ElasticSearch does not run #2
Comments
|
Can't guarantee anything will come out of it but I can take a look into this |
|
That would be great! Here are some pointers: Follow these instructions to enable strace and run elasticsearch. Look for the log file: The entry point for the the ioctl syscall in the Sentry is here. |
|
Note: the elasticsearch image was failing to start due to not being able to find a loopback interface even though Adding |
shentubot
pushed a commit
that referenced
this issue
Jul 3, 2018
glibc's malloc also uses SYS_TIME. Permit it. #0 0x0000000000de6267 in time () #1 0x0000000000db19d8 in get_nprocs () #2 0x0000000000d8a31a in arena_get2.part () #3 0x0000000000d8ab4a in malloc () #4 0x0000000000d3c6b5 in __sanitizer::InternalAlloc(unsigned long, __sanitizer::SizeClassAllocatorLocalCache<__sanitizer::SizeClassAllocator32<0ul, 140737488355328ull, 0ul, __sanitizer::SizeClassMap<3ul, 4ul, 8ul, 17ul, 64ul, 14ul>, 20ul, __sanitizer::TwoLevelByteMap<32768ull, 4096ull, __sanitizer::NoOpMapUnmapCallback>, __sanitizer::NoOpMapUnmapCallback> >*, unsigned long) () #5 0x0000000000d4cd70 in __tsan_go_start () #6 0x00000000004617a3 in racecall () #7 0x00000000010f4ea0 in runtime.findfunctab () #8 0x000000000043f193 in runtime.racegostart () Signed-off-by: Dmitry Vyukov <dvyukov@google.com> [mpratt@google.com: updated comments and commit message] Signed-off-by: Michael Pratt <mpratt@google.com> Change-Id: Ibe2d0dc3035bf5052d5fb802cfaa37c5e0e7a09a PiperOrigin-RevId: 203042627
dvyukov
pushed a commit
to dvyukov/gvisor
that referenced
this issue
Jul 4, 2018
Closes google#2 PiperOrigin-RevId: 202997196 Change-Id: I0c9f6f5a8a1abe1ae427bca5f590bdf9f82a6675
dvyukov
added a commit
to dvyukov/gvisor
that referenced
this issue
Jul 4, 2018
glibc's malloc also uses SYS_TIME. Permit it. #0 0x0000000000de6267 in time () google#1 0x0000000000db19d8 in get_nprocs () google#2 0x0000000000d8a31a in arena_get2.part () google#3 0x0000000000d8ab4a in malloc () google#4 0x0000000000d3c6b5 in __sanitizer::InternalAlloc(unsigned long, __sanitizer::SizeClassAllocatorLocalCache<__sanitizer::SizeClassAllocator32<0ul, 140737488355328ull, 0ul, __sanitizer::SizeClassMap<3ul, 4ul, 8ul, 17ul, 64ul, 14ul>, 20ul, __sanitizer::TwoLevelByteMap<32768ull, 4096ull, __sanitizer::NoOpMapUnmapCallback>, __sanitizer::NoOpMapUnmapCallback> >*, unsigned long) () google#5 0x0000000000d4cd70 in __tsan_go_start () google#6 0x00000000004617a3 in racecall () google#7 0x00000000010f4ea0 in runtime.findfunctab () google#8 0x000000000043f193 in runtime.racegostart () Signed-off-by: Dmitry Vyukov <dvyukov@google.com> [mpratt@google.com: updated comments and commit message] Signed-off-by: Michael Pratt <mpratt@google.com> Change-Id: Ibe2d0dc3035bf5052d5fb802cfaa37c5e0e7a09a PiperOrigin-RevId: 203042627
tonistiigi
referenced
this issue
in tonistiigi/gvisor
Jan 30, 2019
glibc's malloc also uses SYS_TIME. Permit it. #0 0x0000000000de6267 in time () #1 0x0000000000db19d8 in get_nprocs () #2 0x0000000000d8a31a in arena_get2.part () #3 0x0000000000d8ab4a in malloc () google#4 0x0000000000d3c6b5 in __sanitizer::InternalAlloc(unsigned long, __sanitizer::SizeClassAllocatorLocalCache<__sanitizer::SizeClassAllocator32<0ul, 140737488355328ull, 0ul, __sanitizer::SizeClassMap<3ul, 4ul, 8ul, 17ul, 64ul, 14ul>, 20ul, __sanitizer::TwoLevelByteMap<32768ull, 4096ull, __sanitizer::NoOpMapUnmapCallback>, __sanitizer::NoOpMapUnmapCallback> >*, unsigned long) () google#5 0x0000000000d4cd70 in __tsan_go_start () google#6 0x00000000004617a3 in racecall () google#7 0x00000000010f4ea0 in runtime.findfunctab () google#8 0x000000000043f193 in runtime.racegostart () Signed-off-by: Dmitry Vyukov <dvyukov@google.com> [mpratt@google.com: updated comments and commit message] Signed-off-by: Michael Pratt <mpratt@google.com> Change-Id: Ibe2d0dc3035bf5052d5fb802cfaa37c5e0e7a09a PiperOrigin-RevId: 203042627 Upstream-commit: 6144751
Closed
tanjianfeng
pushed a commit
to tanjianfeng/gvisor
that referenced
this issue
Aug 2, 2019
Below command under hostinet network will lead to panic: $ cat /proc/net/tcp It's caused by the wrong SizeOfTCPInfo. #0 runtime.panicindex() google#1 encoding/binary.littleEndian.Uint64 google#2 encoding/binary.(*littleEndian).Uint64 google#3 gvisor.dev/gvisor/pkg/binary.unmarshal google#4 gvisor.dev/gvisor/pkg/binary.unmarshal google#5 gvisor.dev/gvisor/pkg/binary.Unmarshal google#6 gvisor.dev/gvisor/pkg/sentry/socket/hostinet.(*socketOperations).State google#7 gvisor.dev/gvisor/pkg/sentry/fs/proc.(*netTCP).ReadSeqFileData Correct SizeOfTCPInfo from 104 to 192 to fix it. Fixes google#640 Signed-off-by: Jianfeng Tan <henry.tjf@antfin.com>
amscanne
referenced
this issue
in amscanne/gvisor
Nov 14, 2019
copybara-service bot
pushed a commit
that referenced
this issue
Mar 26, 2021
Before this change: ``` $ docker run --runtime=runsc --rm -it -v ~/tmp:/hosttmp ubuntu:focal /hosttmp/issue5732 --bytes1=128 --bytes2=1024 #1: read(128) = 128 #2: read(1024) = EOF $ docker run --runtime=runsc-vfs2 --rm -it -v ~/tmp:/hosttmp ubuntu:focal /hosttmp/issue5732 --bytes1=128 --bytes2=1024 #1: read(128) = 128 #2: read(1024) = 256 ``` After this change: ``` $ docker run --runtime=runsc --rm -it -v ~/tmp:/hosttmp ubuntu:focal /hosttmp/issue5732 --bytes1=128 --bytes2=1024 #1: read(128) = 128 #2: read(1024) = 256 $ docker run --runtime=runsc-vfs2 --rm -it -v ~/tmp:/hosttmp ubuntu:focal /hosttmp/issue5732 --bytes1=128 --bytes2=1024 #1: read(128) = 128 #2: read(1024) = 256 ``` Fixes #5732 PiperOrigin-RevId: 365178386
Closed
copybara-service bot
pushed a commit
that referenced
this issue
Jul 3, 2024
Distributed training isn't working with PyTorch on certain A100 nodes. Adds the missing ioctl `UVM_UNMAP_EXTERNAL` allowing for certain NCCL operations to succeed when using [`torch.distributed`](https://pytorch.org/docs/stable/distributed.html), fixing distributed training. ## Reproduction This affects numerous A100 40GB and 80GB instances in our fleet. This reproduction requires 4 A100 GPUs, either 40GB or 80GB. - **NVIDIA Driver Version**: 550.54.15 - **CUDA Version**: 12.4 - **NVIDIA device**: NVIDIA A100 80GB PCIe ### Steps 1. **Install gvisor** ```bash URL="https://storage.googleapis.com/gvisor/releases/master/latest/${ARCH}" wget -nc "${URL}/runsc" "${URL}/runsc.sha512" chmod +x runsc sudo cp runsc /usr/local/bin/runsc sudo /usr/local/bin/runsc install sudo systemctl reload docker ``` 2. **Add GPU enabling gvisor options** ```json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] }, "runsc": { "path": "/usr/local/bin/runsc", "runtimeArgs": ["--nvproxy", "--nvproxy-docker", "-debug-log=/tmp/runsc/", "-debug", "-strace"] } } } ``` Reload configs with `sudo systemctl reload docker`. 