create branch for v2.9

docs/source/tutorials/quantization_speedup.ipynb

+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# SpeedUp Model with Calibration Config\n\n\n## Introduction\n\nDeep learning network has been computational intensive and memory intensive \nwhich increases the difficulty of deploying deep neural network model. Quantization is a \nfundamental technology which is widely used to reduce memory footprint and speedup inference \nprocess. Many frameworks begin to support quantization, but few of them support mixed precision \nquantization and get real speedup. Frameworks like `HAQ: Hardware-Aware Automated Quantization with Mixed Precision <https://arxiv.org/pdf/1811.08886.pdf>`__\\, only support simulated mixed precision quantization which will \nnot speedup the inference process. To get real speedup of mixed precision quantization and \nhelp people get the real feedback from hardware, we design a general framework with simple interface to allow NNI quantization algorithms to connect different \nDL model optimization backends (e.g., TensorRT, NNFusion), which gives users an end-to-end experience that after quantizing their model \nwith quantization algorithms, the quantized model can be directly speeded up with the connected optimization backend. NNI connects \nTensorRT at this stage, and will support more backends in the future.\n\n\n## Design and Implementation\n\nTo support speeding up mixed precision quantization, we divide framework into two part, frontend and backend.  \nFrontend could be popular training frameworks such as PyTorch, TensorFlow etc. Backend could be inference \nframework for different hardwares, such as TensorRT. At present, we support PyTorch as frontend and \nTensorRT as backend. To convert PyTorch model to TensorRT engine, we leverage onnx as intermediate graph \nrepresentation. In this way, we convert PyTorch model to onnx model, then TensorRT parse onnx \nmodel to generate inference engine. \n\n\nQuantization aware training combines NNI quantization algorithm 'QAT' and NNI quantization speedup tool.\nUsers should set config to train quantized model using QAT algorithm(please refer to :doc:`NNI Quantization Algorithms <../compression/quantizer>`  ).\nAfter quantization aware training, users can get new config with calibration parameters and model with quantized weight. By passing new config and model to quantization speedup tool, users can get real mixed precision speedup engine to do inference.\n\n\nAfter getting mixed precision engine, users can do inference with input data.\n\n\nNote\n\n\n* Recommend using \"cpu\"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not \"cpu\"(host), this tool will transpose it to \"cpu\" which may increases unnecessary overhead.\n* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).\n* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.\n\n\n## Prerequisite\nCUDA version >= 11.0\n\nTensorRT version >= 7.2\n\nNote\n\n* If you haven't installed TensorRT before or use the old version, please refer to `TensorRT Installation Guide <https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html>`__\\  \n\n## Usage\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport torch.nn.functional as F\nfrom torch.optim import SGD\nfrom scripts.compression_mnist_model import TorchModel, device, trainer, evaluator, test_trt\n\nconfig_list = [{\n    'quant_types': ['input', 'weight'],\n    'quant_bits': {'input': 8, 'weight': 8},\n    'op_types': ['Conv2d']\n}, {\n    'quant_types': ['output'],\n    'quant_bits': {'output': 8},\n    'op_types': ['ReLU']\n}, {\n    'quant_types': ['input', 'weight'],\n    'quant_bits': {'input': 8, 'weight': 8},\n    'op_names': ['fc1', 'fc2']\n}]\n\nmodel = TorchModel().to(device)\noptimizer = SGD(model.parameters(), lr=0.01, momentum=0.5)\ncriterion = F.nll_loss\ndummy_input = torch.rand(32, 1, 28, 28).to(device)\n\nfrom nni.algorithms.compression.pytorch.quantization import QAT_Quantizer\nquantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)\nquantizer.compress()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "finetuning the model by using QAT\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for epoch in range(3):\n    trainer(model, optimizer, criterion)\n    evaluator(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "export model and get calibration_config\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import os\nos.makedirs('log', exist_ok=True)\nmodel_path = \"./log/mnist_model.pth\"\ncalibration_path = \"./log/mnist_calibration.pth\"\ncalibration_config = quantizer.export_model(model_path, calibration_path)\n\nprint(\"calibration_config: \", calibration_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "build tensorRT engine to make a real speedup\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT\ninput_shape = (32, 1, 28, 28)\nengine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)\nengine.compress()\ntest_trt(engine)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note that NNI also supports post-training quantization directly, please refer to complete examples for detail.\n\nFor complete examples please refer to :githublink:`the code <examples/model_compress/quantization/mixed_precision_speedup_mnist.py>`.\n\nFor more parameters about the class 'TensorRTModelSpeedUp', you can refer to :doc:`Model Compression API Reference <../reference/compression/quantization_speedup>`.\n\n### Mnist test\n\non one GTX2080 GPU,\ninput tensor: ``torch.randn(128, 1, 28, 28)``\n\n.. list-table::\n   :header-rows: 1\n   :widths: auto\n\n   * - quantization strategy\n     - Latency\n     - accuracy\n   * - all in 32bit\n     - 0.001199961\n     - 96%\n   * - mixed precision(average bit 20.4)\n     - 0.000753688\n     - 96%\n   * - all in 8bit\n     - 0.000229869\n     - 93.7%\n\n### Cifar10 resnet18 test (train one epoch)\n\non one GTX2080 GPU,\ninput tensor: ``torch.randn(128, 3, 32, 32)``\n\n.. list-table::\n   :header-rows: 1\n   :widths: auto\n\n   * - quantization strategy\n     - Latency\n     - accuracy\n   * - all in 32bit\n     - 0.003286268\n     - 54.21%\n   * - mixed precision(average bit 11.55)\n     - 0.001358022\n     - 54.78%\n   * - all in 8bit\n     - 0.000859139\n     - 52.81%\n\n"
+      ]
+    }
+  ],
+  "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": "3.9.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

