffffff 2024-03-09 06:25:06.936 n213-017-210:2252778:2253608 [2] NCCL INFO NVLS multicast support is not available on dev 2 2024-03-09 06:25:06.937 n213-017-210:2252779:2253578 [3] NCCL INFO Setting affinity for GPU 3 to ffffffff,00000000,ffffffff 2024-03-09 06:25:06.937 n213-017-210:2252779:2253578 [3] NCCL INFO NVLS multicast support is not available on dev 3 2024-03-09 06:25:06.937 n213-017-210:2252781:2253577 [5] NCCL INFO Setting affinity for GPU 5 to ffffffff,00000000,ffffffff,00000000 2024-03-09 06:25:06.938 n213-017-210:2252781:2253577 [5] NCCL INFO NVLS multicast support is not available on dev 5 2024-03-09 06:25:06.940 n213-017-210:2252776:2253572 [0] NCCL INFO Setting affinity for GPU 0 to ffffffff,00000000,ffffffff 2024-03-09 06:25:06.942 n213-017-210:2252776:2253572 [0] NCCL INFO NVLS multicast support is not available on dev 0 2024-03-09 06:25:06.942 n213-017-210:2252780:2253646 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 2024-03-09 06:25:06.942 n213-017-210:2252779:2253578 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 2024-03-09 06:25:06.942 n213-017-210:2252780:2253646 [4] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:06.942 n213-017-210:2252779:2253578 [3] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:06.943 n213-017-210:2252782:2253624 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252783:2253658 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 2024-03-09 06:25:06.943 n213-017-210:2252778:2253608 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 2024-03-09 06:25:06.943 n213-017-210:2252781:2253577 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 2024-03-09 06:25:06.943 n213-017-210:2252782:2253624 [6] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:06.943 n213-017-210:2252783:2253658 [7] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252778:2253608 [2] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:06.943 n213-017-210:2252781:2253577 [5] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252777:2253640 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252777:2253640 [1] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 2024-03-09 06:25:06.943 n213-017-210:2252776:2253572 [0] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:07.331 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.331 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.332 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.332 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.333 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.333 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.334 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.334 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.334 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.335 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.335 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.335 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.336 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.336 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.336 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.337 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.337 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.337 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.338 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.338 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.338 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.338 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.339 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.339 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.339 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.339 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.340 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.340 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.340 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.340 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.341 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.341 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.342 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.342 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.343 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.343 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.343 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.343 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.344 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.344 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.344 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.344 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.345 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.345 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.345 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.345 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.346 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.346 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.346 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.347 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.347 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.348 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.348 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.348 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.348 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.348 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.349 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.349 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.350 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.350 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.350 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.350 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.351 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.351 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.351 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.352 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.352 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.352 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.352 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.352 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.353 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.353 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.353 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.354 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.354 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.355 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.355 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.355 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.355 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.356 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.356 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.357 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.357 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.357 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.357 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.357 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.358 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.358 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.358 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.360 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.360 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.360 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.360 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.360 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.361 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.361 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.361 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.362 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.363 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.363 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.363 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.363 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.364 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.364 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.364 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.366 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.366 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.366 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.366 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.366 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.367 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.367 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.367 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.369 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.369 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.369 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.369 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.369 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.370 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.370 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.371 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.371 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.372 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.372 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.372 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.372 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.373 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.373 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.374 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.374 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.375 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.375 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.375 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.375 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.376 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.376 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.377 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.378 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.378 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.378 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.378 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.378 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.379 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.379 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.380 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.380 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.381 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.381 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.381 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.381 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.382 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.382 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.383 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.383 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.384 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.384 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.384 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.384 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.385 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.385 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.385 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.386 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.386 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.386 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.387 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.387 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.387 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.388 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.388 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.389 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.389 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.389 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.390 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.390 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.390 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.391 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.391 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.392 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.392 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.392 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.393 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:07.393 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:07.393 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:07.394 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:07.394 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:07.395 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.395 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.395 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.397 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:07.397 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:07.397 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:07.411 n213-017-210:2252776:2253572 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.021 n213-017-210:2252778:2253608 [2] NCCL INFO Connected all rings 2024-03-09 06:25:08.047 n213-017-210:2252779:2253578 [3] NCCL INFO Connected all rings 2024-03-09 06:25:08.052 n213-017-210:2252777:2253640 [1] NCCL INFO Connected all rings 2024-03-09 06:25:08.052 n213-017-210:2252776:2253572 [0] NCCL INFO Connected all rings 2024-03-09 06:25:08.062 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.064 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.067 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.068 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.070 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.072 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.073 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.075 n213-017-210:2252783:2253658 [7] NCCL INFO Connected all rings 2024-03-09 06:25:08.075 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.075 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.078 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.078 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.080 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.080 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.082 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.083 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.084 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.085 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.086 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.087 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.088 n213-017-210:2252782:2253624 [6] NCCL INFO Connected all rings 2024-03-09 06:25:08.089 n213-017-210:2252780:2253646 [4] NCCL INFO Connected all rings 2024-03-09 06:25:08.089 n213-017-210:2252781:2253577 [5] NCCL INFO Connected all rings 2024-03-09 06:25:08.089 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.089 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.091 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.092 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.095 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.098 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.098 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.099 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.100 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.101 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.102 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.103 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.104 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.104 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.106 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.106 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.107 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.108 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.108 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.109 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.110 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.111 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.111 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.111 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.113 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.113 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.114 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.114 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.115 n213-017-210:2252778:2253608 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:08.116 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.116 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.116 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.118 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.119 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.119 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.121 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.121 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.121 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.123 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.123 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.123 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.125 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.125 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.125 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.127 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.127 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.127 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.129 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.129 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.129 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.131 