3. **Run reproduction NCCL test** This test creates one main process and N peer processes. Each peer process sends a torch `Tensor` to the main process using NCCL. ```Dockerfile # Dockerfile FROM python:3.9.15-slim-bullseye RUN pip install torch numpy COPY <<EOF repro.py import argparse import datetime import os import torch import torch.distributed as dist import torch.multiprocessing as mp def setup(rank, world_size): os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "12355" dist.init_process_group("nccl", rank=rank, world_size=world_size, timeout=datetime.timedelta(seconds=600)) torch.cuda.set_device(rank) def cleanup(): dist.destroy_process_group() def send_tensor(rank, world_size): try: setup(rank, world_size) # rank receiving all tensors target_rank = world_size - 1 dist.barrier() tensor = torch.ones(5).cuda(rank) if rank < target_rank: print(f"[RANK {rank}] sending tensor: {tensor}") dist.send(tensor=tensor, dst=target_rank) elif rank == target_rank: for other_rank in range(target_rank): tensor = torch.zeros(5).cuda(target_rank) dist.recv(tensor=tensor, src=other_rank) print(f"[RANK {target_rank}] received tensor from rank={other_rank}: {tensor}") print("PASS: NCCL working.") except Exception as e: print(f"[RANK {rank}] error in send_tensor: {e}") raise finally: cleanup() def main(world_size: int = 2): mp.spawn(send_tensor, args=(world_size,), nprocs=world_size, join=True) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run torch-based NCCL tests") parser.add_argument("world_size", type=int, help="number of GPUs to run test on") args = parser.parse_args() if args.world_size < 2: raise RuntimeError(f"world_size needs to be larger than 1 {args.world_size}") main(args.world_size) EOF ENTRYPOINT ["python", "repro.py", "4"] ``` Build image with: ``` docker build -f Dockerfile . ``` Then run it with: ``` sudo docker run -it --shm-size=2.00gb --runtime=runsc --gpus='"device=GPU-742ea7fc-dd4f-612c-e860-499bf200a815,GPU-94a801d8-7713-acf6-337d-338b7cfdf19e,GPU-0d19cef2-10ce-e445-a0be-3d330e36c1fd,GPU-ac5046fb-020c-93e8-2784-f44aedbc5bbd"' 040a44863fb1 ``` #### Failure (truncated) ``` ... Exception raised from recvBytes at ../torch/csrc/distributed/c10d/Utils.hpp:672 (most recent call first): frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7edda14cf897 in /usr/local/lib/python3.11/site-packages/torch/lib/libc10.so) frame #1: <unknown function> + 0x5b3a23e (0x7edd8d73a23e in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #2: c10d::TCPStore::doWait(c10::ArrayRef<std::string>, std::chrono::duration<long, std::ratio<1l, 1000l> >) + 0x2c7 (0x7edd8d734c87 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #3: c10d::TCPStore::doGet(std::string const&) + 0x32 (0x7edd8d734f82 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #4: c10d::TCPStore::get(std::string const&) + 0xa1 (0x7edd8d735fd1 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #5: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #6: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #7: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #8: c10d::ProcessGroupNCCL::broadcastUniqueNCCLID(ncclUniqueId*, bool, std::string const&, int) + 0xa9 (0x7edd54da9189 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #9: c10d::ProcessGroupNCCL::getNCCLComm(std::string const&, c10::Device&, c10d::OpType, int, bool) + 0xc50 (0x7edd54db0610 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #10: c10d::ProcessGroupNCCL::recv(std::vector<at::Tensor, std::allocator<at::Tensor> >&, int, int) + 0x5f8 (0x7edd54dcf978 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #11: <unknown function> + 0x5adc309 (0x7edd8d6dc309 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #12: <unknown function> + 0x5ae6f10 (0x7edd8d6e6f10 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #13: <unknown function> + 0x5ae6fa5 (0x7edd8d6e6fa5 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #14: <unknown function> + 0x5124446 (0x7edd8cd24446 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #15: <unknown function> + 0x1acf4b8 (0x7edd896cf4b8 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #16: <unknown function> + 0x5aee004 (0x7edd8d6ee004 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #17: <unknown function> + 0x5af36b5 (0x7edd8d6f36b5 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #18: <unknown function> + 0xd2fe8e (0x7edda032fe8e in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_python.so) frame #19: <unknown function> + 0x47f074 (0x7edd9fa7f074 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_python.so) <omitting python frames> frame #35: <unknown function> + 0x29d90 (0x7edda2029d90 in /usr/lib/x86_64-linux-gnu/libc.so.6) frame #36: __libc_start_main + 0x80 (0x7edda2029e40 in /usr/lib/x86_64-linux-gnu/libc.so.6) frame #37: <unknown function> + 0x108e (0x55f950b0c08e in /usr/local/bin/python) . This may indicate a possible application crash on rank 0 or a network set up issue. ... ``` ### Fix gvisor debug logs show: ``` W0702 20:36:17.577055 445833 uvm.go:148] [ 22: 84] nvproxy: unknown uvm ioctl 66 = 0x42 ``` I've implemented that ioctl in this PR. This is the output after the fix. ``` [RANK 2] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:2') [RANK 0] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:0') [RANK 1] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:1') [RANK 3] received tensor from rank=0: tensor([1., 1., 1., 1., 1.], device='cuda:3') [RANK 3] received tensor from rank=1: tensor([1., 1., 1., 1., 1.], device='cuda:3') [RANK 3] received tensor from rank=2: tensor([1., 1., 1., 1., 1.], device='cuda:3') PASS: NCCL working. ``` FUTURE_COPYBARA_INTEGRATE_REVIEW=#10610 from luiscape:master ee88734 PiperOrigin-RevId: 649146570
copybara-service bot
pushed a commit
that referenced
this issue
Jul 3, 2024
Distributed training isn't working with PyTorch on certain A100 nodes. Adds the missing ioctl `UVM_UNMAP_EXTERNAL` allowing for certain NCCL operations to succeed when using [`torch.distributed`](https://pytorch.org/docs/stable/distributed.html), fixing distributed training. ## Reproduction This affects numerous A100 40GB and 80GB instances in our fleet. This reproduction requires 4 A100 GPUs, either 40GB or 80GB. - **NVIDIA Driver Version**: 550.54.15 - **CUDA Version**: 12.4 - **NVIDIA device**: NVIDIA A100 80GB PCIe ### Steps 1. **Install gvisor** ```bash URL="https://storage.googleapis.com/gvisor/releases/master/latest/${ARCH}" wget -nc "${URL}/runsc" "${URL}/runsc.sha512" chmod +x runsc sudo cp runsc /usr/local/bin/runsc sudo /usr/local/bin/runsc install sudo systemctl reload docker ``` 2. **Add GPU enabling gvisor options** ```json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] }, "runsc": { "path": "/usr/local/bin/runsc", "runtimeArgs": ["--nvproxy", "--nvproxy-docker", "-debug-log=/tmp/runsc/", "-debug", "-strace"] } } } ``` Reload configs with `sudo systemctl reload docker`. 3. **Run reproduction NCCL test** This test creates one main process and N peer processes. Each peer process sends a torch `Tensor` to the main process using NCCL. ```Dockerfile # Dockerfile FROM python:3.9.15-slim-bullseye RUN pip install torch numpy COPY <<EOF repro.py import argparse import datetime import os import torch import torch.distributed as dist import torch.multiprocessing as mp def setup(rank, world_size): os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "12355" dist.init_process_group("nccl", rank=rank, world_size=world_size, timeout=datetime.timedelta(seconds=600)) torch.cuda.set_device(rank) def cleanup(): dist.destroy_process_group() def send_tensor(rank, world_size): try: setup(rank, world_size) # rank receiving all tensors target_rank = world_size - 1 dist.barrier() tensor = torch.ones(5).cuda(rank) if rank < target_rank: print(f"[RANK {rank}] sending tensor: {tensor}") dist.send(tensor=tensor, dst=target_rank) elif rank == target_rank: for other_rank in range(target_rank): tensor = torch.zeros(5).cuda(target_rank) dist.recv(tensor=tensor, src=other_rank) print(f"[RANK {target_rank}] received tensor from rank={other_rank}: {tensor}") print("PASS: NCCL working.") except Exception as e: print(f"[RANK {rank}] error in send_tensor: {e}") raise finally: cleanup() def main(world_size: int = 