Github action: auto-update.

neuraloperator · Dec 16, 2024 · 8be73b1 · 8be73b1
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diff --git a/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/dev/_downloads/0d78e075dd52a34e158d7f5f710dfe89/plot_incremental_FNO_darcy.zip b/dev/_downloads/0d78e075dd52a34e158d7f5f710dfe89/plot_incremental_FNO_darcy.zip
diff --git a/dev/_downloads/20c43dd37baf603889c4dc23e93bdb60/plot_count_flops.zip b/dev/_downloads/20c43dd37baf603889c4dc23e93bdb60/plot_count_flops.zip
diff --git a/dev/_downloads/3864a2d85c7ce11adeac9580559229ab/plot_darcy_flow.zip b/dev/_downloads/3864a2d85c7ce11adeac9580559229ab/plot_darcy_flow.zip
diff --git a/dev/_downloads/3faf9d2eaee5cc8e9f1c631c002ce544/plot_darcy_flow_spectrum.zip b/dev/_downloads/3faf9d2eaee5cc8e9f1c631c002ce544/plot_darcy_flow_spectrum.zip
diff --git a/dev/_downloads/4e1cba46bb51062a073424e1efbaad57/plot_DISCO_convolutions.zip b/dev/_downloads/4e1cba46bb51062a073424e1efbaad57/plot_DISCO_convolutions.zip
diff --git a/dev/_downloads/5e60095ce99919773daa83384f767e02/plot_SFNO_swe.zip b/dev/_downloads/5e60095ce99919773daa83384f767e02/plot_SFNO_swe.zip
diff --git a/dev/_downloads/645da00b8fbbb9bb5cae877fd0f31635/plot_FNO_darcy.zip b/dev/_downloads/645da00b8fbbb9bb5cae877fd0f31635/plot_FNO_darcy.zip
diff --git a/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/dev/_downloads/7296405f6df7c2cfe184e9b258cee33e/checkpoint_FNO_darcy.zip b/dev/_downloads/7296405f6df7c2cfe184e9b258cee33e/checkpoint_FNO_darcy.zip
diff --git a/dev/_downloads/cefc537c5730a6b3e916b83c1fd313d6/plot_UNO_darcy.zip b/dev/_downloads/cefc537c5730a6b3e916b83c1fd313d6/plot_UNO_darcy.zip
diff --git a/dev/_images/sphx_glr_plot_SFNO_swe_001.png b/dev/_images/sphx_glr_plot_SFNO_swe_001.png
diff --git a/dev/_images/sphx_glr_plot_SFNO_swe_thumb.png b/dev/_images/sphx_glr_plot_SFNO_swe_thumb.png
diff --git a/dev/_images/sphx_glr_plot_UNO_darcy_001.png b/dev/_images/sphx_glr_plot_UNO_darcy_001.png
diff --git a/dev/_images/sphx_glr_plot_UNO_darcy_thumb.png b/dev/_images/sphx_glr_plot_UNO_darcy_thumb.png
diff --git a/dev/_sources/auto_examples/plot_DISCO_convolutions.rst.txt b/dev/_sources/auto_examples/plot_DISCO_convolutions.rst.txt
@@ -357,7 +357,7 @@ in order to compute the convolved image, we need to first bring it into the righ
  .. code-block:: none
 
 
-    <matplotlib.colorbar.Colorbar object at 0x7f052afd4880>
+    <matplotlib.colorbar.Colorbar object at 0x7fc32d0138b0>
 
 
 
@@ -448,7 +448,7 @@ in order to compute the convolved image, we need to first bring it into the righ
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 37.420 seconds)
+   **Total running time of the script:** (0 minutes 36.824 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_DISCO_convolutions.py:

diff --git a/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_FNO_darcy.rst.txt
@@ -248,13 +248,13 @@ Training the model
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f0560afcee0>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7fc348be4d90>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f0546730640>
+     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7fc348be4f40>
 
-     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f0546730640>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f05606759a0>}
+     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7fc348be4f40>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7fc348c035b0>}
 
 
 
