pytorch_mnist_ddp | PyTorch mnist distributed data parallel example
kandi X-RAY | pytorch_mnist_ddp Summary
kandi X-RAY | pytorch_mnist_ddp Summary
pytorch_mnist_ddp is a Python library. pytorch_mnist_ddp has no bugs, it has no vulnerabilities and it has low support. However pytorch_mnist_ddp build file is not available. You can download it from GitHub.
大家都知道,mnist 之于深度学习计算机视觉,就像 hello world 对于各大编程语言,我相信很多朋友看各个深度学习框架,都是看一下训练 mnist 的例子,例如 PyTorch 的 mnist 例子,TensorFlow 的 mnist 例子,Paddle 的 mnist 例子。所以说,mnist 对于深度学习框架,是个很好的管中窥豹的机会。. 今天开源一个基于 PyTorch 分布式训练,也就是 DistributedDataParallel,简称 DDP,分布式数据并行。. 其实,PyTorch 有两个版本的数据并行接口,一个是 DataParallel(简称 DP),另外一个是上面说的 DDP,两者的区别是:. 因此,尽量避免使用 DP,直接换成 DDP,其实 DDP 相比 DP,需要改动的代码并不多。. 废话不多数,下面简单介绍一下代码。 为了简洁和规范,我是在 PyTorch 的 mnist 例子上改动的。. | 卡数 | 20 epoch 耗时(秒) | | :--------- | :--: | | 4 | 73.6 | | 2 | 137.1 | | 1 | 242.3 |.
大家都知道,mnist 之于深度学习计算机视觉,就像 hello world 对于各大编程语言,我相信很多朋友看各个深度学习框架,都是看一下训练 mnist 的例子,例如 PyTorch 的 mnist 例子,TensorFlow 的 mnist 例子,Paddle 的 mnist 例子。所以说,mnist 对于深度学习框架,是个很好的管中窥豹的机会。. 今天开源一个基于 PyTorch 分布式训练,也就是 DistributedDataParallel,简称 DDP,分布式数据并行。. 其实,PyTorch 有两个版本的数据并行接口,一个是 DataParallel(简称 DP),另外一个是上面说的 DDP,两者的区别是:. 因此,尽量避免使用 DP,直接换成 DDP,其实 DDP 相比 DP,需要改动的代码并不多。. 废话不多数,下面简单介绍一下代码。 为了简洁和规范,我是在 PyTorch 的 mnist 例子上改动的。. | 卡数 | 20 epoch 耗时(秒) | | :--------- | :--: | | 4 | 73.6 | | 2 | 137.1 | | 1 | 242.3 |.
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pytorch_mnist_ddp has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
pytorch_mnist_ddp has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of pytorch_mnist_ddp is current.
Quality
pytorch_mnist_ddp has no bugs reported.
Security
pytorch_mnist_ddp has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
pytorch_mnist_ddp does not have a standard license declared.
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Without a license, all rights are reserved, and you cannot use the library in your applications.
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pytorch_mnist_ddp releases are not available. You will need to build from source code and install.
pytorch_mnist_ddp has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
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Install pytorch_mnist_ddp
You can download it from GitHub.
You can use pytorch_mnist_ddp 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.
You can use pytorch_mnist_ddp 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.
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