` 以开启你的 NNI 旅程！
为什么选择 NNI？
--------------------
NNI 使得自动机器学习技术即插即用
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. raw:: html
.. codesnippetcard::
:icon: ../img/thumbnails/hpo-small.svg
:title: 超参调优
:link: tutorials/hpo_quickstart_pytorch/main
:seemore: 点这里阅读完整教程
.. code-block::
params = nni.get_next_parameter()
class Net(nn.Module):
...
model = Net()
optimizer = optim.SGD(model.parameters(),
params['lr'],
params['momentum'])
for epoch in range(10):
train(...)
accuracy = test(model)
nni.report_final_result(accuracy)
.. codesnippetcard::
:icon: ../img/thumbnails/pruning-small.svg
:title: 模型剪枝
:link: tutorials/pruning_quick_start_mnist
:seemore: 点这里阅读完整教程
.. code-block::
# define a config_list
config = [{
'sparsity': 0.8,
'op_types': ['Conv2d']
}]
# generate masks for simulated pruning
wrapped_model, masks = \
L1NormPruner(model, config). \
compress()
# apply the masks for real speedup
ModelSpeedup(unwrapped_model, input, masks). \
speedup_model()
.. codesnippetcard::
:icon: ../img/thumbnails/quantization-small.svg
:title: 模型量化
:link: tutorials/quantization_speedup
:seemore: 点这里阅读完整教程
.. code-block::
# define a config_list
config = [{
'quant_types': ['input', 'weight'],
'quant_bits': {'input': 8, 'weight': 8},
'op_types': ['Conv2d']
}]
# in case quantizer needs a extra training
quantizer = QAT_Quantizer(model, config)
quantizer.compress()
# Training...
# export calibration config and
# generate TensorRT engine for real speedup
calibration_config = quantizer.export_model(
model_path, calibration_path)
engine = ModelSpeedupTensorRT(
model, input_shape, config=calib_config)
engine.compress()
.. codesnippetcard::
:icon: ../img/thumbnails/multi-trial-nas-small.svg
:title: 神经网络架构搜索
:link: tutorials/hello_nas
:seemore: 点这里阅读完整教程
.. code-block::
# define model space
- self.conv2 = nn.Conv2d(32, 64, 3, 1)
+ self.conv2 = nn.LayerChoice([
+ nn.Conv2d(32, 64, 3, 1),
+ DepthwiseSeparableConv(32, 64)
+ ])
# search strategy + evaluator
strategy = RegularizedEvolution()
evaluator = FunctionalEvaluator(
train_eval_fn)
# run experiment
RetiariiExperiment(model_space,
evaluator, strategy).run()
.. codesnippetcard::
:icon: ../img/thumbnails/one-shot-nas-small.svg
:title: 单尝试 (One-shot) NAS
:link: nas/exploration_strategy
:seemore: 点这里阅读完整教程
.. code-block::
# define model space
space = AnySearchSpace()
# get a darts trainer
trainer = DartsTrainer(space, loss, metrics)
trainer.fit()
# get final searched architecture
arch = trainer.export()
.. codesnippetcard::
:icon: ../img/thumbnails/feature-engineering-small.svg
:title: 特征工程
:link: feature_engineering/overview
:seemore: 点这里阅读完整教程
.. code-block::
selector = GBDTSelector()
selector.fit(
X_train, y_train,
lgb_params=lgb_params,
eval_ratio=eval_ratio,
early_stopping_rounds=10,
importance_type='gain',
num_boost_round=1000)
# get selected features
features = selector.get_selected_features()
.. End of code snippet card
.. raw:: html
NNI 可降低自动机器学习实验管理的成本
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. codesnippetcard::
:icon: ../img/thumbnails/training-service-small.svg
:title: 训练平台
:link: experiment/training_service/overview
:seemore: 点这里了解更多
一个自动机器学习实验通常需要很多次尝试，来找到合适且具有潜力的模型。
**训练平台** 的目标便是让整个调优过程可以轻松的扩展到分布式平台上，为不同的计算资源（例如本地机器、远端服务器、集群等）提供的统一的用户体验。
目前，NNI 已经支持 **超过九种** 训练平台。
.. codesnippetcard::
:icon: ../img/thumbnails/web-portal-small.svg
:title: 网页控制台
:link: experiment/web_portal/web_portal
:seemore: 点这里了解更多
网页控制台提供了可视化调优过程的能力，让你可以轻松检查、跟踪、控制实验流程。
.. image:: ../static/img/webui.gif
:width: 100%
.. codesnippetcard::
:icon: ../img/thumbnails/experiment-management-small.svg
:title: 多实验管理
:link: experiment/experiment_management
:seemore: 点这里了解更多
深度学习模型往往需要多个实验不断迭代，例如用户可能想尝试不同的调优算法，优化他们的搜索空间，或者切换到其他的计算资源。
**多实验管理** 提供了对多个实验的结果进行聚合和比较的强大能力，极大程度上简化了开发者的开发流程。
获取帮助或参与贡献
-------------------------------
NNI 使用 `NNI GitHub 仓库 `_ 进行维护。我们在 GitHub 上收集反馈，以及新需求和想法。你可以：
* 新建一个 `GitHub issue `_ 反馈一个 bug 或者需求。
* 新建一个 `pull request `_ 以贡献代码（在此之前，请务必确保你已经阅读过 :doc:`贡献指南 `）。
* 如果你有任何问题，都可以加入 `NNI 讨论 `_。
* 加入即时聊天群组：
.. list-table::
:header-rows: 1
:widths: auto
* - Gitter
- 微信
* -
.. image:: https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png
-
.. image:: https://github.com/scarlett2018/nniutil/raw/master/wechat.png
引用 NNI
----------
如果你在你的文献中用到了 NNI，请考虑引用我们：
Microsoft. Neural Network Intelligence (version |release|). https://github.com/microsoft/nni
Bibtex 格式如下（请将版本号替换成你在使用的特定版本）： ::
@software{nni2021,
author = {{Microsoft}},
month = {1},
title = {{Neural Network Intelligence}},
url = {https://github.com/microsoft/nni},
version = {2.0},
year = {2021}
}