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@@ -82,10 +82,10 @@ NNI (Neural Network Intelligence) 是自动机器学习(AutoML)的工具包
## **使用场景**
## **使用场景**
* 在本地 Trial 不同的自动机器学习算法来训练模型。
* 在本机尝试使用不同的自动机器学习(AutoML)算法来训练模型。
* 在分布式环境中加速自动机器学习(如:远程 GPU 工作站和云服务器)。
* 在分布式环境中加速自动机器学习(如:远程 GPU 工作站和云服务器)。
* 定制自动机器学习算法,或比较不同的自动机器学习算法。
* 定制自动机器学习算法,或比较不同的自动机器学习算法。
* 在自己的机器学习平台中支持自动机器学习。
* 在机器学习平台中支持自动机器学习。
## 相关项目
## 相关项目
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@@ -93,7 +93,7 @@ NNI (Neural Network Intelligence) 是自动机器学习(AutoML)的工具包
*[OpenPAI](https://github.com/Microsoft/pai):作为开源平台,提供了完整的 AI 模型训练和资源管理能力,能轻松扩展,并支持各种规模的私有部署、云和混合环境。
*[OpenPAI](https://github.com/Microsoft/pai):作为开源平台,提供了完整的 AI 模型训练和资源管理能力,能轻松扩展,并支持各种规模的私有部署、云和混合环境。
*[FrameworkController](https://github.com/Microsoft/frameworkcontroller):开源的通用 Kubernetes Pod 控制器,通过单个控制器来编排 Kubernetes 上所有类型的应用。
*[FrameworkController](https://github.com/Microsoft/frameworkcontroller):开源的通用 Kubernetes Pod 控制器,通过单个控制器来编排 Kubernetes 上所有类型的应用。
*[MMdnn](https://github.com/Microsoft/MMdnn):一个完成、跨框架的解决方案,能够转换、可视化、诊断深度神经网络模型。 MMdnn 中的 "MM" 表示model management(模型管理),而 "dnn" 是 deep neural network(深度神经网络)的缩写。 我们鼓励研究人员和学生利用这些项目来加速 AI 开发和研究。
*[MMdnn](https://github.com/Microsoft/MMdnn):一个完整、跨框架的解决方案,能够转换、可视化、诊断深度神经网络模型。 MMdnn 中的 "MM" 表示model management(模型管理),而 "dnn" 是 deep neural network(深度神经网络)的缩写。 我们鼓励研究人员和学生利用这些项目来加速 AI 开发和研究。
Note: The maxExecDuration spec set the time of an experiment, not a trial job. If the experiment reach the max duration time, the experiment will not stop, but could not submit new trial jobs any more.
Note: The maxExecDuration spec set the time of an experiment, not a trial job. If the experiment reach the max duration time, the experiment will not stop, but could not submit new trial jobs any more.
* __debug__
* Description
NNI will check the version of nniManager process and the version of trialKeeper in remote, pai and kubernetes platform. If you want to disable version check, you could set debug be false.
If you start a docker image using NNI's offical image `msranni/nni`, you could directly start NNI experiments by using `nnictl` command. Our offical image has NNI's running environment and basic python and deep learning frameworks environment.
If you start a docker image using NNI's offical image `msranni/nni`, you could directly start NNI experiments by using `nnictl` command. Our offical image has NNI's running environment and basic python and deep learning frameworks environment.
If you start your own docker image, you may need to install NNI package first, please [refer](https://github.com/Microsoft/nni/blob/master/docs/en_US/Installation.md).
If you start your own docker image, you may need to install NNI package first, please [refer](Installation.md).
If you want to run NNI's offical examples, you may need to clone NNI repo in github using
If you want to run NNI's offical examples, you may need to clone NNI repo in github using
then you could enter `nni/examples/trials` to start an experiment.
then you could enter `nni/examples/trials` to start an experiment.
After you prepare NNI's environment, you could start a new experiment using `nnictl` command, [refer](https://github.com/Microsoft/nni/blob/master/docs/en_US/QuickStart.md)
After you prepare NNI's environment, you could start a new experiment using `nnictl` command, [refer](QuickStart.md)
## Using docker in remote platform
## Using docker in remote platform
NNI support starting experiments in [remoteTrainingService](https://github.com/Microsoft/nni/blob/master/docs/en_US/RemoteMachineMode.md), and run trial jobs in remote machines. As docker could start an independent Ubuntu system as SSH server, docker container could be used as the remote machine in NNI's remot mode.
NNI support starting experiments in [remoteTrainingService](RemoteMachineMode.md), and run trial jobs in remote machines. As docker could start an independent Ubuntu system as SSH server, docker container could be used as the remote machine in NNI's remot mode.
### Step 1: Setting docker environment
### Step 1: Setting docker environment
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@@ -78,7 +78,7 @@ If you use your own docker image as remote server, please make sure that this im
### Step3: Run NNI experiments
### Step3: Run NNI experiments
You could set your config file as remote platform, and setting the `machineList` configuration to connect your docker SSH server, [refer](https://github.com/Microsoft/nni/blob/master/docs/en_US/RemoteMachineMode.md). Note that you should set correct `port`,`username` and `passwd` or `sshKeyPath` of your host machine.
You could set your config file as remote platform, and setting the `machineList` configuration to connect your docker SSH server, [refer](RemoteMachineMode.md). Note that you should set correct `port`,`username` and `passwd` or `sshKeyPath` of your host machine.
`port:` The host machine's port, mapping to docker's SSH port.
`port:` The host machine's port, mapping to docker's SSH port.
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`sshKeyPath:` The path of private key of docker container.
`sshKeyPath:` The path of private key of docker container.
After the configuration of config file, you could start an experiment, [refer](https://github.com/Microsoft/nni/blob/master/docs/en_US/QuickStart.md)
After the configuration of config file, you could start an experiment, [refer](QuickStart.md)