@@ -311,22 +311,22 @@ Then train the model on our small Darcy-Flow dataset:
     Training on 1000 samples
     Testing on [50, 50] samples         on resolutions [16, 32].
     Raw outputs of shape torch.Size([32, 1, 16, 16])
-    [0] time=2.60, avg_loss=0.6956, train_err=21.7383
+    [0] time=2.55, avg_loss=0.6956, train_err=21.7383
     Eval: 16_h1=0.4298, 16_l2=0.3487, 32_h1=0.5847, 32_l2=0.3542
-    [3] time=2.57, avg_loss=0.2103, train_err=6.5705
+    [3] time=2.52, avg_loss=0.2103, train_err=6.5705
     Eval: 16_h1=0.2030, 16_l2=0.1384, 32_h1=0.5075, 32_l2=0.1774
-    [6] time=2.57, avg_loss=0.1911, train_err=5.9721
+    [6] time=2.54, avg_loss=0.1911, train_err=5.9721
     Eval: 16_h1=0.2099, 16_l2=0.1374, 32_h1=0.4907, 32_l2=0.1783
-    [9] time=2.57, avg_loss=0.1410, train_err=4.4073
+    [9] time=2.53, avg_loss=0.1410, train_err=4.4073
     Eval: 16_h1=0.2052, 16_l2=0.1201, 32_h1=0.5268, 32_l2=0.1615
-    [12] time=2.58, avg_loss=0.1422, train_err=4.4434
+    [12] time=2.53, avg_loss=0.1422, train_err=4.4434
     Eval: 16_h1=0.2131, 16_l2=0.1285, 32_h1=0.5413, 32_l2=0.1741
-    [15] time=2.58, avg_loss=0.1198, train_err=3.7424
+    [15] time=2.52, avg_loss=0.1198, train_err=3.7424
     Eval: 16_h1=0.1984, 16_l2=0.1137, 32_h1=0.5255, 32_l2=0.1569
-    [18] time=2.56, avg_loss=0.1104, train_err=3.4502
+    [18] time=2.54, avg_loss=0.1104, train_err=3.4502
     Eval: 16_h1=0.2039, 16_l2=0.1195, 32_h1=0.5062, 32_l2=0.1603
 
-    {'train_err': 2.9605126455426216, 'avg_loss': 0.0947364046573639, 'avg_lasso_loss': None, 'epoch_train_time': 2.5626969950000102}
+    {'train_err': 2.9605126455426216, 'avg_loss': 0.0947364046573639, 'avg_lasso_loss': None, 'epoch_train_time': 2.5328369260000727}
 
 
 
@@ -476,7 +476,7 @@ are other ways to scale the outputs of the FNO to train a true super-resolution
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 52.899 seconds)
+   **Total running time of the script:** (0 minutes 51.949 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_FNO_darcy.py:

diff --git a/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt b/dev/_sources/auto_examples/plot_SFNO_swe.rst.txt
@@ -234,13 +234,13 @@ Creating the losses
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f05467c8520>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7fc348b03700>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.LpLoss object at 0x7f05467c8c10>
+     * Train: <neuralop.losses.data_losses.LpLoss object at 0x7fc348b035e0>
 
-     * Test: {'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f05467c8c10>}
+     * Test: {'l2': <neuralop.losses.data_losses.LpLoss object at 0x7fc348b035e0>}
 
 
 
@@ -297,22 +297,22 @@ Train the model on the spherical SWE dataset
     Training on 200 samples
     Testing on [50, 50] samples         on resolutions [(32, 64), (64, 128)].
     Raw outputs of shape torch.Size([4, 3, 32, 64])
-    [0] time=3.55, avg_loss=2.5143, train_err=10.0572
-    Eval: (32, 64)_l2=1.9155, (64, 128)_l2=2.4213
-    [3] time=3.55, avg_loss=0.4193, train_err=1.6771
-    Eval: (32, 64)_l2=0.4481, (64, 128)_l2=2.5097
-    [6] time=3.49, avg_loss=0.2898, train_err=1.1590
-    Eval: (32, 64)_l2=0.3605, (64, 128)_l2=2.4361
-    [9] time=3.53, avg_loss=0.2269, train_err=0.9075
-    Eval: (32, 64)_l2=0.3382, (64, 128)_l2=2.4815
-    [12] time=3.52, avg_loss=0.1930, train_err=0.7721
-    Eval: (32, 64)_l2=0.3280, (64, 128)_l2=2.4038
-    [15] time=3.54, avg_loss=0.1609, train_err=0.6438
-    Eval: (32, 64)_l2=0.3029, (64, 128)_l2=2.4056
-    [18] time=3.57, avg_loss=0.1422, train_err=0.5687
-    Eval: (32, 64)_l2=0.2940, (64, 128)_l2=2.3771
+    [0] time=3.52, avg_loss=2.5951, train_err=10.3803
+    Eval: (32, 64)_l2=1.7616, (64, 128)_l2=2.4268
+    [3] time=3.52, avg_loss=0.4486, train_err=1.7945
+    Eval: (32, 64)_l2=0.4180, (64, 128)_l2=2.3742
+    [6] time=3.47, avg_loss=0.2711, train_err=1.0843
+    Eval: (32, 64)_l2=0.3335, (64, 128)_l2=2.3245
+    [9] time=3.48, avg_loss=0.2257, train_err=0.9030
+    Eval: (32, 64)_l2=0.3089, (64, 128)_l2=2.3506
+    [12] time=3.47, avg_loss=0.1931, train_err=0.7726
+    Eval: (32, 64)_l2=0.2473, (64, 128)_l2=2.3245
+    [15] time=3.46, avg_loss=0.1670, train_err=0.6680
+    Eval: (32, 64)_l2=0.2365, (64, 128)_l2=2.3338
+    [18] time=3.46, avg_loss=0.1504, train_err=0.6015
+    Eval: (32, 64)_l2=0.2184, (64, 128)_l2=2.3164
 
