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Python Workflow Definition - workflow interoperability for aiida, jobflow and pyiron
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Python Workflow Definition
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<h1>Python Workflow Definition</h1>
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<h2> Contents </h2>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#definition">Definition</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#format">Format</a></li>
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<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#quantum-espresso-workflow">Quantum Espresso Workflow</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#nfdi4ing-scientific-workflow-requirements">NFDI4Ing Scientific Workflow Requirements</a></li>
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<section class="tex2jax_ignore mathjax_ignore" id="python-workflow-definition">
<h1>Python Workflow Definition<a class="headerlink" href="#python-workflow-definition" title="Link to this heading">#</a></h1>
<p><a class="reference external" href="https://github.com/pyiron-dev/python-workflow-definition/actions/workflows/pipeline.yml"><img alt="Pipeline" src="https://github.com/pyiron-dev/python-workflow-definition/actions/workflows/pipeline.yml/badge.svg" /></a>
<a class="reference external" href="https://mybinder.org/v2/gh/pyiron-dev/python-workflow-definition/HEAD"><img alt="Binder" src="https://mybinder.org/badge_logo.svg" /></a></p>
<section id="definition">
<h2>Definition<a class="headerlink" href="#definition" title="Link to this heading">#</a></h2>
<p>In the Python Workflow Definition (PWD) each node represents a Python function, with the edges defining the connection
between input and output of the different Python functions.</p>
</section>
<section id="format">
<h2>Format<a class="headerlink" href="#format" title="Link to this heading">#</a></h2>
<p>Each workflow consists of three files, a Python module which defines the individual Pythons, a JSON file which defines
the connections between the different Python functions and a conda environment file to define the software dependencies.
The files are not intended to be human readable, but rather interact as a machine readable exchange format between the
different workflow engines to enable interoperability.</p>
</section>
<section id="examples">
<h2>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h2>
<section id="simple-example">
<h3>Simple Example<a class="headerlink" href="#simple-example" title="Link to this heading">#</a></h3>
<p>As a first example we define two Python functions which add multiple inputs:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">add_x_and_y</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_prod_and_div</span><span class="p">(</span><span class="n">x</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-></span> <span class="nb">dict</span><span class="p">:</span>
<span class="k">return</span> <span class="p">{</span><span class="s2">"prod"</span><span class="p">:</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span><span class="p">,</span> <span class="s2">"div"</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="n">y</span><span class="p">}</span>
</pre></div>
</div>
<p>These two Python functions are combined in the following example workflow:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">tmp_dict</span> <span class="o">=</span> <span class="n">get_prod_and_div</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">add_x_and_y</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">tmp_dict</span><span class="p">[</span><span class="s2">"prod"</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">tmp_dict</span><span class="p">[</span><span class="s2">"div"</span><span class="p">])</span>
</pre></div>
</div>
<p>For the workflow representation of these Python functions the Python functions are stored in the <a class="reference download internal" download="" href="_downloads/2733b71b8b860a0550ebcfb326aabf3a/simple_workflow.py"><span class="xref download myst">simple_workflow.py</span></a>
Python module. The connection of the Python functions are stored in the <a class="reference download internal" download="" href="_downloads/ccde96f4155d5d9b945f1f0b662ed925/workflow_simple.json"><span class="xref download myst">workflow_simple.json</span></a>
JSON file:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
<span class="s2">"nodes"</span><span class="p">:</span> <span class="p">[</span>
<span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"function"</span><span class="p">:</span> <span class="s2">"simple_workflow.get_prod_and_div"</span><span class="p">},</span>
<span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"function"</span><span class="p">:</span> <span class="s2">"simple_workflow.add_x_and_y"</span><span class="p">},</span>
<span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"value"</span><span class="p">:</span> <span class="mi">1</span><span class="p">},</span>
