CircleCI update of dev docs (2671).

Author: Circle CI

master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -207,6 +207,7 @@ <h1>Source code for ot.backend</h1><div class="highlight"><pre>
         <span class="kn">import</span> <span class="nn">jax.scipy.special</span> <span class="k">as</span> <span class="nn">jspecial</span>
         <span class="kn">from</span> <span class="nn">jax.lib</span> <span class="kn">import</span> <span class="n">xla_bridge</span>
         <span class="n">jax_type</span> <span class="o">=</span> <span class="n">jax</span><span class="o">.</span><span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span>
+        <span class="n">jax_new_version</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">jax</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]))</span> <span class="o">&gt;</span> <span class="mf">4.24</span>
     <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
         <span class="n">jax</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="n">jax_type</span> <span class="o">=</span> <span class="nb">float</span>
@@ -2035,11 +2036,19 @@ <h1>Source code for ot.backend</h1><div class="highlight"><pre>
                 <span class="n">jax</span><span class="o">.</span><span class="n">device_put</span><span class="p">(</span><span class="n">jnp</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">jnp</span><span class="o">.</span><span class="n">float64</span><span class="p">),</span> <span class="n">d</span><span class="p">)</span>
             <span class="p">]</span>
 
+        <span class="bp">self</span><span class="o">.</span><span class="n">jax_new_version</span> <span class="o">=</span> <span class="n">jax_new_version</span>
+
     <span class="k">def</span> <span class="nf">_to_numpy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
 
+    <span class="k">def</span> <span class="nf">_get_device</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">jax_new_version</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">devices</span><span class="p">())[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">a</span><span class="o">.</span><span class="n">device_buffer</span><span class="o">.</span><span class="n">device</span><span class="p">()</span>
+
     <span class="k">def</span> <span class="nf">_change_device</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">type_as</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">jax</span><span class="o">.</span><span class="n">device_put</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">type_as</span><span class="o">.</span><span class="n">device_buffer</span><span class="o">.</span><span class="n">device</span><span class="p">())</span>
+        <span class="k">return</span> <span class="n">jax</span><span class="o">.</span><span class="n">device_put</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_device</span><span class="p">(</span><span class="n">type_as</span><span class="p">))</span>
 
     <span class="k">def</span> <span class="nf">_from_numpy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">type_as</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
@@ -2457,7 +2466,10 @@ <h1>Source code for ot.backend</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="JaxBackend.dtype_device">
 <a class="viewcode-back" href="../../gen_modules/ot.backend.html#ot.backend.JaxBackend.dtype_device">[docs]</a>
     <span class="k">def</span> <span class="nf">dtype_device</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">a</span><span class="o">.</span><span class="n">device_buffer</span><span class="o">.</span><span class="n">device</span><span class="p">()</span></div>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">jax_new_version</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">devices</span><span class="p">())[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">a</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">a</span><span class="o">.</span><span class="n">device_buffer</span><span class="o">.</span><span class="n">device</span><span class="p">()</span></div>
 
 
 <div class="viewcode-block" id="JaxBackend.assert_same_dtype_device">

master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -195,26 +195,26 @@ Estimating dual variables for entropic OT
 
  .. code-block:: none
 
-    Iter:   0, loss=0.20204949002249922
-    Iter:  10, loss=-19.47584556587466
-    Iter:  20, loss=-31.429525660329517
-    Iter:  30, loss=-36.70549589151293
-    Iter:  40, loss=-40.104393854466224
-    Iter:  50, loss=-41.71970632953193
-    Iter:  60, loss=-42.48323308041214
-    Iter:  70, loss=-42.825683674928264
-    Iter:  80, loss=-42.94068528401017
-    Iter:  90, loss=-42.97380108266532
-    Iter: 100, loss=-42.98796115314187
-    Iter: 110, loss=-42.99433008352598
-    Iter: 120, loss=-42.996860587404996
-    Iter: 130, loss=-42.99834253338184
-    Iter: 140, loss=-42.999047253425104
-    Iter: 150, loss=-42.9995070985144
-    Iter: 160, loss=-42.999839261612365
-    Iter: 170, loss=-43.000076556643705
-    Iter: 180, loss=-43.00026730268771
-    Iter: 190, loss=-43.00041914898498
+    Iter:   0, loss=0.2020494900224745
+    Iter:  10, loss=-19.57618259614961
+    Iter:  20, loss=-31.66301265244961
+    Iter:  30, loss=-36.50666181688356
+    Iter:  40, loss=-39.95100518352593
+    Iter:  50, loss=-41.47867745038081
+    Iter:  60, loss=-42.40982244225048
+    Iter:  70, loss=-42.75576775807454
+    Iter:  80, loss=-42.96194349519
+    Iter:  90, loss=-43.010399265246384
+    Iter: 100, loss=-43.04617124235882
+    Iter: 110, loss=-43.05966450080063
+    Iter: 120, loss=-43.06794354289777
+    Iter: 130, loss=-43.073006381620885
+    Iter: 140, loss=-43.076636666041324
+    Iter: 150, loss=-43.079366227386934
+    Iter: 160, loss=-43.08154681843257
+    Iter: 170, loss=-43.08329470262464
+    Iter: 180, loss=-43.08470031156932
+    Iter: 190, loss=-43.08581430429776
 
 
 
@@ -319,25 +319,25 @@ Estimating dual variables for quadratic OT
  .. code-block:: none
 
     Iter:   0, loss=-0.0018442196020623663
-    Iter:  10, loss=-19.61387664621203
-    Iter:  20, loss=-31.373114762444427
-    Iter:  30, loss=-36.549940622056816
-    Iter:  40, loss=-39.715819804778995
-    Iter:  50, loss=-41.61567235919983
-    Iter:  60, loss=-42.279493349784346
-    Iter:  70, loss=-42.68091682263879
-    Iter:  80, loss=-42.79426450444149
-    Iter:  90, loss=-42.84497179793894
-    Iter: 100, loss=-42.86443425295894
-    Iter: 110, loss=-42.8729932791931
-    Iter: 120, loss=-42.87708688048491
-    Iter: 130, loss=-42.87881265031169
-    Iter: 140, loss=-42.87980692585934
-    Iter: 150, loss=-42.88040226246206
-    Iter: 160, loss=-42.880774215111146
-    Iter: 170, loss=-42.88101077690173
-    Iter: 180, loss=-42.88116300490077
-    Iter: 190, loss=-42.881270999159455
+    Iter:  10, loss=-19.544292446025445
+    Iter:  20, loss=-31.280524158660008
+    Iter:  30, loss=-36.19856648665331
+    Iter:  40, loss=-39.62982578094831
+    Iter:  50, loss=-41.171229459917676
+    Iter:  60, loss=-42.12753723269981
+    Iter:  70, loss=-42.55976656784887
+    Iter:  80, loss=-42.761741861484026
+    Iter:  90, loss=-42.86048643148755
+    Iter: 100, loss=-42.896210186167515
+    Iter: 110, loss=-42.912518604496654
+    Iter: 120, loss=-42.92196834252769
+    Iter: 130, loss=-42.92766529649812
+    Iter: 140, loss=-42.93143626004543
+    Iter: 150, loss=-42.93430243457384
+    Iter: 160, loss=-42.93669507664405
+    Iter: 170, loss=-42.93876197077028
+    Iter: 180, loss=-42.94043926818304
+    Iter: 190, loss=-42.941767213471
 
 
 
@@ -380,7 +380,7 @@ Plot the estimated quadratic OT plan
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 12.396 seconds)
+   **Total running time of the script:** (0 minutes 8.564 seconds)
 
 
 .. _sphx_glr_download_auto_examples_backends_plot_dual_ot_pytorch.py:

master/_images/sphx_glr_plot_OT_2D_samples_001.png

@@ -395,14 +395,14 @@ classes
     Estimated weights :  [0.29985821 0.18926744 0.51087435]
     True proportions :  [0.5 0.3 0.2]
 
-    <matplotlib.colorbar.Colorbar object at 0x7f2ec3daa650>
+    <matplotlib.colorbar.Colorbar object at 0x7f60c09e69e0>
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 9.062 seconds)
+   **Total running time of the script:** (0 minutes 8.575 seconds)
 
 
 .. _sphx_glr_download_auto_examples_backends_plot_optim_gromov_pytorch.py: