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Unverified Commit a911b856 authored by Yuge Zhang's avatar Yuge Zhang Committed by GitHub
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Resolve conflicts for #4760 (#4762)

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docs/source/tutorials/index.rst
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......@@ -11,14 +11,14 @@ Tutorials
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Get started with NAS on MNIST">
<div class="sphx-glr-thumbcontainer" tooltip="Introduction ------------">
.. only:: html
.. figure:: /tutorials/images/thumb/sphx_glr_nas_quick_start_mnist_thumb.png
:alt: Get started with NAS on MNIST
.. figure:: /tutorials/images/thumb/sphx_glr_pruning_speedup_thumb.png
:alt: Speedup Model with Mask
:ref:`sphx_glr_tutorials_nas_quick_start_mnist.py`
:ref:`sphx_glr_tutorials_pruning_speedup.py`
.. raw:: html
......@@ -28,18 +28,18 @@ Tutorials
.. toctree::
:hidden:
/tutorials/nas_quick_start_mnist
/tutorials/pruning_speedup
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Start and Manage a New Experiment">
<div class="sphx-glr-thumbcontainer" tooltip=" Introduction ------------">
.. only:: html
.. figure:: /tutorials/images/thumb/sphx_glr_nni_experiment_thumb.png
:alt: Start and Manage a New Experiment
.. figure:: /tutorials/images/thumb/sphx_glr_quantization_speedup_thumb.png
:alt: SpeedUp Model with Calibration Config
:ref:`sphx_glr_tutorials_nni_experiment.py`
:ref:`sphx_glr_tutorials_quantization_speedup.py`
.. raw:: html
......@@ -49,7 +49,237 @@ Tutorials
.. toctree::
:hidden:
/tutorials/nni_experiment
/tutorials/quantization_speedup
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Quantization reduces model size and speeds up inference time by reducing the number of bits req...">
.. only:: html
.. figure:: /tutorials/images/thumb/sphx_glr_quantization_quick_start_mnist_thumb.png
:alt: Quantization Quickstart
:ref:`sphx_glr_tutorials_quantization_quick_start_mnist.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/quantization_quick_start_mnist
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Model pruning is a technique to reduce the model size and computation by reducing model weight ...">
.. only:: html
.. figure:: /tutorials/images/thumb/sphx_glr_pruning_quick_start_mnist_thumb.png
:alt: Pruning Quickstart
:ref:`sphx_glr_tutorials_pruning_quick_start_mnist.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/pruning_quick_start_mnist
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="To write a new quantization algorithm, you can write a class that inherits nni.compression.pyto...">
.. only:: html
.. figure:: /tutorials/images/thumb/sphx_glr_quantization_customize_thumb.png
:alt: Customize a new quantization algorithm
:ref:`sphx_glr_tutorials_quantization_customize.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/quantization_customize
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we show how to use NAS Benchmarks as datasets. For research purposes we somet...">
.. only:: html
.. figure:: /tutorials/images/thumb/sphx_glr_nasbench_as_dataset_thumb.png
:alt: Use NAS Benchmarks as Datasets
:ref:`sphx_glr_tutorials_nasbench_as_dataset.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/nasbench_as_dataset
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="Users can easily customize a basic pruner in NNI. A large number of basic modules have been pro...">
.. only:: html
.. figure:: /tutorials/images/thumb/sphx_glr_pruning_customize_thumb.png
:alt: Customize Basic Pruner
:ref:`sphx_glr_tutorials_pruning_customize.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/pruning_customize
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="This is the 101 tutorial of Neural Architecture Search (NAS) on NNI. In this tutorial, we will ...">
.. only:: html
.. figure:: /tutorials/images/thumb/sphx_glr_hello_nas_thumb.png
:alt: Hello, NAS!
:ref:`sphx_glr_tutorials_hello_nas.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/hello_nas
.. raw:: html
<div class="sphx-glr-clear"></div>
.. _sphx_glr_tutorials_hpo_quickstart_pytorch:
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="The tutorial consists of 4 steps: ">
.. only:: html
.. figure:: /tutorials/hpo_quickstart_pytorch/images/thumb/sphx_glr_main_thumb.png
:alt: HPO Quickstart with PyTorch
:ref:`sphx_glr_tutorials_hpo_quickstart_pytorch_main.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/hpo_quickstart_pytorch/main
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="It can be run directly and will have the exact same result as original version.">
.. only:: html
.. figure:: /tutorials/hpo_quickstart_pytorch/images/thumb/sphx_glr_model_thumb.png
:alt: Port PyTorch Quickstart to NNI
:ref:`sphx_glr_tutorials_hpo_quickstart_pytorch_model.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/hpo_quickstart_pytorch/model
.. raw:: html
<div class="sphx-glr-clear"></div>
.. _sphx_glr_tutorials_hpo_quickstart_tensorflow:
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="The tutorial consists of 4 steps: ">
.. only:: html
.. figure:: /tutorials/hpo_quickstart_tensorflow/images/thumb/sphx_glr_main_thumb.png
:alt: HPO Quickstart with TensorFlow
:ref:`sphx_glr_tutorials_hpo_quickstart_tensorflow_main.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/hpo_quickstart_tensorflow/main
.. raw:: html
<div class="sphx-glr-thumbcontainer" tooltip="It can be run directly and will have the exact same result as original version.">
.. only:: html
.. figure:: /tutorials/hpo_quickstart_tensorflow/images/thumb/sphx_glr_model_thumb.png
:alt: Port TensorFlow Quickstart to NNI
:ref:`sphx_glr_tutorials_hpo_quickstart_tensorflow_model.py`
.. raw:: html
</div>
.. toctree::
:hidden:
/tutorials/hpo_quickstart_tensorflow/model
.. raw:: html
<div class="sphx-glr-clear"></div>
......
