tutorials.po

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#: ../../source/tutorials/hello_nas.rst:13
msgid ""
"Click :ref:`here <sphx_glr_download_tutorials_hello_nas.py>` to download "
"the full example code"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:22
msgid "Hello, NAS!"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:24
msgid ""
"This is the 101 tutorial of Neural Architecture Search (NAS) on NNI. In "
"this tutorial, we will search for a neural architecture on MNIST dataset "
"with the help of NAS framework of NNI, i.e., *Retiarii*. We use multi-"
"trial NAS as an example to show how to construct and explore a model "
"space."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:28
msgid ""
"There are mainly three crucial components for a neural architecture "
"search task, namely,"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:30
msgid "Model search space that defines a set of models to explore."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:31
msgid "A proper strategy as the method to explore this model space."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:32
msgid ""
"A model evaluator that reports the performance of every model in the "
"space."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:34
msgid ""
"Currently, PyTorch is the only supported framework by Retiarii, and we "
"have only tested **PyTorch 1.7 to 1.10**. This tutorial assumes PyTorch "
"context but it should also apply to other frameworks, which is in our "
"future plan."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:38
msgid "Define your Model Space"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:40
msgid ""
"Model space is defined by users to express a set of models that users "
"want to explore, which contains potentially good-performing models. In "
"this framework, a model space is defined with two parts: a base model and"
" possible mutations on the base model."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:46
msgid "Define Base Model"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:48
msgid ""
"Defining a base model is almost the same as defining a PyTorch (or "
"TensorFlow) model. Usually, you only need to replace the code ``import "
"torch.nn as nn`` with ``import nni.retiarii.nn.pytorch as nn`` to use our"
" wrapped PyTorch modules."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:52
msgid "Below is a very simple example of defining a base model."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:93
msgid ""
"Always keep in mind that you should use ``import nni.retiarii.nn.pytorch "
"as nn`` and :meth:`nni.retiarii.model_wrapper`. Many mistakes are a "
"result of forgetting one of those. Also, please use ``torch.nn`` for "
"submodules of ``nn.init``, e.g., ``torch.nn.init`` instead of "
"``nn.init``."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:98
msgid "Define Model Mutations"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:100
msgid ""
"A base model is only one concrete model not a model space. We provide "
":doc:`API and Primitives </nas/construct_space>` for users to express how"
" the base model can be mutated. That is, to build a model space which "
"includes many models."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:103
msgid "Based on the above base model, we can define a model space as below."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:134
msgid "This results in the following code:"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:189
#: ../../source/tutorials/hello_nas.rst:259
#: ../../source/tutorials/hello_nas.rst:471
#: ../../source/tutorials/hello_nas.rst:564
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:244
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:281
#: ../../source/tutorials/pruning_quick_start_mnist.rst:65
#: ../../source/tutorials/pruning_quick_start_mnist.rst:107
#: ../../source/tutorials/pruning_quick_start_mnist.rst:172
#: ../../source/tutorials/pruning_quick_start_mnist.rst:218
#: ../../source/tutorials/pruning_quick_start_mnist.rst:255
#: ../../source/tutorials/pruning_quick_start_mnist.rst:283
msgid "Out:"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:210
msgid ""
"This example uses two mutation APIs, :class:`nn.LayerChoice "
"<nni.retiarii.nn.pytorch.LayerChoice>` and :class:`nn.InputChoice "
"<nni.retiarii.nn.pytorch.ValueChoice>`. :class:`nn.LayerChoice "
"<nni.retiarii.nn.pytorch.LayerChoice>` takes a list of candidate modules "
"(two in this example), one will be chosen for each sampled model. It can "
"be used like normal PyTorch module. :class:`nn.InputChoice "
"<nni.retiarii.nn.pytorch.ValueChoice>` takes a list of candidate values, "
"one will be chosen to take effect for each sampled model."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:219
msgid ""
"More detailed API description and usage can be found :doc:`here "
"</nas/construct_space>`."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:223
msgid ""
"We are actively enriching the mutation APIs, to facilitate easy "
"construction of model space. If the currently supported mutation APIs "
"cannot express your model space, please refer to :doc:`this doc "
"</nas/mutator>` for customizing mutators."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:228
msgid "Explore the Defined Model Space"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:230
msgid ""
"There are basically two exploration approaches: (1) search by evaluating "
"each sampled model independently, which is the search approach in :ref"
":`multi-trial NAS <multi-trial-nas>` and (2) one-shot weight-sharing "
"based search, which is used in one-shot NAS. We demonstrate the first "
