deploy: SimplexLab/TorchJD@0f815b5

Author: ValerianRey

latest/docs/aggregation/cagrad/index.html

@@ -241,7 +241,7 @@
 <span id="cagrad"></span><h1>CAGrad<a class="headerlink" href="#module-torchjd.aggregation.cagrad" title="Link to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torchjd.aggregation.cagrad.CAGrad">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.cagrad.</span></span><span class="sig-name descname"><span class="pre">CAGrad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">c</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/aggregation/cagrad.py#L10-L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.cagrad.CAGrad" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.cagrad.</span></span><span class="sig-name descname"><span class="pre">CAGrad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">c</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/aggregation/cagrad.py#L14-L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.cagrad.CAGrad" title="Link to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="../bases/#torchjd.aggregation.bases.Aggregator" title="torchjd.aggregation.bases.Aggregator"><code class="xref py py-class docutils literal notranslate"><span class="pre">Aggregator</span></code></a> as defined in Algorithm 1 of
 <a class="reference external" href="https://arxiv.org/pdf/2110.14048.pdf">Conflict-Averse Gradient Descent for Multi-task Learning</a>.</p>
 <dl class="field-list simple">
@@ -268,8 +268,10 @@
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>This aggregator has dependencies that are not included by default when installing
-<code class="docutils literal notranslate"><span class="pre">torchjd</span></code>. To install them, use <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">torchjd[cagrad]</span></code>.</p>
+<p>This aggregator is not installed by default. When not installed, trying to import it should
+result in the following error:
+<code class="docutils literal notranslate"><span class="pre">ImportError:</span> <span class="pre">cannot</span> <span class="pre">import</span> <span class="pre">name</span> <span class="pre">'CAGrad'</span> <span class="pre">from</span> <span class="pre">'torchjd.aggregation'</span></code>.
+To install it, use <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">torchjd[cagrad]</span></code>.</p>
 </div>
 </dd></dl>
 

latest/docs/aggregation/nash_mtl/index.html

@@ -241,7 +241,7 @@
 <span id="nash-mtl"></span><h1>Nash-MTL<a class="headerlink" href="#module-torchjd.aggregation.nash_mtl" title="Link to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torchjd.aggregation.nash_mtl.NashMTL">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.nash_mtl.</span></span><span class="sig-name descname"><span class="pre">NashMTL</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_tasks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_weights_every</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/aggregation/nash_mtl.py#L36-L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.nash_mtl.NashMTL" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.nash_mtl.</span></span><span class="sig-name descname"><span class="pre">NashMTL</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_tasks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_weights_every</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/aggregation/nash_mtl.py#L39-L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.nash_mtl.NashMTL" title="Link to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="../bases/#torchjd.aggregation.bases.Aggregator" title="torchjd.aggregation.bases.Aggregator"><code class="xref py py-class docutils literal notranslate"><span class="pre">Aggregator</span></code></a> as proposed in Algorithm 1 of
 <a class="reference external" href="https://arxiv.org/pdf/2202.01017.pdf">Multi-Task Learning as a Bargaining Game</a>.</p>
 <dl class="field-list simple">
@@ -273,8 +273,10 @@
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>This aggregator has dependencies that are not included by default when installing
-<code class="docutils literal notranslate"><span class="pre">torchjd</span></code>. To install them, use <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">torchjd[nash_mtl]</span></code>.</p>
+<p>This aggregator is not installed by default. When not installed, trying to import it should
+result in the following error:
+<code class="docutils literal notranslate"><span class="pre">ImportError:</span> <span class="pre">cannot</span> <span class="pre">import</span> <span class="pre">name</span> <span class="pre">'NashMTL'</span> <span class="pre">from</span> <span class="pre">'torchjd.aggregation'</span></code>.
+To install it, use <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">torchjd[nash_mtl]</span></code>.</p>
 </div>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -288,7 +290,7 @@
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.nash_mtl.NashMTL.reset">
-<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/aggregation/nash_mtl.py#L93-L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.nash_mtl.NashMTL.reset" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/TorchJD/torchjd/blob/main/src/torchjd/aggregation/nash_mtl.py#L98-L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.nash_mtl.NashMTL.reset" title="Link to this definition">¶</a></dt>
 <dd><p>Resets the internal state of the algorithm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type<span class="colon">:</span></dt>