docs/source/tutorials/quantization_speedup.py

+"""
+SpeedUp Model with Calibration Config
+======================================
+
+
+Introduction
+------------
+
+Deep learning network has been computational intensive and memory intensive 
+which increases the difficulty of deploying deep neural network model. Quantization is a 
+fundamental technology which is widely used to reduce memory footprint and speedup inference 
+process. Many frameworks begin to support quantization, but few of them support mixed precision 
+quantization and get real speedup. Frameworks like `HAQ: Hardware-Aware Automated Quantization with Mixed Precision <https://arxiv.org/pdf/1811.08886.pdf>`__\, only support simulated mixed precision quantization which will 
+not speedup the inference process. To get real speedup of mixed precision quantization and 
+help people get the real feedback from hardware, we design a general framework with simple interface to allow NNI quantization algorithms to connect different 
+DL model optimization backends (e.g., TensorRT, NNFusion), which gives users an end-to-end experience that after quantizing their model 
+with quantization algorithms, the quantized model can be directly speeded up with the connected optimization backend. NNI connects 
+TensorRT at this stage, and will support more backends in the future.
+
+
+Design and Implementation
+-------------------------
+
+To support speeding up mixed precision quantization, we divide framework into two part, frontend and backend.  
+Frontend could be popular training frameworks such as PyTorch, TensorFlow etc. Backend could be inference 
+framework for different hardwares, such as TensorRT. At present, we support PyTorch as frontend and 
+TensorRT as backend. To convert PyTorch model to TensorRT engine, we leverage onnx as intermediate graph 
+representation. In this way, we convert PyTorch model to onnx model, then TensorRT parse onnx 
+model to generate inference engine. 
+
+
+Quantization aware training combines NNI quantization algorithm 'QAT' and NNI quantization speedup tool.
+Users should set config to train quantized model using QAT algorithm(please refer to :doc:`NNI Quantization Algorithms <../compression/quantizer>`  ).
+After quantization aware training, users can get new config with calibration parameters and model with quantized weight. By passing new config and model to quantization speedup tool, users can get real mixed precision speedup engine to do inference.
+
+
+After getting mixed precision engine, users can do inference with input data.
+
+
+Note
+
+
+* Recommend using "cpu"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not "cpu"(host), this tool will transpose it to "cpu" which may increases unnecessary overhead.
+* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).
+* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.
+
+
+Prerequisite
+------------
+CUDA version >= 11.0
+
+TensorRT version >= 7.2
+
+Note
+
+* If you haven't installed TensorRT before or use the old version, please refer to `TensorRT Installation Guide <https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html>`__\  
+
+Usage
+-----
+
+"""
+
+# %%
+import torch
+import torch.nn.functional as F
+from torch.optim import SGD
+from scripts.compression_mnist_model import TorchModel, device, trainer, evaluator, test_trt
+
+config_list = [{
+    'quant_types': ['input', 'weight'],
+    'quant_bits': {'input': 8, 'weight': 8},
+    'op_types': ['Conv2d']
+}, {
+    'quant_types': ['output'],
+    'quant_bits': {'output': 8},
+    'op_types': ['ReLU']
+}, {
+    'quant_types': ['input', 'weight'],
+    'quant_bits': {'input': 8, 'weight': 8},
+    'op_names': ['fc1', 'fc2']
+}]
+
+model = TorchModel().to(device)
+optimizer = SGD(model.parameters(), lr=0.01, momentum=0.5)
+criterion = F.nll_loss
+dummy_input = torch.rand(32, 1, 28, 28).to(device)
+
+from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)
+quantizer.compress()
+
+# %%
+# finetuning the model by using QAT
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
+    evaluator(model)
+
+# %%
+# export model and get calibration_config
+import os
+os.makedirs('log', exist_ok=True)
+model_path = "./log/mnist_model.pth"
+calibration_path = "./log/mnist_calibration.pth"
+calibration_config = quantizer.export_model(model_path, calibration_path)
+
+print("calibration_config: ", calibration_config)
+
+# %%
+# build tensorRT engine to make a real speedup
+
+from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT
+input_shape = (32, 1, 28, 28)
+engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)
+engine.compress()
+test_trt(engine)
+
+# %%
+# Note that NNI also supports post-training quantization directly, please refer to complete examples for detail.
+#
+# For complete examples please refer to :githublink:`the code <examples/model_compress/quantization/mixed_precision_speedup_mnist.py>`.
+#
+# For more parameters about the class 'TensorRTModelSpeedUp', you can refer to :doc:`Model Compression API Reference <../reference/compression/quantization_speedup>`.
+#
+# Mnist test
+# ^^^^^^^^^^
+#
+# on one GTX2080 GPU,
+# input tensor: ``torch.randn(128, 1, 28, 28)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - quantization strategy
+#      - Latency
+#      - accuracy
+#    * - all in 32bit
+#      - 0.001199961
+#      - 96%
+#    * - mixed precision(average bit 20.4)
+#      - 0.000753688
+#      - 96%
+#    * - all in 8bit
+#      - 0.000229869
+#      - 93.7%
+#
+# Cifar10 resnet18 test (train one epoch)
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# on one GTX2080 GPU,
+# input tensor: ``torch.randn(128, 3, 32, 32)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - quantization strategy
+#      - Latency
+#      - accuracy
+#    * - all in 32bit
+#      - 0.003286268
+#      - 54.21%
+#    * - mixed precision(average bit 11.55)
+#      - 0.001358022
+#      - 54.78%
+#    * - all in 8bit
+#      - 0.000859139
+#      - 52.81%

