NNI has supported many training services listed below. Users can go through each page to learning how to configure the corresponding training service. NNI has high extensibility by design, users can customize new training service for their special resource, platform or needs.
@@ -43,8 +43,8 @@ If you want to use AzureBlob, add below to your config. Full config file see :gi
You can find ``storageAccountName``, ``storageAccountKey``, ``containerName`` on azure storage account portal.
.. image:: ../../img/azure_storage.png
:target: ../../img/azure_storage.png
.. image:: ../../../img/azure_storage.png
:target: ../../../img/azure_storage.png
:alt:
If you want to use NFS, add below to your config. Full config file see :githublink:`mnist-sharedstorage/config_nfs.yml <examples/trials/mnist-sharedstorage/config_nfs.yml>`.
Web portal is for users to conveniently visualize their NNI experiments, tuning and training progress, detailed metrics, and error logs. Web portal also allows users to control their NNI experiments, trials, such as updating an experiment of its concurrency, duration, rerunning trials.
* Like compare, select the trials you want to combine to launch the tensorboard at first, then click the ``Tensorboard`` button.
.. image:: ../../img/Tensorboard_1.png
:target: ../../img/Tensorboard_1.png
.. image:: ../../../img/Tensorboard_1.png
:target: ../../../img/Tensorboard_1.png
:alt:
* After click the ``OK`` button in the pop-up box, you will jump to the tensorboard portal.
.. image:: ../../img/Tensorboard_2.png
:target: ../../img/Tensorboard_2.png
.. image:: ../../../img/Tensorboard_2.png
:target: ../../../img/Tensorboard_2.png
:alt:
* You can see the ``SequenceID-TrialID`` on the tensorboard portal.
.. image:: ../../img/Tensorboard_3.png
:target: ../../img/Tensorboard_3.png
.. image:: ../../../img/Tensorboard_3.png
:target: ../../../img/Tensorboard_3.png
:alt:
Stop All
...
...
@@ -48,8 +48,8 @@ Stop All
If you want to open the portal you have already launched, click the tensorboard id. If you don't need the tensorboard anymore, click ``Stop all tensorboard`` button.
Web portal is for users to conveniently visualize their NNI experiments, tuning and training progress, detailed metrics, and error logs. Web portal also allows users to control their NNI experiments, trials, such as updating an experiment of its concurrency, duration, rerunning trials.
.. image:: ../../../static/img/webui.gif
:width: 100%
Q&A
---
...
...
@@ -19,8 +23,8 @@ What should you do when you think the chart is strange, such as ``Default metric
.. image:: ../../img/webui-img/summary.png
:target: ../../img/webui-img/summary.png
.. image:: ../../../img/webui-img/summary.png
:target: ../../../img/webui-img/summary.png
:alt: summary
...
...
@@ -34,8 +38,8 @@ What should you do when your experiment has error
We are glad to announce the alpha release for Feature Engineering toolkit on top of NNI, it's still in the experiment phase which might evolve based on user feedback. We'd like to invite you to use, feedback and even contribute.
.. note::
We are glad to announce the alpha release for Feature Engineering toolkit on top of NNI, it's still in the experiment phase which might evolve based on user feedback. We'd like to invite you to use, feedback and even contribute.
For now, we support the following feature selector:
The objective of NAS is typically to find architectures that achieve high predictive performance on unseen data. Performance estimation refers to the process of estimating this performance. The problem with performance estimation is mostly its scalability, i.e., how can I run and manage multiple trials simultaneously.
In NNI, we standardize this process is implemented with :doc:`evaluator <evaluator>`, which is responsible of estimating a model's performance. The choices of evaluators also range from the simplest option, e.g., to perform a standard training and validation of the architecture on data, to complex configurations and implementations. Evaluators are run in *trials*, where trials can be spawn onto distributed platforms with our powerful :doc:`training service </experiment/training_service>`.
In NNI, we standardize this process is implemented with :doc:`evaluator <evaluator>`, which is responsible of estimating a model's performance. The choices of evaluators also range from the simplest option, e.g., to perform a standard training and validation of the architecture on data, to complex configurations and implementations. Evaluators are run in *trials*, where trials can be spawn onto distributed platforms with our powerful :doc:`training service </experiment/training_service/overview>`.