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.132 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.132 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.133 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.134 n213-017-210:2252783:2253658 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:08.134 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.136 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.136 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.137 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.138 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.139 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.140 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.141 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.142 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.144 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.144 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.146 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.147 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.147 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.148 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.150 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.150 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.150 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.150 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.151 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.152 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.152 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.153 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.153 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.154 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.155 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.156 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.156 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.156 n213-017-210:2252779:2253578 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:08.157 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.158 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.159 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.159 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.160 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.161 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.161 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.161 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.163 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.164 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.164 n213-017-210:2252777:2253640 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:08.164 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.165 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.166 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.166 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.168 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.169 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.169 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.170 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.172 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.172 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.173 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.175 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.175 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.176 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.177 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.177 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.179 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.181 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.181 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.183 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.184 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.185 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.186 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.187 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.188 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.189 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.190 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.190 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.191 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.192 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.192 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.193 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.194 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.194 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.195 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.195 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.195 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.196 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.197 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.198 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.199 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.199 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.199 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.200 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.200 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.201 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.202 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.203 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.203 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.204 n213-017-210:2252781:2253577 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:08.204 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.204 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.206 n213-017-210:2252780:2253646 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:08.206 n213-017-210:2252782:2253624 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:08.670 n213-017-210:2252776:2253572 [0] NCCL INFO Connected all trees 2024-03-09 06:25:08.671 n213-017-210:2252776:2253572 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:08.671 n213-017-210:2252776:2253572 [0] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:08.680 n213-017-210:2252777:2253640 [1] NCCL INFO Connected all trees 2024-03-09 06:25:08.680 n213-017-210:2252777:2253640 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:08.680 n213-017-210:2252777:2253640 [1] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:08.751 n213-017-210:2252778:2253608 [2] NCCL INFO Connected all trees 2024-03-09 06:25:08.751 n213-017-210:2252778:2253608 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:08.751 n213-017-210:2252778:2253608 [2] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:08.755 n213-017-210:2252783:2253658 [7] NCCL INFO Connected all trees 2024-03-09 06:25:08.755 n213-017-210:2252783:2253658 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:08.755 n213-017-210:2252783:2253658 [7] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:08.760 n213-017-210:2252779:2253578 [3] NCCL INFO Connected all trees 2024-03-09 06:25:08.760 n213-017-210:2252779:2253578 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:08.760 n213-017-210:2252779:2253578 [3] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:08.764 n213-017-210:2252780:2253646 [4] NCCL INFO Connected all trees 2024-03-09 06:25:08.764 n213-017-210:2252780:2253646 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:08.764 n213-017-210:2252780:2253646 [4] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:08.764 n213-017-210:2252782:2253624 [6] NCCL INFO Connected all trees 2024-03-09 06:25:08.764 n213-017-210:2252782:2253624 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:08.764 n213-017-210:2252782:2253624 [6] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:08.764 n213-017-210:2252781:2253577 [5] NCCL INFO Connected all trees 2024-03-09 06:25:08.764 n213-017-210:2252781:2253577 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:08.764 n213-017-210:2252781:2253577 [5] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:08.789 n213-017-210:2252778:2253608 [2] NCCL INFO comm 0x7a3a2bd0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 4a000 commId 0x5adbfca4a74bfde - Init COMPLETE 2024-03-09 06:25:08.789 n213-017-210:2252782:2253624 [6] NCCL INFO comm 0x91380c80 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId c5000 commId 0x5adbfca4a74bfde - Init COMPLETE 2024-03-09 06:25:08.789 n213-017-210:2252780:2253646 [4] NCCL INFO comm 0x77ae8fb0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 89000 commId 0x5adbfca4a74bfde - Init COMPLETE 2024-03-09 06:25:08.789 n213-017-210:2252776:2253572 [0] NCCL INFO comm 0x18afec00 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 10000 commId 0x5adbfca4a74bfde - Init COMPLETE 2024-03-09 06:25:08.790 n213-017-210:2252783:2253658 [7] NCCL INFO comm 0x754554d0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId c9000 commId 0x5adbfca4a74bfde - Init COMPLETE 2024-03-09 06:25:08.790 n213-017-210:2252779:2253578 [3] NCCL INFO comm 0x799eb500 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 4e000 commId 0x5adbfca4a74bfde - Init COMPLETE 2024-03-09 06:25:08.791 n213-017-210:2252777:2253640 [1] NCCL INFO comm 0x75bb64c0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 16000 commId 0x5adbfca4a74bfde - Init COMPLETE 2024-03-09 06:25:08.792 n213-017-210:2252781:2253577 [5] NCCL INFO comm 0xa08bc940 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 8e000 commId 0x5adbfca4a74bfde - Init COMPLETE /usr/local/lib/python3.9/dist-packages/bytedmetrics/__init__.py:10: UserWarning: bytedmetrics is renamed to bytedance.metrics, please using `bytedance.metrics` instead of `bytedmetrics` warnings.warn("bytedmetrics is renamed to bytedance.metrics, please using `bytedance.metrics` instead of `bytedmetrics`") wandb: ⭐️ View project at https://ml.byteintl.net/experiment/tracking/detail?Id=project_20230126_e9daa974 wandb: 🚀 View run at https://ml.byteintl.net/experiment/tracking/detail?Id=project_20230126_e9daa974&selectedTrial=run_20240309_98cb39ab wandb: - Waiting for wandb.init()... wandb: \ Waiting for wandb.init()... wandb: | Waiting for wandb.init()... wandb: Tracking run with wandb version 0.13.69 wandb: Run data is saved locally in /mnt/bn/liangkeg/ruohongz/vllm/video_llava/wandb/run-20240309_062525-run_20240309_98cb39ab wandb: Run `wandb offline` to turn off syncing. 0%| | 0/7045 [00:007->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 2024-03-09 06:25:35.611 n213-017-210:2252783:2254206 [7] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:35.611 n213-017-210:2252782:2254213 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.611 n213-017-210:2252782:2254213 [6] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.611 n213-017-210:2252781:2254212 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 2024-03-09 06:25:35.611 n213-017-210:2252780:2254210 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 2024-03-09 06:25:35.611 n213-017-210:2252777:2254209 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.611 n213-017-210:2252780:2254210 [4] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:35.611 n213-017-210:2252781:2254212 [5] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.611 n213-017-210:2252777:2254209 [1] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.611 n213-017-210:2252778:2254211 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.611 n213-017-210:2252779:2254208 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.611 n213-017-210:2252779:2254208 [3] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.611 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252778:2254211 [2] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 2024-03-09 06:25:35.612 n213-017-210:2252776:2254207 [0] NCCL INFO P2P Chunksize set to 524288 2024-03-09 06:25:35.984 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:35.984 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:35.985 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:35.985 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:35.985 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:35.985 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:35.985 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:35.986 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:35.986 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:35.987 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:35.987 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:35.987 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:35.987 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:35.987 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:35.988 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:35.988 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:35.989 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:35.989 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:35.989 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:35.989 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:35.989 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:35.990 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:35.990 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:35.991 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:35.991 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:35.991 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:35.991 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:35.991 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:35.992 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:35.992 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:35.992 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:35.993 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:35.993 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:35.993 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:35.993 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:35.994 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:35.994 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:35.994 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:35.995 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:35.995 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:35.995 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:35.995 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:35.996 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:35.996 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:35.996 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:35.997 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:35.997 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:35.997 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:35.997 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:35.998 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:35.998 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:35.998 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:35.998 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:35.998 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:35.999 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:35.999 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:35.999 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.000 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.000 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.000 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.000 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.000 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.001 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.001 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.001 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.002 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.002 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.002 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.003 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.003 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.003 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.003 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.003 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.004 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.004 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.004 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.005 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.005 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.005 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.006 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.006 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.006 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.006 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.007 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.007 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.007 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.007 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.008 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.009 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.009 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.009 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.009 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.009 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.010 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.010 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.011 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.011 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.011 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.011 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.011 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.012 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.012 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.013 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.013 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.013 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.013 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.014 