"approach in this tutorial. Users can refer to :ref:`here <one-shot-nas>` "
"for the second approach."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:235
msgid ""
"First, users need to pick a proper exploration strategy to explore the "
"defined model space. Second, users need to pick or customize a model "
"evaluator to evaluate the performance of each explored model."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:239
msgid "Pick an exploration strategy"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:241
msgid ""
"Retiarii supports many :doc:`exploration strategies "
"</nas/exploration_strategy>`."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:243
msgid "Simply choosing (i.e., instantiate) an exploration strategy as below."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:273
msgid "Pick or customize a model evaluator"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:275
msgid ""
"In the exploration process, the exploration strategy repeatedly generates"
" new models. A model evaluator is for training and validating each "
"generated model to obtain the model's performance. The performance is "
"sent to the exploration strategy for the strategy to generate better "
"models."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:279
msgid ""
"Retiarii has provided :doc:`built-in model evaluators </nas/evaluator>`, "
"but to start with, it is recommended to use :class:`FunctionalEvaluator "
"<nni.retiarii.evaluator.FunctionalEvaluator>`, that is, to wrap your own "
"training and evaluation code with one single function. This function "
"should receive one single model class and uses "
":func:`nni.report_final_result` to report the final score of this model."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:284
msgid ""
"An example here creates a simple evaluator that runs on MNIST dataset, "
"trains for 2 epochs, and reports its validation accuracy."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:367
msgid "Create the evaluator"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:386
msgid ""
"The ``train_epoch`` and ``test_epoch`` here can be any customized "
"function, where users can write their own training recipe."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:389
msgid ""
"It is recommended that the ``evaluate_model`` here accepts no additional "
"arguments other than ``model_cls``. However, in the :doc:`advanced "
"tutorial </nas/evaluator>`, we will show how to use additional arguments "
"in case you actually need those. In future, we will support mutation on "
"the arguments of evaluators, which is commonly called \"Hyper-parmeter "
"tuning\"."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:394
msgid "Launch an Experiment"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:396
msgid ""
"After all the above are prepared, it is time to start an experiment to do"
" the model search. An example is shown below."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:417
msgid ""
"The following configurations are useful to control how many trials to run"
" at most / at the same time."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:436
msgid ""
"Remember to set the following config if you want to GPU. "
"``use_active_gpu`` should be set true if you wish to use an occupied GPU "
"(possibly running a GUI)."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:456
msgid ""
"Launch the experiment. The experiment should take several minutes to "
"finish on a workstation with 2 GPUs."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:495
msgid ""
"Users can also run Retiarii Experiment with :doc:`different training "
"services </experiment/training_service/overview>` besides ``local`` "
"training service."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:499
msgid "Visualize the Experiment"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:501
msgid ""
"Users can visualize their experiment in the same way as visualizing a "
"normal hyper-parameter tuning experiment. For example, open "
"``localhost:8081`` in your browser, 8081 is the port that you set in "
"``exp.run``. Please refer to :doc:`here "
"</experiment/web_portal/web_portal>` for details."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:505
msgid ""
"We support visualizing models with 3rd-party visualization engines (like "
"`Netron <https://netron.app/>`__). This can be used by clicking "
"``Visualization`` in detail panel for each trial. Note that current "
"visualization is based on `onnx <https://onnx.ai/>`__ , thus "
"visualization is not feasible if the model cannot be exported into onnx."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:510
msgid ""
"Built-in evaluators (e.g., Classification) will automatically export the "
"model into a file. For your own evaluator, you need to save your file "
"into ``$NNI_OUTPUT_DIR/model.onnx`` to make this work. For instance,"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:541
msgid "Relaunch the experiment, and a button is shown on Web portal."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:546
msgid "Export Top Models"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:548
msgid ""
"Users can export top models after the exploration is done using "
"``export_top_models``."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:575
msgid ""
"The output is ``json`` object which records the mutation actions of the "
"top model. If users want to output source code of the top model, they can"
" use :ref:`graph-based execution engine <graph-based-execution-engine>` "
"for the experiment, by simply adding the following two lines."
msgstr ""