-    {'train_err': 0.5653003603219986, 'avg_loss': 0.14132509008049965, 'avg_lasso_loss': None, 'epoch_train_time': 3.540815240000029}
+    {'train_err': 0.5853043901920318, 'avg_loss': 0.14632609754800796, 'avg_lasso_loss': None, 'epoch_train_time': 3.4730680470000834}
 
 
 
@@ -383,7 +383,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 26.316 seconds)
+   **Total running time of the script:** (1 minutes 25.227 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_SFNO_swe.py:

diff --git a/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt b/dev/_sources/auto_examples/plot_UNO_darcy.rst.txt
@@ -345,13 +345,13 @@ Creating the losses
     )
 
     ### SCHEDULER ###
-     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7f0546796370>
+     <torch.optim.lr_scheduler.CosineAnnealingLR object at 0x7fc348ad6460>
 
     ### LOSSES ###
 
-     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7f0546796040>
+     * Train: <neuralop.losses.data_losses.H1Loss object at 0x7fc348ad6820>
 
-     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7f0546796040>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7f05467962e0>}
+     * Test: {'h1': <neuralop.losses.data_losses.H1Loss object at 0x7fc348ad6820>, 'l2': <neuralop.losses.data_losses.LpLoss object at 0x7fc348ad6be0>}
 
 
 
@@ -410,22 +410,22 @@ Actually train the model on our small Darcy-Flow dataset
     Training on 1000 samples
     Testing on [50, 50] samples         on resolutions [16, 32].
     Raw outputs of shape torch.Size([32, 1, 16, 16])
-    [0] time=10.26, avg_loss=0.7033, train_err=21.9780
-    Eval: 16_h1=0.4504, 16_l2=0.2891, 32_h1=0.8744, 32_l2=0.5683
-    [3] time=10.22, avg_loss=0.2421, train_err=7.5659
-    Eval: 16_h1=0.2374, 16_l2=0.1541, 32_h1=0.7924, 32_l2=0.5027
-    [6] time=10.14, avg_loss=0.2417, train_err=7.5527
-    Eval: 16_h1=0.2790, 16_l2=0.1725, 32_h1=0.8010, 32_l2=0.4774
-    [9] time=10.14, avg_loss=0.2402, train_err=7.5063
-    Eval: 16_h1=0.2619, 16_l2=0.1642, 32_h1=0.7790, 32_l2=0.4700
-    [12] time=10.11, avg_loss=0.2047, train_err=6.3980
-    Eval: 16_h1=0.2947, 16_l2=0.1913, 32_h1=0.7847, 32_l2=0.4989
-    [15] time=10.14, avg_loss=0.1683, train_err=5.2603
-    Eval: 16_h1=0.2925, 16_l2=0.1709, 32_h1=0.7781, 32_l2=0.4702
-    [18] time=10.13, avg_loss=0.1472, train_err=4.5987
-    Eval: 16_h1=0.2312, 16_l2=0.1432, 32_h1=0.7338, 32_l2=0.4617
+    [0] time=10.09, avg_loss=0.6742, train_err=21.0681
+    Eval: 16_h1=0.3964, 16_l2=0.2586, 32_h1=0.9592, 32_l2=0.6138
+    [3] time=9.98, avg_loss=0.2421, train_err=7.5670
+    Eval: 16_h1=0.2568, 16_l2=0.1667, 32_h1=0.8420, 32_l2=0.5287
+    [6] time=10.09, avg_loss=0.2110, train_err=6.5937
+    Eval: 16_h1=0.2970, 16_l2=0.1961, 32_h1=0.8152, 32_l2=0.5072
+    [9] time=10.02, avg_loss=0.1948, train_err=6.0878
+    Eval: 16_h1=0.2791, 16_l2=0.1853, 32_h1=0.8134, 32_l2=0.4969
+    [12] time=10.03, avg_loss=0.2015, train_err=6.2963
+    Eval: 16_h1=0.2579, 16_l2=0.1594, 32_h1=0.8114, 32_l2=0.4822
+    [15] time=10.01, avg_loss=0.1410, train_err=4.4049
+    Eval: 16_h1=0.2554, 16_l2=0.1777, 32_h1=0.8068, 32_l2=0.5007
+    [18] time=10.00, avg_loss=0.1490, train_err=4.6550
+    Eval: 16_h1=0.2411, 16_l2=0.1463, 32_h1=0.7796, 32_l2=0.4553
 