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.014 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.014 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.014 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.015 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.015 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.015 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.016 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.016 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.016 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.016 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.017 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.017 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.017 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.018 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.018 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.018 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.019 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.019 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.019 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.019 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.020 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.020 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.021 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.021 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.021 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.021 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.021 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.022 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.022 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.022 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.023 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.023 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.023 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.024 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.024 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.024 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.025 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.025 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.026 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.026 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.026 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.026 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.026 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.026 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.027 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.027 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.028 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.028 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.028 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.028 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.029 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.029 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.029 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.030 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.030 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.030 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.030 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.031 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.031 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.031 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.032 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.032 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.033 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.033 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.033 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.033 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.034 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.034 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.034 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.035 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read 2024-03-09 06:25:36.035 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.035 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.036 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.036 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.036 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.036 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.038 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.038 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.038 n213-017-210:2252776:2254207 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.040 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.041 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.043 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.045 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.047 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.049 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.624 n213-017-210:2252777:2254209 [1] NCCL INFO Connected all rings 2024-03-09 06:25:36.629 n213-017-210:2252776:2254207 [0] NCCL INFO Connected all rings 2024-03-09 06:25:36.636 n213-017-210:2252778:2254211 [2] NCCL INFO Connected all rings 2024-03-09 06:25:36.661 n213-017-210:2252779:2254208 [3] NCCL INFO Connected all rings 2024-03-09 06:25:36.664 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.666 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.667 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.669 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.671 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.672 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.673 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.674 n213-017-210:2252780:2254210 [4] NCCL INFO Connected all rings 2024-03-09 06:25:36.674 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.676 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.677 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.678 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.679 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.680 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.681 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.682 n213-017-210:2252783:2254206 [7] NCCL INFO Connected all rings 2024-03-09 06:25:36.682 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.682 n213-017-210:2252781:2254212 [5] NCCL INFO Connected all rings 2024-03-09 06:25:36.682 n213-017-210:2252782:2254213 [6] NCCL INFO Connected all rings 2024-03-09 06:25:36.682 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.683 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.684 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.685 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.686 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.687 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.689 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.690 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.691 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.692 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.692 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.693 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.693 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.694 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.694 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.695 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.696 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.696 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.697 n213-017-210:2252777:2254209 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read 2024-03-09 06:25:36.698 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.698 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.699 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.699 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.700 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.700 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.701 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.701 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.702 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.703 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.703 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.705 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.705 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.706 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.707 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.707 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.708 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.708 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.709 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.710 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.710 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.710 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.711 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.712 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.712 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.713 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.713 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.714 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.714 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.714 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.715 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.716 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.716 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.717 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.717 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.717 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.718 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.718 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.719 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.719 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.719 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.720 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.720 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.721 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.721 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.722 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.722 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.723 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.723 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.723 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.724 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.724 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.725 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.725 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.725 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.726 n213-017-210:2252780:2254210 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read 2024-03-09 06:25:36.726 n213-017-210:2252779:2254208 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read 2024-03-09 06:25:36.726 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.727 n213-017-210:2252778:2254211 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read 2024-03-09 06:25:36.727 n213-017-210:2252783:2254206 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read 2024-03-09 06:25:36.727 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.728 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Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.761 n213-017-210:2252782:2254213 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read 2024-03-09 06:25:36.762 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:36.765 n213-017-210:2252781:2254212 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read 2024-03-09 06:25:37.194 n213-017-210:2252776:2254207 [0] NCCL INFO Connected all trees 2024-03-09 06:25:37.194 n213-017-210:2252776:2254207 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:37.194 n213-017-210:2252776:2254207 [0] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:37.230 n213-017-210:2252777:2254209 [1] NCCL INFO Connected all trees 2024-03-09 06:25:37.230 n213-017-210:2252777:2254209 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:37.230 n213-017-210:2252777:2254209 [1] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:37.278 n213-017-210:2252778:2254211 [2] NCCL INFO Connected all trees 2024-03-09 06:25:37.278 n213-017-210:2252778:2254211 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:37.278 n213-017-210:2252778:2254211 [2] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:37.286 n213-017-210:2252779:2254208 [3] NCCL INFO Connected all trees 2024-03-09 06:25:37.286 n213-017-210:2252779:2254208 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:37.286 n213-017-210:2252779:2254208 [3] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:37.294 n213-017-210:2252780:2254210 [4] NCCL INFO Connected all trees 2024-03-09 06:25:37.294 n213-017-210:2252780:2254210 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:37.294 n213-017-210:2252780:2254210 [4] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:37.294 n213-017-210:2252782:2254213 [6] NCCL INFO Connected all trees 2024-03-09 06:25:37.295 n213-017-210:2252781:2254212 [5] NCCL INFO Connected all trees 2024-03-09 06:25:37.295 n213-017-210:2252782:2254213 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:37.295 n213-017-210:2252781:2254212 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:37.295 n213-017-210:2252781:2254212 [5] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:37.295 n213-017-210:2252782:2254213 [6] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:37.298 n213-017-210:2252783:2254206 [7] NCCL INFO Connected all trees 2024-03-09 06:25:37.298 n213-017-210:2252783:2254206 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 2024-03-09 06:25:37.298 n213-017-210:2252783:2254206 [7] NCCL INFO 24 coll channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 2024-03-09 06:25:37.321 n213-017-210:2252781:2254212 [5] NCCL INFO comm 0x6f5eb560 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 8e000 commId 0x44ef0d75b7331d7c - Init COMPLETE 2024-03-09 06:25:37.321 n213-017-210:2252783:2254206 [7] NCCL INFO comm 0x6f584bc0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId c9000 commId 0x44ef0d75b7331d7c - Init COMPLETE 2024-03-09 06:25:37.321 n213-017-210:2252780:2254210 [4] NCCL INFO comm 0x6fd97400 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 89000 commId 0x44ef0d75b7331d7c - Init COMPLETE 2024-03-09 06:25:37.321 n213-017-210:2252782:2254213 [6] NCCL INFO comm 0x6f5c01c0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId c5000 commId 0x44ef0d75b7331d7c - Init COMPLETE 2024-03-09 06:25:37.321 n213-017-210:2252779:2254208 [3] NCCL INFO comm 0x70282cc0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 4e000 commId 0x44ef0d75b7331d7c - Init COMPLETE 2024-03-09 06:25:37.321 n213-017-210:2252777:2254209 [1] NCCL INFO comm 0x6ece5cc0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 16000 commId 0x44ef0d75b7331d7c - Init COMPLETE 2024-03-09 06:25:37.321 n213-017-210:2252778:2254211 [2] NCCL INFO comm 0x6f970a00 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 4a000 commId 0x44ef0d75b7331d7c - Init COMPLETE 2024-03-09 06:25:37.321 n213-017-210:2252776:2254207 [0] NCCL INFO comm 0x19413970 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 10000 commId 0x44ef0d75b7331d7c - Init COMPLETE /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True). warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( /usr/local/lib/python3.9/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py:1586: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:85.) total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)]) /usr/local/lib/python3.9/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py:1586: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:85.) total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)]) /usr/local/lib/python3.9/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py:1586: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:85.) total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)]) /usr/local/lib/python3.9/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py:1586: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:85.) total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)]) /usr/local/lib/python3.9/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py:1586: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:85.) total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)]) /usr/local/lib/python3.9/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py:1586: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:85.) total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)]) /usr/local/lib/python3.9/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py:1586: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:85.) total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)]) /usr/local/lib/python3.9/dist-packages/deepspeed/runtime/zero/stage_1_and_2.py:1586: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:85.) total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)]) 0%| | 1/7045 [00:23<45:24:42, 23.21s/it] {'loss': 1.3232, 'learning_rate': 2.358490566037736e-08, 'epoch': 0.0} 0%| | 1/7045 [00:23<45:24:42, 23.21s/it] 0%| | 2/7045 [00:35<32:49:55, 16.78s/it] {'loss': 1.3271, 'learning_rate': 4.716981132075472e-08, 'epoch': 0.0} 0%| | 2/7045 [00:35<32:49:55, 16.78s/it] 0%| | 3/7045 [00:46<27:39:41, 14.14s/it] {'loss': 1.3477, 'learning_rate': 7.075471698113208e-08, 'epoch': 0.0} 0%| | 3/7045 [00:46<27:39:41, 14.14s/it] 0%| | 4/7045 [00:57<25:18:43, 12.94s/it] {'loss': 1.334, 'learning_rate': 9.433962264150944e-08, 'epoch': 0.0} 0%| | 4/7045 [00:57<25:18:43, 12.94s/it] 0%| | 5/7045 [01:08<23:56:43, 12.24s/it] {'loss': 1.3379, 'learning_rate': 1.179245283018868e-07, 'epoch': 0.0} 0%| | 5/7045 [01:08<23:56:43, 12.24s/it] 0%| | 6/7045 [01:19<23:05:28, 11.81s/it] {'loss': 1.3525, 'learning_rate': 1.4150943396226417e-07, 