pythonfix
horovod 0.28.1
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Horovod is a powerful distributed training framework for Python that allows you to train deep learning models across multiple GPUs and servers quickly and efficiently. It falls under the category of distributed computing libraries. Built on top of TensorFlow, PyTorch, and other popular deep learning frameworks, Horovod simplifies the process of scaling up your model training by handling the complexities of distributed training under the hood.
The horovod/horovod repo was created 7 years ago and the last code push was 1 months ago.
The project is extremely popular with a mindblowing 14216 github stars!
How to Install horovod
You can install horovod using pip
pip install horovod
or add it to a project with poetry
poetry add horovod
Package Details
- Author
- The Horovod Authors
- License
- Apache 2.0
- Homepage
- https://github.com/horovod/horovod
- PyPi:
- https://pypi.org/project/horovod/
- GitHub Repo:
- https://github.com/horovod/horovod
Classifiers
- Scientific/Engineering/Artificial Intelligence
Related Packages
Errors
A list of common horovod errors.
Code Examples
Here are some horovod code examples and snippets.
GitHub Issues
The horovod package has 405 open issues on GitHub
- Cannot install horovod with tensorflow framework
- mpirun check failed with version
- Problem during installation
- Elastic Horovod incompatible with tf.compat.v1.train.MonitoredTrainingSession
- Pre-load dataset to memory
- failed call to cuInit after calling hvd.init()
- Build Horovod with temporarily installed CMake if necessary
- learning rate can't update in resuming training
- test_ray.py::test_gpu_ids_num_workers sometimes fails on Buildkite
- can horovod work only with Gloo，without MPI?
- Horovd + AMP Contrastive Learning Imprecision
- GPU Head tests for elastic ray are flaky in CI
- integrate with Lightning ecosystem CI
- Error on installing: pip install horovod[pytorch] in Virtualbox ubuntu 18.04
- Multi worker inference in Databricks
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