test: remove unused dataclass in tf-mnist-classifier integration test 1…

…747fa0

deployment-guide/obtaining-datasets.html

@@ -542,16 +542,16 @@ <h3>Setup<a class="headerlink" href="#setup" title="Permalink to this headline">
 <p>Register an account with Kaggle at <a class="reference external" href="https://www.kaggle.com/">https://www.kaggle.com/</a> so that you can access their content.
 Next, install the Python <code class="docutils literal notranslate"><span class="pre">kaggle</span></code> package so that you can use Python to access the <a class="reference internal" href="../glossary.html#term-API"><span class="xref std std-term">API</span></a>,</p>
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 Pip</label><div class="tabbed-content docutils">
 <div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="c1"># User-level install</span>
 python -m pip install --user kaggle
 </pre></div>
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 <div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="c1"># Conda virtual environment install</span>
 conda create -n kaggle <span class="nv">python</span><span class="o">=</span><span class="m">3</span> pip

deployment-guide/single-machine-deployment.html

@@ -1078,8 +1078,8 @@ <h3>Parameters to Set/Update<a class="headerlink" href="#parameters-to-set-updat
 <p class="rubric">Configure the dataset volume</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">nfs-datasets</span></code> volume will need to be updated based on the details of your deployment,</p>
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 <p>The following Docker volume assumes that the NFS share is the directory <code class="docutils literal notranslate"><span class="pre">/var/nfs/datasets</span></code> on a device with the IP address <code class="docutils literal notranslate"><span class="pre">192.168.1.20</span></code>.
 Update it to match the details of your NFS share.</p>
@@ -1092,8 +1092,8 @@ <h3>Parameters to Set/Update<a class="headerlink" href="#parameters-to-set-updat
 </pre></div>
 </div>
 </div>
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 Datasets in local directory</label><div class="tabbed-content docutils">
 <p>The following Docker volume assumes that the data is stored in the local directory <code class="docutils literal notranslate"><span class="pre">/var/nfs/datasets</span></code>.
 Update this to match the folder where you store your datasets.</p>

getting-started/installation.html

@@ -579,8 +579,8 @@ <h3>Requirements<a class="headerlink" href="#requirements" title="Permalink to t
 The easiest way to accomplish this is using the pre-built configuration files included in the project codebase to create a <a class="reference external" href="https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/environments.html">Conda Environment</a>.</p>
 <p>There are two install options to start using <a class="reference external" href="https://docs.conda.io/projects/conda/en/latest/user-guide/concepts/environments.html">Conda Environments</a>,</p>
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 <p>The following links will provide a installation package for version 2020.11 of <a class="reference external" href="https://docs.anaconda.com/">Anaconda</a> on your host machine (must meet all <span class="xref std std-ref">quickstart-system-requirements</span>).</p>
 <ul class="simple">
@@ -590,8 +590,8 @@ <h3>Requirements<a class="headerlink" href="#requirements" title="Permalink to t
 </ul>
 <p>If your host machine does not meet the <span class="xref std std-ref">quickstart-system-requirements</span>, then go to the <a class="reference external" href="https://docs.anaconda.com/anaconda/install/">Anaconda Installation Documents</a> for more help.</p>
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 <p>The following links will provide a installation package for the latest version of <a class="reference external" href="https://docs.conda.io/en/latest/miniconda.html">Miniconda</a> on your host machine (must meet all <span class="xref std std-ref">quickstart-system-requirements</span>).</p>
 <ul class="simple">
@@ -639,18 +639,18 @@ <h3>Requirements<a class="headerlink" href="#id1" title="Permalink to this headl
 <span id="quickstart-clone-repository"></span><h3>Clone the Repository<a class="headerlink" href="#clone-the-repository" title="Permalink to this headline">¶</a></h3>
 <p>To clone the repository, open a new <strong>Terminal</strong> session for your operating system,</p>
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 MacOS</label><div class="tabbed-content docutils">
 <p>Use the keyboard shortcut <kbd class="kbd docutils literal notranslate">command</kbd> + <kbd class="kbd docutils literal notranslate">space</kbd> to open the <strong>Spotlight Search</strong>, type <code class="docutils literal notranslate"><span class="pre">Terminal</span></code> into the search bar, and click the <em>Terminal</em> application under <em>Top Hit</em> at the top of your results.</p>
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 Linux</label><div class="tabbed-content docutils">
 <p>Use the keyboard shortcut <kbd class="kbd docutils literal notranslate">ctrl</kbd> + <kbd class="kbd docutils literal notranslate">alt</kbd> + <kbd class="kbd docutils literal notranslate">t</kbd> to open the <strong>Terminal</strong>.</p>
 </div>
@@ -673,15 +673,15 @@ <h3>Requirements<a class="headerlink" href="#id1" title="Permalink to this headl
 </div>
 <p>Clone the repository to your local computer,</p>
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 Clone with HTTPS</label><div class="tabbed-content docutils">
 <div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>git clone https://github.com/usnistgov/dioptra.git
 </pre></div>
 </div>
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 Clone with SSH</label><div class="tabbed-content docutils">
 <div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>git clone git@github.com:usnistgov/dioptra.git
 </pre></div>