docs/source/tutorials/quantization_speedup.py.md5

+2404b8d0c3958a0191b77bbe882456e4
\ No newline at end of file

docs/source/tutorials/quantization_speedup.rst

+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/quantization_speedup.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_quantization_speedup.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_quantization_speedup.py:
+
+
+SpeedUp Model with Calibration Config
+======================================
+
+
+Introduction
+------------
+
+Deep learning network has been computational intensive and memory intensive 
+which increases the difficulty of deploying deep neural network model. Quantization is a 
+fundamental technology which is widely used to reduce memory footprint and speedup inference 
+process. Many frameworks begin to support quantization, but few of them support mixed precision 
+quantization and get real speedup. Frameworks like `HAQ: Hardware-Aware Automated Quantization with Mixed Precision <https://arxiv.org/pdf/1811.08886.pdf>`__\, only support simulated mixed precision quantization which will 
+not speedup the inference process. To get real speedup of mixed precision quantization and 
+help people get the real feedback from hardware, we design a general framework with simple interface to allow NNI quantization algorithms to connect different 
+DL model optimization backends (e.g., TensorRT, NNFusion), which gives users an end-to-end experience that after quantizing their model 
+with quantization algorithms, the quantized model can be directly speeded up with the connected optimization backend. NNI connects 
+TensorRT at this stage, and will support more backends in the future.
+
+
+Design and Implementation
+-------------------------
+
+To support speeding up mixed precision quantization, we divide framework into two part, frontend and backend.  
+Frontend could be popular training frameworks such as PyTorch, TensorFlow etc. Backend could be inference 
+framework for different hardwares, such as TensorRT. At present, we support PyTorch as frontend and 
+TensorRT as backend. To convert PyTorch model to TensorRT engine, we leverage onnx as intermediate graph 
+representation. In this way, we convert PyTorch model to onnx model, then TensorRT parse onnx 
+model to generate inference engine. 
+
+
+Quantization aware training combines NNI quantization algorithm 'QAT' and NNI quantization speedup tool.
+Users should set config to train quantized model using QAT algorithm(please refer to :doc:`NNI Quantization Algorithms <../compression/quantizer>`  ).
+After quantization aware training, users can get new config with calibration parameters and model with quantized weight. By passing new config and model to quantization speedup tool, users can get real mixed precision speedup engine to do inference.
+
+
+After getting mixed precision engine, users can do inference with input data.
+
+
+Note
+
+
+* Recommend using "cpu"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not "cpu"(host), this tool will transpose it to "cpu" which may increases unnecessary overhead.
+* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).
+* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.
+
+
+Prerequisite
+------------
+CUDA version >= 11.0
+
+TensorRT version >= 7.2
+
+Note
+
+* If you haven't installed TensorRT before or use the old version, please refer to `TensorRT Installation Guide <https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html>`__\  
+
+Usage
+-----
+
+.. GENERATED FROM PYTHON SOURCE LINES 64-92
+
+.. code-block:: default
+
+    import torch
+    import torch.nn.functional as F
+    from torch.optim import SGD
+    from scripts.compression_mnist_model import TorchModel, device, trainer, evaluator, test_trt
+
+    config_list = [{
+        'quant_types': ['input', 'weight'],
+        'quant_bits': {'input': 8, 'weight': 8},
+        'op_types': ['Conv2d']
+    }, {
+        'quant_types': ['output'],
+        'quant_bits': {'output': 8},
+        'op_types': ['ReLU']
+    }, {
+        'quant_types': ['input', 'weight'],
+        'quant_bits': {'input': 8, 'weight': 8},
+        'op_names': ['fc1', 'fc2']
+    }]
+
+    model = TorchModel().to(device)
+    optimizer = SGD(model.parameters(), lr=0.01, momentum=0.5)
+    criterion = F.nll_loss
+    dummy_input = torch.rand(32, 1, 28, 28).to(device)
+
+    from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+    quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)
+    quantizer.compress()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+    TorchModel(
+      (conv1): QuantizerModuleWrapper(
+        (module): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (conv2): QuantizerModuleWrapper(
+        (module): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (fc1): QuantizerModuleWrapper(
+        (module): Linear(in_features=256, out_features=120, bias=True)
+      )
+      (fc2): QuantizerModuleWrapper(
+        (module): Linear(in_features=120, out_features=84, bias=True)
+      )
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+      (relu1): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu2): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu3): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu4): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (pool1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+      (pool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+    )
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 93-94
+
+finetuning the model by using QAT
+
+.. GENERATED FROM PYTHON SOURCE LINES 94-98
+
+.. code-block:: default
+
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+        evaluator(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Average test loss: 0.5386, Accuracy: 8619/10000 (86%)
+    Average test loss: 0.1553, Accuracy: 9521/10000 (95%)
+    Average test loss: 0.1001, Accuracy: 9686/10000 (97%)
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 99-100
+
+export model and get calibration_config
+
+.. GENERATED FROM PYTHON SOURCE LINES 100-108
+
+.. code-block:: default
+
+    import os
+    os.makedirs('log', exist_ok=True)
+    model_path = "./log/mnist_model.pth"
+    calibration_path = "./log/mnist_calibration.pth"
+    calibration_config = quantizer.export_model(model_path, calibration_path)
+
+    print("calibration_config: ", calibration_config)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    calibration_config:  {'conv1': {'weight_bits': 8, 'weight_scale': tensor([0.0029], device='cuda:0'), 'weight_zero_point': tensor([98.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': -0.4242129623889923, 'tracked_max_input': 2.821486711502075}, 'conv2': {'weight_bits': 8, 'weight_scale': tensor([0.0017], device='cuda:0'), 'weight_zero_point': tensor([124.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 8.848002433776855}, 'fc1': {'weight_bits': 8, 'weight_scale': tensor([0.0010], device='cuda:0'), 'weight_zero_point': tensor([134.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 14.64758586883545}, 'fc2': {'weight_bits': 8, 'weight_scale': tensor([0.0013], device='cuda:0'), 'weight_zero_point': tensor([121.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 15.807988166809082}, 'relu1': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 9.041301727294922}, 'relu2': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 15.143928527832031}, 'relu3': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 16.151935577392578}, 'relu4': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 11.749024391174316}}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 109-110
+
+build tensorRT engine to make a real speedup
+
+.. GENERATED FROM PYTHON SOURCE LINES 110-117
+
+.. code-block:: default
+
+
+    from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT
+    input_shape = (32, 1, 28, 28)
+    engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)
+    engine.compress()
+    test_trt(engine)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Loss: 0.10061546401977539  Accuracy: 96.83%
+    Inference elapsed_time (whole dataset): 0.04322671890258789s
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 118-169
+
+Note that NNI also supports post-training quantization directly, please refer to complete examples for detail.
+
+For complete examples please refer to :githublink:`the code <examples/model_compress/quantization/mixed_precision_speedup_mnist.py>`.
+
+For more parameters about the class 'TensorRTModelSpeedUp', you can refer to :doc:`Model Compression API Reference <../reference/compression/quantization_speedup>`.
+
+Mnist test
+^^^^^^^^^^
+
+on one GTX2080 GPU,
+input tensor: ``torch.randn(128, 1, 28, 28)``
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - quantization strategy
+     - Latency
+     - accuracy
+   * - all in 32bit
+     - 0.001199961
+     - 96%
+   * - mixed precision(average bit 20.4)
+     - 0.000753688
+     - 96%
+   * - all in 8bit
+     - 0.000229869
+     - 93.7%
+
+Cifar10 resnet18 test (train one epoch)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+on one GTX2080 GPU,
+input tensor: ``torch.randn(128, 3, 32, 32)``
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - quantization strategy
+     - Latency
+     - accuracy
+   * - all in 32bit
+     - 0.003286268
+     - 54.21%
+   * - mixed precision(average bit 11.55)
+     - 0.001358022
+     - 54.78%
+   * - all in 8bit
+     - 0.000859139
+     - 52.81%
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  4.509 seconds)
+
+
+.. _sphx_glr_download_tutorials_quantization_speedup.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: quantization_speedup.py <quantization_speedup.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: quantization_speedup.ipynb <quantization_speedup.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