<span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"value"</span><span class="p">:</span> <span class="mi">2</span><span class="p">}</span>
<span class="p">],</span>
<span class="s2">"edges"</span><span class="p">:</span> <span class="p">[</span>
<span class="p">{</span><span class="s2">"target"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"targetPort"</span><span class="p">:</span> <span class="s2">"x"</span><span class="p">,</span> <span class="s2">"source"</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">"sourcePort"</span><span class="p">:</span> <span class="n">null</span><span class="p">},</span>
<span class="p">{</span><span class="s2">"target"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"targetPort"</span><span class="p">:</span> <span class="s2">"y"</span><span class="p">,</span> <span class="s2">"source"</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">"sourcePort"</span><span class="p">:</span> <span class="n">null</span><span class="p">},</span>
<span class="p">{</span><span class="s2">"target"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"targetPort"</span><span class="p">:</span> <span class="s2">"x"</span><span class="p">,</span> <span class="s2">"source"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"sourcePort"</span><span class="p">:</span> <span class="s2">"prod"</span><span class="p">},</span>
<span class="p">{</span><span class="s2">"target"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"targetPort"</span><span class="p">:</span> <span class="s2">"y"</span><span class="p">,</span> <span class="s2">"source"</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">"sourcePort"</span><span class="p">:</span> <span class="s2">"div"</span><span class="p">}</span>
<span class="p">]</span>
<span class="p">}</span>
</pre></div>
</div>
<p>The abbreviations in the definition of the edges are:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">target</span></code> - target node</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">targetPort</span></code> - target port - for a node with multiple input parameters the target port specifies which input parameter to use.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">source</span></code> - source node</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">sourcePort</span></code> - source port - for a node with multiple output parameters the source port specifies which output parameter to use.</p></li>
</ul>
<p>As the workflow does not require any additional resources, as it is only using built-in functionality of the Python standard
library.</p>
<p>The corresponding Jupyter notebooks demonstrate this functionality:</p>
<div class="pst-scrollable-table-container"><table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>Example</p></th>
<th class="head"><p>Explanation</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="aiida_simple.html"><span class="std std-doc">aiida_simple.ipynb</span></a></p></td>
<td><p>Define Workflow with aiida and execute it with jobflow and pyiron_base.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="jobflow_simple.html"><span class="std std-doc">jobflow_simple.ipynb</span></a></p></td>
<td><p>Define Workflow with jobflow and execute it with aiida and pyiron_base.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyiron_base_simple.html"><span class="std std-doc">pyiron_base_simple.ipynb</span></a></p></td>
<td><p>Define Workflow with pyiron_base and execute it with aiida and jobflow.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="universal_workflow_simple.html"><span class="std std-doc">universal_workflow_simple.ipynb</span></a></p></td>
<td><p>Execute workflow defined in the Python Workflow Definition with aiida, executorlib, jobflow, pyiron_base and pure Python.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="quantum-espresso-workflow">
<h3>Quantum Espresso Workflow<a class="headerlink" href="#quantum-espresso-workflow" title="Link to this heading">#</a></h3>
<p>The second workflow example is the calculation of an energy volume curve with Quantum Espresso. In the first step the
initial structure is relaxed, afterward it is strained and the total energy is calculated.</p>
<ul class="simple">
<li><p><a class="reference download internal" download="" href="_downloads/a7264696a1cb39eff4d20169ae2c8b01/quantum_espresso_workflow.py"><span class="xref download myst">quantum_espresso_workflow.py</span></a> Python functions</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/4e50b332423ddf7c6ac6a8f369748242/workflow_qe.json"><span class="xref download myst">workflow_qe.json</span></a> Workflow definition in the Python Workflow Definition.</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/236a3f7d8b0b8b7046fe52f5b24e2f3a/environment_qe.yml"><span class="xref download myst">environment_qe.yml</span></a> Conda environment</p></li>
</ul>
<div class="pst-scrollable-table-container"><table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>Example</p></th>
<th class="head"><p>Explanation</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="aiida_qe.html"><span class="std std-doc">aiida_qe.ipynb</span></a></p></td>
