Horovod

Horovod is a distributed training framework for TensorFlow, Keras, and PyTorch. Azure Databricks supports distributed deep learning training using HorovodRunner and the horovod.spark package. For Spark ML pipeline applications using Keras or PyTorch, you can use the horovod.spark estimator API.

Requirements

Databricks Runtime ML.

Use Horovod

The following articles provide general information about distributed deep learning with Horovod and example notebooks illustrating how to use HorovodRunner and the horovod.spark package.

• HorovodRunner: distributed deep learning with Horovod
• HorovodRunner examples
• horovod.spark: distributed deep learning with Horovod

Install a different version of Horovod

To upgrade or downgrade Horovod from the pre-installed version in your ML cluster, you must recompile Horovod by following these steps:

1. Uninstall the current version of Horovod.

%pip uninstall -y horovod

2. If using a GPU-accelerated cluster, install CUDA development libraries required to compile Horovod. To ensure compatibility, leave the package versions unchanged.

%sh
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"

wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
dpkg -i ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb

apt-get update
apt-get install --allow-downgrades --no-install-recommends -y \
cuda-nvml-dev-11-0=11.0.167-1 \
cuda-nvcc-11-0=11.0.221-1 \
cuda-cudart-dev-11-0=11.0.221-1 \
cuda-libraries-dev-11-0=11.0.3-1 \
libnccl-dev=2.11.4-1+cuda11.5\
libcusparse-dev-11-0=11.1.1.245-1

3. Download the desired version of Horovod's source code and compile with the appropriate flags. If you don't need any of the extensions (such as HOROVOD_WITH_PYTORCH), you can remove those flags.

CPU

%sh
HOROVOD_VERSION=v0.21.3 # Change as necessary
git clone --recursive https://github.com/horovod/horovod.git --branch ${HOROVOD_VERSION}
cd horovod
rm -rf build/ dist/
HOROVOD_WITH_MPI=1 HOROVOD_WITH_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 \
# For Databricks Runtime 8.4 ML and below, replace with /databricks/conda/envs/databricks-ml/bin/python
sudo /databricks/python3/bin/python setup.py bdist_wheel
readlink -f dist/horovod-*.whl

GPU

%sh
HOROVOD_VERSION=v0.21.3 # Change as necessary
git clone --recursive https://github.com/horovod/horovod.git --branch ${HOROVOD_VERSION}
cd horovod
rm -rf build/ dist/
HOROVOD_GPU_ALLREDUCE=NCCL HOROVOD_CUDA_HOME=/usr/local/cuda HOROVOD_WITH_MPI=1 HOROVOD_WITH_TENSORFLOW=1 HOROVOD_WITH_PYTORCH=1 \
# For Databricks Runtime 8.4 ML and below, replace with /databricks/conda/envs/databricks-ml-gpu/bin/python
sudo /databricks/python3/bin/python setup.py bdist_wheel
readlink -f dist/horovod-*.whl

4. Use %pip to reinstall Horovod by specifying the Python wheel path from the previous command's output. 0.21.3 is shown in this example.

%pip install --no-cache-dir /databricks/driver/horovod/dist/horovod-0.21.3-cp38-cp38-linux_x86_64.whl

Troubleshoot Horovod installation

Problem: Importing horovod.{torch|tensorflow} raises ImportError: Extension horovod.{torch|tensorflow} has not been built

Solution: Horovod comes pre-installed on Databricks Runtime ML, so this error typically occurs if updating an environment goes wrong. The error indicates that Horovod was installed before a required library (PyTorch or TensorFlow). Since Horovod is compiled during installation, horovod.{torch|tensorflow} will not get compiled if those packages aren't present during the installation of Horovod. To fix the issue, follow these steps:

1. Verify that you are on a Databricks Runtime ML cluster.
2. Ensure that the PyTorch or TensorFlow package is already installed.
3. Uninstall Horovod (%pip uninstall -y horovod).
4. Install cmake (%pip install cmake).
5. Reinstall horovod.