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Release Note of v3.0 Preview (#5548)
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@ -20,7 +20,7 @@ NNI automates feature engineering, neural architecture search, hyperparameter tu
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## What's NEW! <a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>
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* **New release**: [v2.10 is available](https://github.com/microsoft/nni/releases/tag/v2.10) - _released on Nov-14-2022_
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* **New release**: [v3.0 preview is available](https://github.com/microsoft/nni/releases/tag/v3.0rc1) - _released on May-5-2022_
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* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on June-22-2022_
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* **New research paper**: [SparTA: Deep-Learning Model Sparsity via Tensor-with-Sparsity-Attribute](https://www.usenix.org/system/files/osdi22-zheng-ningxin.pdf) - _published in OSDI 2022_
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* **New research paper**: [Privacy-preserving Online AutoML for Domain-Specific Face Detection](https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Privacy-Preserving_Online_AutoML_for_Domain-Specific_Face_Detection_CVPR_2022_paper.pdf) - _published in CVPR 2022_
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@ -31,7 +31,7 @@ author = 'Microsoft'
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version = ''
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# The full version, including alpha/beta/rc tags
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# FIXME: this should be written somewhere globally
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release = 'v2.10'
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release = 'v3.0rc1'
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# -- General configuration ---------------------------------------------------
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@ -5,6 +5,111 @@
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Change Log
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==========
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Release 3.0 Preview - 5/9/2022
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------------------------------
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Web Portal
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^^^^^^^^^^
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* New look and feel
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Neural Architecture Search
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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* **Breaking change**: ``nni.retiarii`` is no longer maintained and tested. Please migrate to ``nni.nas``.
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* Inherit ``nni.nas.nn.pytorch.ModelSpace``, rather than use ``@model_wrapper``.
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* Use ``nni.choice``, rather than ``nni.nas.nn.pytorch.ValueChoice``.
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* Use ``nni.nas.experiment.NasExperiment`` and ``NasExperimentConfig``, rather than ``RetiariiExperiment``.
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* Use ``nni.nas.model_context``, rather than ``nni.nas.fixed_arch``.
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* Please refer to `quickstart <https://nni.readthedocs.io/en/v3.0rc1/tutorials/hello_nas.html>`_ for more changes.
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* A refreshed experience to construct model space.
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* Enhanced debuggability via ``freeze()`` and ``simplify()`` APIs.
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* Enhanced expressiveness with ``nni.choice``, ``nni.uniform``, ``nni.normal`` and etc.
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* Enhanced experience of customization with ``MutableModule``, ``ModelSpace`` and ``ParamterizedModule``.
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* Search space with constraints is now supported.
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* Improved robustness and stability of strategies.
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* Supported search space types are now enriched for PolicyBaseRL, ENAS and Proxyless.
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* Each step of one-shot strategies can be executed alone: model mutation, evaluator mutation and training.
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* Most multi-trial strategies now supports specifying seed for reproducibility.
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* Performance of strategies have been verified on a set of benchmarks.
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* Strategy/engine middleware.
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* Filtering, replicating, deduplicating or retrying models submitted by any strategy.
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* Merging or transforming models before executing (e.g., CGO).
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* Arbitrarily-long chains of middlewares.
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* New execution engine.
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* Improved debuggability via SequentialExecutionEngine: trials can run in a single process and breakpoints are effective.
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* The old execution engine is now decomposed into execution engine and model format.
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* Enhanced extensibility of execution engines.
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* NAS profiler and hardware-aware NAS.
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* New profilers profile a model space, and quickly compute a profiling result for a sampled architecture or a distribution of architectures (FlopsProfiler, NumParamsProfiler and NnMeterProfiler are officially supported).
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* Assemble profiler with arbitrary strategies, including both multi-trial and one-shot.
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* Profiler are extensible. Strategies can be assembled with arbitrary customized profilers.
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Model Compression
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^^^^^^^^^^^^^^^^^
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* Compression framework is refactored, new framework import path is ``nni.contrib.compression``.
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* Configure keys are refactored, support more detailed compression configurations.
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* Support multi compression methods fusion.
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* Support distillation as a basic compression component.
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* Support more compression targets, like ``input``, ``ouptut`` and any registered paramters.
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* Support compressing any module type by customizing module settings.
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* Pruning
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* Pruner interfaces have fine-tuned for easy to use.
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* Support configuring ``granularity`` in pruners.
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* Support different mask ways, multiply zero or add a large negative value.
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* Support manully setting dependency group and global group.
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* A new powerful pruning speedup is released, applicability and robustness have been greatly improved.
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* The end to end transformer compression tutorial has been updated, achieved more extreme compression performance.
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* Quantization
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* Support using ``Evaluator`` to handle training/inferencing.
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* Support more module fusion combinations.
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* Support configuring ``granularity`` in quantizers.
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* Distillation
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* DynamicLayerwiseDistiller and Adaptive1dLayerwiseDistiller are supported.
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* Compression documents now updated for the new framework, the old version please view `v2.10 <https://nni.readthedocs.io/en/v2.10/>`_ doc.
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* New compression examples are under `nni/examples/compression <https://github.com/microsoft/nni/tree/v3.0rc1/examples/compression>`_
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* Create a evaluator: `nni/examples/compression/evaluator <https://github.com/microsoft/nni/tree/v3.0rc1/examples/compression/evaluator>`_
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* Pruning a model: `nni/examples/compression/pruning <https://github.com/microsoft/nni/tree/v3.0rc1/examples/compression/pruning>`_
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* Quantize a model: `nni/examples/compression/quantization <https://github.com/microsoft/nni/tree/v3.0rc1/examples/compression/quantization>`_
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* Fusion compression: `nni/examples/compression/fusion <https://github.com/microsoft/nni/tree/v3.0rc1/examples/compression/fusion>`_
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Training Services
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^^^^^^^^^^^^^^^^^
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* **Breaking change**: NNI v3.0 cannot resume experiments created by NNI v2.x
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* Local training service:
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* Reduced latency of creating trials
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* Fixed "GPU metric not found"
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* Fixed bugs about resuming trials
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* Remote training service:
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* ``reuse_mode`` now defaults to ``False``; setting it to ``True`` will fallback to v2.x remote training service
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* Reduced latency of creating trials
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* Fixed "GPU metric not found"
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* Fixed bugs about resuming trials
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* Supported viewing trial logs on the web portal
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* Supported automatic recover after temporary server failure (network fluctuation, out of memory, etc)
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Release 2.10 - 11/14/2022
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-------------------------
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