2): mp.spawn(send_tensor, args=(world_size,), nprocs=world_size, join=True) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run torch-based NCCL tests") parser.add_argument("world_size", type=int, help="number of GPUs to run test on") args = parser.parse_args() if args.world_size < 2: raise RuntimeError(f"world_size needs to be larger than 1 {args.world_size}") main(args.world_size) EOF ENTRYPOINT ["python", "repro.py", "4"] ``` Build image with: ``` docker build -f Dockerfile . ``` Then run it with: ``` sudo docker run -it --shm-size=2.00gb --runtime=runsc --gpus='"device=GPU-742ea7fc-dd4f-612c-e860-499bf200a815,GPU-94a801d8-7713-acf6-337d-338b7cfdf19e,GPU-0d19cef2-10ce-e445-a0be-3d330e36c1fd,GPU-ac5046fb-020c-93e8-2784-f44aedbc5bbd"' 040a44863fb1 ``` #### Failure (truncated) ``` ... Exception raised from recvBytes at ../torch/csrc/distributed/c10d/Utils.hpp:672 (most recent call first): frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7edda14cf897 in /usr/local/lib/python3.11/site-packages/torch/lib/libc10.so) frame #1: <unknown function> + 0x5b3a23e (0x7edd8d73a23e in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #2: c10d::TCPStore::doWait(c10::ArrayRef<std::string>, std::chrono::duration<long, std::ratio<1l, 1000l> >) + 0x2c7 (0x7edd8d734c87 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #3: c10d::TCPStore::doGet(std::string const&) + 0x32 (0x7edd8d734f82 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #4: c10d::TCPStore::get(std::string const&) + 0xa1 (0x7edd8d735fd1 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #5: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #6: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #7: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #8: c10d::ProcessGroupNCCL::broadcastUniqueNCCLID(ncclUniqueId*, bool, std::string const&, int) + 0xa9 (0x7edd54da9189 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #9: c10d::ProcessGroupNCCL::getNCCLComm(std::string const&, c10::Device&, c10d::OpType, int, bool) + 0xc50 (0x7edd54db0610 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #10: c10d::ProcessGroupNCCL::recv(std::vector<at::Tensor, std::allocator<at::Tensor> >&, int, int) + 0x5f8 (0x7edd54dcf978 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #11: <unknown function> + 0x5adc309 (0x7edd8d6dc309 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #12: <unknown function> + 0x5ae6f10 (0x7edd8d6e6f10 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #13: <unknown function> + 0x5ae6fa5 (0x7edd8d6e6fa5 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #14: <unknown function> + 0x5124446 (0x7edd8cd24446 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #15: <unknown function> + 0x1acf4b8 (0x7edd896cf4b8 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #16: <unknown function> + 0x5aee004 (0x7edd8d6ee004 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #17: <unknown function> + 0x5af36b5 (0x7edd8d6f36b5 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #18: <unknown function> + 0xd2fe8e (0x7edda032fe8e in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_python.so) frame #19: <unknown function> + 0x47f074 (0x7edd9fa7f074 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_python.so) <omitting python frames> frame #35: <unknown function> + 0x29d90 (0x7edda2029d90 in /usr/lib/x86_64-linux-gnu/libc.so.6) frame #36: __libc_start_main + 0x80 (0x7edda2029e40 in /usr/lib/x86_64-linux-gnu/libc.so.6) frame #37: <unknown function> + 0x108e (0x55f950b0c08e in /usr/local/bin/python) . This may indicate a possible application crash on rank 0 or a network set up issue. ... ``` ### Fix gvisor debug logs show: ``` W0702 20:36:17.577055 445833 uvm.go:148] [ 22: 84] nvproxy: unknown uvm ioctl 66 = 0x42 ``` I've implemented that ioctl in this PR. This is the output after the fix. ``` [RANK 2] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:2') [RANK 0] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:0') [RANK 1] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:1') [RANK 3] received tensor from rank=0: tensor([1., 1., 1., 1., 1.], device='cuda:3') [RANK 3] received tensor from rank=1: tensor([1., 1., 1., 1., 1.], device='cuda:3') [RANK 3] received tensor from rank=2: tensor([1., 1., 1., 1., 1.], device='cuda:3') PASS: NCCL working. ``` FUTURE_COPYBARA_INTEGRATE_REVIEW=#10610 from luiscape:master ee88734 PiperOrigin-RevId: 649146570
copybara-service bot
pushed a commit
that referenced
this issue
Jul 3, 2024
Distributed training isn't working with PyTorch on certain A100 nodes. Adds the missing ioctl `UVM_UNMAP_EXTERNAL` allowing for certain NCCL operations to succeed when using [`torch.distributed`](https://pytorch.org/docs/stable/distributed.html), fixing distributed training. ## Reproduction This affects numerous A100 40GB and 80GB instances in our fleet. This reproduction requires 4 A100 GPUs, either 40GB or 80GB. - **NVIDIA Driver Version**: 550.54.15 - **CUDA Version**: 12.4 - **NVIDIA device**: NVIDIA A100 80GB PCIe ### Steps 1. **Install gvisor** ```bash URL="https://storage.googleapis.com/gvisor/releases/master/latest/${ARCH}" wget -nc "${URL}/runsc" "${URL}/runsc.sha512" chmod +x runsc sudo cp runsc /usr/local/bin/runsc sudo /usr/local/bin/runsc install sudo systemctl reload docker ``` 2. **Add GPU enabling gvisor options** ```json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] }, "runsc": { "path": "/usr/local/bin/runsc", "runtimeArgs": ["--nvproxy", "--nvproxy-docker", "-debug-log=/tmp/runsc/", "-debug", "-strace"] } } } ``` Reload configs with `sudo systemctl reload docker`. 3. **Run reproduction NCCL test** This test creates one main process and N peer processes. Each peer process sends a torch `Tensor` to the main process using NCCL. ```Dockerfile # Dockerfile FROM python:3.9.15-slim-bullseye RUN pip install torch numpy COPY <<EOF repro.py import argparse import datetime import os import torch import torch.distributed as dist import torch.multiprocessing as mp def setup(rank, world_size): os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "12355" dist.init_process_group("nccl", rank=rank, world_size=world_size, timeout=datetime.timedelta(seconds=600)) torch.cuda.set_device(rank) def cleanup(): dist.destroy_process_group() def send_tensor(rank, world_size): try: setup(rank, world_size) # rank receiving all tensors target_rank = world_size - 1 dist.barrier() tensor = torch.ones(5).cuda(rank) if rank < target_rank: print(f"[RANK {rank}] sending tensor: {tensor}") dist.send(tensor=tensor, dst=target_rank) elif rank == target_rank: for other_rank in range(target_rank): tensor = torch.zeros(5).cuda(target_rank) dist.recv(tensor=tensor, src=other_rank) print(f"[RANK {target_rank}] received tensor from rank={other_rank}: {tensor}") print("PASS: NCCL working.") except Exception as e: print(f"[RANK {rank}] error in send_tensor: {e}") raise finally: cleanup() def main(world_size: int = 2): mp.spawn(send_tensor, args=(world_size,), nprocs=world_size, join=True) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run torch-based NCCL tests") parser.add_argument("world_size", type=int, help="number of GPUs to run test on") args = parser.parse_args() if args.world_size < 2: raise RuntimeError(f"world_size needs to be larger than 1 {args.world_size}") main(args.world_size) EOF ENTRYPOINT ["python", "repro.py", "4"] ``` Build image with: ``` docker build -f Dockerfile . ``` Then run it with: ``` sudo docker run -it --shm-size=2.00gb --runtime=runsc --gpus='"device=GPU-742ea7fc-dd4f-612c-e860-499bf200a815,GPU-94a801d8-7713-acf6-337d-338b7cfdf19e,GPU-0d19cef2-10ce-e445-a0be-3d330e36c1fd,GPU-ac5046fb-020c-93e8-2784-f44aedbc5bbd"' 040a44863fb1 ``` #### Failure (truncated) ``` ... Exception raised from recvBytes at ../torch/csrc/distributed/c10d/Utils.hpp:672 (most recent call first): frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7edda14cf897 in /usr/local/lib/python3.11/site-packages/torch/lib/libc10.so) frame #1: <unknown function> + 0x5b3a23e (0x7edd8d73a23e in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #2: c10d::TCPStore::doWait(c10::ArrayRef<std::string>, std::chrono::duration<long, std::ratio<1l, 1000l> >) + 0x2c7 (0x7edd8d734c87 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #3: c10d::TCPStore::doGet(std::string const&) + 0x32 (0x7edd8d734f82 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #4: c10d::TCPStore::get(std::string const&) + 0xa1 (0x7edd8d735fd1 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #5: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #6: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #7: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #8: c10d::ProcessGroupNCCL::broadcastUniqueNCCLID(ncclUniqueId*, bool, std::string const&, int) + 0xa9 (0x7edd54da9189 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #9: c10d::ProcessGroupNCCL::getNCCLComm(std::string const&, c10::Device&, c10d::OpType, int, bool) + 0xc50 (0x7edd54db0610 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #10: c10d::ProcessGroupNCCL::recv(std::vector<at::Tensor, std::allocator<at::Tensor> >&, int, int) + 0x5f8 (0x7edd54dcf978 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #11: <unknown function> + 0x5adc309 (0x7edd8d6dc309 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #12: <unknown function> + 0x5ae6f10 (0x7edd8d6e6f10 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #13: <unknown function> + 0x5ae6fa5 (0x7edd8d6e6fa5 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #14: <unknown function> + 0x5124446 (0x7edd8cd24446 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #15: <unknown function> + 0x1acf4b8 (0x7edd896cf4b8 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #16: <unknown function> + 0x5aee004 (0x7edd8d6ee004 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #17: <unknown function> + 0x5af36b5 (0x7edd8d6f36b5 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #18: <unknown function> + 0xd2fe8e (0x7edda032fe8e in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_python.so) frame #19: <unknown function> + 0x47f074 (0x7edd9fa7f074 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_python.so) <omitting python frames> frame #35: <unknown function> + 0x29d90 (0x7edda2029d90 in /usr/lib/x86_64-linux-gnu/libc.so.6) frame #36: __libc_start_main + 0x80 (0x7edda2029e40 in /usr/lib/x86_64-linux-gnu/libc.so.6) frame #37: <unknown function> + 0x108e (0x55f950b0c08e in /usr/local/bin/python) . This may indicate a possible application crash on rank 0 or a network set up issue. ... ``` ### Fix gvisor debug logs show: ``` W0702 20:36:17.577055 445833 uvm.go:148] [ 22: 84] nvproxy: unknown uvm ioctl 66 = 0x42 ``` I've implemented that ioctl in this PR. This is the output after the fix. ``` [RANK 2] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:2') [RANK 0] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:0') [RANK 1] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:1') [RANK 3] received tensor from rank=0: tensor([1., 1., 1., 1., 1.], device='cuda:3') [RANK 3] received tensor from rank=1: tensor([1., 1., 1., 1., 1.], device='cuda:3') [RANK 3] received tensor from rank=2: tensor([1., 1., 1., 1., 1.], device='cuda:3') PASS: NCCL working. ``` FUTURE_COPYBARA_INTEGRATE_REVIEW=#10610 from luiscape:master ee88734 PiperOrigin-RevId: 649146570
copybara-service bot
pushed a commit
that referenced
this issue
Jul 8, 2024
Distributed training isn't working with PyTorch on certain A100 nodes. Adds the missing ioctl `UVM_UNMAP_EXTERNAL` allowing for certain NCCL operations to succeed when using [`torch.distributed`](https://pytorch.org/docs/stable/distributed.html), fixing distributed training. ## Reproduction This affects numerous A100 40GB and 80GB instances in our fleet. This reproduction requires 4 A100 GPUs, either 40GB or 80GB. - **NVIDIA Driver Version**: 550.54.15 - **CUDA Version**: 12.4 - **NVIDIA device**: NVIDIA A100 80GB PCIe ### Steps 1. **Install gvisor** ```bash URL="https://storage.googleapis.com/gvisor/releases/master/latest/${ARCH}" wget -nc "${URL}/runsc" "${URL}/runsc.sha512" chmod +x runsc sudo cp runsc /usr/local/bin/runsc sudo /usr/local/bin/runsc install sudo systemctl reload docker ``` 2. **Add GPU enabling gvisor options** ```json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] }, "runsc": { "path": "/usr/local/bin/runsc", "runtimeArgs": ["--nvproxy", "--nvproxy-docker", "-debug-log=/tmp/runsc/", "-debug", "-strace"] } } } ``` Reload configs with `sudo systemctl reload docker`. 3. **Run reproduction NCCL test** This test creates one main process and N peer processes. Each peer process sends a torch `Tensor` to the main process using NCCL. ```Dockerfile # Dockerfile FROM python:3.9.15-slim-bullseye RUN pip install torch numpy COPY <<EOF repro.py import argparse import datetime import os import torch import torch.distributed as dist import torch.multiprocessing as mp def setup(rank, world_size): os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "12355" dist.init_process_group("nccl", rank=rank, world_size=world_size, timeout=datetime.timedelta(seconds=600)) torch.cuda.set_device(rank) def cleanup(): dist.destroy_process_group() def send_tensor(rank, world_size): try: setup(rank, world_size) # rank receiving all tensors target_rank = world_size - 1 dist.barrier() tensor = torch.ones(5).cuda(rank) if rank < target_rank: print(f"[RANK {rank}] sending tensor: {tensor}") dist.send(tensor=tensor, dst=target_rank) elif rank == target_rank: for other_rank in range(target_rank): tensor = torch.zeros(5).cuda(target_rank) dist.recv(tensor=tensor, src=other_rank) print(f"[RANK {target_rank}] received tensor from rank={other_rank}: {tensor}") print("PASS: NCCL working.") except Exception as e: print(f"[RANK {rank}] error in send_tensor: {e}") raise finally: cleanup() def main(world_size: int = 2): mp.spawn(send_tensor, args=(world_size,), nprocs=world_size, join=True) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run torch-based NCCL tests") parser.add_argument("world_size", type=int, help="number of GPUs to run test on") args = parser.parse_args() if args.world_size < 2: raise RuntimeError(f"world_size needs to be larger than 1 {args.world_size}") main(args.world_size) EOF ENTRYPOINT ["python", "repro.py", "4"] ``` Build image with: ``` docker build -f Dockerfile . ``` Then run it with: ``` sudo docker run -it --shm-size=2.00gb --runtime=runsc --gpus='"device=GPU-742ea7fc-dd4f-612c-e860-499bf200a815,GPU-94a801d8-7713-acf6-337d-338b7cfdf19e,GPU-0d19cef2-10ce-e445-a0be-3d330e36c1fd,GPU-ac5046fb-020c-93e8-2784-f44aedbc5bbd"' 040a44863fb1 ``` #### Failure (truncated) ``` ... Exception raised from recvBytes at ../torch/csrc/distributed/c10d/Utils.hpp:672 (most recent call first): frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7edda14cf897 in /usr/local/lib/python3.11/site-packages/torch/lib/libc10.so) frame #1: <unknown function> + 0x5b3a23e (0x7edd8d73a23e in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #2: c10d::TCPStore::doWait(c10::ArrayRef<std::string>, std::chrono::duration<long, std::ratio<1l, 