docs/source/tutorials/nas_quick_start_mnist.ipynb deleted 100644 → 0
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Get started with NAS on MNIST\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = (1, 2, 3)\na"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print('hello')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
\ No newline at end of file
docs/source/tutorials/nas_quick_start_mnist.py deleted 100644 → 0
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"""
Get started with NAS on MNIST
=============================
"""
# %%
a = (1, 2, 3)
a
# %%
print('hello')
docs/source/tutorials/nas_quick_start_mnist.py.md5 deleted 100644 → 0
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f87a716bc3274d0f9a77db503198ac4a
\ No newline at end of file
docs/source/tutorials/nas_quick_start_mnist.rst deleted 100644 → 0
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.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/nas_quick_start_mnist.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_tutorials_nas_quick_start_mnist.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_tutorials_nas_quick_start_mnist.py:
Get started with NAS on MNIST
=============================
.. GENERATED FROM PYTHON SOURCE LINES 7-10
.. code-block:: default
a = (1, 2, 3)
a
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
(1, 2, 3)
.. GENERATED FROM PYTHON SOURCE LINES 11-12
.. code-block:: default
print('hello')
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
hello
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.002 seconds)
.. _sphx_glr_download_tutorials_nas_quick_start_mnist.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: nas_quick_start_mnist.py <nas_quick_start_mnist.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: nas_quick_start_mnist.ipynb <nas_quick_start_mnist.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
docs/source/tutorials/nasbench_as_dataset.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Use NAS Benchmarks as Datasets\n\nIn this tutorial, we show how to use NAS Benchmarks as datasets.\nFor research purposes we sometimes desire to query the benchmarks for architecture accuracies,\nrather than train them one by one from scratch.\nNNI has provided query tools so that users can easily get the retrieve the data in NAS benchmarks.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\nThis tutorial assumes that you have already prepared your NAS benchmarks under cache directory\n(by default, ``~/.cache/nni/nasbenchmark``).\nIf you haven't, please follow the data preparation guide in :doc:`/nas/benchmarks`.\n\nAs a result, the directory should look like:\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\nos.listdir(os.path.expanduser('~/.cache/nni/nasbenchmark'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pprint\n\nfrom nni.nas.benchmarks.nasbench101 import query_nb101_trial_stats\nfrom nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats\nfrom nni.nas.benchmarks.nds import query_nds_trial_stats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NAS-Bench-101\n\nUse the following architecture as an example:\n\n<img src=\"file://../../img/nas-bench-101-example.png\">\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"arch = {\n 'op1': 'conv3x3-bn-relu',\n 'op2': 'maxpool3x3',\n 'op3': 'conv3x3-bn-relu',\n 'op4': 'conv3x3-bn-relu',\n 'op5': 'conv1x1-bn-relu',\n 'input1': [0],\n 'input2': [1],\n 'input3': [2],\n 'input4': [0],\n 'input5': [0, 3, 4],\n 'input6': [2, 5]\n}\nfor t in query_nb101_trial_stats(arch, 108, include_intermediates=True):\n pprint.pprint(t)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An architecture of NAS-Bench-101 could be trained more than once.\nEach element of the returned generator is a dict which contains one of the training results of this trial config\n(architecture + hyper-parameters) including train/valid/test accuracy,\ntraining time, number of epochs, etc. The results of NAS-Bench-201 and NDS follow similar formats.\n\n## NAS-Bench-201\n\nUse the following architecture as an example:\n\n<img src=\"file://../../img/nas-bench-201-example.png\">\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"arch = {\n '0_1': 'avg_pool_3x3',\n '0_2': 'conv_1x1',\n '1_2': 'skip_connect',\n '0_3': 'conv_1x1',\n '1_3': 'skip_connect',\n '2_3': 'skip_connect'\n}\nfor t in query_nb201_trial_stats(arch, 200, 'cifar100'):\n pprint.pprint(t)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Intermediate results are also available.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for t in query_nb201_trial_stats(arch, None, 'imagenet16-120', include_intermediates=True):\n print(t['config'])\n print('Intermediates:', len(t['intermediates']))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NDS\n\nUse the following architecture as an example:\n\n<img src=\"file://../../img/nas-bench-nds-example.png\">\n\nHere, ``bot_muls``, ``ds``, ``num_gs``, ``ss`` and ``ws`` stand for \"bottleneck multipliers\",\n\"depths\", \"number of groups\", \"strides\" and \"widths\" respectively.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model_spec = {\n 'bot_muls': [0.0, 0.25, 0.25, 0.25],\n 'ds': [1, 16, 1, 4],\n 'num_gs': [1, 2, 1, 2],\n 'ss': [1, 1, 2, 2],\n 'ws': [16, 64, 128, 16]\n}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use none as a wildcard.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):\n pprint.pprint(t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model_spec = {\n 'bot_muls': [0.0, 0.25, 0.25, 0.25],\n 'ds': [1, 16, 1, 4],\n 'num_gs': [1, 2, 1, 2],\n 'ss': [1, 1, 2, 2],\n 'ws': [16, 64, 128, 16]\n}\nfor t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10', include_intermediates=True):\n pprint.pprint(t['intermediates'][:10])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}\nfor t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):\n pprint.pprint(t)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the first one.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Count number.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}\ncell_spec = {\n 'normal_0_op_x': 'avg_pool_3x3',\n 'normal_0_input_x': 0,\n 'normal_0_op_y': 'conv_7x1_1x7',\n 'normal_0_input_y': 1,\n 'normal_1_op_x': 'sep_conv_3x3',\n 'normal_1_input_x': 2,\n 'normal_1_op_y': 'sep_conv_5x5',\n 'normal_1_input_y': 0,\n 'normal_2_op_x': 'dil_sep_conv_3x3',\n 'normal_2_input_x': 2,\n 'normal_2_op_y': 'dil_sep_conv_3x3',\n 'normal_2_input_y': 2,\n 'normal_3_op_x': 'skip_connect',\n 'normal_3_input_x': 4,\n 'normal_3_op_y': 'dil_sep_conv_3x3',\n 'normal_3_input_y': 4,\n 'normal_4_op_x': 'conv_7x1_1x7',\n 'normal_4_input_x': 2,\n 'normal_4_op_y': 'sep_conv_3x3',\n 'normal_4_input_y': 4,\n 'normal_concat': [3, 5, 6],\n 'reduce_0_op_x': 'avg_pool_3x3',\n 'reduce_0_input_x': 0,\n 'reduce_0_op_y': 'dil_sep_conv_3x3',\n 'reduce_0_input_y': 1,\n 'reduce_1_op_x': 'sep_conv_3x3',\n 'reduce_1_input_x': 0,\n 'reduce_1_op_y': 'sep_conv_3x3',\n 'reduce_1_input_y': 0,\n 'reduce_2_op_x': 'skip_connect',\n 'reduce_2_input_x': 2,\n 'reduce_2_op_y': 'sep_conv_7x7',\n 'reduce_2_input_y': 0,\n 'reduce_3_op_x': 'conv_7x1_1x7',\n 'reduce_3_input_x': 4,\n 'reduce_3_op_y': 'skip_connect',\n 'reduce_3_input_y': 4,\n 'reduce_4_op_x': 'conv_7x1_1x7',\n 'reduce_4_input_x': 0,\n 'reduce_4_op_y': 'conv_7x1_1x7',\n 'reduce_4_input_y': 5,\n 'reduce_concat': [3, 6]\n}\n\nfor t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):\n assert t['config']['model_spec'] == model_spec\n assert t['config']['cell_spec'] == cell_spec\n pprint.pprint(t)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Count number.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
\ No newline at end of file
docs/source/tutorials/nasbench_as_dataset.py 0 → 100644
View file @ a911b856
"""
Use NAS Benchmarks as Datasets
==============================
In this tutorial, we show how to use NAS Benchmarks as datasets.
For research purposes we sometimes desire to query the benchmarks for architecture accuracies,
rather than train them one by one from scratch.
NNI has provided query tools so that users can easily get the retrieve the data in NAS benchmarks.
"""
# %%
# Prerequisites
# -------------
# This tutorial assumes that you have already prepared your NAS benchmarks under cache directory
# (by default, ``~/.cache/nni/nasbenchmark``).
# If you haven't, please follow the data preparation guide in :doc:`/nas/benchmarks`.
#
# As a result, the directory should look like:
import os
os.listdir(os.path.expanduser('~/.cache/nni/nasbenchmark'))
# %%
import pprint
from nni.nas.benchmarks.nasbench101 import query_nb101_trial_stats
from nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats
from nni.nas.benchmarks.nds import query_nds_trial_stats
# %%
# NAS-Bench-101
# -------------
#
# Use the following architecture as an example:
#
# .. image:: ../../img/nas-bench-101-example.png
arch = {
'op1': 'conv3x3-bn-relu',
'op2': 'maxpool3x3',
'op3': 'conv3x3-bn-relu',
'op4': 'conv3x3-bn-relu',
'op5': 'conv1x1-bn-relu',
'input1': [0],
'input2': [1],
'input3': [2],
'input4': [0],
'input5': [0, 3, 4],
'input6': [2, 5]
}
for t in query_nb101_trial_stats(arch, 108, include_intermediates=True):
pprint.pprint(t)
# %%
# An architecture of NAS-Bench-101 could be trained more than once.
# Each element of the returned generator is a dict which contains one of the training results of this trial config
# (architecture + hyper-parameters) including train/valid/test accuracy,
# training time, number of epochs, etc. The results of NAS-Bench-201 and NDS follow similar formats.
#
# NAS-Bench-201
# -------------
#
# Use the following architecture as an example:
#
# .. image:: ../../img/nas-bench-201-example.png
arch = {
'0_1': 'avg_pool_3x3',
'0_2': 'conv_1x1',
'1_2': 'skip_connect',
'0_3': 'conv_1x1',
'1_3': 'skip_connect',
'2_3': 'skip_connect'
}
for t in query_nb201_trial_stats(arch, 200, 'cifar100'):
pprint.pprint(t)
# %%
# Intermediate results are also available.
for t in query_nb201_trial_stats(arch, None, 'imagenet16-120', include_intermediates=True):
print(t['config'])
print('Intermediates:', len(t['intermediates']))
# %%
# NDS
# ---
#
# Use the following architecture as an example:
#
# .. image:: ../../img/nas-bench-nds-example.png
#
# Here, ``bot_muls``, ``ds``, ``num_gs``, ``ss`` and ``ws`` stand for "bottleneck multipliers",
# "depths", "number of groups", "strides" and "widths" respectively.
# %%
model_spec = {
'bot_muls': [0.0, 0.25, 0.25, 0.25],
'ds': [1, 16, 1, 4],
'num_gs': [1, 2, 1, 2],
'ss': [1, 1, 2, 2],
'ws': [16, 64, 128, 16]
}
# %%
# Use none as a wildcard.
for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):
pprint.pprint(t)
# %%
model_spec = {
'bot_muls': [0.0, 0.25, 0.25, 0.25],
'ds': [1, 16, 1, 4],
'num_gs': [1, 2, 1, 2],
'ss': [1, 1, 2, 2],
'ws': [16, 64, 128, 16]
}
for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10', include_intermediates=True):
pprint.pprint(t['intermediates'][:10])
# %%
model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}
for t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):
pprint.pprint(t)
# %%
# Get the first one.
pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))