-    {'train_err': 3.8512582033872604, 'avg_loss': 0.12324026250839233, 'avg_lasso_loss': None, 'epoch_train_time': 10.164662551999982}
+    {'train_err': 4.482214251533151, 'avg_loss': 0.14343085604906083, 'avg_lasso_loss': None, 'epoch_train_time': 9.988673681000023}
 
 
 
@@ -499,7 +499,7 @@ In practice we would train a Neural Operator on one or multiple GPUs
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (3 minutes 26.647 seconds)
+   **Total running time of the script:** (3 minutes 23.629 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_UNO_darcy.py:

diff --git a/dev/_sources/auto_examples/plot_count_flops.rst.txt b/dev/_sources/auto_examples/plot_count_flops.rst.txt
@@ -80,7 +80,7 @@ This output is organized as a defaultdict object that counts the FLOPS used in e
 
  .. code-block:: none
 
-    defaultdict(<function FlopTensorDispatchMode.__init__.<locals>.<lambda> at 0x7f055d093ca0>, {'': defaultdict(<class 'int'>, {'convolution.default': 2982150144, 'bmm.default': 138412032}), 'lifting': defaultdict(<class 'int'>, {'convolution.default': 562036736}), 'lifting.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 25165824}), 'lifting.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 536870912}), 'fno_blocks': defaultdict(<class 'int'>, {'convolution.default': 2147483648, 'bmm.default': 138412032}), 'fno_blocks.fno_skips.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.0.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.0': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.0.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.0.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.1.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.1': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.1.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.1.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.2': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.2.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.2.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.3': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.3.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.3.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'projection': defaultdict(<class 'int'>, {'convolution.default': 272629760}), 'projection.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'projection.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 4194304})})
+    defaultdict(<function FlopTensorDispatchMode.__init__.<locals>.<lambda> at 0x7fc35f094ca0>, {'': defaultdict(<class 'int'>, {'convolution.default': 2982150144, 'bmm.default': 138412032}), 'lifting': defaultdict(<class 'int'>, {'convolution.default': 562036736}), 'lifting.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 25165824}), 'lifting.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 536870912}), 'fno_blocks': defaultdict(<class 'int'>, {'convolution.default': 2147483648, 'bmm.default': 138412032}), 'fno_blocks.fno_skips.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.0.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.0': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.0.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.0.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.1.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.1': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.1': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.1.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.1.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.2.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.2': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.2': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.2.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.2.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.fno_skips.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.fno_skips.3.conv': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.convs.3': defaultdict(<class 'int'>, {'bmm.default': 34603008}), 'fno_blocks.channel_mlp.3': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'fno_blocks.channel_mlp.3.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'fno_blocks.channel_mlp.3.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 134217728}), 'projection': defaultdict(<class 'int'>, {'convolution.default': 272629760}), 'projection.fcs.0': defaultdict(<class 'int'>, {'convolution.default': 268435456}), 'projection.fcs.1': defaultdict(<class 'int'>, {'convolution.default': 4194304})})
 
 
 
@@ -125,7 +125,7 @@ To check the maximum FLOPS used during the forward pass, let's create a recursiv
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 3.530 seconds)
+   **Total running time of the script:** (0 minutes 3.946 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_count_flops.py:

diff --git a/dev/_sources/auto_examples/plot_darcy_flow.rst.txt b/dev/_sources/auto_examples/plot_darcy_flow.rst.txt
@@ -163,7 +163,7 @@ Visualizing the data
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.390 seconds)
+   **Total running time of the script:** (0 minutes 0.408 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_darcy_flow.py:

diff --git a/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt b/dev/_sources/auto_examples/plot_darcy_flow_spectrum.rst.txt
@@ -219,7 +219,7 @@ Loading the Navier-Stokes dataset in 128x128 resolution
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 0.278 seconds)
+   **Total running time of the script:** (0 minutes 0.276 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_darcy_flow_spectrum.py: