deploy: SimplexLab/TorchJD@ef59f7c

Author: ValerianRey

latest/_sources/installation.md.txt

@@ -7,14 +7,14 @@
 
 Note that `torchjd` requires Python 3.10, 3.11, 3.12, 3.13 or 3.14 and `torch>=2.0`.
 
-Some aggregators (CAGrad and Nash-MTL) have additional dependencies that are not included by default
-when installing `torchjd`. To install them, you can use:
-```
-pip install "torchjd[cagrad]"
-```
-```
-pip install "torchjd[nash_mtl]"
-```
+Some aggregators have additional dependencies that are not included by default when installing
+`torchjd`. The following table lists the optional dependency groups and the aggregators they enable:
+
+Group | Classes | Dependencies | Install command |
+|-----|---------|--------------|-----------------|
+| `quadprog_projector` | {class}`~torchjd.linalg.QuadprogProjector` (used in {class}`~torchjd.aggregation.UPGrad` and {class}`~torchjd.aggregation.DualProj`) | [numpy](https://github.com/numpy/numpy), [quadprog](https://github.com/quadprog/quadprog), [qpsolvers](https://github.com/qpsolvers/qpsolvers) | `pip install "torchjd[quadprog_projector]"` |
+| `cagrad` | {class}`~torchjd.aggregation.CAGrad` | [numpy](https://github.com/numpy/numpy), [cvxpy](https://github.com/cvxpy/cvxpy/) | `pip install "torchjd[cagrad]"` |
+| `nash_mtl` | {class}`~torchjd.aggregation.NashMTL` | [numpy](https://github.com/numpy/numpy), [cvxpy](https://github.com/cvxpy/cvxpy/), [ecos](https://github.com/embotech/ecos) | `pip install "torchjd[nash_mtl]"` |
 
 To install `torchjd` with all of its optional dependencies, you can also use:
 ```

latest/docs/aggregation/cagrad/index.html

@@ -304,7 +304,7 @@
 <h1>CAGrad<a class="headerlink" href="#cagrad" title="Link to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torchjd.aggregation.CAGrad">
-<span class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></span><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.</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/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_cagrad.py#L96-L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGrad" title="Link to this definition">¶</a></dt>
+<span class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></span><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.</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/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_cagrad.py#L97-L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGrad" title="Link to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="../#torchjd.aggregation.GramianWeightedAggregator" title="torchjd.aggregation.GramianWeightedAggregator"><code class="xref py py-class docutils literal notranslate"><span class="pre">GramianWeightedAggregator</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">
@@ -323,7 +323,7 @@ <h1>CAGrad<a class="headerlink" href="#cagrad" title="Link to this heading">¶</
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.CAGrad.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L54-L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGrad.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGrad.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the aggregation from the input matrix and applies all registered hooks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -339,10 +339,10 @@ <h1>CAGrad<a class="headerlink" href="#cagrad" title="Link to this heading">¶</
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torchjd.aggregation.CAGradWeighting">
-<span class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></span><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.</span></span><span class="sig-name descname"><span class="pre">CAGradWeighting</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/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_cagrad.py#L20-L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGradWeighting" title="Link to this definition">¶</a></dt>
+<span class="property"><span class="k"><span class="pre">class</span></span><span class="w"> </span></span><span class="sig-prename descclassname"><span class="pre">torchjd.aggregation.</span></span><span class="sig-name descname"><span class="pre">CAGradWeighting</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/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_cagrad.py#L21-L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGradWeighting" title="Link to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.CAGradWeighting.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L54-L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGradWeighting.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.CAGradWeighting.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Call self as a function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type<span class="colon">:</span></dt>

latest/docs/aggregation/config/index.html

@@ -319,7 +319,7 @@ <h1>ConFIG<a class="headerlink" href="#config" title="Link to this heading">¶</
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.ConFIG.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L54-L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.ConFIG.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.ConFIG.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the aggregation from the input matrix and applies all registered hooks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>

latest/docs/aggregation/dualproj/index.html

@@ -319,7 +319,7 @@ <h1>DualProj<a class="headerlink" href="#dualproj" title="Link to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.DualProj.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L54-L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.DualProj.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.DualProj.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the aggregation from the input matrix and applies all registered hooks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -349,7 +349,7 @@ <h1>DualProj<a class="headerlink" href="#dualproj" title="Link to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.DualProjWeighting.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L54-L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.DualProjWeighting.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.DualProjWeighting.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the vector of weights from the input Gramian and applies all registered hooks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>

latest/docs/aggregation/graddrop/index.html

@@ -320,7 +320,7 @@ <h1>GradDrop<a class="headerlink" href="#graddrop" title="Link to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.GradDrop.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L54-L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.GradDrop.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.GradDrop.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the aggregation from the input matrix and applies all registered hooks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>

latest/docs/aggregation/gradvac/index.html

@@ -340,7 +340,7 @@ <h1>GradVac<a class="headerlink" href="#gradvac" title="Link to this heading">¶
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.GradVac.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L54-L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.GradVac.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.GradVac.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Call self as a function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
@@ -402,7 +402,7 @@ <h1>GradVac<a class="headerlink" href="#gradvac" title="Link to this heading">¶
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torchjd.aggregation.GradVacWeighting.__call__">
-<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L54-L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.GradVacWeighting.__call__" title="Link to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__call__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/SimplexLab/TorchJD/blob/main/src/torchjd/aggregation/_mixins.py#L27-L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torchjd.aggregation.GradVacWeighting.__call__" title="Link to this definition">¶</a></dt>
 <dd><p>Call self as a function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type<span class="colon">:</span></dt>