#: ../../source/tutorials/hello_nas.rst:597
msgid "**Total running time of the script:** ( 2 minutes  4.499 seconds)"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:612
msgid ":download:`Download Python source code: hello_nas.py <hello_nas.py>`"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:618
msgid ":download:`Download Jupyter notebook: hello_nas.ipynb <hello_nas.ipynb>`"
msgstr ""

#: ../../source/tutorials/hello_nas.rst:625
#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:335
#: ../../source/tutorials/pruning_quick_start_mnist.rst:357
msgid "`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:13
msgid ""
"Click :ref:`here "
"<sphx_glr_download_tutorials_hpo_quickstart_pytorch_main.py>` to download"
" the full example code"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:22
msgid "HPO Quickstart with PyTorch"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:23
msgid ""
"This tutorial optimizes the model in `official PyTorch quickstart`_ with "
"auto-tuning."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:25
msgid "The tutorial consists of 4 steps:"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:27
msgid "Modify the model for auto-tuning."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:28
msgid "Define hyperparameters' search space."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:29
msgid "Configure the experiment."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:30
msgid "Run the experiment."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:37
msgid "Step 1: Prepare the model"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:38
msgid "In first step, we need to prepare the model to be tuned."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:40
msgid ""
"The model should be put in a separate script. It will be evaluated many "
"times concurrently, and possibly will be trained on distributed "
"platforms."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:44
msgid "In this tutorial, the model is defined in :doc:`model.py <model>`."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:46
msgid "In short, it is a PyTorch model with 3 additional API calls:"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:48
msgid ""
"Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be "
"evalutated."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:49
msgid ""
"Use :func:`nni.report_intermediate_result` to report per-epoch accuracy "
"metrics."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:50
msgid "Use :func:`nni.report_final_result` to report final accuracy."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:52
msgid "Please understand the model code before continue to next step."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:57
msgid "Step 2: Define search space"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:58
msgid ""
"In model code, we have prepared 3 hyperparameters to be tuned: "
"*features*, *lr*, and *momentum*."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:61
msgid ""
"Here we need to define their *search space* so the tuning algorithm can "
"sample them in desired range."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:63
msgid "Assuming we have following prior knowledge for these hyperparameters:"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:65
msgid "*features* should be one of 128, 256, 512, 1024."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:66
msgid ""
"*lr* should be a float between 0.0001 and 0.1, and it follows exponential"
" distribution."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:67
msgid "*momentum* should be a float between 0 and 1."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:69
msgid ""
"In NNI, the space of *features* is called ``choice``; the space of *lr* "
"is called ``loguniform``; and the space of *momentum* is called "
"``uniform``. You may have noticed, these names are derived from "
"``numpy.random``."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:74
msgid ""
"For full specification of search space, check :doc:`the reference "
"</hpo/search_space>`."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:76
msgid "Now we can define the search space as follow:"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:99
msgid "Step 3: Configure the experiment"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:100
msgid ""
"NNI uses an *experiment* to manage the HPO process. The *experiment "
"config* defines how to train the models and how to explore the search "
"space."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:103
msgid ""
"In this tutorial we use a *local* mode experiment, which means models "
"will be trained on local machine, without using any special training "
"platform."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:122
msgid "Now we start to configure the experiment."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:125
msgid "Configure trial code"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:126
msgid ""
"In NNI evaluation of each hyperparameter set is called a *trial*. So the "
"model script is called *trial code*."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:144
msgid ""
"When ``trial_code_directory`` is a relative path, it relates to current "
"working directory. To run ``main.py`` in a different path, you can set "
"trial code directory to ``Path(__file__).parent``. (`__file__ "
"<https://docs.python.org/3.10/reference/datamodel.html#index-43>`__ is "
"only available in standard Python, not in Jupyter Notebook.)"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:151
msgid ""
"If you are using Linux system without Conda, you may need to change "
"``\"python model.py\"`` to ``\"python3 model.py\"``."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:157
msgid "Configure search space"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:175
msgid "Configure tuning algorithm"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:176
msgid "Here we use :doc:`TPE tuner </hpo/tuners>`."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:195
msgid "Configure how many trials to run"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:196
msgid ""
"Here we evaluate 10 sets of hyperparameters in total, and concurrently "
"evaluate 2 sets at a time."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:213
msgid "You may also set ``max_experiment_duration = '1h'`` to limit running time."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:215
msgid ""
"If neither ``max_trial_number`` nor ``max_experiment_duration`` are set, "
"the experiment will run forever until you press Ctrl-C."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:220
msgid ""
"``max_trial_number`` is set to 10 here for a fast example. In real world "
"it should be set to a larger number. With default config TPE tuner "
"requires 20 trials to warm up."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:227
msgid "Step 4: Run the experiment"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:228
msgid ""
"Now the experiment is ready. Choose a port and launch it. (Here we use "
"port 8080.)"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:230
msgid ""
"You can use the web portal to view experiment status: "
"http://localhost:8080."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:260
msgid "After the experiment is done"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:261
msgid "Everything is done and it is safe to exit now. The following are optional."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:263
msgid ""
"If you are using standard Python instead of Jupyter Notebook, you can add"
" ``input()`` or ``signal.pause()`` to prevent Python from exiting, "
"allowing you to view the web portal after the experiment is done."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:293
msgid ""
":meth:`nni.experiment.Experiment.stop` is automatically invoked when "
"Python exits, so it can be omitted in your code."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:296
msgid ""
"After the experiment is stopped, you can run "
":meth:`nni.experiment.Experiment.view` to restart web portal."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:300
msgid ""
"This example uses :doc:`Python API </reference/experiment>` to create "
"experiment."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:302
msgid ""
"You can also create and manage experiments with :doc:`command line tool "
"<../hpo_nnictl/nnictl>`."
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:307
msgid "**Total running time of the script:** ( 1 minutes  24.367 seconds)"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:322
msgid ":download:`Download Python source code: main.py <main.py>`"
msgstr ""