docs/source/tutorials/sg_execution_times.rst

+
+:orphan:
+
+.. _sphx_glr_tutorials_sg_execution_times:
+
+Computation times
+=================
+**00:20.822** total execution time for **tutorials** files:
+
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_bert_glue.py` (``pruning_bert_glue.py``)                           | 00:20.822 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_darts.py` (``darts.py``)                                                   | 01:51.710 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_hello_nas.py` (``hello_nas.py``)                                           | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_nasbench_as_dataset.py` (``nasbench_as_dataset.py``)                       | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_customize.py` (``pruning_customize.py``)                           | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_quick_start_mnist.py` (``pruning_quick_start_mnist.py``)           | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_speedup.py` (``pruning_speedup.py``)                               | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_quantization_customize.py` (``quantization_customize.py``)                 | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_quantization_quick_start_mnist.py` (``quantization_quick_start_mnist.py``) | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_quantization_speedup.py` (``quantization_speedup.py``)                     | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+

docs/static/css/index_page.css

+/* The following are for index.html */
+
+.wy-table-responsive table td, .wy-table-responsive table th {
+    white-space: normal
+}
+
+.gap {
+    margin-top: 24px;
+}
+
+.gap2 {
+    margin-top: 12px;
+}
+
+.rowHeight {
+    line-height: 24px;
+}
+
+.title {
+    padding-bottom: 6px;
+    border-bottom: 1px solid #ccc;
+}
+
+.second-title {
+    margin-top: 24px;
+}
+
+/* command style */
+.command {
+    background-color: #f8f8f8;
+    border: 1px solid #e1e4e5;
+    height: 40px;
+    line-height: 40px;
+    font-size: 12px;
+    padding-left: 10px;
+}
+
+.main-code {
+    background-color: #f8f8f8;
+    padding: 15px 20px;
+}
+
+.command-intro {
+    line-height: 48px;
+}
+
+/* document body width */
+.wy-nav-content {
+    max-width: 100% !important;
+    background-color: #fff;
+}
+
+/* nni logo bgcolor */
+.wy-side-nav-search {
+    background-color: black;
+}
+
+/* document body font-family */
+
+.wy-nav-content ul li {
+    list-style: disc;
+    margin-bottom: 12px;
+    margin-left: 24px;
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+    border: 1px solid #ccc;
+}
+
+.column b {
+    padding-bottom: 4px;
+    border-bottom: 2px solid blue;
+}
+
+.column td {
+    width: 200px;
+    text-align: center;
+    line-height: 36px;
+}
+
+td.framework {
+    width: 300px;
+    -webkit-width: 220px;
+}
+
+.main-table .circle li {
+    list-style-type: circle;
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+
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+    padding-left: 20px;
+}
+
+.main-table .verticalMiddle {
+    vertical-align: middle !important;
+    text-align: center;
+}
+
+.inline img, .inline h2 {
+    display: inline-block;
+    margin-bottom: 0px;
+}
+
+.inline hr {
+    margin-top: 0px;
+}
+
+.inline img {
+    margin-top: -20px;
+}
+
+.ui-img img {
+    height: 350px;
+}
+
+.wy-side-nav-search>div.version {
+    color: #fff !important;
+}
+
+.main-table {
+    width: 90%;
+    margin: 0 auto;
+}
+
+.chinese {
+    margin-bottom: 16px;
+}
+
+.QR {
+    width: 180px;
+}
+
+.or {
+    vertical-align: middle;
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+.wy-plain-list-disc li, .rst-content .section ul li, .rst-content .toctree-wrapper ul li, article ul li {
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+}
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+    width: 300px;
+    height: 300px;
+    float: left;
+    margin-left: 15px;
+    margin-bottom: 60px;
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+    box-sizing: border-box;
+    list-style: none;
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+    text-align: center;
+}
+
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+    width: 250px;
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+    margin: 50px 24px;
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+
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+    margin-top: 6px;
+}
+
+.pipeline tr, .pipeline td, .pipeline th {
+    width: 248px;
+    line-height: 26px;
+    text-align: center;
+    border: 1px solid #ccc;
+}
\ No newline at end of file

docs/static/css/material_custom.css

+/* Global font */
+body, input {
+    font-family: "Roboto", "Noto Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+
+code, kbd, pre {
+    font-family: "Roboto Mono", "Consolas", "Courier New", Courier, monospace;
+}
+
+h1, h2, h3, h4, .md-header, .md-tabs, .md-hero {
+    font-family: "Google Sans", "Noto Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+
+/* Title font */
+@media only screen and (min-width: 45em) {
+    .md-header-nav__title {
+        font-weight: 500;
+    }
+}
+
+.md-typeset h4, .md-typeset h5, .md-typeset h6 {
+    font-weight: 600;
+}
+
+/* viewcode link should have left padding */
+span.viewcode-link {
+    padding-left: 0.6rem;
+}
+
+/* adding an eyebrow for API references */
+dt.sig-object {
+    position: relative;
+    background: #f4f5f7;
+    padding: 0.5rem;
+    border-left: 0.2rem solid var(--custom-color-accent);
+    word-wrap: break-word;
+}
+
+.class > dt.sig-object {
+    border-left: none;                    /* remove left border */
+    border-top: 0.18rem solid var(--custom-color-accent);
+}
+
+.function > dt.sig-object {
+    border-left: none;                    /* remove left border */
+    border-top: 0.18rem solid var(--custom-color-accent);
+}
+
+.exception > dt.sig-object {
+    border-left: none;                    /* remove left border */
+    border-top: 0.18rem solid var(--custom-color-accent);
+}
+
+/* Padding on parameter list is not needed */
+dl.field-list > dt {
+    padding-left: 0 !important;
+}
+
+dl.field-list > dd {
+    margin-left: 1.5em;
+}
+
+/* Version-related */
+span.versionmodified {
+    font-weight: 600;
+}
+
+/* show headerlink when hover/focus */
+dt.sig-object:focus .headerlink, dt.sig-object:hover .headerlink {
+    -webkit-transform: translate(0);
+    transform: translate(0);
+    opacity: 1;
+}
+
+/* logo is too large */
+a.md-logo img {
+    padding: 3px;
+}
+
+/* Split for navigation */
+nav.md-tabs .md-tabs__item:not(:last-child) {
+    padding-right: 0;
+}
+
+nav.md-tabs .md-tabs__arrow {
+    padding-left: .3rem;
+    font-size: inherit;
+    vertical-align: -10%;
+}
+
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+    display: none !important;
+}
+
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+    margin-top: 1rem;
+}
+
+/* make footer appearing inline */
+.md-footer-copyright__highlight {
+    display: inline;
+}
+
+/* toc style */
+li.md-nav__item:not(:first-child) span.caption {
+    margin-top: 1.25em;
+}
+
+@media only screen and (min-width: 76.2em) {
+    .md-nav--primary .md-nav__title--site {
+        display: none;
+    }
+}
+
+.md-nav__overview {
+    font-weight: 500;
+}
+
+@media only screen and (max-width: 76.1875em) {
+    .md-nav__overview {
+        display: none;
+    }
+}
+
+/* hide nav bar in some cases */
+.md-tabs.hidden {
+    display: none;
+}
+
+/* citation style */
+.citation dt {
+    padding-right: 1em;
+}
+
+/* inline code style */
+.md-typeset code {
+    padding-left: 0.07em;
+    padding-right: 0.07em;
+}
+
+/* for release icon, on home page */
+.release-icon {
+    margin-left: 8px;
+    width: 40px;
+}
+
+/* Similar to cardlink, but used in codesnippet in index page. see sphinx_gallery.css */
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+    display: flex;
+    flex-flow: wrap row;
+}
+
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+    padding: 0;
+    margin: .5rem 1rem 1rem 0rem;
+    width: 100%;
+}
+
+/* Controlling the cards in containers only */
+.codesnippet-card-container .codesnippet-card.admonition {
+    width: 47%;
+}
+
+@media only screen and (max-width:59.9375em) {
+    .codesnippet-card-container .codesnippet-card.admonition {
+        width: 100%;
+    }
+}
+
+.codesnippet-card .codesnippet-card-body {
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+    position: relative;
+    padding: 0.9rem 0.9rem 3rem 0.9rem;
+}
+
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+    border-top: 1px solid #ddd;
+    margin: 0 !important;
+    position: absolute;
+    bottom: 0;
+    width: 100%;
+}
+
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+    color: rgba(0, 0, 0, .68);
+}
+
+.codesnippet-card-title-container {
+    margin-top: 0.3rem;
+    position: relative;
+}
+
+.codesnippet-card-title-container h4 {
+    padding-left: 2.3rem;
+    line-height: 1.6rem;
+    height: 1.6rem;
+    margin-top: 0;
+}
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+    left: 0;
+}
+
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+    max-width: 100%;
+    max-height: 100%;
+    /* horizontal and vertical center */
+    /* https://stackoverflow.com/questions/7273338/how-to-vertically-align-an-image-inside-a-div */
+    text-align: center;
+    vertical-align: middle;
+    position: absolute;
+    left: 0;
+    right: 0;
+    top: 0;
+    bottom: 0;
+    margin: auto;
+}
+
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+    width: 1.6rem;
+    height: 1.6rem;
+    padding: 0;
+}
+
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+    position: relative;
+}
+
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+    position: absolute;
+    right: 0;
+}
+
+/* fixes reference overlapping issue */