# %%
# Count number.
model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}
cell_spec = {
'normal_0_op_x': 'avg_pool_3x3',
'normal_0_input_x': 0,
'normal_0_op_y': 'conv_7x1_1x7',
'normal_0_input_y': 1,
'normal_1_op_x': 'sep_conv_3x3',
'normal_1_input_x': 2,
'normal_1_op_y': 'sep_conv_5x5',
'normal_1_input_y': 0,
'normal_2_op_x': 'dil_sep_conv_3x3',
'normal_2_input_x': 2,
'normal_2_op_y': 'dil_sep_conv_3x3',
'normal_2_input_y': 2,
'normal_3_op_x': 'skip_connect',
'normal_3_input_x': 4,
'normal_3_op_y': 'dil_sep_conv_3x3',
'normal_3_input_y': 4,
'normal_4_op_x': 'conv_7x1_1x7',
'normal_4_input_x': 2,
'normal_4_op_y': 'sep_conv_3x3',
'normal_4_input_y': 4,
'normal_concat': [3, 5, 6],
'reduce_0_op_x': 'avg_pool_3x3',
'reduce_0_input_x': 0,
'reduce_0_op_y': 'dil_sep_conv_3x3',
'reduce_0_input_y': 1,
'reduce_1_op_x': 'sep_conv_3x3',
'reduce_1_input_x': 0,
'reduce_1_op_y': 'sep_conv_3x3',
'reduce_1_input_y': 0,
'reduce_2_op_x': 'skip_connect',
'reduce_2_input_x': 2,
'reduce_2_op_y': 'sep_conv_7x7',
'reduce_2_input_y': 0,
'reduce_3_op_x': 'conv_7x1_1x7',
'reduce_3_input_x': 4,
'reduce_3_op_y': 'skip_connect',
'reduce_3_input_y': 4,
'reduce_4_op_x': 'conv_7x1_1x7',
'reduce_4_input_x': 0,
'reduce_4_op_y': 'conv_7x1_1x7',
'reduce_4_input_y': 5,
'reduce_concat': [3, 6]
}
for t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):
assert t['config']['model_spec'] == model_spec
assert t['config']['cell_spec'] == cell_spec
pprint.pprint(t)
# %%
# Count number.
print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))
docs/source/tutorials/nasbench_as_dataset.py.md5 0 → 100644
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docs/source/tutorials/nasbench_as_dataset.rst 0 → 100644
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.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/nasbench_as_dataset.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_tutorials_nasbench_as_dataset.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_tutorials_nasbench_as_dataset.py:
Use NAS Benchmarks as Datasets
==============================
In this tutorial, we show how to use NAS Benchmarks as datasets.
For research purposes we sometimes desire to query the benchmarks for architecture accuracies,
rather than train them one by one from scratch.
NNI has provided query tools so that users can easily get the retrieve the data in NAS benchmarks.
.. GENERATED FROM PYTHON SOURCE LINES 12-19
Prerequisites
-------------
This tutorial assumes that you have already prepared your NAS benchmarks under cache directory
(by default, ``~/.cache/nni/nasbenchmark``).
If you haven't, please follow the data preparation guide in :doc:`/nas/benchmarks`.
As a result, the directory should look like:
.. GENERATED FROM PYTHON SOURCE LINES 19-23
.. code-block:: default
import os
os.listdir(os.path.expanduser('~/.cache/nni/nasbenchmark'))
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
['nasbench101-209f5694.db', 'nasbench201-b2b60732.db', 'nds-5745c235.db']
.. GENERATED FROM PYTHON SOURCE LINES 24-30
.. code-block:: default
import pprint
from nni.nas.benchmarks.nasbench101 import query_nb101_trial_stats
from nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats
from nni.nas.benchmarks.nds import query_nds_trial_stats
.. GENERATED FROM PYTHON SOURCE LINES 31-37
NAS-Bench-101
-------------
Use the following architecture as an example:
.. image:: ../../img/nas-bench-101-example.png
.. GENERATED FROM PYTHON SOURCE LINES 37-54
.. code-block:: default
arch = {
'op1': 'conv3x3-bn-relu',
'op2': 'maxpool3x3',
'op3': 'conv3x3-bn-relu',
'op4': 'conv3x3-bn-relu',
'op5': 'conv1x1-bn-relu',
'input1': [0],
'input2': [1],
'input3': [2],
'input4': [0],
'input5': [0, 3, 4],
'input6': [2, 5]
}
for t in query_nb101_trial_stats(arch, 108, include_intermediates=True):
pprint.pprint(t)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[2022-02-28 13:48:51] INFO (nni.nas.benchmarks.utils/MainThread) "/home/yugzhan/.cache/nni/nasbenchmark/nasbench101-209f5694.db" already exists. Checking hash.
{'config': {'arch': {'input1': [0],
'input2': [1],
'input3': [2],
'input4': [0],
'input5': [0, 3, 4],
'input6': [2, 5],
'op1': 'conv3x3-bn-relu',
'op2': 'maxpool3x3',
'op3': 'conv3x3-bn-relu',
'op4': 'conv3x3-bn-relu',
'op5': 'conv1x1-bn-relu'},
'hash': '00005c142e6f48ac74fdcf73e3439874',
'id': 4,
'num_epochs': 108,
'num_vertices': 7},
'id': 10,
'intermediates': [{'current_epoch': 54,
'id': 19,
'test_acc': 77.40384340286255,
'train_acc': 82.82251358032227,
'training_time': 883.4580078125,
'valid_acc': 77.76442170143127},
{'current_epoch': 108,
'id': 20,
'test_acc': 92.11738705635071,
'train_acc': 100.0,
'training_time': 1769.1279296875,
'valid_acc': 92.41786599159241}],
'parameters': 8.55553,
'test_acc': 92.11738705635071,
'train_acc': 100.0,
'training_time': 106147.67578125,
'valid_acc': 92.41786599159241}
{'config': {'arch': {'input1': [0],
'input2': [1],
'input3': [2],
'input4': [0],
'input5': [0, 3, 4],
'input6': [2, 5],
'op1': 'conv3x3-bn-relu',
'op2': 'maxpool3x3',
'op3': 'conv3x3-bn-relu',
'op4': 'conv3x3-bn-relu',
'op5': 'conv1x1-bn-relu'},
'hash': '00005c142e6f48ac74fdcf73e3439874',
'id': 4,
'num_epochs': 108,
'num_vertices': 7},
'id': 11,
'intermediates': [{'current_epoch': 54,
'id': 21,
'test_acc': 82.04126358032227,
'train_acc': 87.96073794364929,
'training_time': 883.6810302734375,
'valid_acc': 82.91265964508057},
{'current_epoch': 108,
'id': 22,
'test_acc': 91.90705418586731,
'train_acc': 100.0,
'training_time': 1768.2509765625,
'valid_acc': 92.45793223381042}],
'parameters': 8.55553,
'test_acc': 91.90705418586731,
'train_acc': 100.0,
'training_time': 106095.05859375,
'valid_acc': 92.45793223381042}
{'config': {'arch': {'input1': [0],
'input2': [1],
'input3': [2],
'input4': [0],
'input5': [0, 3, 4],
'input6': [2, 5],
'op1': 'conv3x3-bn-relu',
'op2': 'maxpool3x3',
'op3': 'conv3x3-bn-relu',
'op4': 'conv3x3-bn-relu',
'op5': 'conv1x1-bn-relu'},
'hash': '00005c142e6f48ac74fdcf73e3439874',
'id': 4,
'num_epochs': 108,
'num_vertices': 7},
'id': 12,
'intermediates': [{'current_epoch': 54,
'id': 23,
'test_acc': 80.58894276618958,
'train_acc': 86.34815812110901,
'training_time': 883.4569702148438,
'valid_acc': 81.1598539352417},
{'current_epoch': 108,
'id': 24,
'test_acc': 92.15745329856873,
'train_acc': 100.0,
'training_time': 1768.9759521484375,
'valid_acc': 93.04887652397156}],
'parameters': 8.55553,
'test_acc': 92.15745329856873,
'train_acc': 100.0,
'training_time': 106138.55712890625,
'valid_acc': 93.04887652397156}
.. GENERATED FROM PYTHON SOURCE LINES 55-66
An architecture of NAS-Bench-101 could be trained more than once.