'epoch': 0.0} 0%| | 6/7045 [01:19<23:05:28, 11.81s/it] 0%| | 7/7045 [01:30<22:44:05, 11.63s/it] {'loss': 1.3887, 'learning_rate': 1.6509433962264153e-07, 'epoch': 0.0} 0%| | 7/7045 [01:30<22:44:05, 11.63s/it] 0%| | 8/7045 [01:41<22:22:43, 11.45s/it] {'loss': 1.3379, 'learning_rate': 1.886792452830189e-07, 'epoch': 0.0} 0%| | 8/7045 [01:41<22:22:43, 11.45s/it] 0%| | 9/7045 [01:53<22:18:36, 11.42s/it] {'loss': 1.3311, 'learning_rate': 2.1226415094339622e-07, 'epoch': 0.0} 0%| | 9/7045 [01:53<22:18:36, 11.42s/it] 0%| | 10/7045 [02:04<22:06:57, 11.32s/it] {'loss': 1.3672, 'learning_rate': 2.358490566037736e-07, 'epoch': 0.0} 0%| | 10/7045 [02:04<22:06:57, 11.32s/it] 0%| | 11/7045 [02:16<22:33:47, 11.55s/it] {'loss': 1.3203, 'learning_rate': 2.59433962264151e-07, 'epoch': 0.0} 0%| | 11/7045 [02:16<22:33:47, 11.55s/it] 0%| | 12/7045 [02:27<22:28:51, 11.51s/it] {'loss': 1.3379, 'learning_rate': 2.8301886792452833e-07, 'epoch': 0.0} 0%| | 12/7045 [02:27<22:28:51, 11.51s/it] 0%| | 13/7045 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4.982432213986725e-06, 'epoch': 0.07} 7%|▋ | 470/7045 [1:31:48<21:28:57, 11.76s/it] 7%|▋ | 471/7045 [1:31:59<21:18:54, 11.67s/it] {'loss': 1.1855, 'learning_rate': 4.982295926949526e-06, 'epoch': 0.07} 7%|▋ | 471/7045 [1:31:59<21:18:54, 11.67s/it] 7%|▋ | 472/7045 [1:32:11<21:35:28, 11.83s/it] {'loss': 1.1602, 'learning_rate': 4.982159115188904e-06, 'epoch': 0.07} 7%|▋ | 472/7045 [1:32:11<21:35:28, 11.83s/it] 7%|▋ | 473/7045 [1:32:22<21:15:06, 11.64s/it] {'loss': 1.1787, 'learning_rate': 4.982021778733779e-06, 'epoch': 0.07} 7%|▋ | 473/7045 [1:32:22<21:15:06, 11.64s/it] 7%|▋ | 474/7045 [1:32:35<21:58:15, 12.04s/it] {'loss': 1.1338, 'learning_rate': 4.981883917613182e-06, 'epoch': 0.07} 7%|▋ | 474/7045 [1:32:35<21:58:15, 12.04s/it] 7%|▋ | 475/7045 [1:32:47<21:33:24, 11.81s/it] {'loss': 1.1465, 'learning_rate': 4.981745531856255e-06, 'epoch': 0.07} 7%|▋ | 475/7045 [1:32:47<21:33:24, 11.81s/it] 7%|▋ | 476/7045 [1:32:58<21:22:33, 11.71s/it] {'loss': 1.1514, 'learning_rate': 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4.980762144211744e-06, 'epoch': 0.07} 7%|▋ | 482/7045 [1:34:05<20:39:21, 11.33s/it]/usr/local/lib/python3.9/dist-packages/PIL/Image.py:3074: DecompressionBombWarning: Image size (159222407 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack. warnings.warn( 7%|▋ | 483/7045 [1:34:17<20:30:25, 11.25s/it] {'loss': 1.1572, 'learning_rate': 4.980619562426596e-06, 'epoch': 0.07} 7%|▋ | 483/7045 [1:34:17<20:30:25, 11.25s/it] 7%|▋ | 484/7045 [1:34:29<20:59:39, 11.52s/it] {'loss': 1.1338, 'learning_rate': 4.980476456272386e-06, 'epoch': 0.07} 7%|▋ | 484/7045 [1:34:29<20:59:39, 11.52s/it] 7%|▋ | 485/7045 [1:34:42<21:59:09, 12.07s/it] {'loss': 1.1484, 'learning_rate': 4.980332825779364e-06, 'epoch': 0.07} 7%|▋ | 485/7045 [1:34:42<21:59:09, 12.07s/it] 7%|▋ | 486/7045 [1:34:55<22:14:24, 12.21s/it] {'loss': 1.1553, 'learning_rate': 4.980188670977891e-06, 'epoch': 0.07} 7%|▋ | 486/7045 [1:34:55<22:14:24, 12.21s/it] 7%|▋ | 487/7045 [1:35:06<21:52:54, 12.01s/it] {'loss': 1.165, 'learning_rate': 4.980043991898441e-06, 'epoch': 0.07} 7%|▋ | 487/7045 [1:35:06<21:52:54, 12.01s/it] 7%|▋ | 488/7045 [1:35:17<21:20:39, 11.72s/it] {'loss': 1.1758, 'learning_rate': 4.979898788571595e-06, 'epoch': 0.07} 7%|▋ | 488/7045 [1:35:17<21:20:39, 11.72s/it] 7%|▋ | 489/7045 [1:35:28<21:00:54, 11.54s/it] {'loss': 1.1582, 'learning_rate': 4.9797530610280495e-06, 'epoch': 0.07} 7%|▋ | 489/7045 [1:35:28<21:00:54, 11.54s/it] 7%|▋ | 490/7045 [1:35:40<21:22:53, 11.74s/it] {'loss': 1.1064, 'learning_rate': 4.979606809298608e-06, 'epoch': 0.07} 7%|▋ | 490/7045 [1:35:40<21:22:53, 11.74s/it] 7%|▋ | 491/7045 [1:35:51<20:58:28, 11.52s/it] {'loss': 1.1934, 'learning_rate': 4.979460033414186e-06, 'epoch': 0.07} 7%|▋ | 491/7045 [1:35:51<20:58:28, 11.52s/it] 7%|▋ | 492/7045 [1:36:03<20:43:20, 11.38s/it] {'loss': 1.1426, 'learning_rate': 4.979312733405811e-06, 'epoch': 0.07} 7%|▋ | 492/7045 [1:36:03<20:43:20, 11.38s/it] 7%|▋ | 493/7045 [1:36:16<21:37:19, 11.88s/it] {'loss': 1.1816, 'learning_rate': 4.979164909304619e-06, 'epoch': 0.07} 7%|▋ | 493/7045 [1:36:16<21:37:19, 11.88s/it] 7%|▋ | 494/7045 [1:36:29<22:28:31, 12.35s/it] {'loss': 1.1465, 'learning_rate': 4.979016561141857e-06, 'epoch': 0.07} 7%|▋ | 494/7045 [1:36:29<22:28:31, 12.35s/it] 7%|▋ | 495/7045 [1:36:41<22:32:27, 12.39s/it] {'loss': 1.1514, 'learning_rate': 4.9788676889488865e-06, 'epoch': 0.07} 7%|▋ | 495/7045 [1:36:41<22:32:27, 12.39s/it] 7%|▋ | 496/7045 [1:36:53<21:50:18, 12.00s/it] {'loss': 1.167, 'learning_rate': 4.978718292757176e-06, 'epoch': 0.07} 7%|▋ | 496/7045 [1:36:53<21:50:18, 12.00s/it] 7%|▋ | 497/7045 [1:37:04<21:15:42, 11.69s/it] {'loss': 1.1875, 'learning_rate': 4.978568372598304e-06, 'epoch': 0.07} 7%|▋ | 497/7045 [1:37:04<21:15:42, 11.69s/it] 7%|▋ | 498/7045 [1:37:15<20:51:57, 11.47s/it] {'loss': 1.1504, 'learning_rate': 4.9784179285039646e-06, 'epoch': 0.07} 7%|▋ | 498/7045 [1:37:15<20:51:57, 11.47s/it] 7%|▋ | 499/7045 [1:37:26<21:05:08, 11.60s/it] {'loss': 1.209, 'learning_rate': 4.978266960505957e-06, 'epoch': 0.07} 7%|▋ | 499/7045 [1:37:26<21:05:08, 11.60s/it] 7%|▋ | 500/7045 [1:37:37<20:48:20, 11.44s/it] {'loss': 1.1562, 'learning_rate': 4.978115468636195e-06, 'epoch': 0.07} 7%|▋ | 500/7045 [1:37:37<20:48:20, 11.44s/it] 7%|▋ | 501/7045 [1:37:48<20:31:28, 11.29s/it] {'loss': 1.1729, 'learning_rate': 4.977963452926703e-06, 'epoch': 0.07} 7%|▋ | 501/7045 [1:37:48<20:31:28, 11.29s/it] 7%|▋ | 502/7045 [1:38:00<20:44:14, 11.41s/it] {'loss': 1.1201, 'learning_rate': 4.9778109134096125e-06, 'epoch': 0.07} 7%|▋ | 502/7045 [1:38:00<20:44:14, 11.41s/it] 7%|▋ | 503/7045 [1:38:14<21:51:12, 12.03s/it] {'loss': 1.1426, 'learning_rate': 4.97765785011717e-06, 'epoch': 0.07} 7%|▋ | 503/7045 [1:38:14<21:51:12, 12.03s/it] 7%|▋ | 504/7045 [1:38:26<21:48:00, 12.00s/it] {'loss': 1.1992, 'learning_rate': 4.97750426308173e-06, 'epoch': 0.07} 7%|▋ | 504/7045 [1:38:26<21:48:00, 12.00s/it] 7%|▋ | 505/7045 [1:38:38<22:11:57, 12.22s/it] {'loss': 1.1206, 'learning_rate': 4.97735015233576e-06, 'epoch': 0.07} 7%|▋ | 505/7045 [1:38:38<22:11:57, 12.22s/it] 7%|▋ | 506/7045 [1:38:51<22:31:24, 12.40s/it] {'loss': 1.1494, 'learning_rate': 4.977195517911836e-06, 'epoch': 0.07} 7%|▋ | 506/7045 [1:38:51<22:31:24, 12.40s/it] 7%|▋ | 507/7045 [1:39:02<21:50:36, 12.03s/it] {'loss': 1.168, 'learning_rate': 4.9770403598426465e-06, 'epoch': 0.07} 7%|▋ | 507/7045 [1:39:02<21:50:36, 12.03s/it] 7%|▋ | 508/7045 [1:39:13<21:19:36, 11.74s/it] {'loss': 1.1768, 'learning_rate': 4.976884678160988e-06, 'epoch': 0.07} 7%|▋ | 508/7045 [1:39:13<21:19:36, 11.74s/it] 7%|▋ | 509/7045 [1:39:26<22:04:26, 12.16s/it] {'loss': 1.1113, 'learning_rate': 4.976728472899771e-06, 'epoch': 0.07} 7%|▋ | 509/7045 [1:39:26<22:04:26, 12.16s/it] 7%|▋ | 510/7045 [1:39:38<21:40:15, 11.94s/it] {'loss': 1.1582, 'learning_rate': 4.976571744092015e-06, 'epoch': 0.07} 7%|▋ | 510/7045 [1:39:38<21:40:15, 11.94s/it] 7%|▋ | 511/7045 [1:39:49<21:19:19, 11.75s/it] {'loss': 1.1572, 'learning_rate': 4.976414491770849e-06, 'epoch': 0.07} 7%|▋ | 511/7045 [1:39:49<21:19:19, 11.75s/it] 7%|▋ | 512/7045 [1:40:01<21:32:11, 11.87s/it] {'loss': 1.1699, 'learning_rate': 4.9762567159695155e-06, 'epoch': 0.07} 7%|▋ | 512/7045 [1:40:01<21:32:11, 11.87s/it] 7%|▋ | 513/7045 [1:40:14<21:56:36, 12.09s/it] {'loss': 1.1553, 'learning_rate': 4.976098416721366e-06, 'epoch': 0.07} 7%|▋ | 513/7045 [1:40:14<21:56:36, 12.09s/it] 7%|▋ | 514/7045 [1:40:26<21:40:04, 11.94s/it] {'loss': 1.1553, 'learning_rate': 4.975939594059862e-06, 'epoch': 0.07} 7%|▋ | 514/7045 [1:40:26<21:40:04, 11.94s/it] 7%|▋ | 515/7045 [1:40:37<21:11:10, 11.68s/it] {'loss': 1.1387, 'learning_rate': 4.975780248018578e-06, 'epoch': 0.07} 7%|▋ | 515/7045 [1:40:37<21:11:10, 11.68s/it] 7%|▋ | 516/7045 [1:40:48<20:46:51, 11.46s/it] {'loss': 1.1611, 'learning_rate': 4.975620378631195e-06, 'epoch': 0.07} 7%|▋ | 516/7045 [1:40:48<20:46:51, 11.46s/it] 7%|▋ | 517/7045 [1:40:59<20:48:31, 11.48s/it] {'loss': 1.1875, 'learning_rate': 4.97545998593151e-06, 'epoch': 0.07} 7%|▋ | 517/7045 [1:40:59<20:48:31, 11.48s/it] 7%|▋ | 518/7045 [1:41:10<20:47:19, 11.47s/it] {'loss': 1.1709, 'learning_rate': 4.975299069953426e-06, 'epoch': 0.07} 7%|▋ | 518/7045 [1:41:10<20:47:19, 11.47s/it] 7%|▋ | 519/7045 [1:41:22<20:46:03, 11.46s/it] {'loss': 1.1621, 'learning_rate': 4.9751376307309585e-06, 'epoch': 0.07} 7%|▋ | 519/7045 [1:41:22<20:46:03, 11.46s/it] 7%|▋ | 520/7045 [1:41:33<20:26:05, 11.27s/it] {'loss': 1.1797, 'learning_rate': 4.9749756682982344e-06, 'epoch': 0.07} 7%|▋ | 520/7045 [1:41:33<20:26:05, 11.27s/it] 7%|▋ | 521/7045 [1:41:44<20:32:37, 11.34s/it] {'loss': 1.1475, 'learning_rate': 4.97481318268949e-06, 'epoch': 0.07} 7%|▋ | 521/7045 [1:41:44<20:32:37, 11.34s/it] 7%|▋ | 522/7045 [1:41:57<21:06:48, 11.65s/it] {'loss': 1.1719, 'learning_rate': 4.974650173939072e-06, 'epoch': 0.07} 7%|▋ | 522/7045 [1:41:57<21:06:48, 11.65s/it] 7%|▋ | 523/7045 [1:42:08<20:47:11, 11.47s/it] {'loss': 1.1377, 'learning_rate': 4.974486642081439e-06, 'epoch': 0.07} 7%|▋ | 523/7045 [1:42:08<20:47:11, 11.47s/it] 7%|▋ | 524/7045 [1:42:19<20:51:27, 11.51s/it] {'loss': 1.1523, 'learning_rate': 4.974322587151159e-06, 'epoch': 0.07} 7%|▋ | 524/7045 [1:42:19<20:51:27, 11.51s/it] 7%|▋ | 525/7045 [1:42:31<21:11:56, 11.70s/it] {'loss': 1.1738, 'learning_rate': 4.974158009182912e-06, 'epoch': 0.07} 7%|▋ | 525/7045 [1:42:31<21:11:56, 11.70s/it] 7%|▋ | 526/7045 [1:42:42<20:47:01, 11.48s/it] {'loss': 1.1689, 'learning_rate': 4.973992908211486e-06, 'epoch': 0.07} 7%|▋ | 526/7045 [1:42:42<20:47:01, 11.48s/it] 7%|▋ | 527/7045 [1:42:54<20:50:07, 11.51s/it] {'loss': 1.1416, 'learning_rate': 4.973827284271781e-06, 'epoch': 0.07} 7%|▋ | 527/7045 [1:42:54<20:50:07, 11.51s/it] 7%|▋ | 528/7045 [1:43:05<20:38:30, 11.40s/it] {'loss': 1.1416, 'learning_rate': 4.973661137398809e-06, 'epoch': 0.07} 7%|▋ | 528/7045 [1:43:05<20:38:30, 11.40s/it] 8%|▊ | 529/7045 [1:43:16<20:28:54, 11.32s/it] {'loss': 1.1875, 'learning_rate': 4.97349446762769e-06, 'epoch': 0.08} 8%|▊ | 529/7045 [1:43:16<20:28:54, 11.32s/it] 8%|▊ | 530/7045 [1:43:29<21:04:57, 11.65s/it] {'loss': 1.1475, 'learning_rate': 4.973327274993657e-06, 'epoch': 0.08} 8%|▊ | 530/7045 [1:43:29<21:04:57, 11.65s/it] 8%|▊ | 531/7045 [1:43:41<21:10:15, 11.70s/it] {'loss': 1.1875, 'learning_rate': 4.973159559532051e-06, 'epoch': 0.08} 8%|▊ | 531/7045 [1:43:41<21:10:15, 11.70s/it] 8%|▊ | 532/7045 [1:43:52<20:54:32, 11.56s/it] {'loss': 1.1602, 'learning_rate': 4.972991321278325e-06, 'epoch': 0.08} 8%|▊ | 532/7045 [1:43:52<20:54:32, 11.56s/it] 8%|▊ | 533/7045 [1:44:03<20:44:07, 11.46s/it] {'loss': 1.1699, 'learning_rate': 4.9728225602680425e-06, 'epoch': 0.08} 8%|▊ | 533/7045 [1:44:03<20:44:07, 11.46s/it] 8%|▊ | 534/7045 [1:44:14<20:30:38, 11.34s/it] {'loss': 1.1787, 'learning_rate': 4.972653276536878e-06, 'epoch': 0.08} 8%|▊ | 534/7045 [1:44:14<20:30:38, 11.34s/it] 8%|▊ | 535/7045 [1:44:26<21:02:03, 11.63s/it] {'loss': 1.166, 'learning_rate': 4.972483470120614e-06, 'epoch': 0.08} 8%|▊ | 535/7045 [1:44:26<21:02:03, 11.63s/it] 8%|▊ | 536/7045 [1:44:39<21:31:02, 11.90s/it] {'loss': 1.1177, 'learning_rate': 4.972313141055146e-06, 'epoch': 0.08} 8%|▊ | 536/7045 [1:44:39<21:31:02, 11.90s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2541 > 2048). Running this sequence through the model will result in indexing errors 8%|▊ | 537/7045 [1:44:50<21:15:28, 11.76s/it] {'loss': 1.1895, 'learning_rate': 4.9721422893764805e-06, 'epoch': 0.08} 8%|▊ | 537/7045 [1:44:50<21:15:28, 11.76s/it] 8%|▊ | 538/7045 [1:45:01<20:54:10, 11.56s/it] {'loss': 1.1514, 'learning_rate': 4.9719709151207315e-06, 'epoch': 0.08} 8%|▊ | 538/7045 [1:45:01<20:54:10, 11.56s/it] 8%|▊ | 539/7045 [1:45:12<20:34:34, 11.39s/it] {'loss': 1.168, 'learning_rate': 4.9717990183241265e-06, 'epoch': 0.08} 8%|▊ | 539/7045 [1:45:12<20:34:34, 11.39s/it] 8%|▊ | 540/7045 [1:45:25<21:25:33, 11.86s/it] {'loss': 1.1475, 'learning_rate': 4.971626599023002e-06, 'epoch': 0.08} 8%|▊ | 540/7045 [1:45:25<21:25:33, 11.86s/it] 8%|▊ | 541/7045 [1:45:37<21:09:47, 11.71s/it] {'loss': 1.1641, 'learning_rate': 4.971453657253803e-06, 'epoch': 0.08} 8%|▊ | 541/7045 [1:45:37<21:09:47, 11.71s/it] 8%|▊ | 542/7045 [1:45:49<21:39:04, 11.99s/it] {'loss': 1.1543, 'learning_rate': 4.971280193053089e-06, 'epoch': 0.08} 8%|▊ | 542/7045 [1:45:49<21:39:04, 11.99s/it] 8%|▊ | 543/7045 [1:46:01<21:20:32, 11.82s/it] {'loss': 1.1992, 'learning_rate': 4.971106206457529e-06, 'epoch': 0.08} 8%|▊ | 543/7045 [1:46:01<21:20:32, 11.82s/it] 8%|▊ | 544/7045 [1:46:12<21:06:29, 11.69s/it] {'loss': 1.1455, 'learning_rate': 4.970931697503899e-06, 'epoch': 0.08} 8%|▊ | 544/7045 [1:46:12<21:06:29, 11.69s/it] 8%|▊ | 545/7045 [1:46:24<20:56:08, 11.60s/it] {'loss': 1.1445, 'learning_rate': 4.970756666229089e-06, 'epoch': 0.08} 8%|▊ | 545/7045 [1:46:24<20:56:08, 11.60s/it] 8%|▊ | 546/7045 [1:46:36<21:20:51, 11.83s/it] {'loss': 1.1514, 'learning_rate': 4.9705811126700975e-06, 'epoch': 0.08} 8%|▊ | 546/7045 [1:46:36<21:20:51, 11.83s/it] 8%|▊ | 547/7045 [1:46:47<20:57:09, 11.61s/it] {'loss': 1.165, 'learning_rate': 4.970405036864035e-06, 'epoch': 0.08} 8%|▊ | 547/7045 [1:46:47<20:57:09, 11.61s/it] 8%|▊ | 548/7045 [1:46:58<20:47:41, 11.52s/it] {'loss': 1.1943, 'learning_rate': 4.970228438848122e-06, 'epoch': 0.08} 8%|▊ | 548/7045 [1:46:58<20:47:41, 11.52s/it] 8%|▊ | 549/7045 [1:47:09<20:25:42, 11.32s/it] {'loss': 1.1152, 'learning_rate': 4.970051318659687e-06, 'epoch': 0.08} 8%|▊ | 549/7045 [1:47:09<20:25:42, 11.32s/it] 8%|▊ | 550/7045 [1:47:20<20:21:11, 11.28s/it] {'loss': 1.1162, 'learning_rate': 4.969873676336171e-06, 'epoch': 0.08} 8%|▊ | 550/7045 [1:47:20<20:21:11, 11.28s/it] 8%|▊ | 551/7045 [1:47:32<20:44:26, 11.50s/it] {'loss': 1.1826, 'learning_rate': 4.969695511915127e-06, 'epoch': 0.08} 8%|▊ | 551/7045 [1:47:32<20:44:26, 11.50s/it] 8%|▊ | 552/7045 [1:47:44<20:52:02, 11.57s/it] {'loss': 1.1543, 'learning_rate': 4.969516825434215e-06, 'epoch': 0.08} 8%|▊ | 552/7045 [1:47:44<20:52:02, 11.57s/it] 8%|▊ | 553/7045 [1:47:56<21:14:43, 11.78s/it] {'loss': 1.1943, 'learning_rate': 4.969337616931208e-06, 'epoch': 0.08} 8%|▊ | 553/7045 [1:47:56<21:14:43, 11.78s/it] 8%|▊ | 554/7045 [1:48:14<24:11:45, 13.42s/it] {'loss': 1.0889, 'learning_rate': 4.969157886443988e-06, 'epoch': 0.08} 8%|▊ | 554/7045 [1:48:14<24:11:45, 13.42s/it]/usr/local/lib/python3.9/dist-packages/PIL/TiffImagePlugin.py:850: UserWarning: Corrupt EXIF data. Expecting to read 4 bytes but only got 0. warnings.warn(str(msg)) 8%|▊ | 555/7045 [1:48:25<23:08:58, 12.84s/it] {'loss': 1.1572, 'learning_rate': 4.9689776340105466e-06, 'epoch': 0.08} 8%|▊ | 555/7045 [1:48:25<23:08:58, 12.84s/it] 8%|▊ | 556/7045 [1:48:39<23:29:42, 13.03s/it] {'loss': 1.1294, 'learning_rate': 4.968796859668987e-06, 'epoch': 0.08} 8%|▊ | 556/7045 [1:48:39<23:29:42, 13.03s/it] 8%|▊ | 557/7045 [1:48:51<23:22:57, 12.97s/it] {'loss': 1.1416, 'learning_rate': 4.968615563457523e-06, 'epoch': 0.08} 8%|▊ | 557/7045 [1:48:51<23:22:57, 12.97s/it] 8%|▊ | 558/7045 [1:49:05<23:27:38, 13.02s/it] {'loss': 1.1387, 'learning_rate': 4.968433745414478e-06, 'epoch': 0.08} 8%|▊ | 558/7045 [1:49:05<23:27:38, 13.02s/it] 8%|▊ | 559/7045 [1:49:16<22:24:32, 12.44s/it] {'loss': 1.1709, 'learning_rate': 4.968251405578286e-06, 'epoch': 0.08} 8%|▊ | 559/7045 [1:49:16<22:24:32, 12.44s/it] 8%|▊ | 560/7045 [1:49:27<21:43:07, 12.06s/it] {'loss': 1.1465, 'learning_rate': 4.9680685439874895e-06, 'epoch': 0.08} 8%|▊ | 560/7045 [1:49:27<21:43:07, 12.06s/it] 8%|▊ | 561/7045 [1:49:38<21:08:04, 11.73s/it] {'loss': 1.1631, 'learning_rate': 4.967885160680746e-06, 'epoch': 0.08} 8%|▊ | 561/7045 [1:49:38<21:08:04, 11.73s/it] 8%|▊ | 562/7045 [1:49:52<22:30:10, 12.50s/it] {'loss': 1.1772, 'learning_rate': 4.9677012556968175e-06, 'epoch': 0.08} 8%|▊ | 562/7045 [1:49:52<22:30:10, 12.50s/it] 8%|▊ | 563/7045 [1:50:03<21:54:11, 12.16s/it] {'loss': 1.1436, 'learning_rate': 4.96751682907458e-06, 'epoch': 0.08} 8%|▊ | 563/7045 [1:50:03<21:54:11, 12.16s/it] 8%|▊ | 564/7045 [1:50:15<21:22:27, 11.87s/it] {'loss': 1.1582, 'learning_rate': 4.967331880853019e-06, 'epoch': 0.08} 8%|▊ | 564/7045 [1:50:15<21:22:27, 11.87s/it] 8%|▊ | 565/7045 [1:50:26<21:17:57, 11.83s/it] {'loss': 1.1514, 'learning_rate': 4.9671464110712306e-06, 'epoch': 0.08} 8%|▊ | 565/7045 [1:50:26<21:17:57, 11.83s/it] 8%|▊ | 566/7045 [1:50:38<20:58:20, 11.65s/it] {'loss': 1.1152, 'learning_rate': 4.966960419768419e-06, 'epoch': 0.08} 8%|▊ | 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[1:57:53<20:56:22, 11.70s/it] {'loss': 1.127, 'learning_rate': 4.959712480656715e-06, 'epoch': 0.09} 9%|▊ | 603/7045 [1:57:53<20:56:22, 11.70s/it] 9%|▊ | 604/7045 [1:58:06<21:35:14, 12.07s/it] {'loss': 1.1006, 'learning_rate': 4.959506701694953e-06, 'epoch': 0.09} 9%|▊ | 604/7045 [1:58:06<21:35:14, 12.07s/it] 9%|▊ | 605/7045 [1:58:18<21:23:05, 11.95s/it] {'loss': 1.1689, 'learning_rate': 4.959300402827098e-06, 'epoch': 0.09} 9%|▊ | 605/7045 [1:58:18<21:23:05, 11.95s/it] 9%|▊ | 606/7045 [1:58:29<20:51:00, 11.66s/it] {'loss': 1.1768, 'learning_rate': 4.959093584096758e-06, 'epoch': 0.09} 9%|▊ | 606/7045 [1:58:29<20:51:00, 11.66s/it] 9%|▊ | 607/7045 [1:58:40<20:32:25, 11.49s/it] {'loss': 1.1436, 'learning_rate': 4.958886245547654e-06, 'epoch': 0.09} 9%|▊ | 607/7045 [1:58:40<20:32:25, 11.49s/it] 9%|▊ | 608/7045 [1:58:50<20:10:28, 11.28s/it] {'loss': 1.1323, 'learning_rate': 4.958678387223614e-06, 'epoch': 0.09} 9%|▊ | 608/7045 [1:58:50<20:10:28, 11.28s/it] 9%|▊ | 609/7045 [1:59:03<20:58:19, 11.73s/it] {'loss': 1.1318, 'learning_rate': 4.9584700091685765e-06, 'epoch': 0.09} 9%|▊ | 609/7045 [1:59:03<20:58:19, 11.73s/it] 9%|▊ | 610/7045 [1:59:16<21:15:43, 11.89s/it] {'loss': 1.1416, 'learning_rate': 4.958261111426589e-06, 'epoch': 0.09} 9%|▊ | 610/7045 [1:59:16<21:15:43, 11.89s/it] 9%|▊ | 611/7045 [1:59:28<21:21:13, 11.95s/it] {'loss': 1.1592, 'learning_rate': 4.958051694041809e-06, 'epoch': 0.09} 9%|▊ | 611/7045 [1:59:28<21:21:13, 11.95s/it] 9%|▊ | 612/7045 [1:59:39<20:52:51, 11.69s/it] {'loss': 1.1357, 'learning_rate': 4.957841757058506e-06, 'epoch': 0.09} 9%|▊ | 612/7045 [1:59:39<20:52:51, 11.69s/it] 9%|▊ | 613/7045 [1:59:50<20:36:40, 11.54s/it] {'loss': 1.1211, 'learning_rate': 4.957631300521058e-06, 'epoch': 0.09} 9%|▊ | 613/7045 [1:59:50<20:36:40, 11.54s/it] 9%|▊ | 614/7045 [2:00:01<20:19:19, 11.38s/it] {'loss': 1.1318, 'learning_rate': 4.95742032447395e-06, 'epoch': 0.09} 9%|▊ | 614/7045 [2:00:01<20:19:19, 11.38s/it] 9%|▊ | 615/7045 [2:00:14<21:07:24, 11.83s/it] {'loss': 1.1572, 'learning_rate': 4.957208828961784e-06, 'epoch': 0.09} 9%|▊ | 615/7045 [2:00:14<21:07:24, 11.83s/it] 9%|▊ | 616/7045 [2:00:26<21:10:44, 11.86s/it] {'loss': 1.1445, 'learning_rate': 4.956996814029262e-06, 'epoch': 0.09} 9%|▊ | 616/7045 [2:00:26<21:10:44, 11.86s/it] 9%|▉ | 617/7045 [2:00:37<21:08:41, 11.84s/it] {'loss': 1.166, 'learning_rate': 4.956784279721205e-06, 'epoch': 0.09} 9%|▉ | 617/7045 [2:00:37<21:08:41, 11.84s/it] 9%|▉ | 618/7045 [2:00:48<20:35:37, 11.54s/it] {'loss': 1.126, 'learning_rate': 4.956571226082538e-06, 'epoch': 0.09} 9%|▉ | 618/7045 [2:00:48<20:35:37, 11.54s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2330 > 2048). Running this sequence through the model will result in indexing errors 9%|▉ | 619/7045 [2:01:01<20:58:41, 11.75s/it] {'loss': 1.1504, 'learning_rate': 4.956357653158299e-06, 'epoch': 0.09} 9%|▉ | 619/7045 [2:01:01<20:58:41, 11.75s/it] 9%|▉ | 620/7045 [2:01:12<20:38:04, 11.56s/it] {'loss': 1.1797, 'learning_rate': 4.956143560993633e-06, 'epoch': 0.09} 9%|▉ | 620/7045 [2:01:12<20:38:04, 11.56s/it] 9%|▉ | 621/7045 [2:01:25<21:20:24, 11.96s/it] {'loss': 1.1396, 'learning_rate': 4.955928949633796e-06, 'epoch': 0.09} 9%|▉ | 621/7045 [2:01:25<21:20:24, 11.96s/it] 9%|▉ | 622/7045 [2:01:36<20:52:29, 11.70s/it] {'loss': 1.1611, 'learning_rate': 4.955713819124155e-06, 'epoch': 0.09} 9%|▉ | 622/7045 [2:01:36<20:52:29, 11.70s/it] 9%|▉ | 623/7045 [2:01:48<21:00:39, 11.78s/it] {'loss': 1.1582, 'learning_rate': 4.955498169510186e-06, 'epoch': 0.09} 9%|▉ | 623/7045 [2:01:48<21:00:39, 11.78s/it] 9%|▉ | 624/7045 [2:01:58<20:31:03, 11.50s/it] {'loss': 1.1631, 'learning_rate': 4.955282000837472e-06, 'epoch': 0.09} 9%|▉ | 624/7045 [2:01:58<20:31:03, 11.50s/it] 9%|▉ | 625/7045 [2:02:12<21:36:38, 12.12s/it] {'loss': 1.1675, 'learning_rate': 4.955065313151711e-06, 'epoch': 0.09} 9%|▉ | 625/7045 [2:02:12<21:36:38, 12.12s/it] 9%|▉ | 626/7045 [2:02:23<21:04:51, 11.82s/it] {'loss': 1.1367, 'learning_rate': 4.954848106498706e-06, 'epoch': 0.09} 9%|▉ | 626/7045 [2:02:23<21:04:51, 11.82s/it] 9%|▉ | 627/7045 [2:02:34<20:43:25, 11.62s/it] {'loss': 1.1709, 'learning_rate': 4.954630380924373e-06, 'epoch': 0.09} 9%|▉ | 627/7045 [2:02:34<20:43:25, 11.62s/it] 9%|▉ | 628/7045 [2:02:47<21:06:01, 11.84s/it] {'loss': 1.1216, 'learning_rate': 4.9544121364747335e-06, 'epoch': 0.09} 9%|▉ | 628/7045 [2:02:47<21:06:01, 11.84s/it] 9%|▉ | 629/7045 [2:02:58<20:40:15, 11.60s/it] {'loss': 1.1338, 'learning_rate': 4.954193373195925e-06, 'epoch': 0.09} 9%|▉ | 629/7045 [2:02:58<20:40:15, 11.60s/it] 9%|▉ | 630/7045 [2:03:09<20:28:54, 11.49s/it] {'loss': 1.1436, 'learning_rate': 4.9539740911341874e-06, 'epoch': 0.09} 9%|▉ | 630/7045 [2:03:09<20:28:54, 11.49s/it] 9%|▉ | 631/7045 [2:03:22<21:15:37, 11.93s/it] {'loss': 1.1392, 'learning_rate': 4.953754290335877e-06, 'epoch': 0.09} 9%|▉ | 631/7045 [2:03:22<21:15:37, 11.93s/it] 9%|▉ | 632/7045 [2:03:33<20:50:23, 11.70s/it] {'loss': 1.1768, 'learning_rate': 4.953533970847455e-06, 'epoch': 0.09} 9%|▉ | 632/7045 [2:03:33<20:50:23, 11.70s/it] 9%|▉ | 633/7045 [2:03:46<21:40:45, 12.17s/it] {'loss': 1.0889, 'learning_rate': 4.953313132715494e-06, 'epoch': 0.09} 9%|▉ | 633/7045 [2:03:46<21:40:45, 12.17s/it] 9%|▉ | 634/7045 [2:04:00<22:20:13, 12.54s/it] {'loss': 1.1582, 'learning_rate': 4.953091775986677e-06, 'epoch': 0.09} 9%|▉ | 634/7045 [2:04:00<22:20:13, 12.54s/it] 9%|▉ | 635/7045 [2:04:11<21:39:49, 12.17s/it] {'loss': 1.168, 'learning_rate': 4.952869900707795e-06, 'epoch': 0.09} 9%|▉ | 635/7045 [2:04:11<21:39:49, 12.17s/it] 9%|▉ | 636/7045 [2:04:29<24:33:44, 13.80s/it] {'loss': 1.1729, 'learning_rate': 4.952647506925749e-06, 'epoch': 0.09} 9%|▉ | 636/7045 [2:04:29<24:33:44, 13.80s/it] 9%|▉ | 637/7045 [2:04:40<23:15:57, 13.07s/it] {'loss': 1.1328, 'learning_rate': 4.952424594687553e-06, 'epoch': 0.09} 9%|▉ | 637/7045 [2:04:40<23:15:57, 13.07s/it] 9%|▉ | 638/7045 [2:04:53<23:25:30, 13.16s/it] {'loss': 1.1455, 'learning_rate': 4.952201164040323e-06, 'epoch': 0.09} 9%|▉ | 638/7045 [2:04:53<23:25:30, 13.16s/it] 9%|▉ | 639/7045 [2:05:04<22:15:20, 12.51s/it] {'loss': 1.1245, 'learning_rate': 4.951977215031293e-06, 'epoch': 0.09} 9%|▉ | 639/7045 [2:05:04<22:15:20, 12.51s/it] 9%|▉ | 640/7045 [2:05:15<21:28:29, 12.07s/it] {'loss': 1.1182, 'learning_rate': 4.9517527477078e-06, 'epoch': 0.09} 9%|▉ | 640/7045 [2:05:15<21:28:29, 12.07s/it] 9%|▉ | 641/7045 [2:05:27<21:07:45, 11.88s/it] {'loss': 1.165, 'learning_rate': 4.951527762117295e-06, 'epoch': 0.09} 9%|▉ | 641/7045 [2:05:27<21:07:45, 11.88s/it] 9%|▉ | 642/7045 [2:05:38<20:47:42, 11.69s/it] {'loss': 1.166, 'learning_rate': 4.9513022583073364e-06, 'epoch': 0.09} 9%|▉ | 642/7045 [2:05:38<20:47:42, 11.69s/it] 9%|▉ | 643/7045 [2:05:50<20:56:08, 11.77s/it] {'loss': 1.1118, 'learning_rate': 4.951076236325593e-06, 'epoch': 0.09} 9%|▉ | 643/7045 [2:05:50<20:56:08, 11.77s/it] 9%|▉ | 644/7045 [2:06:02<21:13:53, 11.94s/it] {'loss': 1.1787, 'learning_rate': 4.950849696219842e-06, 'epoch': 0.09} 9%|▉ | 644/7045 [2:06:02<21:13:53, 11.94s/it] 9%|▉ | 645/7045 [2:06:13<20:39:14, 11.62s/it] {'loss': 1.1504, 'learning_rate': 4.9506226380379715e-06, 'epoch': 0.09} 9%|▉ | 645/7045 [2:06:13<20:39:14, 11.62s/it] 9%|▉ | 646/7045 [2:06:25<20:28:52, 11.52s/it] {'loss': 1.1191, 'learning_rate': 4.95039506182798e-06, 'epoch': 0.09} 9%|▉ | 646/7045 [2:06:25<20:28:52, 11.52s/it] 9%|▉ | 647/7045 [2:06:35<20:10:33, 11.35s/it] {'loss': 1.1523, 'learning_rate': 4.95016696763797e-06, 'epoch': 0.09} 9%|▉ | 647/7045 [2:06:35<20:10:33, 11.35s/it] 9%|▉ | 648/7045 [2:06:47<19:59:35, 11.25s/it] {'loss': 1.1572, 'learning_rate': 4.94993835551616e-06, 'epoch': 0.09} 9%|▉ | 648/7045 [2:06:47<19:59:35, 11.25s/it] 9%|▉ | 649/7045 [2:06:58<20:00:52, 11.27s/it] {'loss': 1.1953, 'learning_rate': 4.949709225510876e-06, 'epoch': 0.09} 9%|▉ | 649/7045 [2:06:58<20:00:52, 11.27s/it] 9%|▉ | 650/7045 [2:07:11<20:47:44, 11.71s/it] {'loss': 1.1504, 'learning_rate': 4.949479577670552e-06, 'epoch': 0.09} 9%|▉ | 650/7045 [2:07:11<20:47:44, 11.71s/it] 9%|▉ | 651/7045 [2:07:21<20:23:04, 11.48s/it] {'loss': 1.1436, 'learning_rate': 4.949249412043733e-06, 'epoch': 0.09} 9%|▉ | 651/7045 [2:07:21<20:23:04, 11.48s/it] 9%|▉ | 652/7045 [2:07:33<20:32:34, 11.57s/it] {'loss': 1.1309, 'learning_rate': 4.949018728679071e-06, 'epoch': 0.09} 9%|▉ | 652/7045 [2:07:33<20:32:34, 11.57s/it] 9%|▉ | 653/7045 [2:07:44<20:14:44, 11.40s/it] {'loss': 1.1895, 'learning_rate': 4.948787527625332e-06, 'epoch': 0.09} 9%|▉ | 653/7045 [2:07:44<20:14:44, 11.40s/it] 9%|▉ | 654/7045 [2:07:56<20:11:15, 11.37s/it] {'loss': 1.1465, 'learning_rate': 4.948555808931388e-06, 'epoch': 0.09} 9%|▉ | 654/7045 [2:07:56<20:11:15, 11.37s/it] 9%|▉ | 655/7045 [2:08:07<20:06:10, 11.33s/it] {'loss': 1.1377, 'learning_rate': 4.94832357264622e-06, 'epoch': 0.09} 9%|▉ | 655/7045 [2:08:07<20:06:10, 11.33s/it] 9%|▉ | 656/7045 [2:08:20<20:51:04, 11.75s/it] {'loss': 1.0732, 'learning_rate': 4.94809081881892e-06, 'epoch': 0.09} 9%|▉ | 656/7045 [2:08:20<20:51:04, 11.75s/it] 9%|▉ | 657/7045 [2:08:31<20:29:22, 11.55s/it] {'loss': 1.1494, 'learning_rate': 4.94785754749869e-06, 'epoch': 0.09} 9%|▉ | 657/7045 [2:08:31<20:29:22, 11.55s/it] 9%|▉ | 658/7045 [2:08:41<20:08:05, 11.35s/it] {'loss': 1.1406, 'learning_rate': 4.94762375873484e-06, 'epoch': 0.09} 9%|▉ | 658/7045 [2:08:41<20:08:05, 11.35s/it] 9%|▉ | 659/7045 [2:08:54<20:35:43, 11.61s/it] {'loss': 1.1416, 'learning_rate': 4.9473894525767885e-06, 'epoch': 0.09} 9%|▉ | 659/7045 [2:08:54<20:35:43, 11.61s/it] 9%|▉ | 660/7045 [2:09:05<20:37:34, 11.63s/it] {'loss': 1.1201, 'learning_rate': 4.947154629074066e-06, 'epoch': 0.09} 9%|▉ | 660/7045 [2:09:05<20:37:34, 11.63s/it] 9%|▉ | 661/7045 [2:09:17<20:21:20, 11.48s/it] {'loss': 1.1777, 'learning_rate': 4.94691928827631e-06, 'epoch': 0.09} 9%|▉ | 661/7045 [2:09:17<20:21:20, 11.48s/it] 9%|▉ | 662/7045 [2:09:28<20:21:14, 11.48s/it] {'loss': 1.1201, 'learning_rate': 4.946683430233269e-06, 'epoch': 0.09} 9%|▉ | 662/7045 [2:09:28<20:21:14, 11.48s/it] 9%|▉ | 663/7045 [2:09:39<19:59:47, 11.28s/it] {'loss': 1.1074, 'learning_rate': 4.9464470549948e-06, 'epoch': 0.09} 9%|▉ | 663/7045 [2:09:39<19:59:47, 11.28s/it] 9%|▉ | 664/7045 [2:09:50<19:52:28, 11.21s/it] {'loss': 1.166, 'learning_rate': 4.9462101626108696e-06, 'epoch': 0.09} 9%|▉ | 664/7045 [2:09:50<19:52:28, 11.21s/it] 9%|▉ | 665/7045 [2:10:01<19:45:29, 11.15s/it] {'loss': 1.1514, 'learning_rate': 4.945972753131554e-06, 'epoch': 0.09} 9%|▉ | 665/7045 [2:10:01<19:45:29, 11.15s/it] 9%|▉ | 666/7045 [2:10:14<20:48:06, 11.74s/it] {'loss': 1.1162, 'learning_rate': 4.945734826607037e-06, 'epoch': 0.09} 9%|▉ | 666/7045 [2:10:14<20:48:06, 11.74s/it] 9%|▉ | 667/7045 [2:10:25<20:32:12, 11.59s/it] {'loss': 1.1582, 'learning_rate': 4.945496383087613e-06, 'epoch': 0.09} 9%|▉ | 667/7045 [2:10:25<20:32:12, 11.59s/it] 9%|▉ | 668/7045 [2:10:37<20:41:40, 11.68s/it] {'loss': 1.1377, 'learning_rate': 4.945257422623688e-06, 'epoch': 0.09} 9%|▉ | 668/7045 [2:10:37<20:41:40, 11.68s/it] 9%|▉ | 669/7045 [2:10:50<21:20:45, 12.05s/it] {'loss': 1.1382, 'learning_rate': 4.945017945265773e-06, 'epoch': 0.09} 9%|▉ | 669/7045 [2:10:50<21:20:45, 12.05s/it] 10%|▉ | 670/7045 [2:11:01<21:00:06, 11.86s/it] {'loss': 1.1748, 'learning_rate': 4.944777951064491e-06, 'epoch': 0.1} 10%|▉ | 670/7045 [2:11:01<21:00:06, 11.86s/it] 10%|▉ | 671/7045 [2:11:13<20:55:23, 11.82s/it] {'loss': 1.1582, 'learning_rate': 4.944537440070572e-06, 'epoch': 0.1} 10%|▉ | 671/7045 [2:11:13<20:55:23, 11.82s/it] 10%|▉ | 672/7045 [2:11:24<20:32:49, 11.61s/it] {'loss': 1.168, 'learning_rate': 4.9442964123348604e-06, 'epoch': 0.1} 10%|▉ | 672/7045 [2:11:24<20:32:49, 11.61s/it] 10%|▉ | 673/7045 [2:11:36<20:20:48, 11.50s/it] {'loss': 1.1543, 'learning_rate': 4.944054867908302e-06, 'epoch': 0.1} 10%|▉ | 673/7045 [2:11:36<20:20:48, 11.50s/it] 10%|▉ | 674/7045 [2:11:48<20:52:48, 11.80s/it] {'loss': 1.1699, 'learning_rate': 4.943812806841958e-06, 'epoch': 0.1} 10%|▉ | 674/7045 [2:11:48<20:52:48, 11.80s/it] 10%|▉ | 675/7045 [2:11:59<20:35:47, 11.64s/it] {'loss': 1.1631, 'learning_rate': 4.943570229186996e-06, 'epoch': 0.1} 10%|▉ | 675/7045 [2:11:59<20:35:47, 11.64s/it] 10%|▉ | 676/7045 [2:12:11<20:26:48, 11.56s/it] {'loss': 1.1436, 'learning_rate': 4.943327134994695e-06, 'epoch': 0.1} 10%|▉ | 676/7045 [2:12:11<20:26:48, 11.56s/it] 10%|▉ | 677/7045 [2:12:24<21:17:43, 12.04s/it] {'loss': 1.1519, 'learning_rate': 4.94308352431644e-06, 'epoch': 0.1} 10%|▉ | 677/7045 [2:12:24<21:17:43, 12.04s/it] 10%|▉ | 678/7045 [2:12:35<20:51:08, 11.79s/it] {'loss': 1.1152, 'learning_rate': 4.942839397203728e-06, 'epoch': 0.1} 10%|▉ | 678/7045 [2:12:35<20:51:08, 11.79s/it] 10%|▉ | 679/7045 [2:12:46<20:29:07, 11.58s/it] {'loss': 1.1816, 'learning_rate': 4.942594753708165e-06, 'epoch': 0.1} 10%|▉ | 679/7045 [2:12:46<20:29:07, 11.58s/it] 10%|▉ | 680/7045 [2:12:58<20:39:03, 11.68s/it] {'loss': 1.1719, 'learning_rate': 4.942349593881464e-06, 'epoch': 0.1} 10%|▉ | 680/7045 [2:12:58<20:39:03, 11.68s/it] 10%|▉ | 681/7045 [2:13:09<20:22:47, 11.53s/it] {'loss': 1.1689, 'learning_rate': 4.942103917775448e-06, 'epoch': 0.1} 10%|▉ | 681/7045 [2:13:09<20:22:47, 11.53s/it] 10%|▉ | 682/7045 [2:13:21<20:40:47, 11.70s/it] {'loss': 1.1348, 'learning_rate': 4.941857725442051e-06, 'epoch': 0.1} 10%|▉ | 682/7045 [2:13:21<20:40:47, 11.70s/it] 10%|▉ | 683/7045 [2:13:34<20:59:11, 11.88s/it] {'loss': 1.1211, 'learning_rate': 4.941611016933313e-06, 'epoch': 0.1} 10%|▉ | 683/7045 [2:13:34<20:59:11, 11.88s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2250 > 2048). Running this sequence through the model will result in indexing errors 10%|▉ | 684/7045 [2:13:44<20:24:33, 11.55s/it] {'loss': 1.1699, 'learning_rate': 4.941363792301387e-06, 'epoch': 0.1} 10%|▉ | 684/7045 [2:13:44<20:24:33, 11.55s/it] 10%|▉ | 685/7045 [2:13:55<20:10:04, 11.42s/it] {'loss': 1.1719, 'learning_rate': 4.941116051598531e-06, 'epoch': 0.1} 10%|▉ | 685/7045 [2:13:56<20:10:04, 11.42s/it] 10%|▉ | 686/7045 [2:14:08<20:35:24, 11.66s/it] {'loss': 1.1504, 'learning_rate': 4.940867794877116e-06, 'epoch': 0.1} 10%|▉ | 686/7045 [2:14:08<20:35:24, 11.66s/it] 10%|▉ | 687/7045 [2:14:19<20:21:13, 11.52s/it] {'loss': 1.1523, 'learning_rate': 4.940619022189616e-06, 'epoch': 0.1} 10%|▉ | 687/7045 [2:14:19<20:21:13, 11.52s/it] 10%|▉ | 688/7045 [2:14:30<20:12:21, 11.44s/it] {'loss': 1.168, 'learning_rate': 4.940369733588624e-06, 'epoch': 0.1} 10%|▉ | 688/7045 [2:14:30<20:12:21, 11.44s/it] 10%|▉ | 689/7045 [2:14:43<20:41:18, 11.72s/it] {'loss': 1.1289, 'learning_rate': 4.9401199291268314e-06, 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0.1} 10%|▉ | 695/7045 [2:15:53<20:10:50, 11.44s/it] 10%|▉ | 696/7045 [2:16:05<20:05:20, 11.39s/it] {'loss': 1.165, 'learning_rate': 4.938356858241295e-06, 'epoch': 0.1} 10%|▉ | 696/7045 [2:16:05<20:05:20, 11.39s/it] 10%|▉ | 697/7045 [2:16:17<20:44:38, 11.76s/it] {'loss': 1.1836, 'learning_rate': 4.938102928803259e-06, 'epoch': 0.1} 10%|▉ | 697/7045 [2:16:17<20:44:38, 11.76s/it] 10%|▉ | 698/7045 [2:16:29<20:38:08, 11.70s/it] {'loss': 1.1348, 'learning_rate': 4.937848483983594e-06, 'epoch': 0.1} 10%|▉ | 698/7045 [2:16:29<20:38:08, 11.70s/it] 10%|▉ | 699/7045 [2:16:40<20:19:15, 11.53s/it] {'loss': 1.126, 'learning_rate': 4.9375935238360896e-06, 'epoch': 0.1} 10%|▉ | 699/7045 [2:16:40<20:19:15, 11.53s/it] 10%|▉ | 700/7045 [2:16:51<19:58:36, 11.33s/it] {'loss': 1.1211, 'learning_rate': 4.937338048414638e-06, 'epoch': 0.1} 10%|▉ | 700/7045 [2:16:51<19:58:36, 11.33s/it] 10%|▉ | 701/7045 [2:17:08<23:03:36, 13.09s/it] {'loss': 1.1416, 'learning_rate': 4.937082057773245e-06, 'epoch': 0.1} 10%|▉ 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1.1084, 'learning_rate': 4.734234427014533e-06, 'epoch': 0.17} 17%|█▋ | 1224/7045 [3:58:46<19:31:44, 12.08s/it]/usr/local/lib/python3.9/dist-packages/PIL/TiffImagePlugin.py:850: UserWarning: Corrupt EXIF data. 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[5:23:55<17:52:24, 11.95s/it] 24%|██▎ | 1662/7045 [5:24:06<17:27:21, 11.67s/it] {'loss': 1.1396, 'learning_rate': 4.464723578532104e-06, 'epoch': 0.24} 24%|██▎ | 1662/7045 [5:24:06<17:27:21, 11.67s/it] 24%|██▎ | 1663/7045 [5:24:18<17:28:01, 11.68s/it] {'loss': 1.1035, 'learning_rate': 4.464012607924065e-06, 'epoch': 0.24} 24%|██▎ | 1663/7045 [5:24:18<17:28:01, 11.68s/it] 24%|██▎ | 1664/7045 [5:24:29<17:10:05, 11.49s/it] {'loss': 1.1514, 'learning_rate': 4.4633012221506114e-06, 'epoch': 0.24} 24%|██▎ | 1664/7045 [5:24:29<17:10:05, 11.49s/it] 24%|██▎ | 1665/7045 [5:24:41<17:10:40, 11.49s/it] {'loss': 1.1631, 'learning_rate': 4.462589421362119e-06, 'epoch': 0.24} 24%|██▎ | 1665/7045 [5:24:41<17:10:40, 11.49s/it] 24%|██▎ | 1666/7045 [5:24:52<17:00:08, 11.38s/it] {'loss': 1.0996, 'learning_rate': 4.461877205709055e-06, 'epoch': 0.24} 24%|██▎ | 1666/7045 [5:24:52<17:00:08, 11.38s/it] 24%|██▎ | 1667/7045 [5:25:04<17:32:13, 11.74s/it] {'loss': 1.1074, 'learning_rate': 4.461164575341971e-06, 'epoch': 0.24} 24%|██▎ | 1667/7045 [5:25:04<17:32:13, 11.74s/it] 24%|██▎ | 1668/7045 [5:25:15<17:13:19, 11.53s/it] {'loss': 1.1055, 'learning_rate': 4.460451530411507e-06, 'epoch': 0.24} 24%|██▎ | 1668/7045 [5:25:15<17:13:19, 11.53s/it] 24%|██▎ | 1669/7045 [5:25:26<17:00:04, 11.38s/it] {'loss': 1.1055, 'learning_rate': 4.459738071068391e-06, 'epoch': 0.24} 24%|██▎ | 1669/7045 [5:25:26<17:00:04, 11.38s/it] 24%|██▎ | 1670/7045 [5:25:38<16:52:48, 11.31s/it] {'loss': 1.1641, 'learning_rate': 4.459024197463438e-06, 'epoch': 0.24} 24%|██▎ | 1670/7045 [5:25:38<16:52:48, 11.31s/it] 24%|██▎ | 1671/7045 [5:25:49<17:00:13, 11.39s/it] {'loss': 1.1553, 'learning_rate': 4.458309909747554e-06, 'epoch': 0.24} 24%|██▎ | 1671/7045 [5:25:49<17:00:13, 11.39s/it] 24%|██▎ | 1672/7045 [5:26:02<17:48:30, 11.93s/it] {'loss': 1.0732, 'learning_rate': 4.457595208071726e-06, 'epoch': 0.24} 24%|██▎ | 1672/7045 [5:26:02<17:48:30, 11.93s/it] 24%|██▎ | 1673/7045 [5:26:15<17:58:30, 12.05s/it] {'loss': 1.0825, 'learning_rate': 4.4568800925870335e-06, 'epoch': 0.24} 24%|██▎ | 1673/7045 [5:26:15<17:58:30, 12.05s/it] 24%|██▍ | 1674/7045 [5:26:26<17:38:25, 11.82s/it] {'loss': 1.1348, 'learning_rate': 4.456164563444643e-06, 'epoch': 0.24} 24%|██▍ | 1674/7045 [5:26:26<17:38:25, 11.82s/it] 24%|██▍ | 1675/7045 [5:26:43<19:57:07, 13.38s/it] {'loss': 1.1602, 'learning_rate': 4.455448620795807e-06, 'epoch': 0.24} 24%|██▍ | 1675/7045 [5:26:43<19:57:07, 13.38s/it] 24%|██▍ | 1676/7045 [5:26:57<20:09:58, 13.52s/it] {'loss': 1.1304, 'learning_rate': 4.454732264791866e-06, 'epoch': 0.24} 24%|██▍ | 1676/7045 [5:26:57<20:09:58, 13.52s/it] 24%|██▍ | 1677/7045 [5:27:08<19:05:11, 12.80s/it] {'loss': 1.0806, 'learning_rate': 4.454015495584247e-06, 'epoch': 0.24} 24%|██▍ | 1677/7045 [5:27:08<19:05:11, 12.80s/it] 24%|██▍ | 1678/7045 [5:27:19<18:13:02, 12.22s/it] {'loss': 1.1523, 'learning_rate': 4.4532983133244665e-06, 'epoch': 0.24} 24%|██▍ | 1678/7045 [5:27:19<18:13:02, 12.22s/it] 24%|██▍ | 1679/7045 [5:27:30<17:36:16, 11.81s/it] {'loss': 1.1279, 'learning_rate': 4.452580718164127e-06, 'epoch': 0.24} 24%|██▍ | 1679/7045 [5:27:30<17:36:16, 11.81s/it] 24%|██▍ | 1680/7045 [5:27:41<17:13:02, 11.55s/it] {'loss': 1.1357, 'learning_rate': 4.451862710254916e-06, 'epoch': 0.24} 24%|██▍ | 1680/7045 [5:27:41<17:13:02, 11.55s/it] 24%|██▍ | 1681/7045 [5:27:51<16:53:26, 11.34s/it] {'loss': 1.1113, 'learning_rate': 4.451144289748614e-06, 'epoch': 0.24} 24%|██▍ | 1681/7045 [5:27:51<16:53:26, 11.34s/it] 24%|██▍ | 1682/7045 [5:28:02<16:43:40, 11.23s/it] {'loss': 1.1592, 'learning_rate': 4.450425456797084e-06, 'epoch': 0.24} 24%|██▍ | 1682/7045 [5:28:02<16:43:40, 11.23s/it] 24%|██▍ | 1683/7045 [5:28:15<17:24:32, 11.69s/it] {'loss': 1.0713, 'learning_rate': 4.449706211552276e-06, 'epoch': 0.24} 24%|██▍ | 1683/7045 [5:28:15<17:24:32, 11.69s/it] 24%|██▍ | 1684/7045 [5:28:26<17:04:37, 11.47s/it] {'loss': 1.1396, 'learning_rate': 4.4489865541662305e-06, 'epoch': 0.24} 24%|██▍ | 1684/7045 [5:28:26<17:04:37, 11.47s/it] 24%|██▍ | 1685/7045 [5:28:37<17:00:16, 11.42s/it] {'loss': 1.1367, 'learning_rate': 4.448266484791071e-06, 'epoch': 0.24} 24%|██▍ | 1685/7045 [5:28:37<17:00:16, 11.42s/it] 24%|██▍ | 1686/7045 [5:28:49<16:58:34, 11.40s/it] {'loss': 1.1406, 'learning_rate': 4.447546003579014e-06, 'epoch': 0.24} 24%|██▍ | 1686/7045 [5:28:49<16:58:34, 11.40s/it] 24%|██▍ | 1687/7045 [5:29:02<17:45:55, 11.94s/it] {'loss': 1.1514, 'learning_rate': 4.446825110682356e-06, 'epoch': 0.24} 24%|██▍ | 1687/7045 [5:29:02<17:45:55, 11.94s/it] 24%|██▍ | 1688/7045 [5:29:14<17:57:09, 12.06s/it] {'loss': 1.0796, 'learning_rate': 4.446103806253485e-06, 'epoch': 0.24} 24%|██▍ | 1688/7045 [5:29:14<17:57:09, 12.06s/it] 24%|██▍ | 1689/7045 [5:29:25<17:33:49, 11.81s/it] {'loss': 1.165, 'learning_rate': 4.445382090444875e-06, 'epoch': 0.24} 24%|██▍ | 1689/7045 [5:29:26<17:33:49, 11.81s/it] 24%|██▍ | 1690/7045 [5:29:38<17:49:11, 11.98s/it] {'loss': 1.124, 'learning_rate': 4.444659963409087e-06, 'epoch': 0.24} 24%|██▍ | 1690/7045 [5:29:38<17:49:11, 11.98s/it] 24%|██▍ | 1691/7045 [5:29:49<17:33:06, 11.80s/it] {'loss': 1.1475, 'learning_rate': 4.4439374252987685e-06, 'epoch': 0.24} 24%|██▍ | 1691/7045 [5:29:49<17:33:06, 11.80s/it] 24%|██▍ | 1692/7045 [5:30:01<17:22:52, 11.69s/it] {'loss': 1.1396, 'learning_rate': 4.443214476266655e-06, 'epoch': 0.24} 24%|██▍ | 1692/7045 [5:30:01<17:22:52, 11.69s/it] 24%|██▍ | 1693/7045 [5:30:13<17:27:41, 11.75s/it] {'loss': 1.123, 'learning_rate': 4.442491116465566e-06, 'epoch': 0.24} 24%|██▍ | 1693/7045 [5:30:13<17:27:41, 11.75s/it] 24%|██▍ | 1694/7045 [5:30:23<17:03:58, 11.48s/it] {'loss': 1.1182, 'learning_rate': 4.4417673460484125e-06, 'epoch': 0.24} 24%|██▍ | 1694/7045 [5:30:23<17:03:58, 11.48s/it] 24%|██▍ | 1695/7045 [5:30:35<17:04:23, 11.49s/it] {'loss': 1.2002, 'learning_rate': 4.441043165168187e-06, 'epoch': 0.24} 24%|██▍ | 1695/7045 [5:30:35<17:04:23, 11.49s/it] 24%|██▍ | 1696/7045 [5:30:47<17:13:08, 11.59s/it] {'loss': 1.1494, 'learning_rate': 4.440318573977973e-06, 'epoch': 0.24} 24%|██▍ | 1696/7045 [5:30:47<17:13:08, 11.59s/it] 24%|██▍ | 1697/7045 [5:31:00<17:53:04, 12.04s/it] {'loss': 1.0938, 'learning_rate': 4.439593572630939e-06, 'epoch': 0.24} 24%|██▍ | 1697/7045 [5:31:00<17:53:04, 12.04s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2896 > 2048). Running this sequence through the model will result in indexing errors 24%|██▍ | 1698/7045 [5:31:11<17:19:34, 11.67s/it] {'loss': 1.0996, 'learning_rate': 4.438868161280342e-06, 'epoch': 0.24} 24%|██▍ | 1698/7045 [5:31:11<17:19:34, 11.67s/it] 24%|██▍ | 1699/7045 [5:31:22<17:14:59, 11.62s/it] {'loss': 1.1377, 'learning_rate': 4.438142340079521e-06, 'epoch': 0.24} 24%|██▍ | 1699/7045 [5:31:22<17:14:59, 11.62s/it] 24%|██▍ | 1700/7045 [5:31:33<17:01:34, 11.47s/it] {'loss': 1.124, 'learning_rate': 4.437416109181905e-06, 'epoch': 0.24} 24%|██▍ | 1700/7045 [5:31:33<17:01:34, 11.47s/it] 24%|██▍ | 1701/7045 [5:31:44<16:54:25, 11.39s/it] {'loss': 1.1328, 'learning_rate': 4.436689468741012e-06, 'epoch': 0.24} 24%|██▍ | 1701/7045 [5:31:44<16:54:25, 11.39s/it] 24%|██▍ | 1702/7045 [5:31:56<16:59:03, 11.44s/it] {'loss': 1.1157, 'learning_rate': 4.435962418910442e-06, 'epoch': 0.24} 24%|██▍ | 1702/7045 [5:31:56<16:59:03, 11.44s/it] 24%|██▍ | 1703/7045 [5:32:09<17:41:10, 11.92s/it] {'loss': 1.082, 'learning_rate': 4.4352349598438834e-06, 'epoch': 0.24} 24%|██▍ | 1703/7045 [5:32:09<17:41:10, 11.92s/it] 24%|██▍ | 1704/7045 [5:32:20<17:18:27, 11.67s/it] {'loss': 1.1377, 'learning_rate': 4.434507091695112e-06, 'epoch': 0.24} 24%|██▍ | 1704/7045 [5:32:20<17:18:27, 11.67s/it] 24%|██▍ | 1705/7045 [5:32:33<17:54:17, 12.07s/it] {'loss': 1.127, 'learning_rate': 4.433778814617987e-06, 'epoch': 0.24} 24%|██▍ | 1705/7045 [5:32:33<17:54:17, 12.07s/it] 24%|██▍ | 1706/7045 [5:32:45<17:39:26, 11.91s/it] {'loss': 1.1484, 'learning_rate': 4.433050128766458e-06, 'epoch': 0.24} 24%|██▍ | 1706/7045 [5:32:45<17:39:26, 11.91s/it] 24%|██▍ | 1707/7045 [5:32:56<17:27:26, 11.77s/it] {'loss': 1.1543, 'learning_rate': 4.43232103429456e-06, 'epoch': 0.24} 24%|██▍ | 1707/7045 [5:32:56<17:27:26, 11.77s/it] 24%|██▍ | 1708/7045 [5:33:07<17:13:26, 11.62s/it] {'loss': 1.1426, 'learning_rate': 4.431591531356412e-06, 'epoch': 0.24} 24%|██▍ | 1708/7045 [5:33:07<17:13:26, 11.62s/it] 24%|██▍ | 1709/7045 [5:33:19<17:04:14, 11.52s/it] {'loss': 1.1348, 'learning_rate': 4.430861620106221e-06, 'epoch': 0.24} 24%|██▍ | 1709/7045 [5:33:19<17:04:14, 11.52s/it] 24%|██▍ | 1710/7045 [5:33:30<16:58:03, 11.45s/it] {'loss': 1.1582, 'learning_rate': 4.43013130069828e-06, 'epoch': 0.24} 24%|██▍ | 1710/7045 [5:33:30<16:58:03, 11.45s/it] 24%|██▍ | 1711/7045 [5:33:41<16:45:10, 11.31s/it] {'loss': 1.1143, 'learning_rate': 4.429400573286972e-06, 'epoch': 0.24} 24%|██▍ | 1711/7045 [5:33:41<16:45:10, 11.31s/it] 24%|██▍ | 1712/7045 [5:33:52<16:40:34, 11.26s/it] {'loss': 1.1592, 'learning_rate': 4.428669438026757e-06, 'epoch': 0.24} 24%|██▍ | 1712/7045 [5:33:52<16:40:34, 11.26s/it] 24%|██▍ | 1713/7045 [5:34:04<16:44:16, 11.30s/it] {'loss': 1.1172, 'learning_rate': 4.427937895072192e-06, 'epoch': 0.24} 24%|██▍ | 1713/7045 [5:34:04<16:44:16, 11.30s/it] 24%|██▍ | 1714/7045 [5:34:15<16:56:55, 11.45s/it] {'loss': 1.1494, 'learning_rate': 4.427205944577913e-06, 'epoch': 0.24} 24%|██▍ | 1714/7045 [5:34:15<16:56:55, 11.45s/it] 24%|██▍ | 1715/7045 [5:34:33<19:42:36, 13.31s/it] {'loss': 1.1104, 'learning_rate': 4.426473586698643e-06, 'epoch': 0.24} 24%|██▍ | 1715/7045 [5:34:33<19:42:36, 13.31s/it] 24%|██▍ | 1716/7045 [5:34:45<19:13:21, 12.99s/it] {'loss': 1.1152, 'learning_rate': 4.425740821589196e-06, 'epoch': 0.24} 24%|██▍ | 1716/7045 [5:34:45<19:13:21, 12.99s/it] 24%|██▍ | 1717/7045 [5:34:56<18:25:29, 12.45s/it] {'loss': 1.1689, 'learning_rate': 4.425007649404466e-06, 'epoch': 0.24} 24%|██▍ | 1717/7045 [5:34:56<18:25:29, 12.45s/it] 24%|██▍ | 1718/7045 [5:35:08<18:16:26, 12.35s/it] {'loss': 1.1123, 'learning_rate': 4.424274070299435e-06, 'epoch': 0.24} 24%|██▍ | 1718/7045 [5:35:08<18:16:26, 12.35s/it] 24%|██▍ | 1719/7045 [5:35:20<18:04:35, 12.22s/it] {'loss': 1.0957, 'learning_rate': 4.423540084429174e-06, 'epoch': 0.24} 24%|██▍ | 1719/7045 [5:35:20<18:04:35, 12.22s/it] 24%|██▍ | 1720/7045 [5:35:32<17:38:57, 11.93s/it] {'loss': 1.1514, 'learning_rate': 4.422805691948835e-06, 'epoch': 0.24} 24%|██▍ 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Expecting to read 2 bytes but only got 0. warnings.warn(str(msg)) 27%|██▋ | 1932/7045 [6:16:38<16:07:25, 11.35s/it] {'loss': 1.1318, 'learning_rate': 4.258191248667423e-06, 'epoch': 0.27} 27%|██▋ | 1932/7045 [6:16:38<16:07:25, 11.35s/it] 27%|██▋ | 1933/7045 [6:16:52<17:09:13, 12.08s/it] {'loss': 1.1357, 'learning_rate': 4.2573739218523284e-06, 'epoch': 0.27} 27%|██▋ | 1933/7045 [6:16:52<17:09:13, 12.08s/it] 27%|██▋ | 1934/7045 [6:17:04<17:02:36, 12.00s/it] {'loss': 1.105, 'learning_rate': 4.256556223552413e-06, 'epoch': 0.27} 27%|██▋ | 1934/7045 [6:17:04<17:02:36, 12.00s/it] 27%|██▋ | 1935/7045 [6:17:15<16:44:06, 11.79s/it] {'loss': 1.1475, 'learning_rate': 4.255738153940526e-06, 'epoch': 0.27} 27%|██▋ | 1935/7045 [6:17:15<16:44:06, 11.79s/it] 27%|██▋ | 1936/7045 [6:17:26<16:30:15, 11.63s/it] {'loss': 1.1309, 'learning_rate': 4.254919713189596e-06, 'epoch': 0.27} 27%|██▋ | 1936/7045 [6:17:26<16:30:15, 11.63s/it] 27%|██▋ | 1937/7045 [6:17:39<17:03:56, 12.03s/it] {'loss': 1.1401, 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{'loss': 1.1182, 'learning_rate': 4.230194771935002e-06, 'epoch': 0.28} 28%|██▊ | 1966/7045 [6:23:19<17:16:08, 12.24s/it] 28%|██▊ | 1967/7045 [6:23:31<17:15:54, 12.24s/it] {'loss': 1.1309, 'learning_rate': 4.229364912072943e-06, 'epoch': 0.28} 28%|██▊ | 1967/7045 [6:23:31<17:15:54, 12.24s/it] 28%|██▊ | 1968/7045 [6:23:44<17:35:44, 12.48s/it] {'loss': 1.0938, 'learning_rate': 4.228534686646785e-06, 'epoch': 0.28} 28%|██▊ | 1968/7045 [6:23:44<17:35:44, 12.48s/it] 28%|██▊ | 1969/7045 [6:23:56<17:07:58, 12.15s/it] {'loss': 1.1338, 'learning_rate': 4.227704095832025e-06, 'epoch': 0.28} 28%|██▊ | 1969/7045 [6:23:56<17:07:58, 12.15s/it] 28%|██▊ | 1970/7045 [6:24:09<17:36:18, 12.49s/it] {'loss': 1.1333, 'learning_rate': 4.226873139804239e-06, 'epoch': 0.28} 28%|██▊ | 1970/7045 [6:24:09<17:36:18, 12.49s/it] 28%|██▊ | 1971/7045 [6:24:24<18:31:59, 13.15s/it] {'loss': 1.0742, 'learning_rate': 4.226041818739079e-06, 'epoch': 0.28} 28%|██▊ | 1971/7045 [6:24:24<18:31:59, 13.15s/it] 28%|██▊ | 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'epoch': 0.28} 28%|██▊ | 1983/7045 [6:26:48<16:42:00, 11.88s/it] 28%|██▊ | 1984/7045 [6:26:59<16:37:09, 11.82s/it] {'loss': 1.1182, 'learning_rate': 4.2152015065533125e-06, 'epoch': 0.28} 28%|██▊ | 1984/7045 [6:26:59<16:37:09, 11.82s/it] 28%|██▊ | 1985/7045 [6:27:10<16:17:36, 11.59s/it] {'loss': 1.1123, 'learning_rate': 4.214365093451609e-06, 'epoch': 0.28} 28%|██▊ | 1985/7045 [6:27:10<16:17:36, 11.59s/it] 28%|██▊ | 1986/7045 [6:27:24<16:58:35, 12.08s/it] {'loss': 1.1104, 'learning_rate': 4.213528317956563e-06, 'epoch': 0.28} 28%|██▊ | 1986/7045 [6:27:24<16:58:35, 12.08s/it] 28%|██▊ | 1987/7045 [6:27:35<16:35:51, 11.81s/it] {'loss': 1.1104, 'learning_rate': 4.212691180245056e-06, 'epoch': 0.28} 28%|██▊ | 1987/7045 [6:27:35<16:35:51, 11.81s/it] 28%|██▊ | 1988/7045 [6:27:46<16:11:57, 11.53s/it] {'loss': 1.1318, 'learning_rate': 4.211853680494048e-06, 'epoch': 0.28} 28%|██▊ | 1988/7045 [6:27:46<16:11:57, 11.53s/it] 28%|██▊ | 1989/7045 [6:27:57<16:14:32, 11.56s/it] {'loss': 1.0996, 'learning_rate': 4.211015818880575e-06, 'epoch': 0.28} 28%|██▊ | 1989/7045 [6:27:57<16:14:32, 11.56s/it] 28%|██▊ | 1990/7045 [6:28:09<16:09:51, 11.51s/it] {'loss': 1.1377, 'learning_rate': 4.210177595581749e-06, 'epoch': 0.28} 28%|██▊ | 1990/7045 [6:28:09<16:09:51, 11.51s/it] 28%|██▊ | 1991/7045 [6:28:22<16:45:38, 11.94s/it] {'loss': 1.1201, 'learning_rate': 4.209339010774761e-06, 'epoch': 0.28} 28%|██▊ | 1991/7045 [6:28:22<16:45:38, 11.94s/it] 28%|██▊ | 1992/7045 [6:28:33<16:26:51, 11.72s/it] {'loss': 1.1543, 'learning_rate': 4.208500064636874e-06, 'epoch': 0.28} 28%|██▊ | 1992/7045 [6:28:33<16:26:51, 11.72s/it] 28%|██▊ | 1993/7045 [6:28:45<16:40:34, 11.88s/it] {'loss': 1.1084, 'learning_rate': 4.2076607573454304e-06, 'epoch': 0.28} 28%|██▊ | 1993/7045 [6:28:45<16:40:34, 11.88s/it] 28%|██▊ | 1994/7045 [6:28:59<17:15:49, 12.30s/it] {'loss': 1.1396, 'learning_rate': 4.206821089077848e-06, 'epoch': 0.28} 28%|██▊ | 1994/7045 [6:28:59<17:15:49, 12.30s/it] 28%|██▊ | 1995/7045 [6:29:10<16:58:01, 12.10s/it] {'loss': 1.1338, 'learning_rate': 4.2059810600116225e-06, 'epoch': 0.28} 28%|██▊ | 1995/7045 [6:29:10<16:58:01, 12.10s/it] 28%|██▊ | 1996/7045 [6:29:21<16:27:39, 11.74s/it] {'loss': 1.1621, 'learning_rate': 4.205140670324324e-06, 'epoch': 0.28} 28%|██▊ | 1996/7045 [6:29:21<16:27:39, 11.74s/it] 28%|██▊ | 1997/7045 [6:29:32<16:09:32, 11.52s/it] {'loss': 1.125, 'learning_rate': 4.204299920193599e-06, 'epoch': 0.28} 28%|██▊ | 1997/7045 [6:29:32<16:09:32, 11.52s/it] 28%|██▊ | 1998/7045 [6:29:43<15:53:31, 11.34s/it] {'loss': 1.1182, 'learning_rate': 4.20345880979717e-06, 'epoch': 0.28} 28%|██▊ | 1998/7045 [6:29:43<15:53:31, 11.34s/it] 28%|██▊ | 1999/7045 [6:29:54<15:51:49, 11.32s/it] {'loss': 1.1475, 'learning_rate': 4.202617339312838e-06, 'epoch': 0.28} 28%|██▊ | 1999/7045 [6:29:54<15:51:49, 11.32s/it] 28%|██▊ | 2000/7045 [6:30:06<16:02:44, 11.45s/it] {'loss': 1.1738, 'learning_rate': 4.201775508918477e-06, 'epoch': 0.28} 28%|██▊ | 2000/7045 [6:30:06<16:02:44, 11.45s/it]/usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( 28%|██▊ | 2001/7045 [6:30:49<29:19:20, 20.93s/it] {'loss': 1.1045, 'learning_rate': 4.2009333187920375e-06, 'epoch': 0.28} 28%|██▊ | 2001/7045 [6:30:49<29:19:20, 20.93s/it] 28%|██▊ | 2002/7045 [6:31:02<25:50:12, 18.44s/it] {'loss': 1.0596, 'learning_rate': 4.20009076911155e-06, 'epoch': 0.28} 28%|██▊ | 2002/7045 [6:31:02<25:50:12, 18.44s/it] 28%|██▊ | 2003/7045 [6:31:13<22:49:17, 16.29s/it] {'loss': 1.1172, 'learning_rate': 4.199247860055115e-06, 'epoch': 0.28} 28%|██▊ | 2003/7045 [6:31:13<22:49:17, 16.29s/it] 28%|██▊ | 2004/7045 [6:31:24<20:41:07, 14.77s/it] {'loss': 1.147, 'learning_rate': 4.198404591800913e-06, 'epoch': 0.28} 28%|██▊ | 2004/7045 [6:31:24<20:41:07, 14.77s/it] 28%|██▊ | 2005/7045 [6:31:36<19:33:00, 13.96s/it] {'loss': 1.0967, 'learning_rate': 4.197560964527201e-06, 'epoch': 0.28} 28%|██▊ | 2005/7045 [6:31:36<19:33:00, 13.96s/it] 28%|██▊ | 2006/7045 [6:31:47<18:17:01, 13.06s/it] {'loss': 1.1182, 'learning_rate': 4.196716978412307e-06, 'epoch': 0.28} 28%|██▊ | 2006/7045 [6:31:47<18:17:01, 13.06s/it] 28%|██▊ | 2007/7045 [6:31:58<17:27:05, 12.47s/it] {'loss': 1.1304, 'learning_rate': 4.195872633634641e-06, 'epoch': 0.28} 28%|██▊ | 2007/7045 [6:31:58<17:27:05, 12.47s/it] 29%|██▊ | 2008/7045 [6:32:12<17:50:12, 12.75s/it] {'loss': 1.0908, 'learning_rate': 4.195027930372685e-06, 'epoch': 0.29} 29%|██▊ | 2008/7045 [6:32:12<17:50:12, 12.75s/it] 29%|██▊ | 2009/7045 [6:32:24<17:32:26, 12.54s/it] {'loss': 1.1475, 'learning_rate': 4.194182868804997e-06, 'epoch': 0.29} 29%|██▊ | 2009/7045 [6:32:24<17:32:26, 12.54s/it] 29%|██▊ | 2010/7045 [6:32:35<16:54:19, 12.09s/it] {'loss': 1.1533, 'learning_rate': 4.193337449110213e-06, 'epoch': 0.29} 29%|██▊ | 2010/7045 [6:32:35<16:54:19, 12.09s/it] 29%|██▊ | 2011/7045 [6:32:46<16:37:03, 11.88s/it] {'loss': 1.1436, 'learning_rate': 4.192491671467041e-06, 'epoch': 0.29} 29%|██▊ | 2011/7045 [6:32:46<16:37:03, 11.88s/it] 29%|██▊ | 2012/7045 [6:32:59<16:57:26, 12.13s/it] {'loss': 1.1133, 'learning_rate': 4.191645536054268e-06, 'epoch': 0.29} 29%|██▊ | 2012/7045 [6:32:59<16:57:26, 12.13s/it] 29%|██▊ | 2013/7045 [6:33:11<16:54:53, 12.10s/it] {'loss': 1.1465, 'learning_rate': 4.190799043050757e-06, 'epoch': 0.29} 29%|██▊ | 2013/7045 [6:33:11<16:54:53, 12.10s/it] 29%|██▊ | 2014/7045 [6:33:23<16:52:48, 12.08s/it] {'loss': 1.1313, 'learning_rate': 4.189952192635443e-06, 'epoch': 0.29} 29%|██▊ | 2014/7045 [6:33:23<16:52:48, 12.08s/it] 29%|██▊ | 2015/7045 [6:33:36<17:05:55, 12.24s/it] {'loss': 1.1484, 'learning_rate': 4.189104984987339e-06, 'epoch': 0.29} 29%|██▊ | 2015/7045 [6:33:36<17:05:55, 12.24s/it] 29%|██▊ | 2016/7045 [6:33:48<17:12:00, 12.31s/it] {'loss': 1.1025, 'learning_rate': 4.1882574202855334e-06, 'epoch': 0.29} 29%|██▊ | 2016/7045 [6:33:48<17:12:00, 12.31s/it] 29%|██▊ | 2017/7045 [6:33:59<16:44:55, 11.99s/it] {'loss': 1.1318, 'learning_rate': 4.18740949870919e-06, 'epoch': 0.29} 29%|██▊ | 2017/7045 [6:33:59<16:44:55, 11.99s/it] 29%|██▊ | 2018/7045 [6:34:12<17:07:23, 12.26s/it] {'loss': 1.1455, 'learning_rate': 4.186561220437546e-06, 'epoch': 0.29} 29%|██▊ | 2018/7045 [6:34:12<17:07:23, 12.26s/it] 29%|██▊ | 2019/7045 [6:34:23<16:33:47, 11.86s/it] {'loss': 1.1328, 'learning_rate': 4.185712585649919e-06, 'epoch': 0.29} 29%|██▊ | 2019/7045 [6:34:23<16:33:47, 11.86s/it] 29%|██▊ | 2020/7045 [6:34:35<16:44:09, 11.99s/it] {'loss': 1.0996, 'learning_rate': 4.184863594525697e-06, 'epoch': 0.29} 29%|██▊ | 2020/7045 [6:34:35<16:44:09, 11.99s/it] 29%|██▊ | 2021/7045 [6:34:48<17:00:23, 12.19s/it] {'loss': 1.1279, 'learning_rate': 4.184014247244344e-06, 'epoch': 0.29} 29%|██▊ | 2021/7045 [6:34:48<17:00:23, 12.19s/it] 29%|██▊ | 2022/7045 [6:35:00<16:54:59, 12.12s/it] {'loss': 1.1299, 'learning_rate': 4.183164543985402e-06, 'epoch': 0.29} 29%|██▊ | 2022/7045 [6:35:00<16:54:59, 12.12s/it] 29%|██▊ | 2023/7045 [6:35:12<16:47:13, 12.03s/it] {'loss': 1.1147, 'learning_rate': 4.182314484928487e-06, 'epoch': 0.29} 29%|██▊ | 2023/7045 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'learning_rate': 3.881569923485026e-06, 'epoch': 0.33} 33%|███▎ | 2355/7045 [7:39:27<14:44:53, 11.32s/it] 33%|███▎ | 2356/7045 [7:39:40<15:29:04, 11.89s/it] {'loss': 1.0645, 'learning_rate': 3.880611819255565e-06, 'epoch': 0.33} 33%|███▎ | 2356/7045 [7:39:40<15:29:04, 11.89s/it] 33%|███▎ | 2357/7045 [7:39:53<15:45:23, 12.10s/it] {'loss': 1.0967, 'learning_rate': 3.879653423183639e-06, 'epoch': 0.33} 33%|███▎ | 2357/7045 [7:39:53<15:45:23, 12.10s/it] 33%|███▎ | 2358/7045 [7:40:04<15:22:08, 11.80s/it] {'loss': 1.167, 'learning_rate': 3.878694735471842e-06, 'epoch': 0.33} 33%|███▎ | 2358/7045 [7:40:04<15:22:08, 11.80s/it] 33%|███▎ | 2359/7045 [7:40:15<14:58:30, 11.50s/it] {'loss': 1.1104, 'learning_rate': 3.877735756322825e-06, 'epoch': 0.33} 33%|███▎ | 2359/7045 [7:40:15<14:58:30, 11.50s/it] 33%|███▎ | 2360/7045 [7:40:26<14:59:34, 11.52s/it] {'loss': 1.0918, 'learning_rate': 3.876776485939305e-06, 'epoch': 0.33} 33%|███▎ | 2360/7045 [7:40:26<14:59:34, 11.52s/it] 34%|███▎ | 2361/7045 [7:40:37<14:49:15, 11.39s/it] {'loss': 1.1748, 'learning_rate': 3.875816924524059e-06, 'epoch': 0.34} 34%|███▎ | 2361/7045 [7:40:37<14:49:15, 11.39s/it] 34%|███▎ | 2362/7045 [7:40:49<14:51:28, 11.42s/it] {'loss': 1.1123, 'learning_rate': 3.874857072279924e-06, 'epoch': 0.34} 34%|███▎ | 2362/7045 [7:40:49<14:51:28, 11.42s/it] 34%|███▎ | 2363/7045 [7:41:01<15:12:52, 11.70s/it] {'loss': 1.1216, 'learning_rate': 3.873896929409799e-06, 'epoch': 0.34} 34%|███▎ | 2363/7045 [7:41:01<15:12:52, 11.70s/it] 34%|███▎ | 2364/7045 [7:41:12<15:05:11, 11.60s/it] {'loss': 1.1348, 'learning_rate': 3.8729364961166475e-06, 'epoch': 0.34} 34%|███▎ | 2364/7045 [7:41:13<15:05:11, 11.60s/it] 34%|███▎ | 2365/7045 [7:41:23<14:48:48, 11.39s/it] {'loss': 1.1538, 'learning_rate': 3.871975772603488e-06, 'epoch': 0.34} 34%|███▎ | 2365/7045 [7:41:23<14:48:48, 11.39s/it] 34%|███▎ | 2366/7045 [7:41:37<15:45:21, 12.12s/it] {'loss': 1.0908, 'learning_rate': 3.871014759073408e-06, 'epoch': 0.34} 34%|███▎ | 2366/7045 [7:41:37<15:45:21, 12.12s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2540 > 2048). Running this sequence through the model will result in indexing errors 34%|███▎ | 2367/7045 [7:41:48<15:19:57, 11.80s/it] {'loss': 1.1152, 'learning_rate': 3.870053455729552e-06, 'epoch': 0.34} 34%|███▎ | 2367/7045 [7:41:48<15:19:57, 11.80s/it] 34%|███▎ | 2368/7045 [7:41:59<15:05:38, 11.62s/it] {'loss': 1.1475, 'learning_rate': 3.869091862775125e-06, 'epoch': 0.34} 34%|███▎ | 2368/7045 [7:41:59<15:05:38, 11.62s/it] 34%|███▎ | 2369/7045 [7:42:11<14:54:58, 11.48s/it] {'loss': 1.166, 'learning_rate': 3.8681299804133955e-06, 'epoch': 0.34} 34%|███▎ | 2369/7045 [7:42:11<14:54:58, 11.48s/it] 34%|███▎ | 2370/7045 [7:42:22<14:53:11, 11.46s/it] {'loss': 1.1709, 'learning_rate': 3.867167808847693e-06, 'epoch': 0.34} 34%|███▎ | 2370/7045 [7:42:22<14:53:11, 11.46s/it] 34%|███▎ | 2371/7045 [7:42:35<15:29:48, 11.94s/it] {'loss': 1.1377, 'learning_rate': 3.866205348281405e-06, 'epoch': 0.34} 34%|███▎ | 2371/7045 [7:42:35<15:29:48, 11.94s/it] 34%|███▎ | 2372/7045 [7:42:46<15:10:59, 11.70s/it] {'loss': 1.1318, 'learning_rate': 3.865242598917985e-06, 'epoch': 0.34} 34%|███▎ | 2372/7045 [7:42:46<15:10:59, 11.70s/it] 34%|███▎ | 2373/7045 [7:42:57<14:59:56, 11.56s/it] {'loss': 1.0894, 'learning_rate': 3.8642795609609444e-06, 'epoch': 0.34} 34%|███▎ | 2373/7045 [7:42:57<14:59:56, 11.56s/it] 34%|███▎ | 2374/7045 [7:43:08<14:47:06, 11.40s/it] {'loss': 1.1543, 'learning_rate': 3.863316234613855e-06, 'epoch': 0.34} 34%|███▎ | 2374/7045 [7:43:08<14:47:06, 11.40s/it] 34%|███▎ | 2375/7045 [7:43:21<15:13:39, 11.74s/it] {'loss': 1.1123, 'learning_rate': 3.862352620080353e-06, 'epoch': 0.34} 34%|███▎ | 2375/7045 [7:43:21<15:13:39, 11.74s/it] 34%|███▎ | 2376/7045 [7:43:32<15:02:06, 11.59s/it] {'loss': 1.1592, 'learning_rate': 3.86138871756413e-06, 'epoch': 0.34} 34%|███▎ | 2376/7045 [7:43:32<15:02:06, 11.59s/it] 34%|███▎ | 2377/7045 [7:43:43<14:52:15, 11.47s/it] {'loss': 1.082, 'learning_rate': 3.8604245272689466e-06, 'epoch': 0.34} 34%|███▎ | 2377/7045 [7:43:43<14:52:15, 11.47s/it] 34%|███▍ | 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3.848831871217635e-06, 'epoch': 0.34} 34%|███▍ | 2389/7045 [7:46:09<15:41:50, 12.14s/it] 34%|███▍ | 2390/7045 [7:46:20<15:20:23, 11.86s/it] {'loss': 1.1157, 'learning_rate': 3.8478639583653295e-06, 'epoch': 0.34} 34%|███▍ | 2390/7045 [7:46:20<15:20:23, 11.86s/it] 34%|███▍ | 2391/7045 [7:46:31<15:00:53, 11.61s/it] {'loss': 1.1025, 'learning_rate': 3.8468957605930106e-06, 'epoch': 0.34} 34%|███▍ | 2391/7045 [7:46:31<15:00:53, 11.61s/it] 34%|███▍ | 2392/7045 [7:46:42<14:55:39, 11.55s/it] {'loss': 1.1123, 'learning_rate': 3.845927278105341e-06, 'epoch': 0.34} 34%|███▍ | 2392/7045 [7:46:42<14:55:39, 11.55s/it] 34%|███▍ | 2393/7045 [7:46:53<14:43:22, 11.39s/it] {'loss': 1.1494, 'learning_rate': 3.844958511107045e-06, 'epoch': 0.34} 34%|███▍ | 2393/7045 [7:46:53<14:43:22, 11.39s/it] 34%|███▍ | 2394/7045 [7:47:05<14:43:30, 11.40s/it] {'loss': 1.1406, 'learning_rate': 3.843989459802908e-06, 'epoch': 0.34} 34%|███▍ | 2394/7045 [7:47:05<14:43:30, 11.40s/it] 34%|███▍ | 2395/7045 [7:47:17<14:54:27, 11.54s/it] {'loss': 1.1807, 'learning_rate': 3.843020124397773e-06, 'epoch': 0.34} 34%|███▍ | 2395/7045 [7:47:17<14:54:27, 11.54s/it] 34%|███▍ | 2396/7045 [7:47:28<14:41:17, 11.37s/it] {'loss': 1.1318, 'learning_rate': 3.842050505096544e-06, 'epoch': 0.34} 34%|███▍ | 2396/7045 [7:47:28<14:41:17, 11.37s/it] 34%|███▍ | 2397/7045 [7:47:39<14:38:18, 11.34s/it] {'loss': 1.1094, 'learning_rate': 3.841080602104186e-06, 'epoch': 0.34} 34%|███▍ | 2397/7045 [7:47:39<14:38:18, 11.34s/it] 34%|███▍ | 2398/7045 [7:47:50<14:32:52, 11.27s/it] {'loss': 1.0791, 'learning_rate': 3.840110415625723e-06, 'epoch': 0.34} 34%|███▍ | 2398/7045 [7:47:50<14:32:52, 11.27s/it] 34%|███▍ | 2399/7045 [7:48:01<14:28:13, 11.21s/it] {'loss': 1.0781, 'learning_rate': 3.839139945866238e-06, 'epoch': 0.34} 34%|███▍ | 2399/7045 [7:48:01<14:28:13, 11.21s/it] 34%|███▍ | 2400/7045 [7:48:12<14:24:13, 11.16s/it] {'loss': 1.1592, 'learning_rate': 3.838169193030877e-06, 'epoch': 0.34} 34%|███▍ | 2400/7045 [7:48:12<14:24:13, 11.16s/it] 34%|███▍ | 2401/7045 [7:48:23<14:16:45, 11.07s/it] {'loss': 1.1035, 'learning_rate': 3.8371981573248425e-06, 'epoch': 0.34} 34%|███▍ | 2401/7045 [7:48:23<14:16:45, 11.07s/it] 34%|███▍ | 2402/7045 [7:48:34<14:22:38, 11.15s/it] {'loss': 1.1372, 'learning_rate': 3.836226838953399e-06, 'epoch': 0.34} 34%|███▍ | 2402/7045 [7:48:34<14:22:38, 11.15s/it] 34%|███▍ | 2403/7045 [7:48:48<15:08:56, 11.75s/it] {'loss': 1.1123, 'learning_rate': 3.83525523812187e-06, 'epoch': 0.34} 34%|███▍ | 2403/7045 [7:48:48<15:08:56, 11.75s/it] 34%|███▍ | 2404/7045 [7:48:58<14:47:03, 11.47s/it] {'loss': 1.1396, 'learning_rate': 3.834283355035637e-06, 'epoch': 0.34} 34%|███▍ | 2404/7045 [7:48:58<14:47:03, 11.47s/it] 34%|███▍ | 2405/7045 [7:49:10<15:03:10, 11.68s/it] {'loss': 1.1562, 'learning_rate': 3.833311189900145e-06, 'epoch': 0.34} 34%|███▍ | 2405/7045 [7:49:10<15:03:10, 11.68s/it] 34%|███▍ | 2406/7045 [7:49:22<14:49:32, 11.51s/it] {'loss': 1.105, 'learning_rate': 3.832338742920896e-06, 'epoch': 0.34} 34%|███▍ | 2406/7045 [7:49:22<14:49:32, 11.51s/it] 34%|███▍ | 2407/7045 [7:49:34<15:14:31, 11.83s/it] {'loss': 1.1162, 'learning_rate': 3.8313660143034504e-06, 'epoch': 0.34} 34%|███▍ | 2407/7045 [7:49:34<15:14:31, 11.83s/it] 34%|███▍ | 2408/7045 [7:49:46<15:22:48, 11.94s/it] {'loss': 1.083, 'learning_rate': 3.830393004253431e-06, 'epoch': 0.34} 34%|███▍ | 2408/7045 [7:49:46<15:22:48, 11.94s/it] 34%|███▍ | 2409/7045 [7:49:57<15:01:58, 11.67s/it] {'loss': 1.1025, 'learning_rate': 3.8294197129765185e-06, 'epoch': 0.34} 34%|███▍ | 2409/7045 [7:49:57<15:01:58, 11.67s/it] 34%|███▍ | 2410/7045 [7:50:09<14:54:16, 11.58s/it] {'loss': 1.1084, 'learning_rate': 3.828446140678454e-06, 'epoch': 0.34} 34%|███▍ | 2410/7045 [7:50:09<14:54:16, 11.58s/it] 34%|███▍ | 2411/7045 [7:50:20<14:43:36, 11.44s/it] {'loss': 1.1562, 'learning_rate': 3.827472287565036e-06, 'epoch': 0.34} 34%|███▍ | 2411/7045 [7:50:20<14:43:36, 11.44s/it] 34%|███▍ | 2412/7045 [7:50:33<15:13:43, 11.83s/it] {'loss': 1.1357, 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UserWarning: Corrupt EXIF data. 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[13:00:00<9:38:53, 11.40s/it] 57%|█████▋ | 3999/7045 [13:00:14<10:18:24, 12.18s/it] {'loss': 1.0518, 'learning_rate': 2.076196720332299e-06, 'epoch': 0.57} 57%|█████▋ | 3999/7045 [13:00:14<10:18:24, 12.18s/it] 57%|█████▋ | 4000/7045 [13:00:26<10:15:34, 12.13s/it] {'loss': 1.0684, 'learning_rate': 2.0750639820245862e-06, 'epoch': 0.57} 57%|█████▋ | 4000/7045 [13:00:26<10:15:34, 12.13s/it]/usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. 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DecompressionBombWarning: Image size (97200000 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack. warnings.warn( 62%|██████▏ | 4384/7045 [14:15:20<8:23:18, 11.35s/it] {'loss': 1.1094, 'learning_rate': 1.6489712669364466e-06, 'epoch': 0.62} 62%|██████▏ | 4384/7045 [14:15:20<8:23:18, 11.35s/it] 62%|██████▏ | 4385/7045 [14:15:33<8:41:22, 11.76s/it] {'loss': 1.0718, 'learning_rate': 1.6478905849543282e-06, 'epoch': 0.62} 62%|██████▏ | 4385/7045 [14:15:33<8:41:22, 11.76s/it] 62%|██████▏ | 4386/7045 [14:15:44<8:32:15, 11.56s/it] {'loss': 1.1191, 'learning_rate': 1.64681008309649e-06, 'epoch': 0.62} 62%|██████▏ | 4386/7045 [14:15:44<8:32:15, 11.56s/it] 62%|██████▏ | 4387/7045 [14:15:57<8:48:12, 11.92s/it] {'loss': 1.085, 'learning_rate': 1.6457297615913364e-06, 'epoch': 0.62} 62%|██████▏ | 4387/7045 [14:15:57<8:48:12, 11.92s/it] 62%|██████▏ | 4388/7045 [14:16:08<8:38:07, 11.70s/it] {'loss': 1.1064, 'learning_rate': 1.6446496206672325e-06, 'epoch': 0.62} 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{'loss': 1.123, 'learning_rate': 9.419174737849904e-07, 'epoch': 0.72} 72%|███████▏ | 5092/7045 [16:32:52<6:16:31, 11.57s/it] 72%|███████▏ | 5093/7045 [16:33:04<6:12:40, 11.46s/it] {'loss': 1.0903, 'learning_rate': 9.410187510469962e-07, 'epoch': 0.72} 72%|███████▏ | 5093/7045 [16:33:04<6:12:40, 11.46s/it] 72%|███████▏ | 5094/7045 [16:33:15<6:08:18, 11.33s/it] {'loss': 1.1211, 'learning_rate': 9.401203578563239e-07, 'epoch': 0.72} 72%|███████▏ | 5094/7045 [16:33:15<6:08:18, 11.33s/it] 72%|███████▏ | 5095/7045 [16:33:27<6:16:44, 11.59s/it] {'loss': 1.0967, 'learning_rate': 9.392222944028836e-07, 'epoch': 0.72} 72%|███████▏ | 5095/7045 [16:33:27<6:16:44, 11.59s/it] 72%|███████▏ | 5096/7045 [16:33:39<6:20:47, 11.72s/it] {'loss': 1.166, 'learning_rate': 9.383245608765124e-07, 'epoch': 0.72} 72%|███████▏ | 5096/7045 [16:33:39<6:20:47, 11.72s/it] 72%|███████▏ | 5097/7045 [16:33:52<6:33:54, 12.13s/it] {'loss': 1.0825, 'learning_rate': 9.374271574669782e-07, 'epoch': 0.72} 72%|███████▏ | 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73%|███████▎ | 5108/7045 [16:35:57<6:16:41, 11.67s/it] 73%|███████▎ | 5109/7045 [16:36:09<6:16:59, 11.68s/it] {'loss': 1.0977, 'learning_rate': 9.266841346463204e-07, 'epoch': 0.73} 73%|███████▎ | 5109/7045 [16:36:09<6:16:59, 11.68s/it] 73%|███████▎ | 5110/7045 [16:36:20<6:10:30, 11.49s/it] {'loss': 1.0928, 'learning_rate': 9.257910399928155e-07, 'epoch': 0.73} 73%|███████▎ | 5110/7045 [16:36:20<6:10:30, 11.49s/it] 73%|███████▎ | 5111/7045 [16:36:31<6:05:15, 11.33s/it] {'loss': 1.124, 'learning_rate': 9.248982781055624e-07, 'epoch': 0.73} 73%|███████▎ | 5111/7045 [16:36:31<6:05:15, 11.33s/it] 73%|███████▎ | 5112/7045 [16:36:43<6:07:17, 11.40s/it] {'loss': 1.1006, 'learning_rate': 9.240058491732806e-07, 'epoch': 0.73} 73%|███████▎ | 5112/7045 [16:36:43<6:07:17, 11.40s/it] 73%|███████▎ | 5113/7045 [16:36:54<6:05:06, 11.34s/it] {'loss': 1.1108, 'learning_rate': 9.231137533846158e-07, 'epoch': 0.73} 73%|███████▎ | 5113/7045 [16:36:54<6:05:06, 11.34s/it] 73%|███████▎ | 5114/7045 [16:37:05<6:04:28, 11.33s/it] {'loss': 1.0801, 'learning_rate': 9.222219909281466e-07, 'epoch': 0.73} 73%|███████▎ | 5114/7045 [16:37:05<6:04:28, 11.33s/it] 73%|███████▎ | 5115/7045 [16:37:16<6:03:50, 11.31s/it] {'loss': 1.1289, 'learning_rate': 9.213305619923771e-07, 'epoch': 0.73} 73%|███████▎ | 5115/7045 [16:37:16<6:03:50, 11.31s/it] 73%|███████▎ | 5116/7045 [16:37:29<6:13:03, 11.60s/it] {'loss': 1.1084, 'learning_rate': 9.204394667657448e-07, 'epoch': 0.73} 73%|███████▎ | 5116/7045 [16:37:29<6:13:03, 11.60s/it] 73%|███████▎ | 5117/7045 [16:37:41<6:15:54, 11.70s/it] {'loss': 1.1191, 'learning_rate': 9.195487054366153e-07, 'epoch': 0.73} 73%|███████▎ | 5117/7045 [16:37:41<6:15:54, 11.70s/it] 73%|███████▎ | 5118/7045 [16:37:52<6:08:56, 11.49s/it] {'loss': 1.123, 'learning_rate': 9.186582781932832e-07, 'epoch': 0.73} 73%|███████▎ | 5118/7045 [16:37:52<6:08:56, 11.49s/it] 73%|███████▎ | 5119/7045 [16:38:03<6:12:50, 11.62s/it] {'loss': 1.127, 'learning_rate': 9.177681852239711e-07, 'epoch': 0.73} 73%|███████▎ | 5119/7045 [16:38:03<6:12:50, 11.62s/it] 73%|███████▎ | 5120/7045 [16:38:15<6:08:21, 11.48s/it] {'loss': 1.124, 'learning_rate': 9.168784267168346e-07, 'epoch': 0.73} 73%|███████▎ | 5120/7045 [16:38:15<6:08:21, 11.48s/it] 73%|███████▎ | 5121/7045 [16:38:27<6:13:39, 11.65s/it] {'loss': 1.0771, 'learning_rate': 9.159890028599552e-07, 'epoch': 0.73} 73%|███████▎ | 5121/7045 [16:38:27<6:13:39, 11.65s/it] 73%|███████▎ | 5122/7045 [16:38:38<6:07:59, 11.48s/it] {'loss': 1.1113, 'learning_rate': 9.150999138413446e-07, 'epoch': 0.73} 73%|███████▎ | 5122/7045 [16:38:38<6:07:59, 11.48s/it] 73%|███████▎ | 5123/7045 [16:38:49<6:05:30, 11.41s/it] {'loss': 1.1211, 'learning_rate': 9.142111598489455e-07, 'epoch': 0.73} 73%|███████▎ | 5123/7045 [16:38:49<6:05:30, 11.41s/it] 73%|███████▎ | 5124/7045 [16:39:01<6:09:27, 11.54s/it] {'loss': 1.0825, 'learning_rate': 9.133227410706269e-07, 'epoch': 0.73} 73%|███████▎ | 5124/7045 [16:39:01<6:09:27, 11.54s/it] 73%|███████▎ | 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9.079992784082328e-07, 'epoch': 0.73} 73%|███████▎ | 5130/7045 [16:40:09<6:01:48, 11.34s/it] 73%|███████▎ | 5131/7045 [16:40:20<5:57:44, 11.21s/it] {'loss': 1.1465, 'learning_rate': 9.071132113828982e-07, 'epoch': 0.73} 73%|███████▎ | 5131/7045 [16:40:20<5:57:44, 11.21s/it] 73%|███████▎ | 5132/7045 [16:40:32<6:06:05, 11.48s/it] {'loss': 1.1035, 'learning_rate': 9.06227481072054e-07, 'epoch': 0.73} 73%|███████▎ | 5132/7045 [16:40:32<6:06:05, 11.48s/it] 73%|███████▎ | 5133/7045 [16:40:43<6:03:06, 11.39s/it] {'loss': 1.0864, 'learning_rate': 9.053420876629307e-07, 'epoch': 0.73} 73%|███████▎ | 5133/7045 [16:40:43<6:03:06, 11.39s/it] 73%|███████▎ | 5134/7045 [16:40:56<6:19:55, 11.93s/it] {'loss': 1.1079, 'learning_rate': 9.044570313426898e-07, 'epoch': 0.73} 73%|███████▎ | 5134/7045 [16:40:56<6:19:55, 11.93s/it] 73%|███████▎ | 5135/7045 [16:41:09<6:21:36, 11.99s/it] {'loss': 1.1611, 'learning_rate': 9.035723122984189e-07, 'epoch': 0.73} 73%|███████▎ | 5135/7045 [16:41:09<6:21:36, 11.99s/it] 73%|███████▎ | 5136/7045 [16:41:21<6:21:54, 12.00s/it] {'loss': 1.1104, 'learning_rate': 9.026879307171368e-07, 'epoch': 0.73} 73%|███████▎ | 5136/7045 [16:41:21<6:21:54, 12.00s/it] 73%|███████▎ | 5137/7045 [16:41:33<6:21:21, 11.99s/it] {'loss': 1.1396, 'learning_rate': 9.01803886785789e-07, 'epoch': 0.73} 73%|███████▎ | 5137/7045 [16:41:33<6:21:21, 11.99s/it] 73%|███████▎ | 5138/7045 [16:41:43<6:10:25, 11.65s/it] {'loss': 1.1094, 'learning_rate': 9.009201806912494e-07, 'epoch': 0.73} 73%|███████▎ | 5138/7045 [16:41:43<6:10:25, 11.65s/it] 73%|███████▎ | 5139/7045 [16:41:55<6:05:14, 11.50s/it] {'loss': 1.1167, 'learning_rate': 9.000368126203221e-07, 'epoch': 0.73} 73%|███████▎ | 5139/7045 [16:41:55<6:05:14, 11.50s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2171 > 2048). Running this sequence through the model will result in indexing errors 73%|███████▎ | 5140/7045 [16:42:07<6:12:05, 11.72s/it] {'loss': 1.0859, 'learning_rate': 8.991537827597402e-07, 'epoch': 0.73} 73%|███████▎ | 5140/7045 [16:42:07<6:12:05, 11.72s/it] 73%|███████▎ | 5141/7045 [16:42:19<6:15:30, 11.83s/it] {'loss': 1.0942, 'learning_rate': 8.982710912961623e-07, 'epoch': 0.73} 73%|███████▎ | 5141/7045 [16:42:19<6:15:30, 11.83s/it] 73%|███████▎ | 5142/7045 [16:42:30<6:08:55, 11.63s/it] {'loss': 1.0801, 'learning_rate': 8.973887384161789e-07, 'epoch': 0.73} 73%|███████▎ | 5142/7045 [16:42:30<6:08:55, 11.63s/it] 73%|███████▎ | 5143/7045 [16:42:42<6:09:43, 11.66s/it] {'loss': 1.1123, 'learning_rate': 8.965067243063063e-07, 'epoch': 0.73} 73%|███████▎ | 5143/7045 [16:42:42<6:09:43, 11.66s/it] 73%|███████▎ | 5144/7045 [16:42:53<6:04:50, 11.52s/it] {'loss': 1.0903, 'learning_rate': 8.9562504915299e-07, 'epoch': 0.73} 73%|███████▎ | 5144/7045 [16:42:53<6:04:50, 11.52s/it] 73%|███████▎ | 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8.903421267527152e-07, 'epoch': 0.73} 73%|███████▎ | 5150/7045 [16:44:02<6:04:16, 11.53s/it] 73%|███████▎ | 5151/7045 [16:44:14<5:59:42, 11.40s/it] {'loss': 1.104, 'learning_rate': 8.89462829509753e-07, 'epoch': 0.73} 73%|███████▎ | 5151/7045 [16:44:14<5:59:42, 11.40s/it] 73%|███████▎ | 5152/7045 [16:44:26<6:07:37, 11.65s/it] {'loss': 1.1079, 'learning_rate': 8.885838727123306e-07, 'epoch': 0.73} 73%|███████▎ | 5152/7045 [16:44:26<6:07:37, 11.65s/it] 73%|███████▎ | 5153/7045 [16:44:37<6:01:54, 11.48s/it] {'loss': 1.0898, 'learning_rate': 8.877052565462483e-07, 'epoch': 0.73} 73%|███████▎ | 5153/7045 [16:44:37<6:01:54, 11.48s/it] 73%|███████▎ | 5154/7045 [16:44:48<6:01:29, 11.47s/it] {'loss': 1.1611, 'learning_rate': 8.868269811972327e-07, 'epoch': 0.73} 73%|███████▎ | 5154/7045 [16:44:48<6:01:29, 11.47s/it] 73%|███████▎ | 5155/7045 [16:44:59<5:55:32, 11.29s/it] {'loss': 1.1377, 'learning_rate': 8.859490468509385e-07, 'epoch': 0.73} 73%|███████▎ | 5155/7045 [16:44:59<5:55:32, 11.29s/it] 73%|███████▎ | 5156/7045 [16:45:10<5:53:14, 11.22s/it] {'loss': 1.1641, 'learning_rate': 8.850714536929506e-07, 'epoch': 0.73} 73%|███████▎ | 5156/7045 [16:45:10<5:53:14, 11.22s/it] 73%|███████▎ | 5157/7045 [16:45:28<6:51:15, 13.07s/it] {'loss': 1.0947, 'learning_rate': 8.841942019087784e-07, 'epoch': 0.73} 73%|███████▎ | 5157/7045 [16:45:28<6:51:15, 13.07s/it] 73%|███████▎ | 5158/7045 [16:45:39<6:36:18, 12.60s/it] {'loss': 1.1172, 'learning_rate': 8.833172916838629e-07, 'epoch': 0.73} 73%|███████▎ | 5158/7045 [16:45:39<6:36:18, 12.60s/it] 73%|███████▎ | 5159/7045 [16:45:51<6:27:48, 12.34s/it] {'loss': 1.0996, 'learning_rate': 8.824407232035698e-07, 'epoch': 0.73} 73%|███████▎ | 5159/7045 [16:45:51<6:27:48, 12.34s/it] 73%|███████▎ | 5160/7045 [16:46:04<6:33:47, 12.53s/it] {'loss': 1.1045, 'learning_rate': 8.815644966531931e-07, 'epoch': 0.73} 73%|███████▎ | 5160/7045 [16:46:04<6:33:47, 12.53s/it] 73%|███████▎ | 5161/7045 [16:46:17<6:40:32, 12.76s/it] {'loss': 1.0913, 'learning_rate': 8.806886122179567e-07, 'epoch': 0.73} 73%|███████▎ | 5161/7045 [16:46:17<6:40:32, 12.76s/it] 73%|███████▎ | 5162/7045 [16:46:29<6:31:07, 12.46s/it] {'loss': 1.1069, 'learning_rate': 8.798130700830087e-07, 'epoch': 0.73} 73%|███████▎ | 5162/7045 [16:46:29<6:31:07, 12.46s/it] 73%|███████▎ | 5163/7045 [16:46:40<6:19:22, 12.10s/it] {'loss': 1.0928, 'learning_rate': 8.789378704334287e-07, 'epoch': 0.73} 73%|███████▎ | 5163/7045 [16:46:40<6:19:22, 12.10s/it] 73%|███████▎ | 5164/7045 [16:46:51<6:08:23, 11.75s/it] {'loss': 1.1016, 'learning_rate': 8.7806301345422e-07, 'epoch': 0.73} 73%|███████▎ | 5164/7045 [16:46:51<6:08:23, 11.75s/it] 73%|███████▎ | 5165/7045 [16:47:04<6:19:56, 12.13s/it] {'loss': 1.1299, 'learning_rate': 8.771884993303176e-07, 'epoch': 0.73} 73%|███████▎ | 5165/7045 [16:47:04<6:19:56, 12.13s/it] 73%|███████▎ | 5166/7045 [16:47:16<6:17:24, 12.05s/it] {'loss': 1.1035, 'learning_rate': 8.763143282465797e-07, 'epoch': 0.73} 73%|███████▎ | 5166/7045 [16:47:16<6:17:24, 12.05s/it] 73%|███████▎ | 5167/7045 [16:47:28<6:13:24, 11.93s/it] {'loss': 1.1123, 'learning_rate': 8.754405003877958e-07, 'epoch': 0.73} 73%|███████▎ | 5167/7045 [16:47:28<6:13:24, 11.93s/it] 73%|███████▎ | 5168/7045 [16:47:39<6:07:05, 11.73s/it] {'loss': 1.127, 'learning_rate': 8.745670159386796e-07, 'epoch': 0.73} 73%|███████▎ | 5168/7045 [16:47:39<6:07:05, 11.73s/it] 73%|███████▎ | 5169/7045 [16:47:50<6:01:03, 11.55s/it] {'loss': 1.123, 'learning_rate': 8.736938750838752e-07, 'epoch': 0.73} 73%|███████▎ | 5169/7045 [16:47:50<6:01:03, 11.55s/it] 73%|███████▎ | 5170/7045 [16:48:01<5:56:09, 11.40s/it] {'loss': 1.125, 'learning_rate': 8.728210780079524e-07, 'epoch': 0.73} 73%|███████▎ | 5170/7045 [16:48:01<5:56:09, 11.40s/it] 73%|███████▎ | 5171/7045 [16:48:12<5:52:05, 11.27s/it] {'loss': 1.0986, 'learning_rate': 8.719486248954074e-07, 'epoch': 0.73} 73%|███████▎ | 5171/7045 [16:48:12<5:52:05, 11.27s/it] 73%|███████▎ | 5172/7045 [16:48:25<6:07:48, 11.78s/it] {'loss': 1.0786, 'learning_rate': 8.710765159306661e-07, 'epoch': 0.73} 73%|███████▎ | 5172/7045 [16:48:25<6:07:48, 11.78s/it] 73%|███████▎ | 5173/7045 [16:48:36<6:03:46, 11.66s/it] {'loss': 1.0854, 'learning_rate': 8.702047512980794e-07, 'epoch': 0.73} 73%|███████▎ | 5173/7045 [16:48:36<6:03:46, 11.66s/it] 73%|███████▎ | 5174/7045 [16:48:47<5:57:29, 11.46s/it] {'loss': 1.1143, 'learning_rate': 8.693333311819279e-07, 'epoch': 