+/* This is originally defined to be negative in application_fixes.css */
+/* They did that to ensure the header doesn't disappear in jump links */
+/* We did this by using scroll-margin-top instead */
+dt:target {
+    margin-top: 0.15rem !important;
+    padding-top: 0.5rem !important;
+}
+
+:target {
+    /* header height */
+    scroll-margin-top: 3.5rem;
+}
+
+/* fix code block style on mobile */
+@media only screen and (max-width: 44.9375em) {
+    .md-typeset pre {
+        margin: 1em -0.3em;
+    }
+}
+
+/* Responsive nav bar */
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+    padding: 0 !important;
+}
+
+/* collapsible toctree */
+.md-nav--primary ul li {
+    padding-left: .8rem;
+}
+
+.md-nav__item {
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+
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+    transition: all 0.1s ease;
+    position: absolute;
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+}
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+}
+
+@media only screen and (max-width:76.1875em) {
+    .md-nav--primary .md-nav__link {
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+    }
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+    }
+    .md-nav--primary .md-nav__item .md-nav__list .md-nav__item {
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+    }
+    html .md-nav--primary .md-nav__title--site .md-nav__button {
+        height: auto;
+        font-size: inherit;
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+    }
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+        padding-left: .6rem;
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+    }
+}
+
+/* Increase TOC padding */
+.md-nav--primary ul li ul li {
+    padding-left: 0.8rem;
+}
+
+/* Nav bar and heroes */
+@media only screen and (min-width:60em) {
+    .md-search__form, .md-search__input {
+        border-radius: .3rem;   /* even rounder */
+    }
+}
+
+.md-header-nav__source, .md-source {
+    padding-right: 0 !important;
+}
+
+.md-hero {
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+}
+
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+    max-width: 73rem;
+    position: absolute;
+    bottom: -46px;
+    left: 0;
+    right: 0;
+    margin-left: auto;
+    margin-right: auto;
+    width: 100%;
+    z-index: 0;
+}
+
+.md-hero__background img {
+    width: 100%;
+}
+
+@media only screen and (max-width:59.9375em) {
+    .md-hero__background {
+        display: none;
+    }
+}
+
+@media only screen and (max-width:76.1875em) {
+    .md-hero__background {
+        bottom: -5%;
+        top: auto;
+    }
+}
+
+.md-hero__inner {
+    z-index: 1;
+    position: relative;
+    padding-right: 35%;
+}
+
+@media only screen and (min-width:76.2em) {
+    .md-hero__inner {
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+        padding-bottom: 1.2rem;
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+}
+
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+    color: rgba(0,0,0,.85);
+    font-size: 1.5625rem;
+    line-height: 1.3;
+}
+
+.md-typeset h1, .md-typeset h2, .md-typeset h3 {
+    font-weight: 400;
+    letter-spacing: 0;
+}
+
+/* Enlarge table */
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+    font-size: 0.7rem;
+    box-shadow: 0 2px 2px 0 rgb(0 0 0 / 8%), 0 1px 5px 0 rgb(0 0 0 / 7%), 0 3px 1px -2px rgb(0 0 0 / 14%);
+}
+
+.md-typeset table:not([class]) th {
+    padding: .5rem .7rem;
+    background-color: #e6e7e8;
+    color: inherit;
+    font-weight: 500;
+}
+
+.md-typeset table:not([class]) td {
+    padding: .4rem .7rem;
+}
+
+/* On this page TOC */
+.md-sidebar--secondary .md-nav--secondary {
+    border-inline-start: 4px solid var(--custom-color-primary);
+}
+
+.md-nav__link {
+    margin-top: 0.45em;
+}
+
+/* Override style for copy button */
+button.copybtn {
+    opacity: 1;
+}
+
+.o-tooltip--left:after {
+    transform: translateX(-5%) translateY(-125%);
+    padding: .4em;
+    font-size: .5rem;
+    font-weight: 600;
+    background: #5f6368;
+}
+
+.o-tooltip--left:hover:after {
+    transform: translateX(-5%) translateY(-120%);
+}
+
+/* Sphinx tabs */
+/* Copied from https://github.com/executablebooks/sphinx-tabs/blob/master/sphinx_tabs/static/tabs.css with modifications */
+.sphinx-tabs.container {
+    margin-bottom: 1rem;
+    border: 1px solid rgb(232, 234, 237);
+    border-radius: 8px;
+}
+
+[role="tablist"] {
+    padding: .3rem 0 0 0;
+    border-bottom: 1px solid #a0b3bf;
+}
+
+.sphinx-tabs-tab {
+    position: relative;
+    line-height: 2rem;
+    font-weight: 600;
+    padding: 0 1rem;
+    color: #80868b;
+}
+
+.sphinx-tabs-tab[aria-selected="true"] {
+    color: #3f51b5;                     /* primary color */
+    border-bottom: 2px solid #3f51b5;
+}
+
+.sphinx-tabs-tab:focus {
+    z-index: 1;
+    outline-offset: 1px;
+}
+
+.sphinx-tabs-panel {
+    position: relative;
+    padding: 1rem;
+}
+
+.sphinx-tabs-panel.code-tab {
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+}
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+.sphinx-tabs-panel.code-tab .highlight {
+    margin: 0;
+    padding: .5rem;
+}
+
+.sphinx-tab img {
+    margin-bottom: 2rem;
+}

