<a href="TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a>, other cloud options and even <a href="TrainingService/HybridMode.html">Hybrid mode</a>.
</p>
<!-- Who should consider using NNI -->
<div>
<h2 class="title">Who should consider using NNI</h2>
<ul>
<li>Those who want to <b>try different AutoML algorithms</b> in their training code/model.</li>
<li>Those who want to run AutoML trial jobs <b>in different environments</b> to speed up search.</li>
<li class="rowHeight">Researchers and data scientists who want to easily <b>implement and experiement new AutoML
algorithms</b>
, may it be: hyperparameter tuning algorithm,
neural architect search algorithm or model compression algorithm.
</li>
<li>ML Platform owners who want to <b>support AutoML in their platform</b></li>
<div>NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by filling an issue.</div>
<br/>
<div>We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.</div>
<br/>
<div class="rowHeight">If you plan to contribute new features, new tuners, new training services, etc. please first open an issue or reuse an exisiting issue, and discuss the feature with us. We will discuss with you on the issue timely or set up conference calls if needed.</div>
<br/>
<div>To learn more about making a contribution to NNI, please refer to our <a href="contribution.html"">How-to contribution page</a>.</div>
<br/>
<div>We appreciate all contributions and thank all the contributors!</div>
<li><a href="https://github.com/microsoft/nni/issues/new/choose">File an issue</a> on GitHub.</li>
<li>Open or participate in a <a href="https://github.com/microsoft/nni/discussions">discussion</a>.</li>
<li>Discuss on the <a href="https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge">NNI Gitter</a> in NNI.</li>
</ul>
<div>
<div class="rowHeight">Join IM discussion groups:</div>
* **New webinar**: `Introducing Retiarii, A deep learning exploratory-training framework on NNI <https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html>`_ - *scheduled on June-24-2021*
* **New community channel**: `Discussions <https://github.com/microsoft/nni/discussions>`_
The DNN model tuning often requires more than one experiment.
Users might try different tuning algorithms, fine-tune their search space, or switch to another training service.
**Experiment management** provides the power to aggregate and compare tuning results from multiple experiments,
so that the tuning workflow becomes clean and organized.
.. raw:: html
<h2>Get Support and Contribute Back</h2>
NNI is maintained on the `NNI GitHub repository <https://github.com/microsoft/nni>`_. We collect feedbacks and new proposals/ideas on GitHub. You can:
* Open a `GitHub issue <https://github.com/microsoft/nni/issues>`_ for bugs and feature requests.
* Open a `pull request <https://github.com/microsoft/nni/pulls>`_ to contribute code (make sure to read the `contribution guide </contribution>` before doing this).
* Participate in `NNI Discussion <https://github.com/microsoft/nni/discussions>`_ for general questions and new ideas.
NNI can be applied on various model tuning tasks. Some state-of-the-art model search algorithms, such as EfficientNet, can be easily built on NNI. Popular models, e.g., recommendation models, can be tuned with NNI. The following are some use cases to illustrate how to leverage NNI in your model tuning tasks and how to build your own pipeline with NNI.
.. toctree::
:maxdepth: 1
Tuning SVD automatically <RecommendersSvd>
EfficientNet on NNI <../TrialExample/EfficientNet>
Automatic Model Architecture Search for Reading Comprehension <../TrialExample/SquadEvolutionExamples>
Parallelizing Optimization for TPE <ParallelizingTpeSearch>
\ No newline at end of file
Tuning SVD automatically <recommenders_svd>
EfficientNet on NNI <efficientnet>
Automatic Model Architecture Search for Reading Comprehension <squad_evolution_examples>
Parallelizing Optimization for TPE <parallelizing_tpe_search>
The performance of systems, such as database, tensor operator implementaion, often need to be tuned to adapt to specific hardware configuration, targeted workload, etc. Manually tuning a system is complicated and often requires detailed understanding of hardware and workload. NNI can make such tasks much easier and help system owners find the best configuration to the system automatically. The detailed design philosophy of automatic system tuning can be found in this `paper <https://dl.acm.org/doi/10.1145/3352020.3352031>`__\ . The following are some typical cases that NNI can help.
.. toctree::
:maxdepth: 1
Tuning SPTAG (Space Partition Tree And Graph) automatically <SptagAutoTune>
Tuning the performance of RocksDB <../TrialExample/RocksdbExamples>
Different from the tutorials and examples in the rest of the document which show the usage of a feature, this part mainly introduces end-to-end scenarios and use cases to help users further understand how NNI can help them. NNI can be widely adopted in various scenarios. We also encourage community contributors to share their AutoML practices especially the NNI usage practices from their experience.
.. toctree::
:maxdepth: 1
Automatic Model Tuning (HPO/NAS) <automodel>
Automatic System Tuning (AutoSys) <autosys>
Model Compression <model_compression>
Feature Engineering <feature_engineering>
Performance measurement, comparison and analysis <perf_compare>
The following is an article about how NNI helps in auto feature engineering shared by a community contributor. More use cases and solutions will be added in the future.
.. toctree::
:maxdepth: 1
NNI review article from Zhihu: - By Garvin Li <NNI_AutoFeatureEng>
\ No newline at end of file
NNI review article from Zhihu: - By Garvin Li <nni_autofeatureeng>