Each element of the returned generator is a dict which contains one of the training results of this trial config
(architecture + hyper-parameters) including train/valid/test accuracy,
training time, number of epochs, etc. The results of NAS-Bench-201 and NDS follow similar formats.
NAS-Bench-201
-------------
Use the following architecture as an example:
.. image:: ../../img/nas-bench-201-example.png
.. GENERATED FROM PYTHON SOURCE LINES 66-78
.. code-block:: default
arch = {
'0_1': 'avg_pool_3x3',
'0_2': 'conv_1x1',
'1_2': 'skip_connect',
'0_3': 'conv_1x1',
'1_3': 'skip_connect',
'2_3': 'skip_connect'
}
for t in query_nb201_trial_stats(arch, 200, 'cifar100'):
pprint.pprint(t)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[2022-02-28 13:49:09] INFO (nni.nas.benchmarks.utils/MainThread) "/home/yugzhan/.cache/nni/nasbenchmark/nasbench201-b2b60732.db" already exists. Checking hash.
{'config': {'arch': {'0_1': 'avg_pool_3x3',
'0_2': 'conv_1x1',
'0_3': 'conv_1x1',
'1_2': 'skip_connect',
'1_3': 'skip_connect',
'2_3': 'skip_connect'},
'dataset': 'cifar100',
'id': 7,
'num_cells': 5,
'num_channels': 16,
'num_epochs': 200},
'flops': 15.65322,
'id': 3,
'latency': 0.013182918230692545,
'ori_test_acc': 53.11,
'ori_test_evaluation_time': 1.0195916947864352,
'ori_test_loss': 1.7307863704681397,
'parameters': 0.135156,
'seed': 999,
'test_acc': 53.07999995727539,
'test_evaluation_time': 0.5097958473932176,
'test_loss': 1.731276072692871,
'train_acc': 57.82,
'train_loss': 1.5116578379058838,
'training_time': 2888.4371995925903,
'valid_acc': 53.14000000610351,
'valid_evaluation_time': 0.5097958473932176,
'valid_loss': 1.7302966793060304}
{'config': {'arch': {'0_1': 'avg_pool_3x3',
'0_2': 'conv_1x1',
'0_3': 'conv_1x1',
'1_2': 'skip_connect',
'1_3': 'skip_connect',
'2_3': 'skip_connect'},
'dataset': 'cifar100',
'id': 7,
'num_cells': 5,
'num_channels': 16,
'num_epochs': 200},
'flops': 15.65322,
'id': 7,
'latency': 0.013182918230692545,
'ori_test_acc': 51.93,
'ori_test_evaluation_time': 1.0195916947864352,
'ori_test_loss': 1.7572312774658203,
'parameters': 0.135156,
'seed': 777,
'test_acc': 51.979999938964845,
'test_evaluation_time': 0.5097958473932176,
'test_loss': 1.7429540189743042,
'train_acc': 57.578,
'train_loss': 1.5114233912658692,
'training_time': 2888.4371995925903,
'valid_acc': 51.88,
'valid_evaluation_time': 0.5097958473932176,
'valid_loss': 1.7715086591720581}
{'config': {'arch': {'0_1': 'avg_pool_3x3',
'0_2': 'conv_1x1',
'0_3': 'conv_1x1',
'1_2': 'skip_connect',
'1_3': 'skip_connect',
'2_3': 'skip_connect'},
'dataset': 'cifar100',
'id': 7,
'num_cells': 5,
'num_channels': 16,
'num_epochs': 200},
'flops': 15.65322,
'id': 11,
'latency': 0.013182918230692545,
'ori_test_acc': 53.38,
'ori_test_evaluation_time': 1.0195916947864352,
'ori_test_loss': 1.7281623031616211,
'parameters': 0.135156,
'seed': 888,
'test_acc': 53.67999998779297,
'test_evaluation_time': 0.5097958473932176,
'test_loss': 1.7327697801589965,
'train_acc': 57.792,
'train_loss': 1.5091403088760376,
'training_time': 2888.4371995925903,
'valid_acc': 53.08000000610352,
'valid_evaluation_time': 0.5097958473932176,
'valid_loss': 1.7235548280715942}
.. GENERATED FROM PYTHON SOURCE LINES 79-80
Intermediate results are also available.