0.73} 73%|███████▎ | 5174/7045 [16:48:47<5:57:29, 11.46s/it] 73%|███████▎ | 5175/7045 [16:48:58<5:53:00, 11.33s/it] {'loss': 1.1025, 'learning_rate': 8.684622557664168e-07, 'epoch': 0.73} 73%|███████▎ | 5175/7045 [16:48:58<5:53:00, 11.33s/it] 73%|███████▎ | 5176/7045 [16:49:10<5:52:40, 11.32s/it] {'loss': 1.1211, 'learning_rate': 8.67591525235679e-07, 'epoch': 0.73} 73%|███████▎ | 5176/7045 [16:49:10<5:52:40, 11.32s/it] 73%|███████▎ | 5177/7045 [16:49:21<5:50:01, 11.24s/it] {'loss': 1.0903, 'learning_rate': 8.66721139773776e-07, 'epoch': 0.73} 73%|███████▎ | 5177/7045 [16:49:21<5:50:01, 11.24s/it] 73%|███████▎ | 5178/7045 [16:49:32<5:49:05, 11.22s/it] {'loss': 1.0889, 'learning_rate': 8.658510995646957e-07, 'epoch': 0.73} 73%|███████▎ | 5178/7045 [16:49:32<5:49:05, 11.22s/it] 74%|███████▎ | 5179/7045 [16:49:43<5:46:30, 11.14s/it] {'loss': 1.1289, 'learning_rate': 8.649814047923513e-07, 'epoch': 0.74} 74%|███████▎ | 5179/7045 [16:49:43<5:46:30, 11.14s/it] 74%|███████▎ | 5180/7045 [16:49:54<5:49:26, 11.24s/it] {'loss': 1.0923, 'learning_rate': 8.64112055640586e-07, 'epoch': 0.74} 74%|███████▎ | 5180/7045 [16:49:54<5:49:26, 11.24s/it] 74%|███████▎ | 5181/7045 [16:50:07<6:00:24, 11.60s/it] {'loss': 1.1113, 'learning_rate': 8.632430522931679e-07, 'epoch': 0.74} 74%|███████▎ | 5181/7045 [16:50:07<6:00:24, 11.60s/it] 74%|███████▎ | 5182/7045 [16:50:19<6:04:38, 11.74s/it] {'loss': 1.1123, 'learning_rate': 8.623743949337909e-07, 'epoch': 0.74} 74%|███████▎ | 5182/7045 [16:50:19<6:04:38, 11.74s/it] 74%|███████▎ | 5183/7045 [16:50:32<6:19:11, 12.22s/it] {'loss': 1.0898, 'learning_rate': 8.615060837460795e-07, 'epoch': 0.74} 74%|███████▎ | 5183/7045 [16:50:32<6:19:11, 12.22s/it] 74%|███████▎ | 5184/7045 [16:50:44<6:19:53, 12.25s/it] {'loss': 1.1104, 'learning_rate': 8.60638118913581e-07, 'epoch': 0.74} 74%|███████▎ | 5184/7045 [16:50:45<6:19:53, 12.25s/it] 74%|███████▎ | 5185/7045 [16:50:56<6:09:37, 11.92s/it] {'loss': 1.0908, 'learning_rate': 8.597705006197729e-07, 'epoch': 0.74} 74%|███████▎ | 5185/7045 [16:50:56<6:09:37, 11.92s/it] 74%|███████▎ | 5186/7045 [16:51:07<6:01:24, 11.66s/it] {'loss': 1.1064, 'learning_rate': 8.589032290480571e-07, 'epoch': 0.74} 74%|███████▎ | 5186/7045 [16:51:07<6:01:24, 11.66s/it] 74%|███████▎ | 5187/7045 [16:51:18<6:00:16, 11.63s/it] {'loss': 1.1299, 'learning_rate': 8.580363043817618e-07, 'epoch': 0.74} 74%|███████▎ | 5187/7045 [16:51:18<6:00:16, 11.63s/it] 74%|███████▎ | 5188/7045 [16:51:31<6:08:34, 11.91s/it] {'loss': 1.0908, 'learning_rate': 8.571697268041446e-07, 'epoch': 0.74} 74%|███████▎ | 5188/7045 [16:51:31<6:08:34, 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'learning_rate': 8.519775604537606e-07, 'epoch': 0.74} 74%|███████▎ | 5194/7045 [16:52:38<5:49:48, 11.34s/it] 74%|███████▎ | 5195/7045 [16:52:49<5:50:58, 11.38s/it] {'loss': 1.1201, 'learning_rate': 8.511134176218871e-07, 'epoch': 0.74} 74%|███████▎ | 5195/7045 [16:52:49<5:50:58, 11.38s/it] 74%|███████▍ | 5196/7045 [16:53:00<5:46:51, 11.26s/it] {'loss': 1.1494, 'learning_rate': 8.502496233420959e-07, 'epoch': 0.74} 74%|███████▍ | 5196/7045 [16:53:00<5:46:51, 11.26s/it] 74%|███████▍ | 5197/7045 [16:53:11<5:44:27, 11.18s/it] {'loss': 1.1113, 'learning_rate': 8.4938617779698e-07, 'epoch': 0.74} 74%|███████▍ | 5197/7045 [16:53:11<5:44:27, 11.18s/it] 74%|███████▍ | 5198/7045 [16:53:22<5:43:39, 11.16s/it] {'loss': 1.1172, 'learning_rate': 8.485230811690595e-07, 'epoch': 0.74} 74%|███████▍ | 5198/7045 [16:53:22<5:43:39, 11.16s/it] 74%|███████▍ | 5199/7045 [16:53:33<5:42:58, 11.15s/it] {'loss': 1.1025, 'learning_rate': 8.476603336407827e-07, 'epoch': 0.74} 74%|███████▍ | 5199/7045 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The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants. warnings.warn( /usr/local/lib/python3.9/dist-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None warnings.warn( 85%|████████▌ | 6001/7045 [19:30:19<6:03:46, 20.91s/it] {'loss': 1.1206, 'learning_rate': 2.825097753193065e-07, 'epoch': 0.85} 85%|████████▌ | 6001/7045 [19:30:19<6:03:46, 20.91s/it] 85%|████████▌ | 6002/7045 [19:30:31<5:16:01, 18.18s/it] {'loss': 1.1021, 'learning_rate': 2.8197923442154163e-07, 'epoch': 0.85} 85%|████████▌ | 6002/7045 [19:30:31<5:16:01, 18.18s/it] 85%|████████▌ | 6003/7045 [19:30:44<4:52:27, 16.84s/it] {'loss': 1.0264, 'learning_rate': 2.814491623830432e-07, 'epoch': 0.85} 85%|████████▌ | 6003/7045 [19:30:44<4:52:27, 16.84s/it] 85%|████████▌ | 6004/7045 [19:30:55<4:21:47, 15.09s/it] {'loss': 1.1191, 'learning_rate': 2.8091955931586144e-07, 'epoch': 0.85} 85%|████████▌ | 6004/7045 [19:30:55<4:21:47, 15.09s/it] 85%|████████▌ | 6005/7045 [19:31:07<4:01:28, 13.93s/it] {'loss': 1.124, 'learning_rate': 2.8039042533194813e-07, 'epoch': 0.85} 85%|████████▌ | 6005/7045 [19:31:07<4:01:28, 13.93s/it] 85%|████████▌ | 6006/7045 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11.60s/it] {'loss': 1.0898, 'learning_rate': 2.5757460416626105e-07, 'epoch': 0.86} 86%|████████▌ | 6049/7045 [19:39:37<3:12:33, 11.60s/it]Error with [Errno 2] No such file or directory: '/mnt/bn/liangkeg/data/allava-4v/ALLaVA/allava_laion/images/99998.jpeg', [{'id': 'allava_laion_inst_99998', 'image': '/mnt/bn/liangkeg/data/allava-4v/ALLaVA/allava_laion/images/99998.jpeg', 'conversations': [{'from': 'human', 'value': "\nConsidering the ring's unique design and the way the gemstones are mounted, what could be the specific method used in setting these stones, and how might it contribute to the ring's aesthetic appeal?"}, {'from': 'gpt', 'value': "The setting used for the gemstones appears to be a tension setting, a technique where the gemstones are held in place by the pressure of the metal band itself, creating the illusion that the stones are floating. This method not only enhances the ring's modern and sleek appearance but also allows more light to pass through the gemstones, increasing their brilliance and sparkle. The absence of prongs or bezels contributes to a minimalist and contemporary aesthetic, emphasizing the stones' clarity and the smooth curvature of the metal."}], 'original_caption': 'Love Comet Rose Gold Ring', 'url': 'https://ak1.ostkcdn.com/images/products/is/images/direct/2071d97c795c1623c75134e3ff0018595b49764d/Love-Comet-Rose-Gold-Ring.jpg'}] 86%|████████▌ | 6050/7045 [19:39:49<3:15:52, 11.81s/it] {'loss': 1.1001, 'learning_rate': 2.570666931864901e-07, 'epoch': 0.86} 86%|████████▌ | 6050/7045 [19:39:49<3:15:52, 11.81s/it] 86%|████████▌ | 6051/7045 [19:40:01<3:14:04, 11.72s/it] {'loss': 1.125, 'learning_rate': 2.56559256332157e-07, 'epoch': 0.86} 86%|████████▌ | 6051/7045 [19:40:01<3:14:04, 11.72s/it] 86%|████████▌ | 6052/7045 [19:40:12<3:11:53, 11.60s/it] {'loss': 1.1338, 'learning_rate': 2.5605229371052615e-07, 'epoch': 0.86} 86%|████████▌ | 6052/7045 [19:40:12<3:11:53, 11.60s/it] 86%|████████▌ | 6053/7045 [19:40:24<3:11:58, 11.61s/it] {'loss': 1.1074, 'learning_rate': 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11.54s/it] {'loss': 1.1123, 'learning_rate': 1.8284017217221395e-07, 'epoch': 0.88} 88%|████████▊ | 6208/7045 [20:10:33<2:40:58, 11.54s/it] 88%|████████▊ | 6209/7045 [20:10:44<2:38:33, 11.38s/it] {'loss': 1.0825, 'learning_rate': 1.8240892803371268e-07, 'epoch': 0.88} 88%|████████▊ | 6209/7045 [20:10:44<2:38:33, 11.38s/it] 88%|████████▊ | 6210/7045 [20:10:57<2:46:50, 11.99s/it] {'loss': 1.1196, 'learning_rate': 1.8197817380227968e-07, 'epoch': 0.88} 88%|████████▊ | 6210/7045 [20:10:57<2:46:50, 11.99s/it] 88%|████████▊ | 6211/7045 [20:11:08<2:43:35, 11.77s/it] {'loss': 1.1396, 'learning_rate': 1.815479095689704e-07, 'epoch': 0.88} 88%|████████▊ | 6211/7045 [20:11:08<2:43:35, 11.77s/it] 88%|████████▊ | 6212/7045 [20:11:21<2:45:48, 11.94s/it] {'loss': 1.0923, 'learning_rate': 1.811181354247371e-07, 'epoch': 0.88} 88%|████████▊ | 6212/7045 [20:11:21<2:45:48, 11.94s/it] 88%|████████▊ | 6213/7045 [20:11:32<2:42:18, 11.71s/it] {'loss': 1.124, 'learning_rate': 1.8068885146042824e-07, 'epoch': 0.88} 88%|████████▊ | 6213/7045 [20:11:32<2:42:18, 11.71s/it] 88%|████████▊ | 6214/7045 [20:11:45<2:47:27, 12.09s/it] {'loss': 1.0757, 'learning_rate': 1.8026005776678817e-07, 'epoch': 0.88} 88%|████████▊ | 6214/7045 [20:11:45<2:47:27, 12.09s/it] 88%|████████▊ | 6215/7045 [20:11:56<2:42:03, 11.71s/it] {'loss': 1.063, 'learning_rate': 1.7983175443445855e-07, 'epoch': 0.88} 88%|████████▊ | 6215/7045 [20:11:56<2:42:03, 11.71s/it] 88%|████████▊ | 6216/7045 [20:12:07<2:41:27, 11.69s/it] {'loss': 1.1025, 'learning_rate': 1.794039415539764e-07, 'epoch': 0.88} 88%|████████▊ | 6216/7045 [20:12:07<2:41:27, 11.69s/it] 88%|████████▊ | 6217/7045 [20:12:19<2:42:49, 11.80s/it] {'loss': 1.1602, 'learning_rate': 1.7897661921577547e-07, 'epoch': 0.88} 88%|████████▊ | 6217/7045 [20:12:19<2:42:49, 11.80s/it] 88%|████████▊ | 6218/7045 [20:12:30<2:38:59, 11.53s/it] {'loss': 1.0791, 'learning_rate': 1.785497875101863e-07, 'epoch': 0.88} 88%|████████▊ | 6218/7045 [20:12:30<2:38:59, 11.53s/it] 88%|████████▊ | 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1.7599910560690686e-07, 'epoch': 0.88} 88%|████████▊ | 6224/7045 [20:13:39<2:38:51, 11.61s/it] 88%|████████▊ | 6225/7045 [20:13:50<2:36:15, 11.43s/it] {'loss': 1.1025, 'learning_rate': 1.755757108501263e-07, 'epoch': 0.88} 88%|████████▊ | 6225/7045 [20:13:50<2:36:15, 11.43s/it] 88%|████████▊ | 6226/7045 [20:14:03<2:41:56, 11.86s/it] {'loss': 1.0747, 'learning_rate': 1.751528074448633e-07, 'epoch': 0.88} 88%|████████▊ | 6226/7045 [20:14:03<2:41:56, 11.86s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2485 > 2048). Running this sequence through the model will result in indexing errors 88%|████████▊ | 6227/7045 [20:14:14<2:40:00, 11.74s/it] {'loss': 1.1318, 'learning_rate': 1.7473039548051296e-07, 'epoch': 0.88} 88%|████████▊ | 6227/7045 [20:14:14<2:40:00, 11.74s/it] 88%|████████▊ | 6228/7045 [20:14:27<2:45:28, 12.15s/it] {'loss': 1.0801, 'learning_rate': 1.7430847504636778e-07, 'epoch': 0.88} 88%|████████▊ | 6228/7045 [20:14:27<2:45:28, 12.15s/it] 88%|████████▊ | 6229/7045 [20:14:38<2:39:41, 11.74s/it] {'loss': 1.084, 'learning_rate': 1.738870462316161e-07, 'epoch': 0.88} 88%|████████▊ | 6229/7045 [20:14:38<2:39:41, 11.74s/it] 88%|████████▊ | 6230/7045 [20:14:49<2:37:11, 11.57s/it] {'loss': 1.0874, 'learning_rate': 1.73466109125342e-07, 'epoch': 0.88} 88%|████████▊ | 6230/7045 [20:14:49<2:37:11, 11.57s/it] 88%|████████▊ | 6231/7045 [20:15:01<2:37:11, 11.59s/it] {'loss': 1.1304, 'learning_rate': 1.7304566381652565e-07, 'epoch': 0.88} 88%|████████▊ | 6231/7045 [20:15:01<2:37:11, 11.59s/it] 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> 2048). 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Expecting to read 4 bytes but only got 0. warnings.warn(str(msg)) 100%|█████████▉| 7025/7045 [22:49:58<03:57, 11.86s/it] {'loss': 1.0547, 'learning_rate': 1.0569242152147497e-10, 'epoch': 1.0} 100%|█████████▉| 7025/7045 [22:49:58<03:57, 11.86s/it] 100%|█████████▉| 7026/7045 [22:50:11<03:53, 12.28s/it] {'loss': 1.0947, 'learning_rate': 9.538747595516651e-11, 'epoch': 1.0} 100%|█████████▉| 7026/7045 [22:50:11<03:53, 12.28s/it] 100%|█████████▉| 7027/7045 [22:50:23<03:40, 12.24s/it] {'loss': 1.1475, 'learning_rate': 8.56109760469237e-11, 'epoch': 1.0} 100%|█████████▉| 7027/7045 [22:50:23<03:40, 12.24s/it] 100%|█████████▉| 7028/7045 [22:50:36<03:31, 12.45s/it] {'loss': 1.0493, 'learning_rate': 7.636292386370425e-11, 'epoch': 1.0} 100%|█████████▉| 7028/7045 [22:50:36<03:31, 12.45s/it] 100%|█████████▉| 7029/7045 [22:50:48<03:17, 12.33s/it] {'loss': 1.0513, 'learning_rate': 6.764332136033336e-11, 'epoch': 1.0} 100%|█████████▉| 7029/7045 [22:50:48<03:17, 12.33s/it] 100%|█████████▉| 7030/7045 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[22:53:09<00:45, 11.35s/it] {'loss': 1.0737, 'learning_rate': 4.227725458605392e-12, 'epoch': 1.0} 100%|█████████▉| 7041/7045 [22:53:09<00:45, 11.35s/it] 100%|█████████▉| 7042/7045 [22:53:21<00:34, 11.39s/it] {'loss': 1.0996, 'learning_rate': 2.378095863841967e-12, 'epoch': 1.0} 100%|█████████▉| 7042/7045 [22:53:21<00:34, 11.39s/it] 100%|█████████▉| 7043/7045 [22:53:32<00:22, 11.39s/it] {'loss': 1.1064, 'learning_rate': 1.0569315880837316e-12, 'epoch': 1.0} 100%|█████████▉| 7043/7045 [22:53:32<00:22, 11.39s/it] 100%|█████████▉| 7044/7045 [22:53:43<00:11, 11.28s/it] {'loss': 1.1108, 'learning_rate': 2.6423291110688754e-13, 'epoch': 1.0} 100%|█████████▉| 7044/7045 [22:53:43<00:11, 11.28s/it] 100%|██████████| 7045/7045 [22:53:57<00:00, 12.13s/it] {'loss': 1.0869, 'learning_rate': 0.0, 'epoch': 1.0} 100%|██████████| 7045/7045 [22:53:57<00:00, 12.13s/it] {'train_runtime': 82446.7613, 'train_samples_per_second': 10.937, 'train_steps_per_second': 0.085, 'train_loss': 1.1225040476401704, 'epoch': 1.0} 100%|██████████| 7045/7045 [22:53:57<00:00, 12.13s/it] 100%|██████████| 7045/7045 [22:53:57<00:00, 11.70s/it] 2024-03-10 05:19:42.110 n213-017-210:2252780:2254222 [4] NCCL INFO [Service thread] Connection closed by localRank 5 2024-03-10 05:19:42.110 n213-017-210:2252782:2254221 [6] NCCL INFO [Service thread] Connection closed by localRank 5 2024-03-10 05:19:42.110 n213-017-210:2252781:2254223 [5] NCCL INFO [Service thread] Connection closed by localRank 5 2024-03-10 05:19:42.299 n213-017-210:2252782:2254221 [6] NCCL INFO [Service thread] Connection closed by localRank 7 2024-03-10 05:19:42.299 n213-017-210:2252783:2254220 [7] NCCL INFO [Service thread] Connection closed by localRank 7 2024-03-10 05:19:42.299 n213-017-210:2252776:2254227 [0] NCCL INFO [Service thread] Connection closed by localRank 7 2024-03-10 05:19:42.658 n213-017-210:2252780:2254222 [4] NCCL INFO [Service thread] Connection closed by localRank 3 2024-03-10 05:19:42.658 n213-017-210:2252778:2254226 [2] NCCL INFO [Service thread] Connection closed by localRank 3 2024-03-10 05:19:42.658 n213-017-210:2252779:2254225 [3] NCCL INFO [Service thread] Connection closed by localRank 3 2024-03-10 05:19:42.658 n213-017-210:2252778:2254226 [2] NCCL INFO [Service thread] Connection closed by localRank 2 2024-03-10 05:19:42.658 n213-017-210:2252779:2254225 [3] NCCL INFO [Service thread] Connection closed by localRank 2 2024-03-10 05:19:42.658 n213-017-210:2252777:2254224 [1] NCCL INFO [Service thread] Connection closed by localRank 2 2024-03-10 05:19:42.683 n213-017-210:2252778:2254226 [2] NCCL INFO [Service thread] Connection closed by localRank 1 2024-03-10 05:19:42.683 n213-017-210:2252777:2254224 [1] NCCL INFO [Service thread] Connection closed by localRank 1 2024-03-10 05:19:42.683 n213-017-210:2252776:2254227 [0] NCCL INFO [Service thread] Connection closed by localRank 1 2024-03-10 05:19:42.735 n213-017-210:2252781:2254223 [5] NCCL INFO [Service thread] Connection closed by localRank 6 2024-03-10 05:19:42.735 n213-017-210:2252782:2254221 [6] NCCL INFO [Service thread] Connection closed by localRank 6 2024-03-10 05:19:42.735 n213-017-210:2252783:2254220 [7] NCCL INFO [Service thread] Connection closed by localRank 6 2024-03-10 05:19:42.786 n213-017-210:2252779:2254225 [3] NCCL INFO [Service thread] Connection closed by localRank 4 2024-03-10 05:19:42.786 n213-017-210:2252780:2254222 [4] NCCL INFO [Service thread] Connection closed by localRank 4 2024-03-10 05:19:42.786 n213-017-210:2252781:2254223 [5] NCCL INFO [Service thread] Connection closed by localRank 4 2024-03-10 05:19:43.052 n213-017-210:2252778:2252778 [2] NCCL INFO comm 0x6f970a00 rank 2 nranks 8 cudaDev 2 busId 4a000 - Abort COMPLETE 2024-03-10 05:19:43.117 n213-017-210:2252781:2252781 [5] NCCL INFO comm 0x6f5eb560 rank 5 nranks 8 cudaDev 5 busId 8e000 - Abort COMPLETE 2024-03-10 05:19:43.119 n213-017-210:2252779:2252779 [3] NCCL INFO comm 0x70282cc0 rank 3 nranks 8 cudaDev 3 busId 4e000 - Abort COMPLETE 2024-03-10 05:19:43.524 n213-017-210:2252780:2252780 [4] NCCL INFO comm 0x6fd97400 rank 4 nranks 8 cudaDev 4 busId 89000 - Abort COMPLETE 2024-03-10 05:19:43.525 n213-017-210:2252782:2252782 [6] NCCL INFO comm 0x6f5c01c0 rank 6 nranks 8 cudaDev 6 busId c5000 - Abort COMPLETE 2024-03-10 05:19:46.932 n213-017-210:2252783:2252783 [7] NCCL INFO comm 0x6f584bc0 rank 7 nranks 8 cudaDev 7 busId c9000 - Abort COMPLETE 2024-03-10 05:19:46.938 n213-017-210:2252777:2252777 [1] NCCL INFO comm 0x6ece5cc0 rank 1 nranks 8 cudaDev 1 busId 16000 - Abort COMPLETE 2024-03-10 05:19:46.964 n213-017-210:2252780:2253716 [4] NCCL INFO [Service thread] Connection closed by localRank 5 2024-03-10 05:19:47.077 n213-017-210:2252780:2253716 [4] NCCL INFO [Service thread] Connection closed by localRank 3 2024-03-10 05:19:47.302 n213-017-210:2252777:2253722 [1] NCCL INFO [Service thread] Connection closed by localRank 2 2024-03-10 05:19:47.563 n213-017-210:2252783:2253719 [7] NCCL INFO [Service thread] Connection closed by localRank 6 2024-03-10 05:19:51.118 n213-017-210:2252776:2253723 [0] NCCL INFO [Service thread] Connection closed by localRank 7 2024-03-10 05:19:51.120 n213-017-210:2252776:2253723 [0] NCCL INFO [Service thread] Connection closed by localRank 1 wandb: Waiting for W&B process to finish... (success). wandb: wandb: Run history: wandb: train/epoch ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███ wandb: train/global_step ▁▁▁▂▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███ wandb: train/learning_rate ▃▇██████▇▇▇▇▇▆▆▆▆▅▅▅▅▄▄▄▃▃▃▃▂▂▂▂▂▁▁▁▁▁▁▁ wandb: train/loss █▇▄▄▅▅▄▃▃▅▃▃▂▄▄▃▃▁▄▃▄▂▂▂▂▃▃▅▁▅▂▁▄▂▃▃▁▃▂▂ wandb: train/total_flos ▁ wandb: train/train_loss ▁ wandb: train/train_runtime ▁ wandb: train/train_samples_per_second ▁ wandb: train/train_steps_per_second ▁ wandb: wandb: Run summary: wandb: train/epoch 1.0 wandb: train/global_step 7045 wandb: train/learning_rate 0.0 wandb: train/loss 1.0869 wandb: train/total_flos 1.4118353085990437e+19 wandb: train/train_loss 1.1225 wandb: train/train_runtime 82446.7613 wandb: train/train_samples_per_second 10.937 wandb: train/train_steps_per_second 0.085 wandb: wandb: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s) wandb: Find logs at: ./wandb/run-20240309_062525-run_20240309_98cb39ab/logs 2024-03-10 05:20:07.273 n213-017-210:2252776:2254227 [0] NCCL INFO [Service thread] Connection closed by localRank 0 2024-03-10 05:20:07.911 n213-017-210:2252776:2252776 [0] NCCL INFO comm 0x19413970 rank 0 nranks 8 cudaDev 0 busId 10000 - Abort COMPLETE