Horovod is an open-source distributed deep learning framework that makes it easy to train models across multiple GPUs and nodes. It is built on top of popular deep learning frameworks like TensorFlow, Keras, PyTorch, and Apache MXNet, and is designed to improve the speed and efficiency of model training by leveraging data parallelism.
When using Horovod, you might encounter the error message: NCCL error: unhandled system error. This error typically appears during the initialization or execution of distributed training tasks and can halt the training process.
The error NCCL error: unhandled system error often arises due to an incompatibility between the NCCL (NVIDIA Collective Communications Library) version and the installed CUDA version. NCCL is crucial for efficient communication between GPUs, and any mismatch can lead to system errors.
For more information on NCCL, you can visit the NVIDIA NCCL page.
First, check the versions of NCCL and CUDA installed on your system. You can do this by running the following commands:
nvcc --version
This will display the CUDA version. To check the NCCL version, you can use:
dpkg -l | grep nccl
Ensure that the versions of NCCL and CUDA are compatible. You can refer to the NCCL Release Notes for compatibility information.
If there is an incompatibility, update either NCCL or CUDA to a compatible version. For updating CUDA, follow the instructions on the CUDA Toolkit Download page. For NCCL, you can download the latest version from the NCCL Download page.
After updating, you may need to rebuild Horovod to ensure it links against the correct versions of NCCL and CUDA. Use the following command:
HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_WITH_TENSORFLOW=1 pip install --no-cache-dir horovod
By ensuring compatibility between NCCL and CUDA, and rebuilding Horovod if necessary, you can resolve the NCCL error: unhandled system error and continue with your distributed training tasks efficiently.
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