.. GENERATED FROM PYTHON SOURCE LINES 80-85
.. code-block:: default
for t in query_nb201_trial_stats(arch, None, 'imagenet16-120', include_intermediates=True):
print(t['config'])
print('Intermediates:', len(t['intermediates']))
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
{'id': 4, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 12, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
Intermediates: 12
{'id': 8, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 200, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
Intermediates: 200
{'id': 8, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 200, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
Intermediates: 200
{'id': 8, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 200, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
Intermediates: 200
.. GENERATED FROM PYTHON SOURCE LINES 86-95
NDS
---
Use the following architecture as an example:
.. image:: ../../img/nas-bench-nds-example.png
Here, ``bot_muls``, ``ds``, ``num_gs``, ``ss`` and ``ws`` stand for "bottleneck multipliers",
"depths", "number of groups", "strides" and "widths" respectively.
.. GENERATED FROM PYTHON SOURCE LINES 97-105
.. code-block:: default
model_spec = {
'bot_muls': [0.0, 0.25, 0.25, 0.25],
'ds': [1, 16, 1, 4],
'num_gs': [1, 2, 1, 2],
'ss': [1, 1, 2, 2],
'ws': [16, 64, 128, 16]
}
.. GENERATED FROM PYTHON SOURCE LINES 106-107
Use none as a wildcard.
.. GENERATED FROM PYTHON SOURCE LINES 107-110
.. code-block:: default
for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):
pprint.pprint(t)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[2022-02-28 13:49:36] INFO (nni.nas.benchmarks.utils/MainThread) "/home/yugzhan/.cache/nni/nasbenchmark/nds-5745c235.db" already exists. Checking hash.
{'best_test_acc': 90.48,
'best_train_acc': 96.356,
'best_train_loss': 0.116,
'config': {'base_lr': 0.1,
'cell_spec': {},
'dataset': 'cifar10',
'generator': 'random',
'id': 45505,
'model_family': 'residual_bottleneck',
'model_spec': {'bot_muls': [0.0, 0.25, 0.25, 0.25],
'ds': [1, 16, 1, 4],
'num_gs': [1, 2, 1, 2],
'ss': [1, 1, 2, 2],
'ws': [16, 64, 128, 16]},
'num_epochs': 100,
'proposer': 'resnext-a',
'weight_decay': 0.0005},
'final_test_acc': 90.39,
'final_train_acc': 96.298,
'final_train_loss': 0.116,
'flops': 69.890986,
'id': 45505,
'iter_time': 0.065,
'parameters': 0.083002,
'seed': 1}
.. GENERATED FROM PYTHON SOURCE LINES 111-121
.. code-block:: default
model_spec = {
'bot_muls': [0.0, 0.25, 0.25, 0.25],
'ds': [1, 16, 1, 4],
'num_gs': [1, 2, 1, 2],
'ss': [1, 1, 2, 2],
'ws': [16, 64, 128, 16]
}
for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10', include_intermediates=True):
pprint.pprint(t['intermediates'][:10])
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[{'current_epoch': 1,
'id': 4494501,
'test_acc': 41.76,
'train_acc': 30.421000000000006,
'train_loss': 1.793},
{'current_epoch': 2,
'id': 4494502,
'test_acc': 54.66,
'train_acc': 47.24,
'train_loss': 1.415},
{'current_epoch': 3,
'id': 4494503,
'test_acc': 59.97,
'train_acc': 56.983,
'train_loss': 1.179},
{'current_epoch': 4,
'id': 4494504,
'test_acc': 62.91,
'train_acc': 61.955,
'train_loss': 1.048},
{'current_epoch': 5,
'id': 4494505,
'test_acc': 66.16,
'train_acc': 64.493,
'train_loss': 0.983},
{'current_epoch': 6,
'id': 4494506,
'test_acc': 66.5,
'train_acc': 66.274,
'train_loss': 0.937},
{'current_epoch': 7,
'id': 4494507,
'test_acc': 67.55,
'train_acc': 67.426,
'train_loss': 0.907},
{'current_epoch': 8,
'id': 4494508,
'test_acc': 69.45,
'train_acc': 68.45400000000001,
'train_loss': 0.878},
{'current_epoch': 9,
'id': 4494509,
'test_acc': 70.14,
'train_acc': 69.295,
'train_loss': 0.857},
{'current_epoch': 10,
'id': 4494510,
'test_acc': 69.47,
'train_acc': 70.304,
'train_loss': 0.832}]
.. GENERATED FROM PYTHON SOURCE LINES 122-126
.. code-block:: default
model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}
for t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):
pprint.pprint(t)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
{'best_test_acc': 93.58,
'best_train_acc': 99.772,
'best_train_loss': 0.011,
'config': {'base_lr': 0.1,
'cell_spec': {},
'dataset': 'cifar10',
'generator': 'random',
'id': 108998,
'model_family': 'residual_basic',
'model_spec': {'ds': [1, 12, 12, 12],
'ss': [1, 1, 2, 2],
'ws': [16, 24, 24, 40]},
'num_epochs': 100,
'proposer': 'resnet',
'weight_decay': 0.0005},
'final_test_acc': 93.49,
'final_train_acc': 99.772,
'final_train_loss': 0.011,
'flops': 184.519578,
'id': 108998,
'iter_time': 0.059,
'parameters': 0.594138,
'seed': 1}
.. GENERATED FROM PYTHON SOURCE LINES 127-128
Get the first one.
.. GENERATED FROM PYTHON SOURCE LINES 128-130
.. code-block:: default
pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
{'best_test_acc': 84.5,
'best_train_acc': 89.66499999999999,
'best_train_loss': 0.302,
'config': {'base_lr': 0.1,
'cell_spec': {},
'dataset': 'cifar10',
'generator': 'random',
'id': 139492,
'model_family': 'vanilla',
'model_spec': {'ds': [1, 12, 12, 12],
'ss': [1, 1, 2, 2],
'ws': [16, 24, 32, 40]},
'num_epochs': 100,
'proposer': 'vanilla',
'weight_decay': 0.0005},
'final_test_acc': 84.35,
'final_train_acc': 89.633,
'final_train_loss': 0.303,
'flops': 208.36393,
'id': 154692,
'iter_time': 0.058,
'parameters': 0.68977,
'seed': 1}
.. GENERATED FROM PYTHON SOURCE LINES 131-132
Count number.
