horovod | Distributed training framework for TensorFlow Keras | Machine Learning library

 by   horovod Python Version: 0.28.1 License: Non-SPDX

kandi X-RAY | horovod Summary

kandi X-RAY | horovod Summary

horovod is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. horovod has no bugs, it has no vulnerabilities, it has build file available and it has high support. However horovod has a Non-SPDX License. You can install using 'pip install horovod' or download it from GitHub, PyPI.

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
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            kandi-support Support

              horovod has a highly active ecosystem.
              It has 13329 star(s) with 2191 fork(s). There are 333 watchers for this library.
              There were 2 major release(s) in the last 12 months.
              There are 342 open issues and 1833 have been closed. On average issues are closed in 114 days. There are 6 open pull requests and 0 closed requests.
              OutlinedDot
              It has a negative sentiment in the developer community.
              The latest version of horovod is 0.28.1

            kandi-Quality Quality

              horovod has 0 bugs and 0 code smells.

            kandi-Security Security

              horovod has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              horovod code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              horovod has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              horovod releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              horovod saves you 13920 person hours of effort in developing the same functionality from scratch.
              It has 36804 lines of code, 2714 functions and 236 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed horovod and discovered the below as its top functions. This is intended to give you an instant insight into horovod implemented functionality, and help decide if they suit your requirements.
            • Parse command line arguments .
            • Trainer .
            • Builds a build and test .
            • Train the MNIST dataset .
            • Runs an elastic training .
            • Create a DistributedOptimizer .
            • Create a DistributedOptimizer .
            • Broadcast the state of an optimizer .
            • Run MPI .
            • Build a test and test - macos test .
            Get all kandi verified functions for this library.

            horovod Key Features

            No Key Features are available at this moment for horovod.

            horovod Examples and Code Snippets

            README.rst
            Pythondot img1Lines of Code : 0dot img1License : Non-SPDX (NOASSERTION)
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            Horovod-Install
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            If you've installed PyTorch from `PyPI `__, make sure that ``g++-5`` or above is installed. If you've installed either package from `Conda `_, make sure that the ``gxx_linux-64`` Conda package is installed. To run on CPUs: $ pip install horov
            Horovod-Citation
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            @article{sergeev2018horovod,
              Author = {Alexander Sergeev and Mike Del Balso},
              Journal = {arXiv preprint arXiv:1802.05799},
              Title = {Horovod: fast and easy distributed deep learning in {TensorFlow}},
              Year = {2018}
            }  
            horovod - keras spark3 rossmann
            Pythondot img4Lines of Code : 382dot img4License : Non-SPDX
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            # Copyright 2017 onwards, fast.ai, Inc.
            # Modifications copyright (C) 2018 Uber Technologies, Inc.
            #
            # Licensed under the Apache License, Version 2.0 (the "License");
            # you may not use this file except in compliance with the License.
            # You may obtain  
            horovod - mxnet imagenet resnet50
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            # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
            #
            # Permission is hereby granted, free of charge, to any person obtaining a copy
            # of this software and associated documentation files (the "Software"), to deal
            # in the Softwa  
            horovod - keras spark rossmann run
            Pythondot img6Lines of Code : 358dot img6License : Non-SPDX
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            # Copyright 2017 onwards, fast.ai, Inc.
            # Modifications copyright (C) 2018 Uber Technologies, Inc.
            #
            # Licensed under the Apache License, Version 2.0 (the "License");
            # you may not use this file except in compliance with the License.
            # You may obtain  
            copy iconCopy
            conda env create -f environment.yml
            
            conda activate 
            conda list
            
            How to ensure neural net performance comparability?
            Pythondot img8Lines of Code : 11dot img8License : Strong Copyleft (CC BY-SA 4.0)
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            SEED = 123
            os.environ['PYTHONHASHSEED']=str(SEED)
            random.seed(SEED)
            np.random.seed(SEED)
            tf.set_random_seed(SEED)
            
            session_config.intra_op_parallelism_threads = 1
            session_config.inter_op_parallelism_threads = 1
            
            Tensorflow error with dataset iterator initialization in monitoredtrainingsession
            Pythondot img9Lines of Code : 4dot img9License : Strong Copyleft (CC BY-SA 4.0)
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            scaffold = tf.train.Scaffold(local_init_op=train_init_operator)
            
            with tf.train.MonitoredTrainingSession(scaffold=scaffold, ...
            
            How to train PyTorch transfer learning tutorial with more then 1 GPU
            Pythondot img10Lines of Code : 12dot img10License : Strong Copyleft (CC BY-SA 4.0)
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                # Horovod: pin GPU to local rank.
                torch.cuda.set_device(hvd.local_rank())
                torch.cuda.manual_seed(args.seed)
            
                train_sampler = torch.utils.data.distributed.DistributedSampler(
                train_dataset, num_re

            Community Discussions

            QUESTION

            No module named 'Cython' setting up Azure ML docker instance
            Asked 2021-Jul-03 at 14:57

            I'm trying to install the following library in my Azure ML instance:

            https://github.com/philferriere/cocoapi#egg=pycocotools&subdirectory=PythonAPI

            My Dockerfile looks like this:

            ...