docs/static/css/material_dropdown.css

+/* https://codepen.io/mildrenben/pen/RPwQEY */
+
+@media only screen and (max-width:44.9375em) {
+    .drop {
+        display: none;
+    }
+}
+
+.drop {
+    width: 5.3rem;
+    vertical-align: middle;
+}
+
+@media only screen and (min-width:60em) and (max-width:70em) {
+    .drop {
+        width: 4.7rem;
+    }
+    /* also narrow nav source width */
+    .md-header-nav__source {
+        width: 10rem;
+    }
+}
+
+.drop button {
+    color: inherit;
+    font-weight: 700;
+    font-size: .65rem;
+    cursor: pointer;
+}
+
+.drop button:after {
+    content: "\e5c5";
+    padding: 0 0 0.3rem 0.2rem;
+    font-size: 0.5rem;
+    font-family: "Material Icons";
+    display: inline-block;
+    transition: all 0.2s ease;
+    transform: rotate(0);
+    transform-origin: 65% 30%;
+}
+
+.drop button.active:after {
+    transform: rotate(180deg);
+}
+
+.drop ul {
+    position: absolute;
+    left: 0;
+    top: 6em;
+    transition: all 0.1s ease;
+    padding: .625em 0;
+    margin-top: -0.9rem;
+    transform: scale(0);
+    transform-origin: 0 0;
+    background: #fcfcfc;
+    box-shadow: 0 2px 4px 0 rgba(0, 0, 0, 0.16), 0 2px 8px 0 rgba(0, 0, 0, 0.12);
+}
+
+.drop ul.active {
+    transform: scale(1);
+}
+
+.drop ul li {
+    display: block;
+    width: 100%;
+}
+
+.drop ul li a.md-nav__link {
+    width: 100%;
+    padding: .4rem 1em;
+    display: inline-block;
+    white-space: pre;
+    box-sizing: border-box;
+    color: rgba(0, 0, 0, .87);
+    margin-top: 0;
+    font-size: .75rem;
+}