.. GENERATED FROM PYTHON SOURCE LINES 132-183
.. code-block:: default
model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}
cell_spec = {
'normal_0_op_x': 'avg_pool_3x3',
'normal_0_input_x': 0,
'normal_0_op_y': 'conv_7x1_1x7',
'normal_0_input_y': 1,
'normal_1_op_x': 'sep_conv_3x3',
'normal_1_input_x': 2,
'normal_1_op_y': 'sep_conv_5x5',
'normal_1_input_y': 0,
'normal_2_op_x': 'dil_sep_conv_3x3',
'normal_2_input_x': 2,
'normal_2_op_y': 'dil_sep_conv_3x3',
'normal_2_input_y': 2,
'normal_3_op_x': 'skip_connect',
'normal_3_input_x': 4,
'normal_3_op_y': 'dil_sep_conv_3x3',
'normal_3_input_y': 4,
'normal_4_op_x': 'conv_7x1_1x7',
'normal_4_input_x': 2,
'normal_4_op_y': 'sep_conv_3x3',
'normal_4_input_y': 4,
'normal_concat': [3, 5, 6],
'reduce_0_op_x': 'avg_pool_3x3',
'reduce_0_input_x': 0,
'reduce_0_op_y': 'dil_sep_conv_3x3',
'reduce_0_input_y': 1,
'reduce_1_op_x': 'sep_conv_3x3',
'reduce_1_input_x': 0,
'reduce_1_op_y': 'sep_conv_3x3',
'reduce_1_input_y': 0,
'reduce_2_op_x': 'skip_connect',
'reduce_2_input_x': 2,
'reduce_2_op_y': 'sep_conv_7x7',
'reduce_2_input_y': 0,
'reduce_3_op_x': 'conv_7x1_1x7',
'reduce_3_input_x': 4,
'reduce_3_op_y': 'skip_connect',
'reduce_3_input_y': 4,
'reduce_4_op_x': 'conv_7x1_1x7',
'reduce_4_input_x': 0,
'reduce_4_op_y': 'conv_7x1_1x7',
'reduce_4_input_y': 5,
'reduce_concat': [3, 6]
}
for t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):
assert t['config']['model_spec'] == model_spec
assert t['config']['cell_spec'] == cell_spec
pprint.pprint(t)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
{'best_test_acc': 93.37,
'best_train_acc': 99.91,
'best_train_loss': 0.006,
'config': {'base_lr': 0.1,
'cell_spec': {'normal_0_input_x': 0,
'normal_0_input_y': 1,
'normal_0_op_x': 'avg_pool_3x3',
'normal_0_op_y': 'conv_7x1_1x7',
'normal_1_input_x': 2,
'normal_1_input_y': 0,
'normal_1_op_x': 'sep_conv_3x3',
'normal_1_op_y': 'sep_conv_5x5',
'normal_2_input_x': 2,
'normal_2_input_y': 2,
'normal_2_op_x': 'dil_sep_conv_3x3',
'normal_2_op_y': 'dil_sep_conv_3x3',
'normal_3_input_x': 4,
'normal_3_input_y': 4,
'normal_3_op_x': 'skip_connect',
'normal_3_op_y': 'dil_sep_conv_3x3',
'normal_4_input_x': 2,
'normal_4_input_y': 4,
'normal_4_op_x': 'conv_7x1_1x7',
'normal_4_op_y': 'sep_conv_3x3',
'normal_concat': [3, 5, 6],
'reduce_0_input_x': 0,
'reduce_0_input_y': 1,
'reduce_0_op_x': 'avg_pool_3x3',
'reduce_0_op_y': 'dil_sep_conv_3x3',
'reduce_1_input_x': 0,
'reduce_1_input_y': 0,
'reduce_1_op_x': 'sep_conv_3x3',
'reduce_1_op_y': 'sep_conv_3x3',
'reduce_2_input_x': 2,
'reduce_2_input_y': 0,
'reduce_2_op_x': 'skip_connect',
'reduce_2_op_y': 'sep_conv_7x7',
'reduce_3_input_x': 4,
'reduce_3_input_y': 4,
'reduce_3_op_x': 'conv_7x1_1x7',
'reduce_3_op_y': 'skip_connect',
'reduce_4_input_x': 0,
'reduce_4_input_y': 5,
'reduce_4_op_x': 'conv_7x1_1x7',
'reduce_4_op_y': 'conv_7x1_1x7',
'reduce_concat': [3, 6]},
'dataset': 'cifar10',
'generator': 'random',
'id': 1,
'model_family': 'nas_cell',
'model_spec': {'aux': False,
'depth': 12,
'drop_prob': 0.0,
'num_nodes_normal': 5,
'num_nodes_reduce': 5,
'width': 32},
'num_epochs': 100,
'proposer': 'amoeba',
'weight_decay': 0.0005},
'final_test_acc': 93.27,
'final_train_acc': 99.91,
'final_train_loss': 0.006,
'flops': 664.400586,
'id': 1,
'iter_time': 0.281,
'parameters': 4.190314,
'seed': 1}
.. GENERATED FROM PYTHON SOURCE LINES 184-185
Count number.
