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.
Use Cases and Solutions
=======================
.. toctree::
:maxdepth: 2
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>
Use NNI on Google Colab <NNI_colab_support>
External Repositories and References
====================================
With authors' permission, we listed a set of NNI usage examples and relevant articles.
External Repositories
=====================
* `Hyperparameter Tuning for Matrix Factorization <https://github.com/microsoft/recommenders/blob/master/examples/04_model_select_and_optimize/nni_surprise_svd.ipynb>`__ with NNI
* `scikit-nni <https://github.com/ksachdeva/scikit-nni>`__ Hyper-parameter search for scikit-learn pipelines using NNI
Relevant Articles
=================
* `Cost-effective Hyper-parameter Tuning using AdaptDL with NNI - Feb 23, 2021 <https://medium.com/casl-project/cost-effective-hyper-parameter-tuning-using-adaptdl-with-nni-e55642888761>`__
* `(in Chinese) A summary of NNI new capabilities in NNI 2.0 - Jan 21, 2021 <https://www.msra.cn/zh-cn/news/features/nni-2>`__
* `(in Chinese) A summary of NNI new capabilities in 2019 - Dec 26, 2019 <https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA>`__
* `Find thy hyper-parameters for scikit-learn pipelines using Microsoft NNI - Nov 6, 2019 <https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1>`__
* `(in Chinese) AutoML tools (Advisor, NNI and Google Vizier) comparison - Aug 05, 2019 <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>`__
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.
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::
.. toctree::
:maxdepth: 1
:maxdepth: 1
Tuning SVD automatically <RecommendersSvd>
Tuning SVD automatically <recommenders_svd>
EfficientNet on NNI <../TrialExample/EfficientNet>
EfficientNet on NNI <efficientnet>
Automatic Model Architecture Search for Reading Comprehension <../TrialExample/SquadEvolutionExamples>
Automatic Model Architecture Search for Reading Comprehension <squad_evolution_examples>
Parallelizing Optimization for TPE <ParallelizingTpeSearch>
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.
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::
.. toctree::
:maxdepth: 1
:maxdepth: 1
Tuning SPTAG (Space Partition Tree And Graph) automatically <SptagAutoTune>
Tuning SPTAG (Space Partition Tree And Graph) automatically <sptag_auto_tune>
Tuning the performance of RocksDB <../TrialExample/RocksdbExamples>
Tuning the performance of RocksDB <rocksdb_examples>
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.
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::
.. toctree::
:maxdepth: 1
:maxdepth: 1
NNI review article from Zhihu: - By Garvin Li <NNI_AutoFeatureEng>
NNI review article from Zhihu: - By Garvin Li <nni_autofeatureeng>
