@@ -18,7 +18,7 @@ NNI (Neural Network Intelligence) is a toolkit to help users run automated machi
The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
### **NNI [v0.9](https://github.com/Microsoft/nni/releases) has been released! <a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
### **NNI [v1.0](https://github.com/Microsoft/nni/releases) has been released! <a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
Now we have some external usage examples run in NNI from our contributors. Thanks our lovely contributors. And welcome more and more people to join us!
* Run [ENAS](examples/tuners/enas_nni/README.md) in NNI
* Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) in NNI
*[Automatic Feature Engineering](examples/trials/auto-feature-engineering/README.md) in NNI
## **External Repositories and References**
With authors' permission, we listed a set of NNI usage examples and relevant articles.
***Repo** - Run [ENAS](examples/tuners/enas_nni/README.md) with NNI
***Repo** - Run [Neural Network Architecture Search](examples/trials/nas_cifar10/README.md) with NNI
***Repo** - [Automatic Feature Engineering](examples/trials/auto-feature-engineering/README.md) with NNI
***Repo** - [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI
***Blog (in Chinese)** - AutoML tools (Advisor, NNI and Google Vizier) comparison by [@gaocegege](https://github.com/gaocegege) - [总结与分析 section of design and implementation of kubeflow/katib](http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90)
## **Feedback**
* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI
@@ -55,6 +55,32 @@ Compared with [LocalMode](LocalMode.md) and [RemoteMachineMode](RemoteMachineMod
* Optional key. Set the shmMB configuration of OpenPAI, it set the shared memory for one task in the task role.
* authFile
* Optional key, Set the auth file path for private registry while using PAI mode, [Refer](https://github.com/microsoft/pai/blob/2ea69b45faa018662bc164ed7733f6fdbb4c42b3/docs/faq.md#q-how-to-use-private-docker-registry-job-image-when-submitting-an-openpai-job), you can prepare the authFile and simply provide the local path of this file, NNI will upload this file to HDFS for you.
* portList
* Optional key. Set the portList configuration of OpenPAI, it specifies a list of port used in container, [Refer](https://github.com/microsoft/pai/blob/b2324866d0280a2d22958717ea6025740f71b9f0/docs/job_tutorial.md#specification).
The config schema in NNI is shown below:
```
portList:
- label: test
beginAt: 8080
portNumber: 2
```
Let's say you want to launch a tensorboard in the mnist example using the port. So the first step is to write a wrapper script `launch_pai.sh` of `mnist.py`.
@@ -33,4 +33,4 @@ abstract class TrainingService {
}
```
The parent class of TrainingService has a few abstract functions, users need to inherit the parent class and implement all of these abstract functions.
For more information about how to write your own TrainingService, please [refer](https://github.com/SparkSnail/nni/blob/dev-trainingServiceDoc/docs/en_US/TrainingService/HowToImplementTrainingService.md).
For more information about how to write your own TrainingService, please [refer](https://github.com/microsoft/nni/blob/master/docs/en_US/TrainingService/HowToImplementTrainingService.md).
Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. Note that the only acceptable types of search space are `choice`, `quniform`, `qloguniform`. **The number `q` in `quniform` and `qloguniform` has special meaning (different from the spec in [search space spec](../Tutorial/SearchSpaceSpec.md)). It means the number of values that will be sampled evenly from the range `low` and `high`.**
\ No newline at end of file
Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file.
Note that the only acceptable types of search space are `choice`, `quniform`, `randint`.
@@ -27,9 +27,8 @@ All types of sampling strategies and their parameter are listed here:
*`{"_type": "choice", "_value": options}`
* Which means the variable's value is one of the options. Here 'options' should be a list. Each element of options is a number of string. It could also be a nested sub-search-space, this sub-search-space takes effect only when the corresponding element is chosen. The variables in this sub-search-space could be seen as conditional variables.
* An simple [example](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/search_space.json) of [nested] search space definition. If an element in the options list is a dict, it is a sub-search-space, and for our built-in tuners you have to add a key `_name` in this dict, which helps you to identify which element is chosen. Accordingly, here is a [sample](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/sample.json) which users can get from nni with nested search space definition. Tuners which support nested search space is as follows:
* Which means the variable's value is one of the options. Here `options` should be a list of numbers or a list of strings. Using arbitrary objects as members of this list (like sublists, a mixture of numbers and strings, or null values) should work in most cases, but may trigger undefined behaviors.
*`options` could also be a nested sub-search-space, this sub-search-space takes effect only when the corresponding element is chosen. The variables in this sub-search-space could be seen as conditional variables. Here is an simple [example of nested search space definition](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/search_space.json). If an element in the options list is a dict, it is a sub-search-space, and for our built-in tuners you have to add a key `_name` in this dict, which helps you to identify which element is chosen. Accordingly, here is a [sample](https://github.com/microsoft/nni/tree/master/examples/trials/mnist-nested-search-space/sample.json) which users can get from nni with nested search space definition. Tuners which support nested search space are as follows:
这只是 NNI 支持的一小部分库。 如果对 NNI 感兴趣，可参考[教程](TrialExample/Trials.md)来继续学习。
除了这些案例，也欢迎更多的用户将 NNI 应用到自己的工作中，如果有任何疑问，请参考[实现 Trial](TrialExample/Trials.md)。 如果想成为 NNI 的贡献者，无论是分享示例，还是实现 Tuner 或其它内容，我们都非常期待您的参与。更多信息请[参考这里](Tutorial/Contributing.md)。
* 可选。在使用 pai 模式时，为私有 Docker 仓库设置认证文件，[见参考文档](https://github.com/microsoft/pai/blob/2ea69b45faa018662bc164ed7733f6fdbb4c42b3/docs/faq.md#q-how-to-use-private-docker-registry-job-image-when-submitting-an-openpai-job)。提供 authFile 的本地路径即可， NNI 会上传此文件。
完成并保存 NNI Experiment 配置文件后（例如可保存为：exp_pai.yml），运行以下命令：