#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:328
msgid ":download:`Download Jupyter notebook: main.ipynb <main.ipynb>`"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:13
msgid ""
"Click :ref:`here "
"<sphx_glr_download_tutorials_pruning_quick_start_mnist.py>` to download "
"the full example code"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:22
msgid "Pruning Quickstart"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:24
msgid ""
"Model pruning is a technique to reduce the model size and computation by "
"reducing model weight size or intermediate state size. There are three "
"common practices for pruning a DNN model:"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:27
msgid "Pre-training a model -> Pruning the model -> Fine-tuning the pruned model"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:28
msgid ""
"Pruning a model during training (i.e., pruning aware training) -> Fine-"
"tuning the pruned model"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:29
msgid "Pruning a model -> Training the pruned model from scratch"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:31
msgid ""
"NNI supports all of the above pruning practices by working on the key "
"pruning stage. Following this tutorial for a quick look at how to use NNI"
" to prune a model in a common practice."
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:37
msgid "Preparation"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:39
msgid ""
"In this tutorial, we use a simple model and pre-trained on MNIST dataset."
" If you are familiar with defining a model and training in pytorch, you "
"can skip directly to `Pruning Model`_."
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:121
msgid "Pruning Model"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:123
msgid ""
"Using L1NormPruner to prune the model and generate the masks. Usually, a "
"pruner requires original model and ``config_list`` as its inputs. "
"Detailed about how to write ``config_list`` please refer "
":doc:`compression config specification "
"<../compression/compression_config_list>`."
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:127
msgid ""
"The following `config_list` means all layers whose type is `Linear` or "
"`Conv2d` will be pruned, except the layer named `fc3`, because `fc3` is "
"`exclude`. The final sparsity ratio for each layer is 50%. The layer "
"named `fc3` will not be pruned."
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:153
msgid "Pruners usually require `model` and `config_list` as input arguments."
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:232
msgid ""
"Speedup the original model with masks, note that `ModelSpeedup` requires "
"an unwrapped model. The model becomes smaller after speedup, and reaches "
"a higher sparsity ratio because `ModelSpeedup` will propagate the masks "
"across layers."
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:269
msgid "the model will become real smaller after speedup"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:307
msgid "Fine-tuning Compacted Model"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:308
msgid ""
"Note that if the model has been sped up, you need to re-initialize a new "
"optimizer for fine-tuning. Because speedup will replace the masked big "
"layers with dense small ones."
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:329
msgid "**Total running time of the script:** ( 0 minutes  58.337 seconds)"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:344
msgid ""
":download:`Download Python source code: pruning_quick_start_mnist.py "
"<pruning_quick_start_mnist.py>`"
msgstr ""

#: ../../source/tutorials/pruning_quick_start_mnist.rst:350
msgid ""
":download:`Download Jupyter notebook: pruning_quick_start_mnist.ipynb "
"<pruning_quick_start_mnist.ipynb>`"
msgstr ""

#~ msgid "**Total running time of the script:** ( 2 minutes  15.810 seconds)"
#~ msgstr ""

#~ msgid "NNI HPO Quickstart with PyTorch"
#~ msgstr ""

#~ msgid ""
#~ "There is also a :doc:`TensorFlow "
#~ "version<../hpo_quickstart_tensorflow/main>` if you "
#~ "prefer it."
#~ msgstr ""

#~ msgid ""
#~ "You can also create and manage "
#~ "experiments with :doc:`command line tool "
#~ "</reference/nnictl>`."
#~ msgstr ""

#~ msgid "**Total running time of the script:** ( 1 minutes  24.393 seconds)"
#~ msgstr ""