1000l> >) + 0x2c7 (0x7edd8d734c87 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #3: c10d::TCPStore::doGet(std::string const&) + 0x32 (0x7edd8d734f82 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #4: c10d::TCPStore::get(std::string const&) + 0xa1 (0x7edd8d735fd1 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #5: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #6: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #7: c10d::PrefixStore::get(std::string const&) + 0x31 (0x7edd8d6ea371 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #8: c10d::ProcessGroupNCCL::broadcastUniqueNCCLID(ncclUniqueId*, bool, std::string const&, int) + 0xa9 (0x7edd54da9189 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #9: c10d::ProcessGroupNCCL::getNCCLComm(std::string const&, c10::Device&, c10d::OpType, int, bool) + 0xc50 (0x7edd54db0610 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #10: c10d::ProcessGroupNCCL::recv(std::vector<at::Tensor, std::allocator<at::Tensor> >&, int, int) + 0x5f8 (0x7edd54dcf978 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cuda.so) frame #11: <unknown function> + 0x5adc309 (0x7edd8d6dc309 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #12: <unknown function> + 0x5ae6f10 (0x7edd8d6e6f10 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #13: <unknown function> + 0x5ae6fa5 (0x7edd8d6e6fa5 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #14: <unknown function> + 0x5124446 (0x7edd8cd24446 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #15: <unknown function> + 0x1acf4b8 (0x7edd896cf4b8 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #16: <unknown function> + 0x5aee004 (0x7edd8d6ee004 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #17: <unknown function> + 0x5af36b5 (0x7edd8d6f36b5 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_cpu.so) frame #18: <unknown function> + 0xd2fe8e (0x7edda032fe8e in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_python.so) frame #19: <unknown function> + 0x47f074 (0x7edd9fa7f074 in /usr/local/lib/python3.11/site-packages/torch/lib/libtorch_python.so) <omitting python frames> frame #35: <unknown function> + 0x29d90 (0x7edda2029d90 in /usr/lib/x86_64-linux-gnu/libc.so.6) frame #36: __libc_start_main + 0x80 (0x7edda2029e40 in /usr/lib/x86_64-linux-gnu/libc.so.6) frame #37: <unknown function> + 0x108e (0x55f950b0c08e in /usr/local/bin/python) . This may indicate a possible application crash on rank 0 or a network set up issue. ... ``` ### Fix gvisor debug logs show: ``` W0702 20:36:17.577055 445833 uvm.go:148] [ 22: 84] nvproxy: unknown uvm ioctl 66 = 0x42 ``` I've implemented that ioctl in this PR. This is the output after the fix. ``` [RANK 2] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:2') [RANK 0] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:0') [RANK 1] sending tensor: tensor([1., 1., 1., 1., 1.], device='cuda:1') [RANK 3] received tensor from rank=0: tensor([1., 1., 1., 1., 1.], device='cuda:3') [RANK 3] received tensor from rank=1: tensor([1., 1., 1., 1., 1.], device='cuda:3') [RANK 3] received tensor from rank=2: tensor([1., 1., 1., 1., 1.], device='cuda:3') PASS: NCCL working. ``` FUTURE_COPYBARA_INTEGRATE_REVIEW=#10610 from luiscape:master ee88734 PiperOrigin-RevId: 649146570
copybara-service bot
pushed a commit
that referenced
this issue
Jul 13, 2024
Adding ioctls to fix a simple multi-GPU Huggingface`accelerate` program that does not work on GCP H100s. --- ### System details * **instance type:** `a3-highgpu-8g` (GCP, us-east4-a) * **NVIDIA driver:** `Driver Version: 550.54.15 CUDA Version: 12.4` * **NVIDIA device:** 4 x NVIDIA H100 HBM3 * **uname -a:** `Linux gcp-h100-us-east4-a-0-bb25baf985414f8899dfdfcb82d6796d 5.15.0-208.159.3.el9uek.x86_64 #2 SMP Wed Jun 19 09:05:13 PDT 2024 x86_64 x86_64 x86_64 GNU/Linux` ``` runsc version release-20240513.0-173-gc526d251933a-dirty spec: 1.1.0-rc.1 ``` --- ## Reproduction steps 1. **Install gVisor** **2. Add GPU enabling gvisor options** In `/etc/docker/daemon.json`: ```json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] }, "runsc": { "path": "/home/modal/runsc", "runtimeArgs": ["--nvproxy", "--nvproxy-docker", "-debug-log=/tmp/runsc/", "-debug", "-strace"] } } } ``` **3. Run Dockerfile** ```Dockerfile # Dockerfile FROM winglian/axolotl@sha256:5c724f7accd8188b0f84ead93b7efbfa8f8661f40e133646bd6d946bc3423d6d RUN pip install fastapi==0.111.0 RUN pip install huggingface-hub~=0.23.0 pydantic==2.6.3 python-dateutil ENV HUGGINGFACE_HUB_CACHE="/pretrained" ENV TQDM_DISABLE="true" ENV AXOLOTL_NCCL_TIMEOUT="60" COPY <<EOF repro.py import os import subprocess from pathlib import Path print("[MOD-3226] hello from the repro!!!") from accelerate import Accelerator accelerator = Accelerator() with accelerator.main_process_first(): print(f"hello! {accelerator.process_index}") EOF ENTRYPOINT ["accelerate", "launch", "repro.py"] ``` ``` sudo docker run -it --runtime=$RUNTIME --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 ``` ### Results **`runc`** ``` sudo docker run -it --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! hello! 0 hello! 1 hello! 2hello! 3 ``` **`runsc` (main)** <details> <summary>💥 Failure logs</summary> ``` sudo docker run -it --runtime=runsc --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. hello! 0 Traceback (most recent call last): File "/workspace/axolotl/repro.py", line 10, in <module> with accelerator.main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/accelerator.py", line 884, in main_process_first with self.state.main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 1056, in main_process_first with PartialState().main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 502, in main_process_first yield from self._goes_first(self.is_main_process) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 390, in _goes_first self.wait_for_everyone() File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 379, in wait_for_everyone torch.distributed.barrier() File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 47, in wrapper return func(*args, **kwargs) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 3696, in barrier work = default_pg.barrier(opts=opts) torch.distributed.DistBackendError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1333, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.18.6 ncclUnhandledCudaError: Call to CUDA function failed. Last error: Cuda failure 'unknown error' [2024-07-11 19:52:01,530] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 68 closing signal SIGTERM [2024-07-11 19:52:01,532] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 69 closing signal SIGTERM [2024-07-11 19:52:01,533] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 70 closing signal SIGTERM [2024-07-11 19:52:02,108] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 0 (pid: 67) of binary: /root/miniconda3/envs/py3.10/bin/python Traceback (most recent call last): File "/root/miniconda3/envs/py3.10/bin/accelerate", line 8, in <module> sys.exit(main()) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 46, in main args.func(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1073, in launch_command multi_gpu_launcher(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/launch.py", line 718, in multi_gpu_launcher distrib_run.run(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/run.py", line 797, in run elastic_launch( File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 264, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: repro.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-11_19:52:01 host : d45a08528293 rank : 0 (local_rank: 0) exitcode : 1 (pid: 67) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ``` --- </details> **`runsc` (this pull request)** <details> <summary>✅ Success logs</summary> ``` [modal@gcp-h100-us-east4-a-0-bb25baf985414f8899dfdfcb82d6796d ~]$ sudo docker run -it --runtime=runsc --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. hello! 0 hello! 1 hello! 3hello! 2 ``` </details> FUTURE_COPYBARA_INTEGRATE_REVIEW=#10649 from thundergolfer:master d3d19f1 PiperOrigin-RevId: 651754677