            ANSWER

            Answered 2021-Jul-03 at 14:57

            Solution was to add the following to the Dockerfile:

            Source https://stackoverflow.com/questions/68237132

            QUESTION

            Running a .sh script when container starts up after executing docker run
            Asked 2021-Apr-23 at 04:35

            I know multiple instances of this question exist already, but I wanted to get suggestions as to what is the best way to approach this particular problem would be. My command is:

            ...

            ANSWER

            Answered 2021-Apr-23 at 01:33

            In order to run multiple commands in docker, use /bin/bash -c with a semicolon ;

            Source https://stackoverflow.com/questions/67222223

            QUESTION

            Gradient Exploding Using CIFAR-10 Dataset from the Official Website
            Asked 2020-Nov-10 at 11:25

            I want to train a VGG16 model with Horovod PyTorch on 4 GPUs. Instead of using the CIFAR10 dataset of torch vision.datasets.CIFAR10, I would like to split the dataset on my own. So I downloaded the dataset from the official website and split the dataset. This is how I split the data:

            ...

            ANSWER

            Answered 2020-Nov-10 at 11:25

            Maybe it is because I did not normalize the dataset. Thanks for everyone's help!

            Source https://stackoverflow.com/questions/64745590

            QUESTION

            Run hydra configured project with SLURM and Horovod
            Asked 2020-Sep-28 at 16:38

            Right now, I am using Horovod to run distributed training of my pytorch models. I would like to start using hydra config for the --multirun feature and enqueue all jobs with SLURM. I know there is the Submitid plugin. But I am not sure, how would the whole pipeline work with Horovod. Right now, my command for training looks as follows:

            ...

            ANSWER

            Answered 2020-Sep-28 at 16:38

            The Submitit plugin does support GPU allocation, but I am not familiar with Horovod and have no idea if this can work in conjunction with it. One new feature of Hydra 1.0 is the ability to set or copy environment variables from the launching process. This might come in handy in case Horovod is trying to set some environment variables. See the docs for info about it.

            Source https://stackoverflow.com/questions/64104272

            QUESTION

            Apache Spark 3 GPU cluster
            Asked 2020-Jul-23 at 16:10

            I'm very new to Apache Spark. Before I was experimenting with Dask, Ray , and Horovod which can easily create GPU clusters. I'm currently using Apache Spark 3.0 (which added NVIDIA GPU support) but having trouble with creating GPU clusters. I attemped to configure the spark-defaults.conf as follows:

            ...

            ANSWER

            Answered 2020-Jul-23 at 16:10

            After reviewing several hidden websites, I compiled the instructions to setting up the GPU cluster in Apache Spark 3.0 in the following blog: http://deeplearningyogi.com/ Please comment.

            Thanks,

            vinhdiesal

            Source https://stackoverflow.com/questions/62979742

            QUESTION

            Are Apache Spark 2.0 parquet files incompatible with Apache Arrow?
            Asked 2020-Apr-29 at 13:40

            The problem

            I have written an Apache Spark DataFrame as a parquet file for a deep learning application in a Python environment ; I am currently experiencing issues in implementing basic examples of both petastorm (following this notebook) and horovod frameworks, in reading the aforementioned file namely. The DataFrame has the following type : DataFrame[features: array, next: int, weight: int] (much like in DataBricks' notebook, I had features be a VectorUDT, which I converted to an array).
            In both cases, Apache Arrow throws an ArrowIOError : Invalid parquet file. Corrupt footer. error.

            What I found until now

            I discovered in this question and in this PR that as of version 2.0, Spark doesn't write _metadata or _common_metadata files, unless spark.hadoop.parquet.enable.summary-metadata is set to true in Spark's configuration ; those files are indeed missing.
            I thus tried rewriting my DataFrame with this environment, still no _common_metadata file. What also works is to explicitely pass a schema to petastorm when constructing a reader (passing schema_fields to make_batch_reader for instance ; which is a problem with horovod as there is no such parameter in horovod.spark.keras.KerasEstimator's constructor).

            How would I be able, if at all possible, to either make Spark output those files, or in Arrow to infer the schema, just like Spark seems to be doing ?

            Minimal example with horovod ...

            ANSWER

            Answered 2020-Apr-29 at 13:40

            The problem is solved in pyarrow 0.14+ (issues.apache.org/jira/browse/ARROW-4723), be sure to install the updated version with pip (up until Databricks Runtime 6.5, the included version is 0.13).
            Thanks to @joris' comment for pointing this out.

            Source https://stackoverflow.com/questions/61234955

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install horovod

            You can install using 'pip install horovod' or download it from GitHub, PyPI.
            You can use horovod like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            Install
          • PyPI

            pip install horovod

          • CLONE
          • HTTPS

            https://github.com/horovod/horovod.git

          • CLI

            gh repo clone horovod/horovod

          • sshUrl

            git@github.com:horovod/horovod.git

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