<td><p>Define Workflow with aiida and execute it with jobflow and pyiron_base.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="jobflow_qe.html"><span class="std std-doc">jobflow_qe.ipynb</span></a></p></td>
<td><p>Define Workflow with jobflow and execute it with aiida and pyiron_base.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyiron_base_qe.html"><span class="std std-doc">pyiron_base_qe.ipynb</span></a></p></td>
<td><p>Define Workflow with pyiron_base and execute it with aiida and jobflow.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="universal_workflow_qe.html"><span class="std std-doc">universal_workflow_qe.ipynb</span></a></p></td>
<td><p>Execute workflow defined in the Python Workflow Definition with aiida, executorlib, jobflow, pyiron_base and pure Python.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="nfdi4ing-scientific-workflow-requirements">
<h3>NFDI4Ing Scientific Workflow Requirements<a class="headerlink" href="#nfdi4ing-scientific-workflow-requirements" title="Link to this heading">#</a></h3>
<p>To demonstrate the compatibility of the Python Workflow Definition to file based workflows, the workflow benchmark developed as part of <a class="reference external" href="https://github.com/BAMresearch/NFDI4IngScientificWorkflowRequirements">NFDI4Ing</a>
is implemented for all three simulation codes based on a shared workflow definition.</p>
<ul class="simple">
<li><p><a class="reference download internal" download="" href="_downloads/9b33cb965729263c315b9517407c13c5/nfdi_ing_workflow.py"><span class="xref download myst">nfdi_ing_workflow.py</span></a> Python functions</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/99eb77c77821cee4b1c6b4acc6b55ea7/workflow_nfdi.json"><span class="xref download myst">workflow_nfdi.json</span></a> Workflow definition in the Python Workflow Definition.</p></li>
</ul>
<p>Additional source files provided with the workflow benchmark:</p>
<ul class="simple">
<li><p><a class="reference download internal" download="" href="_downloads/4ff352d789da8f614eef033f89f6e617/preprocessing.yaml"><span class="xref download myst">source/envs/preprocessing.yaml</span></a> Conda environment for preprocessing</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/7ce8b93e79c1ee48a655ac7e627b9960/processing.yaml"><span class="xref download myst">source/envs/processing.yaml</span></a> Conda environment for processing</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/b38b5718bfe9395cdcb7374b3a935e24/postprocessing.yaml"><span class="xref download myst">source/envs/postprocessing.yaml</span></a> Conda environment for postprocessing</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/0e02e07d859c81f2c61d7ccfd9eb429a/macros.tex.template"><span class="xref download myst">source/macros.tex.template</span></a> LaTeX module template</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/b8785b7ed7173a4aaec2e39f99a9245e/paper.tex"><span class="xref download myst">source/paper.tex</span></a> LaTeX paper template</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/a5735e3474ded79139738adc04a114b5/poisson.py"><span class="xref download myst">source/poisson.py</span></a> Poisson Python script</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/10bc4abf7c38577d6f29c14e69dc9484/postprocessing.py"><span class="xref download myst">source/postprocessing.py</span></a> Postprocessing Python script</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/f86f3d7e625c419e8b464ba265abfbe5/prepare_paper_macros.py"><span class="xref download myst">source/prepare_paper_macros.py</span></a> LaTeX preprocessing Python script</p></li>
<li><p><a class="reference download internal" download="" href="_downloads/871bf11770153207ad73692d0ed16f79/unit_square.geo"><span class="xref download myst">source/unit_square.geo</span></a> Input structure</p></li>
</ul>
<div class="pst-scrollable-table-container"><table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>Example</p></th>
<th class="head"><p>Explanation</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference internal" href="aiida_nfdi.html"><span class="std std-doc">aiida_nfdi.ipynb</span></a></p></td>
<td><p>Define Workflow with aiida and execute it with jobflow and pyiron_base.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="jobflow_nfdi.html"><span class="std std-doc">jobflow_nfdi.ipynb</span></a></p></td>
<td><p>Define Workflow with jobflow and execute it with aiida and pyiron_base.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="pyiron_base_nfdi.html"><span class="std std-doc">pyiron_base_nfdi.ipynb</span></a></p></td>
<td><p>Define Workflow with pyiron_base and execute it with aiida and jobflow.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="universal_workflow_nfdi.html"><span class="std std-doc">universal_workflow_nfdi.ipynb</span></a></p></td>
<td><p>Execute workflow defined in the Python Workflow Definition with aiida, executorlib, jobflow, pyiron_base and pure Python.</p></td>
</tr>
</tbody>
</table>
</div>
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Python Workflow Definition - workflow interoperability for aiida, jobflow and pyiron