copybara-service bot
pushed a commit
that referenced
this issue
Jul 13, 2024
Adding ioctls to fix a simple multi-GPU Huggingface`accelerate` program that does not work on GCP H100s. --- ### System details * **instance type:** `a3-highgpu-8g` (GCP, us-east4-a) * **NVIDIA driver:** `Driver Version: 550.54.15 CUDA Version: 12.4` * **NVIDIA device:** 4 x NVIDIA H100 HBM3 * **uname -a:** `Linux gcp-h100-us-east4-a-0-bb25baf985414f8899dfdfcb82d6796d 5.15.0-208.159.3.el9uek.x86_64 #2 SMP Wed Jun 19 09:05:13 PDT 2024 x86_64 x86_64 x86_64 GNU/Linux` ``` runsc version release-20240513.0-173-gc526d251933a-dirty spec: 1.1.0-rc.1 ``` --- ## Reproduction steps 1. **Install gVisor** **2. Add GPU enabling gvisor options** In `/etc/docker/daemon.json`: ```json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] }, "runsc": { "path": "/home/modal/runsc", "runtimeArgs": ["--nvproxy", "--nvproxy-docker", "-debug-log=/tmp/runsc/", "-debug", "-strace"] } } } ``` **3. Run Dockerfile** ```Dockerfile # Dockerfile FROM winglian/axolotl@sha256:5c724f7accd8188b0f84ead93b7efbfa8f8661f40e133646bd6d946bc3423d6d RUN pip install fastapi==0.111.0 RUN pip install huggingface-hub~=0.23.0 pydantic==2.6.3 python-dateutil ENV HUGGINGFACE_HUB_CACHE="/pretrained" ENV TQDM_DISABLE="true" ENV AXOLOTL_NCCL_TIMEOUT="60" COPY <<EOF repro.py import os import subprocess from pathlib import Path print("[MOD-3226] hello from the repro!!!") from accelerate import Accelerator accelerator = Accelerator() with accelerator.main_process_first(): print(f"hello! {accelerator.process_index}") EOF ENTRYPOINT ["accelerate", "launch", "repro.py"] ``` ``` sudo docker run -it --runtime=$RUNTIME --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 ``` ### Results **`runc`** ``` sudo docker run -it --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! hello! 0 hello! 1 hello! 2hello! 3 ``` **`runsc` (main)** <details> <summary>💥 Failure logs</summary> ``` sudo docker run -it --runtime=runsc --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. hello! 0 Traceback (most recent call last): File "/workspace/axolotl/repro.py", line 10, in <module> with accelerator.main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/accelerator.py", line 884, in main_process_first with self.state.main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 1056, in main_process_first with PartialState().main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 502, in main_process_first yield from self._goes_first(self.is_main_process) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 390, in _goes_first self.wait_for_everyone() File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 379, in wait_for_everyone torch.distributed.barrier() File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 47, in wrapper return func(*args, **kwargs) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 3696, in barrier work = default_pg.barrier(opts=opts) torch.distributed.DistBackendError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1333, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.18.6 ncclUnhandledCudaError: Call to CUDA function failed. Last error: Cuda failure 'unknown error' [2024-07-11 19:52:01,530] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 68 closing signal SIGTERM [2024-07-11 19:52:01,532] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 69 closing signal SIGTERM [2024-07-11 19:52:01,533] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 70 closing signal SIGTERM [2024-07-11 19:52:02,108] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 0 (pid: 67) of binary: /root/miniconda3/envs/py3.10/bin/python Traceback (most recent call last): File "/root/miniconda3/envs/py3.10/bin/accelerate", line 8, in <module> sys.exit(main()) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 46, in main args.func(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1073, in launch_command multi_gpu_launcher(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/launch.py", line 718, in multi_gpu_launcher distrib_run.run(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/run.py", line 797, in run elastic_launch( File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 264, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: repro.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-11_19:52:01 host : d45a08528293 rank : 0 (local_rank: 0) exitcode : 1 (pid: 67) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ``` --- </details> **`runsc` (this pull request)** <details> <summary>✅ Success logs</summary> ``` [modal@gcp-h100-us-east4-a-0-bb25baf985414f8899dfdfcb82d6796d ~]$ sudo docker run -it --runtime=runsc --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. hello! 0 hello! 1 hello! 3hello! 2 ``` </details> FUTURE_COPYBARA_INTEGRATE_REVIEW=#10649 from thundergolfer:master d3d19f1 PiperOrigin-RevId: 651754677
copybara-service bot
pushed a commit
that referenced
this issue
Jul 13, 2024
Adding ioctls to fix a simple multi-GPU Huggingface`accelerate` program that does not work on GCP H100s. --- ### System details * **instance type:** `a3-highgpu-8g` (GCP, us-east4-a) * **NVIDIA driver:** `Driver Version: 550.54.15 CUDA Version: 12.4` * **NVIDIA device:** 4 x NVIDIA H100 HBM3 * **uname -a:** `Linux gcp-h100-us-east4-a-0-bb25baf985414f8899dfdfcb82d6796d 5.15.0-208.159.3.el9uek.x86_64 #2 SMP Wed Jun 19 09:05:13 PDT 2024 x86_64 x86_64 x86_64 GNU/Linux` ``` runsc version release-20240513.0-173-gc526d251933a-dirty spec: 1.1.0-rc.1 ``` --- ## Reproduction steps 1. **Install gVisor** **2. Add GPU enabling gvisor options** In `/etc/docker/daemon.json`: ```json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] }, "runsc": { "path": "/home/modal/runsc", "runtimeArgs": ["--nvproxy", "--nvproxy-docker", "-debug-log=/tmp/runsc/", "-debug", "-strace"] } } } ``` **3. Run Dockerfile** ```Dockerfile # Dockerfile FROM winglian/axolotl@sha256:5c724f7accd8188b0f84ead93b7efbfa8f8661f40e133646bd6d946bc3423d6d RUN pip install fastapi==0.111.0 RUN pip install huggingface-hub~=0.23.0 pydantic==2.6.3 python-dateutil ENV HUGGINGFACE_HUB_CACHE="/pretrained" ENV TQDM_DISABLE="true" ENV AXOLOTL_NCCL_TIMEOUT="60" COPY <<EOF repro.py import os import subprocess from pathlib import Path print("[MOD-3226] hello from the repro!!!") from accelerate import Accelerator accelerator = Accelerator() with accelerator.main_process_first(): print(f"hello! {accelerator.process_index}") EOF ENTRYPOINT ["accelerate", "launch", "repro.py"] ``` ``` sudo docker run -it --runtime=$RUNTIME --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 ``` ### Results **`runc`** ``` sudo docker run -it --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! hello! 0 hello! 1 hello! 2hello! 3 ``` **`runsc` (main)** <details> <summary>💥 Failure logs</summary> ``` sudo docker run -it --runtime=runsc --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. hello! 0 Traceback (most recent call last): File "/workspace/axolotl/repro.py", line 10, in <module> with accelerator.main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/accelerator.py", line 884, in main_process_first with self.state.main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 1056, in main_process_first with PartialState().main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 502, in main_process_first yield from self._goes_first(self.is_main_process) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 390, in _goes_first self.wait_for_everyone() File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 379, in wait_for_everyone torch.distributed.barrier() File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 47, in wrapper return func(*args, **kwargs) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 3696, in barrier work = default_pg.barrier(opts=opts) torch.distributed.DistBackendError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1333, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.18.6 ncclUnhandledCudaError: Call to CUDA function failed. Last error: Cuda failure 'unknown error' [2024-07-11 19:52:01,530] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 68 closing signal SIGTERM [2024-07-11 19:52:01,532] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 69 closing signal SIGTERM [2024-07-11 19:52:01,533] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 70 closing signal SIGTERM [2024-07-11 19:52:02,108] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 0 (pid: 67) of binary: /root/miniconda3/envs/py3.10/bin/python Traceback (most recent call last): File "/root/miniconda3/envs/py3.10/bin/accelerate", line 8, in <module> sys.exit(main()) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 46, in main args.func(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1073, in launch_command multi_gpu_launcher(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/launch.py", line 718, in multi_gpu_launcher distrib_run.run(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/run.py", line 797, in run elastic_launch( File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 264, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: repro.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-11_19:52:01 host : d45a08528293 rank : 0 (local_rank: 0) exitcode : 1 (pid: 67) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ``` --- </details> **`runsc` (this pull request)** <details> <summary>✅ Success logs</summary> ``` [modal@gcp-h100-us-east4-a-0-bb25baf985414f8899dfdfcb82d6796d ~]$ sudo docker run -it --runtime=runsc --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. hello! 0 hello! 1 hello! 3hello! 2 ``` </details> FUTURE_COPYBARA_INTEGRATE_REVIEW=#10649 from thundergolfer:master d3d19f1 PiperOrigin-RevId: 651754677
copybara-service bot
pushed a commit
that referenced
this issue
Jul 15, 2024
Adding ioctls to fix a simple multi-GPU Huggingface`accelerate` program that does not work on GCP H100s. --- ### System details * **instance type:** `a3-highgpu-8g` (GCP, us-east4-a) * **NVIDIA driver:** `Driver Version: 550.54.15 CUDA Version: 12.4` * **NVIDIA device:** 4 x NVIDIA H100 HBM3 * **uname -a:** `Linux gcp-h100-us-east4-a-0-bb25baf985414f8899dfdfcb82d6796d 5.15.0-208.159.3.el9uek.x86_64 #2 SMP Wed Jun 19 09:05:13 PDT 2024 x86_64 x86_64 x86_64 GNU/Linux` ``` runsc version release-20240513.0-173-gc526d251933a-dirty spec: 1.1.0-rc.1 ``` --- ## Reproduction steps 1. **Install gVisor** **2. Add GPU enabling gvisor options** In `/etc/docker/daemon.json`: ```json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] }, "runsc": { "path": "/home/modal/runsc", "runtimeArgs": ["--nvproxy", "--nvproxy-docker", "-debug-log=/tmp/runsc/", "-debug", "-strace"] } } } ``` **3. Run Dockerfile** ```Dockerfile # Dockerfile FROM winglian/axolotl@sha256:5c724f7accd8188b0f84ead93b7efbfa8f8661f40e133646bd6d946bc3423d6d RUN pip install fastapi==0.111.0 RUN pip install huggingface-hub~=0.23.0 pydantic==2.6.3 python-dateutil ENV HUGGINGFACE_HUB_CACHE="/pretrained" ENV TQDM_DISABLE="true" ENV AXOLOTL_NCCL_TIMEOUT="60" COPY <<EOF repro.py import os import subprocess from pathlib import Path print("[MOD-3226] hello from the repro!!!") from accelerate import Accelerator accelerator = Accelerator() with accelerator.main_process_first(): print(f"hello! {accelerator.process_index}") EOF ENTRYPOINT ["accelerate", "launch", "repro.py"] ``` ``` sudo docker run -it --runtime=$RUNTIME --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 ``` ### Results **`runc`** ``` sudo docker run -it --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! hello! 0 hello! 1 hello! 2hello! 3 ``` **`runsc` (main)** <details> <summary>💥 Failure logs</summary> ``` sudo docker run -it --runtime=runsc --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. hello! 0 Traceback (most recent call last): File "/workspace/axolotl/repro.py", line 10, in <module> with accelerator.main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/accelerator.py", line 884, in main_process_first with self.state.main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 1056, in main_process_first with PartialState().main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 502, in main_process_first yield from self._goes_first(self.is_main_process) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 390, in _goes_first self.wait_for_everyone() File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 379, in wait_for_everyone torch.distributed.barrier() File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 47, in wrapper return func(*args, **kwargs) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 3696, in barrier work = default_pg.barrier(opts=opts) torch.distributed.DistBackendError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1333, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.18.6 ncclUnhandledCudaError: Call to CUDA function failed. Last error: Cuda failure 'unknown error' [2024-07-11 19:52:01,530] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 68 closing signal SIGTERM [2024-07-11 19:52:01,532] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 69 closing signal SIGTERM [2024-07-11 19:52:01,533] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 70 closing signal SIGTERM [2024-07-11 19:52:02,108] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 0 (pid: 67) of binary: /root/miniconda3/envs/py3.10/bin/python Traceback (most recent call last): File "/root/miniconda3/envs/py3.10/bin/accelerate", line 8, in <module> sys.exit(main()) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 46, in main args.func(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1073, in launch_command multi_gpu_launcher(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/launch.py", line 718, in multi_gpu_launcher distrib_run.run(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/run.py", line 797, in run elastic_launch( File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 264, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: repro.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-11_19:52:01 host : d45a08528293 rank : 0 (local_rank: 0) exitcode : 1 (pid: 67) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ``` --- </details> **`runsc` (this pull request)** <details> <summary>✅ Success logs</summary> ``` [modal@gcp-h100-us-east4-a-0-bb25baf985414f8899dfdfcb82d6796d ~]$ sudo docker run -it --runtime=runsc --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. hello! 0 hello! 1 hello! 3hello! 2 ``` </details> FUTURE_COPYBARA_INTEGRATE_REVIEW=#10649 from thundergolfer:master d3d19f1 PiperOrigin-RevId: 651754677