.. GENERATED FROM PYTHON SOURCE LINES 185-186
.. code-block:: default
print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
NDS (amoeba) count: 5107
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 1 minutes 2.214 seconds)
.. _sphx_glr_download_tutorials_nasbench_as_dataset.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: nasbench_as_dataset.py <nasbench_as_dataset.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: nasbench_as_dataset.ipynb <nasbench_as_dataset.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
docs/source/tutorials/nni_experiment.py deleted 100644 → 0
View file @ 14d2966b
"""
Start and Manage a New Experiment
=================================
"""
# %%
# Configure Search Space
# ----------------------
search_space = {
"C": {"_type": "quniform", "_value": [0.1, 1, 0.1]},
"kernel": {"_type": "choice", "_value": ["linear", "rbf", "poly", "sigmoid"]},
"degree": {"_type": "choice", "_value": [1, 2, 3, 4]},
"gamma": {"_type": "quniform", "_value": [0.01, 0.1, 0.01]},
"coef0": {"_type": "quniform", "_value": [0.01, 0.1, 0.01]}
}
# %%
# Configure Experiment
# --------------------
from nni.experiment import Experiment
experiment = Experiment('local')
experiment.config.experiment_name = 'Example'
experiment.config.trial_concurrency = 2
experiment.config.max_trial_number = 10
experiment.config.search_space = search_space
experiment.config.trial_command = 'python scripts/trial_sklearn.py'
experiment.config.trial_code_directory = './'
experiment.config.tuner.name = 'TPE'
experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
experiment.config.training_service.use_active_gpu = True
# %%
# Start Experiment
# ----------------
experiment.start(8080)
# %%
# Experiment View & Control
# -------------------------
#
# View the status of experiment.
experiment.get_status()
# %%
# Wait until at least one trial finishes.
import time
for _ in range(10):
stats = experiment.get_job_statistics()
if any(stat['trialJobStatus'] == 'SUCCEEDED' for stat in stats):
break
time.sleep(10)
# %%
# Export the experiment data.
experiment.export_data()
# %%
# Get metric of jobs
experiment.get_job_metrics()
# %%
# Stop Experiment
# ---------------
experiment.stop()
docs/source/tutorials/nni_experiment.py.md5 deleted 100644 → 0
View file @ 14d2966b
9f822647d89f05264b70d1ae1c473be1
\ No newline at end of file
docs/source/tutorials/nni_experiment.rst deleted 100644 → 0
View file @ 14d2966b
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/nni_experiment.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_tutorials_nni_experiment.py>`
to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_tutorials_nni_experiment.py:
Start and Manage a New Experiment
=================================
.. GENERATED FROM PYTHON SOURCE LINES 7-9
Configure Search Space
----------------------
.. GENERATED FROM PYTHON SOURCE LINES 9-18
.. code-block:: default
search_space = {
"C": {"_type": "quniform", "_value": [0.1, 1, 0.1]},
"kernel": {"_type": "choice", "_value": ["linear", "rbf", "poly", "sigmoid"]},
"degree": {"_type": "choice", "_value": [1, 2, 3, 4]},
"gamma": {"_type": "quniform", "_value": [0.01, 0.1, 0.01]},
"coef0": {"_type": "quniform", "_value": [0.01, 0.1, 0.01]}
}
.. GENERATED FROM PYTHON SOURCE LINES 19-21
Configure Experiment
--------------------
.. GENERATED FROM PYTHON SOURCE LINES 21-34
.. code-block:: default
from nni.experiment import Experiment
experiment = Experiment('local')
experiment.config.experiment_name = 'Example'
experiment.config.trial_concurrency = 2
experiment.config.max_trial_number = 10
experiment.config.search_space = search_space
experiment.config.trial_command = 'python scripts/trial_sklearn.py'
experiment.config.trial_code_directory = './'
experiment.config.tuner.name = 'TPE'
experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
experiment.config.training_service.use_active_gpu = True
.. GENERATED FROM PYTHON SOURCE LINES 35-37
Start Experiment
----------------
.. GENERATED FROM PYTHON SOURCE LINES 37-39
.. code-block:: default
experiment.start(8080)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[2022-02-07 18:56:04] Creating experiment, Experiment ID: fl9vu67z
[2022-02-07 18:56:04] Starting web server...
[2022-02-07 18:56:05] Setting up...
[2022-02-07 18:56:05] Web UI URLs: http://127.0.0.1:8080 http://10.190.173.211:8080 http://172.17.0.1:8080 http://192.168.49.1:8080
.. GENERATED FROM PYTHON SOURCE LINES 40-44
Experiment View & Control
-------------------------
View the status of experiment.
.. GENERATED FROM PYTHON SOURCE LINES 44-46
.. code-block:: default
experiment.get_status()
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
'RUNNING'
.. GENERATED FROM PYTHON SOURCE LINES 47-48
Wait until at least one trial finishes.
.. GENERATED FROM PYTHON SOURCE LINES 48-56
.. code-block:: default
import time
for _ in range(10):
stats = experiment.get_job_statistics()
if any(stat['trialJobStatus'] == 'SUCCEEDED' for stat in stats):
break
time.sleep(10)
.. GENERATED FROM PYTHON SOURCE LINES 57-58
Export the experiment data.
.. GENERATED FROM PYTHON SOURCE LINES 58-60
.. code-block:: default
experiment.export_data()
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[TrialResult(parameter={'C': 0.9, 'kernel': 'rbf', 'degree': 4, 'gamma': 0.07, 'coef0': 0.03}, value=0.9733333333333334, trialJobId='dNOZt'), TrialResult(parameter={'C': 0.8, 'kernel': 'sigmoid', 'degree': 2, 'gamma': 0.01, 'coef0': 0.01}, value=0.9733333333333334, trialJobId='okYSD')]
.. GENERATED FROM PYTHON SOURCE LINES 61-62
Get metric of jobs
.. GENERATED FROM PYTHON SOURCE LINES 62-64
.. code-block:: default
experiment.get_job_metrics()
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
{'okYSD': [TrialMetricData(timestamp=1644227777089, trialJobId='okYSD', parameterId='1', type='FINAL', sequence=0, data=0.9733333333333334)], 'dNOZt': [TrialMetricData(timestamp=1644227777357, trialJobId='dNOZt', parameterId='0', type='FINAL', sequence=0, data=0.9733333333333334)]}
.. GENERATED FROM PYTHON SOURCE LINES 65-67
Stop Experiment
---------------
.. GENERATED FROM PYTHON SOURCE LINES 67-68
.. code-block:: default
experiment.stop()
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
[2022-02-07 18:56:25] Stopping experiment, please wait...
[2022-02-07 18:56:28] Experiment stopped
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 24.662 seconds)
.. _sphx_glr_download_tutorials_nni_experiment.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: nni_experiment.py <nni_experiment.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: nni_experiment.ipynb <nni_experiment.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
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