tutorials/example-pytorch-mnist-membership-inference.html

@@ -531,8 +531,8 @@ <h2>Using this Demo<a class="headerlink" href="#using-this-demo" title="Permalin
 <p>The steps for getting your environment ready to run this demo depend on whether you intend to run the demo locally (i.e. on your personal computer) or on an existing on-prem deployment.
 Navigate to the tab below that best describes your setup in order to proceed.</p>
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 Once you have downloaded the images, stay in the terminal and navigate to this example’s directory and run the demo startup sequence,</p>
@@ -555,8 +555,8 @@ <h2>Using this Demo<a class="headerlink" href="#using-this-demo" title="Permalin
 </div>
 <p>If you were watching the output logs, you will need to press <kbd class="kbd compound docutils literal notranslate"><kbd class="kbd docutils literal notranslate">Ctrl</kbd>-<kbd class="kbd docutils literal notranslate">C</kbd></kbd> to stop following the logs before you can run <code class="docutils literal notranslate"><span class="pre">make</span> <span class="pre">teardown</span></code>.</p>
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 On-Prem Deployment</label><div class="tabbed-content docutils">
 <p>To run the demo on an on-prem deployment, all you need to do is download and start the <strong>jupyter</strong> service defined in this example’s <code class="docutils literal notranslate"><span class="pre">docker-compose.yml</span></code> file.
 Open a terminal and navigate to this example’s directory and run the <strong>jupyter</strong> startup sequence,</p>

tutorials/example-tensorflow-mnist-feature-squeezing.html

@@ -576,8 +576,8 @@ <h2>Using this Demo<a class="headerlink" href="#using-this-demo" title="Permalin
 <p>The steps for getting your environment ready to run this demo depend on whether you intend to run the demo locally (i.e. on your personal computer) or on an existing on-prem deployment.
 Navigate to the tab below that best describes your setup in order to proceed.</p>
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 Once you have downloaded the images, stay in the terminal and navigate to this example’s directory and run the demo startup sequence,</p>
@@ -607,8 +607,8 @@ <h2>Using this Demo<a class="headerlink" href="#using-this-demo" title="Permalin
 </div>
 <p>If you were watching the output logs, you will need to press <kbd class="kbd compound docutils literal notranslate"><kbd class="kbd docutils literal notranslate">Ctrl</kbd>-<kbd class="kbd docutils literal notranslate">C</kbd></kbd> to stop following the logs before you can run <code class="docutils literal notranslate"><span class="pre">make</span> <span class="pre">teardown</span></code>.</p>
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 On-Prem Deployment</label><div class="tabbed-content docutils">
 <p>To run the demo on an on-prem deployment, all you need to do is download and start the <strong>jupyter</strong> service defined in this example’s <code class="docutils literal notranslate"><span class="pre">docker-compose.yml</span></code> file.
 Open a terminal and navigate to this example’s directory and run the <strong>jupyter</strong> startup sequence,</p>

tutorials/example-tensorflow-mnist-model-inversion.html

@@ -543,8 +543,8 @@ <h2>Using this Demo<a class="headerlink" href="#using-this-demo" title="Permalin
 <p>The steps for getting your environment ready to run this demo depend on whether you intend to run the demo locally (i.e. on your personal computer) or on an existing on-prem deployment.
 Navigate to the tab below that best describes your setup in order to proceed.</p>
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 Once you have downloaded the images, stay in the terminal and navigate to this example’s directory and run the demo startup sequence,</p>
@@ -567,8 +567,8 @@ <h2>Using this Demo<a class="headerlink" href="#using-this-demo" title="Permalin
 </div>
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 On-Prem Deployment</label><div class="tabbed-content docutils">
 <p>To run the demo on an on-prem deployment, all you need to do is download and start the <strong>jupyter</strong> service defined in this example’s <code class="docutils literal notranslate"><span class="pre">docker-compose.yml</span></code> file.
 Open a terminal and navigate to this example’s directory and run the <strong>jupyter</strong> startup sequence,</p>