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Horovod Alternatives
Similar projects and alternatives to horovod
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accelerate
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
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petastorm
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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DeepDanbooru
AI based multi-label girl image classification system, implemented by using TensorFlow.
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mpi4jax
Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:
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onepanel
The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.
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thinc
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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seq2seq
Discontinued A general-purpose encoder-decoder framework for Tensorflow
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pytorch-summary
Model summary in PyTorch similar to `model.summary()` in Keras
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stoke
A lightweight wrapper for PyTorch that provides a simple declarative API for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions. (by fidelity)
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einops
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better horovod alternative or higher similarity.
horovod reviews and mentions
Posts with mentions or reviews of horovod.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-08.
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Discussion Thread
Broke: using Horovod
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[D] What is the recommended approach to training NN on big data set?
And in case scaling is really important to you. May I suggest you look into Horovod?
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Anyone know of any papers or models for segmenting satellite images of a city into things like roads, buildings, parks, etc?
Training is not the same as inference (doing the segmentation), so that scale is probably off by a lot. One or two orders of magnitude just depending on the specifics of what hardware you're running on, and your training and eval dataset would be several orders of magnitude smaller. FAANGs would parallelize that training as well (don't remember if UNet is inherently parallelizable for training) via their internal equivalent of Horovod, so they'll do a GPU-month worth of training in less than a day.
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Embedding Python
[[email protected]] match_arg (utils/args/args.c:163): unrecognized argument quiet [[email protected]] HYDU_parse_array (utils/args/args.c:178): argument matching returned error [[email protected]] parse_args (ui/mpich/utils.c:1639): error parsing input array [[email protected]] HYD_uii_mpx_get_parameters (ui/mpich/utils.c:1691): unable to parse user arguments [[email protected]] main (ui/mpich/mpiexec.c:127): error parsing parameters I believe this is due to mpich being installed: https://github.com/horovod/horovod/issues/1637
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[D] PyTorch Distributed Training Libraries: What are the current options?
Check out Horovod - https://github.com/horovod/horovod
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[D] GPU buying recommendation
If you just want to run tensorflow or pytorch for a Jupyter notebook, setting the environment shouldn't be difficult. I know that AWS has a marketplace of preconfigured images. However, you can go as advanced as setting up a cluster of gpu-equipped nodes to setup Horovod (https://github.com/horovod/horovod) to do distributed machine learning. Yes, there's a learning curve, but you cannot acquire this skillet any other way.
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SKLean, TensorFlow, etc vs Spark ML?
I'm the maintainer for an open source project called Horovod that allows you to distribute deep learning training (e.g., TensorFlow) on platforms like Spark.
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Cluster machine learning
You'll want to use horovod to run keras in a distributed system. Then use Slurm to manage the cluster and run the job.
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A note from our sponsor - SaaSHub
www.saashub.com | 18 Apr 2024
Stats
Basic horovod repo stats
8
13,930
5.8
22 days ago
horovod/horovod is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of horovod is Python.
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