docs/static/css/material_theme.css

+/* Theme related customization */
+/* https://github.com/bashtage/sphinx-material/pull/122 */
+
+/* first part */
+button[data-md-color-primary=custom] {
+    background-color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-typeset a {
+    color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-header,
+[data-md-color-primary=custom] .md-hero {
+    background-color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-nav__link--active,
+[data-md-color-primary=custom] .md-nav__link:active {
+    color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-nav__item--nested>.md-nav__link {
+    color: inherit
+}
+
+[data-md-color-primary=custom] .md-nav__extra_link:active {
+    color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-nav__item--nested > .md-nav__extra_link {
+    color: inherit
+}
+
+/* second part */
+button[data-md-color-accent=custom] {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-typeset a:active,
+[data-md-color-accent=custom] .md-typeset a:hover {
+    color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,
+[data-md-color-accent=custom] .md-typeset pre code::-webkit-scrollbar-thumb:hover {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-nav__link:focus,
+[data-md-color-accent=custom] .md-nav__link:hover,
+[data-md-color-accent=custom] .md-typeset .footnote li:hover .footnote-backref:hover,
+[data-md-color-accent=custom] .md-typeset .footnote li:target .footnote-backref,
+[data-md-color-accent=custom] .md-typeset .md-clipboard:active:before,
+[data-md-color-accent=custom] .md-typeset .md-clipboard:hover:before,
+[data-md-color-accent=custom] .md-typeset [id] .headerlink:focus,
+[data-md-color-accent=custom] .md-typeset [id]:hover .headerlink:hover,
+[data-md-color-accent=custom] .md-typeset [id]:target .headerlink {
+    color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-search-result__link:hover,
+[data-md-color-accent=custom] .md-search-result__link[data-md-state=active] {
+    background-color: rgba(83, 109, 254, .1)
+}
+
+[data-md-color-accent=custom] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-source-file:hover:before {
+    background-color: var(--custom-color-accent)
+}
+
+/* third part */
+@media only screen and (max-width:59.9375em) {
+    [data-md-color-primary=custom] .md-nav__source {
+        background-color: var(--custom-color-primary);
+        opacity: 0.9675;
+    }
+}
+
+@media only screen and (max-width:76.1875em) {
+    html [data-md-color-primary=custom] .md-nav--primary .md-nav__title--site {
+        background-color: var(--custom-color-primary)
+    }
+}
+
+@media only screen and (min-width:76.25em) {
+    [data-md-color-primary=custom] .md-tabs {
+        background-color: var(--custom-color-primary)
+    }
+}

docs/static/css/sphinx_gallery.css

+/* Customization to sphinx-gallery generated notebooks */
+
+p.sphx-glr-script-out {
+    margin: 0;
+    padding: 0;
+    font-size: .7rem;
+}
+
+.sphx-glr-script-out .highlight {
+    margin: 0 !important;
+}
+
+.sphx-glr-script-out .highlight pre {
+    background-color: #f0f6fa !important;
+    padding: .6rem !important;
+    margin-bottom: 1.5rem;
+}
+
+div.sphx-glr-footer {
+    display: none;
+}
+
+.notebook-action-link {
+    padding-right: 1rem;
+}
+
+.sphx-glr-download-link-note {
+    margin-bottom: 1.5rem;
+    line-height: 2.5rem;
+}
+
+.notebook-action-div {
+    display: inline;
+    border-bottom: 1px solid #f3f4f7;
+    padding-right: 2rem;
+    padding-bottom: 0.625rem;
+}
+
+.notebook-action-link:hover .notebook-action-div {
+    /* match theme */
+    border-bottom-color: var(--custom-color-accent);
+}
+
+.notebook-action-link img {
+    width: 1.1rem;
+    height: 1.1rem;
+    margin-right: 10px;
+    vertical-align: middle;
+}
+
+.notebook-action-link div {
+    display: inline;
+}
+
+.notebook-action-link:not(:hover) div {
+    color: rgba(0, 0, 0, .68);
+}
+
+.card-link.admonition {
+    border-left: 0;
+    padding: 0.9rem;
+}
+
+.card-link .card-link-body {
+    min-height: 4rem;
+    position: relative;
+}
+
+.card-link-text {
+    padding-left: 4.9rem;
+}
+
+.card-link a:not(:hover) .card-link-text {
+    color: rgba(0, 0, 0, .68);
+}
+
+.card-link-text .card-link-title-container h4 {
+    margin: 0;
+}
+
+.card-link-text .card-link-summary {
+    margin-top: 0.3rem;
+    margin-bottom: 0.5rem;
+    font-size: 0.75rem;
+}
+
+/* hide link */
+.card-link-anchor {
+    display: none;
+}
+
+.card-link-tag {
+    margin-right: 0.4rem;
+    background: #eeeff2;
+    border: 1px solid #e0dcda;
+    color: rgba(0, 0, 0, 0.7);
+    border-radius: 18px;
+    cursor: pointer;
+    display: inline-block;
+    position: relative;
+    padding: 0.1rem 0.6rem;
+    font-size: 0.7rem;
+}
+
+.card-link-icon {
+    position: absolute;
+    top: 0;
+    left: 0;
+}
+
+.card-link-icon img {
+    max-width: 75%;
+    max-height: 75%;
+    /* horizontal and vertical center */
+    /* https://stackoverflow.com/questions/7273338/how-to-vertically-align-an-image-inside-a-div */
+    text-align: center;
+    vertical-align: middle;
+    position: absolute;
+    left: 0;
+    right: 0;
+    top: 0;
+    bottom: 0;
+    margin: auto;
+}
+
+/* link icon background color */
+.card-link-icon.circle {
+    background-color: #E8DCEE;
+    border-radius: 50%;
+    width: 4rem;
+    height: 4rem;
+    padding: 0;
+}
+
+/* pallette */
+.card-link-icon.cyan {
+    background-color: #DDEFF2;
+}
+
+.card-link-icon.purple {
+    background-color: #E8DCEE;
+}
+
+.card-link-icon.blue {
+    background-color: #DBE8FC;
+}
+
+.card-link-icon.indigo {
+    background-color: #E1E4F3;
+}

docs/static/img/README.md

+This folder is deprecated. Please do not put files here in future!

docs/static/img/gallery-colab.svg

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docs/static/img/gallery-github.svg

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