copybara-service bot
pushed a commit
that referenced
this issue
Jul 15, 2024
Adding ioctls to fix a simple multi-GPU Huggingface`accelerate` program that does not work on GCP H100s. --- ### System details * **instance type:** `a3-highgpu-8g` (GCP, us-east4-a) * **NVIDIA driver:** `Driver Version: 550.54.15 CUDA Version: 12.4` * **NVIDIA device:** 4 x NVIDIA H100 HBM3 * **uname -a:** `Linux gcp-h100-us-east4-a-0-bb25baf985414f8899dfdfcb82d6796d 5.15.0-208.159.3.el9uek.x86_64 #2 SMP Wed Jun 19 09:05:13 PDT 2024 x86_64 x86_64 x86_64 GNU/Linux` ``` runsc version release-20240513.0-173-gc526d251933a-dirty spec: 1.1.0-rc.1 ``` --- ## Reproduction steps 1. **Install gVisor** **2. Add GPU enabling gvisor options** In `/etc/docker/daemon.json`: ```json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] }, "runsc": { "path": "/home/modal/runsc", "runtimeArgs": ["--nvproxy", "--nvproxy-docker", "-debug-log=/tmp/runsc/", "-debug", "-strace"] } } } ``` **3. Run Dockerfile** ```Dockerfile # Dockerfile FROM winglian/axolotl@sha256:5c724f7accd8188b0f84ead93b7efbfa8f8661f40e133646bd6d946bc3423d6d RUN pip install fastapi==0.111.0 RUN pip install huggingface-hub~=0.23.0 pydantic==2.6.3 python-dateutil ENV HUGGINGFACE_HUB_CACHE="/pretrained" ENV TQDM_DISABLE="true" ENV AXOLOTL_NCCL_TIMEOUT="60" COPY <<EOF repro.py import os import subprocess from pathlib import Path print("[MOD-3226] hello from the repro!!!") from accelerate import Accelerator accelerator = Accelerator() with accelerator.main_process_first(): print(f"hello! {accelerator.process_index}") EOF ENTRYPOINT ["accelerate", "launch", "repro.py"] ``` ``` sudo docker run -it --runtime=$RUNTIME --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 ``` ### Results **`runc`** ``` sudo docker run -it --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! hello! 0 hello! 1 hello! 2hello! 3 ``` **`runsc` (main)** <details> <summary>💥 Failure logs</summary> ``` sudo docker run -it --runtime=runsc --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. hello! 0 Traceback (most recent call last): File "/workspace/axolotl/repro.py", line 10, in <module> with accelerator.main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/accelerator.py", line 884, in main_process_first with self.state.main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 1056, in main_process_first with PartialState().main_process_first(): File "/root/miniconda3/envs/py3.10/lib/python3.10/contextlib.py", line 142, in __exit__ next(self.gen) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 502, in main_process_first yield from self._goes_first(self.is_main_process) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 390, in _goes_first self.wait_for_everyone() File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/state.py", line 379, in wait_for_everyone torch.distributed.barrier() File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/c10d_logger.py", line 47, in wrapper return func(*args, **kwargs) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py", line 3696, in barrier work = default_pg.barrier(opts=opts) torch.distributed.DistBackendError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1333, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.18.6 ncclUnhandledCudaError: Call to CUDA function failed. Last error: Cuda failure 'unknown error' [2024-07-11 19:52:01,530] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 68 closing signal SIGTERM [2024-07-11 19:52:01,532] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 69 closing signal SIGTERM [2024-07-11 19:52:01,533] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 70 closing signal SIGTERM [2024-07-11 19:52:02,108] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 0 (pid: 67) of binary: /root/miniconda3/envs/py3.10/bin/python Traceback (most recent call last): File "/root/miniconda3/envs/py3.10/bin/accelerate", line 8, in <module> sys.exit(main()) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 46, in main args.func(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1073, in launch_command multi_gpu_launcher(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/accelerate/commands/launch.py", line 718, in multi_gpu_launcher distrib_run.run(args) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/run.py", line 797, in run elastic_launch( File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 134, in __call__ return launch_agent(self._config, self._entrypoint, list(args)) File "/root/miniconda3/envs/py3.10/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 264, in launch_agent raise ChildFailedError( torch.distributed.elastic.multiprocessing.errors.ChildFailedError: repro.py FAILED ------------------------------------------------------------ Failures: <NO_OTHER_FAILURES> ------------------------------------------------------------ Root Cause (first observed failure): [0]: time : 2024-07-11_19:52:01 host : d45a08528293 rank : 0 (local_rank: 0) exitcode : 1 (pid: 67) error_file: <N/A> traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html ``` --- </details> **`runsc` (this pull request)** <details> <summary>✅ Success logs</summary> ``` [modal@gcp-h100-us-east4-a-0-bb25baf985414f8899dfdfcb82d6796d ~]$ sudo docker run -it --runtime=runsc --gpus='"device=GPU-c453e5c7-a56d-70bf-78ce-61be6cb8e0db,GPU-4703196a-e3df-9e3f-bb8b-6fa91c8e9970,GPU-4a9c162c-9280-eaa8-215a-2c681e82a99f,GPU-1660d344-e18b-e48a-cced-38380e903c31"' ce4326479c8412b13bba27416e3e77093d4411279b432ca1b25050f17ef57a67 The following values were not passed to `accelerate launch` and had defaults used instead: `--num_processes` was set to a value of `4` More than one GPU was found, enabling multi-GPU training. If this was unintended please pass in `--num_processes=1`. `--num_machines` was set to a value of `1` `--mixed_precision` was set to a value of `'no'` `--dynamo_backend` was set to a value of `'no'` To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! [MOD-3226] hello from the repro!!! Detected kernel version 4.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. hello! 0 hello! 1 hello! 3hello! 2 ``` </details> FUTURE_COPYBARA_INTEGRATE_REVIEW=#10649 from thundergolfer:master d3d19f1 PiperOrigin-RevId: 651754677
copybara-service bot
pushed a commit
that referenced
this issue
Oct 23, 2024
Attempt #2. This runs in continuous mode only. PiperOrigin-RevId: 689056926
copybara-service bot
pushed a commit
that referenced
this issue
Oct 23, 2024
Attempt #2. This runs in continuous mode only. PiperOrigin-RevId: 689056926
copybara-service bot
pushed a commit
that referenced
this issue
Oct 23, 2024
Attempt #2. This runs in continuous mode only. PiperOrigin-RevId: 689056926
copybara-service bot
pushed a commit
that referenced
this issue
Oct 24, 2024
Attempt #2. This runs in continuous mode only. PiperOrigin-RevId: 689056926
copybara-service bot
pushed a commit
that referenced
this issue
Oct 30, 2024
Attempt #2. This runs in continuous mode only. PiperOrigin-RevId: 689056926
copybara-service bot
pushed a commit
that referenced
this issue
Oct 30, 2024
Attempt #2. This runs in continuous mode only. PiperOrigin-RevId: 691516066
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
This requires some socket ioctls that are not currently implemented.
The text was updated successfully, but these errors were encountered: