auto-generating sphinx docs

pytorch · Jan 13, 2025 · ee6257e · ee6257e
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diff --git a/main/_downloads/0935354946f64f2fcf766478aa4572c3/tensordict_module.zip b/main/_downloads/0935354946f64f2fcf766478aa4572c3/tensordict_module.zip
diff --git a/main/_downloads/150528e38f6816824f1e81ed67476a9f/export.zip b/main/_downloads/150528e38f6816824f1e81ed67476a9f/export.zip
diff --git a/main/_downloads/30cf47ff46cc4701c97ed46f4d91a0f2/tensordict_keys.zip b/main/_downloads/30cf47ff46cc4701c97ed46f4d91a0f2/tensordict_keys.zip
diff --git a/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip
diff --git a/main/_downloads/5b6beccb0b7dcf81c69b35cec5a445ee/tensorclass_imagenet.zip b/main/_downloads/5b6beccb0b7dcf81c69b35cec5a445ee/tensorclass_imagenet.zip
diff --git a/main/_downloads/71264da051c82d595130ca8e60350024/tensordict_shapes.zip b/main/_downloads/71264da051c82d595130ca8e60350024/tensordict_shapes.zip
diff --git a/main/_downloads/777fb49680f0121bef451f089b823d24/tensorclass_fashion.zip b/main/_downloads/777fb49680f0121bef451f089b823d24/tensorclass_fashion.zip
diff --git a/main/_downloads/89ddb148138028541dafab3ced0e0421/tensordict_memory.zip b/main/_downloads/89ddb148138028541dafab3ced0e0421/tensordict_memory.zip
diff --git a/main/_downloads/a3a53a08487286803e7e9713be78fb54/tensordict_preallocation.zip b/main/_downloads/a3a53a08487286803e7e9713be78fb54/tensordict_preallocation.zip
diff --git a/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip b/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip
diff --git a/main/_downloads/bddca9d9cab285e6fedc82c7769291ef/data_fashion.zip b/main/_downloads/bddca9d9cab285e6fedc82c7769291ef/data_fashion.zip
diff --git a/main/_downloads/c1f74b953ed757c75e2d2bc64666eae1/streamed_tensordict.zip b/main/_downloads/c1f74b953ed757c75e2d2bc64666eae1/streamed_tensordict.zip
diff --git a/main/_downloads/c1fc5aa7548e6b1437d36fb47bc36ce5/tensordict_slicing.zip b/main/_downloads/c1fc5aa7548e6b1437d36fb47bc36ce5/tensordict_slicing.zip
diff --git a/main/_sources/sg_execution_times.rst.txt b/main/_sources/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:26.914** total execution time for 11 files **from all galleries**:
+**02:25.782** total execution time for 11 files **from all galleries**:
 
 .. container::
 
@@ -33,22 +33,22 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_tutorials_tensorclass_fashion.py` (``reference/generated/tutorials/tensorclass_fashion.py``)
-     - 01:00.584
+     - 01:00.236
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:55.435
+     - 00:54.475
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:16.832
+     - 00:16.965
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
-     - 00:11.023
+     - 00:11.020
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.583
+     - 00:01.632
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
-     - 00:01.429
+     - 00:01.425
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``reference/generated/tutorials/tensordict_keys.py``)
      - 00:00.009
@@ -57,7 +57,7 @@ Computation times
      - 00:00.008
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_slicing.py` (``reference/generated/tutorials/tensordict_slicing.py``)
-     - 00:00.005
+     - 00:00.004
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_memory.py` (``reference/generated/tutorials/tensordict_memory.py``)
      - 00:00.004

diff --git a/main/_sources/tutorials/data_fashion.rst.txt b/main/_sources/tutorials/data_fashion.rst.txt
@@ -423,164 +423,164 @@ adjust how we unpack the data to the more explicit key-based retrieval offered b
         is_shared=False)
     Epoch 1
     -------------------------
-    loss: 2.300854 [    0/60000]
-    loss: 2.293746 [ 6400/60000]
-    loss: 2.268699 [12800/60000]
-    loss: 2.266856 [19200/60000]
-    loss: 2.235213 [25600/60000]
-    loss: 2.211900 [32000/60000]
-    loss: 2.222166 [38400/60000]
-    loss: 2.179719 [44800/60000]
-    loss: 2.183011 [51200/60000]
-    loss: 2.141797 [57600/60000]
+    loss: 2.294307 [    0/60000]
+    loss: 2.281618 [ 6400/60000]
+    loss: 2.252174 [12800/60000]
+    loss: 2.252936 [19200/60000]
+    loss: 2.225810 [25600/60000]
+    loss: 2.196889 [32000/60000]
+    loss: 2.205427 [38400/60000]
+    loss: 2.159450 [44800/60000]
+    loss: 2.156655 [51200/60000]
+    loss: 2.119258 [57600/60000]
     Test Error: 
-     Accuracy: 41.4%, Avg loss: 2.138343 
+     Accuracy: 46.3%, Avg loss: 2.110689 
 
     Epoch 2
     -------------------------
-    loss: 2.152298 [    0/60000]
-    loss: 2.143745 [ 6400/60000]
-    loss: 2.074318 [12800/60000]
-    loss: 2.096534 [19200/60000]
-    loss: 2.027858 [25600/60000]
-    loss: 1.974181 [32000/60000]
-    loss: 2.008433 [38400/60000]
-    loss: 1.913542 [44800/60000]
-    loss: 1.927643 [51200/60000]
-    loss: 1.848629 [57600/60000]
+    loss: 2.125491 [    0/60000]
+    loss: 2.111995 [ 6400/60000]
+    loss: 2.033796 [12800/60000]
+    loss: 2.059668 [19200/60000]
+    loss: 1.989268 [25600/60000]
+    loss: 1.936978 [32000/60000]
+    loss: 1.959576 [38400/60000]
+    loss: 1.862608 [44800/60000]
+    loss: 1.872021 [51200/60000]
+    loss: 1.797924 [57600/60000]
     Test Error: 
-     Accuracy: 50.9%, Avg loss: 1.844980 
+     Accuracy: 53.7%, Avg loss: 1.789212 
 
     Epoch 3
     -------------------------
-    loss: 1.882247 [    0/60000]
-    loss: 1.855337 [ 6400/60000]
-    loss: 1.723125 [12800/60000]
-    loss: 1.775211 [19200/60000]
-    loss: 1.646122 [25600/60000]
-    loss: 1.609284 [32000/60000]
-    loss: 1.644226 [38400/60000]
-    loss: 1.530861 [44800/60000]
-    loss: 1.566059 [51200/60000]
-    loss: 1.462497 [57600/60000]
+    loss: 1.834876 [    0/60000]
+    loss: 1.799562 [ 6400/60000]
+    loss: 1.658334 [12800/60000]
+    loss: 1.716297 [19200/60000]
+    loss: 1.600255 [25600/60000]
+    loss: 1.571958 [32000/60000]
+    loss: 1.586597 [38400/60000]
+    loss: 1.484927 [44800/60000]
+    loss: 1.517752 [51200/60000]
+    loss: 1.421714 [57600/60000]
     Test Error: 
-     Accuracy: 60.3%, Avg loss: 1.476044 
+     Accuracy: 59.6%, Avg loss: 1.435836 
 
     Epoch 4
     -------------------------
-    loss: 1.545485 [    0/60000]
-    loss: 1.518976 [ 6400/60000]
-    loss: 1.357983 [12800/60000]
-    loss: 1.438691 [19200/60000]
-    loss: 1.309775 [25600/60000]
-    loss: 1.318142 [32000/60000]
-    loss: 1.343395 [38400/60000]
-    loss: 1.253499 [44800/60000]
-    loss: 1.295117 [51200/60000]
-    loss: 1.202819 [57600/60000]
+    loss: 1.509605 [    0/60000]
+    loss: 1.479262 [ 6400/60000]
+    loss: 1.314702 [12800/60000]
+    loss: 1.404015 [19200/60000]
+    loss: 1.285816 [25600/60000]
+    loss: 1.299244 [32000/60000]
+    loss: 1.307629 [38400/60000]
+    loss: 1.230591 [44800/60000]
+    loss: 1.270229 [51200/60000]
+    loss: 1.182587 [57600/60000]
     Test Error: 
-     Accuracy: 63.6%, Avg loss: 1.223719 
+     Accuracy: 62.5%, Avg loss: 1.203891 
 
     Epoch 5
     -------------------------
-    loss: 1.299295 [    0/60000]
-    loss: 1.292917 [ 6400/60000]
-    loss: 1.116714 [12800/60000]
-    loss: 1.229689 [19200/60000]
-    loss: 1.096139 [25600/60000]
-    loss: 1.131263 [32000/60000]
-    loss: 1.165007 [38400/60000]
-    loss: 1.084519 [44800/60000]
-    loss: 1.130223 [51200/60000]
-    loss: 1.054687 [57600/60000]
+    loss: 1.280522 [    0/60000]
+    loss: 1.268702 [ 6400/60000]
+    loss: 1.090299 [12800/60000]
+    loss: 1.209925 [19200/60000]
+    loss: 1.084458 [25600/60000]
+    loss: 1.124142 [32000/60000]
+    loss: 1.139163 [38400/60000]
+    loss: 1.074788 [44800/60000]
+    loss: 1.117219 [51200/60000]
+    loss: 1.040546 [57600/60000]
     Test Error: 
-     Accuracy: 64.9%, Avg loss: 1.069978 
+     Accuracy: 64.2%, Avg loss: 1.058886 
 
-    TensorDict training done! time:  8.2689 s
+    TensorDict training done! time:  8.6153 s
     Epoch 1
     -------------------------
-    loss: 2.299620 [    0/60000]
-    loss: 2.286566 [ 6400/60000]
-    loss: 2.260642 [12800/60000]
-    loss: 2.262955 [19200/60000]
-    loss: 2.231959 [25600/60000]
-    loss: 2.192373 [32000/60000]
-    loss: 2.216669 [38400/60000]
-    loss: 2.164340 [44800/60000]
-    loss: 2.164060 [51200/60000]
-    loss: 2.128082 [57600/60000]
+    loss: 2.308265 [    0/60000]
+    loss: 2.293171 [ 6400/60000]
+    loss: 2.271529 [12800/60000]
+    loss: 2.267321 [19200/60000]
+    loss: 2.235325 [25600/60000]
+    loss: 2.219271 [32000/60000]
+    loss: 2.215106 [38400/60000]
+    loss: 2.186461 [44800/60000]
+    loss: 2.184157 [51200/60000]
+    loss: 2.144828 [57600/60000]
     Test Error: 
-     Accuracy: 38.2%, Avg loss: 2.119627 
+     Accuracy: 47.1%, Avg loss: 2.140672 
 
     Epoch 2
     -------------------------
-    loss: 2.129410 [    0/60000]
-    loss: 2.115433 [ 6400/60000]
-    loss: 2.048279 [12800/60000]
-    loss: 2.071231 [19200/60000]
-    loss: 1.996241 [25600/60000]
-    loss: 1.938681 [32000/60000]
-    loss: 1.971183 [38400/60000]
-    loss: 1.876342 [44800/60000]
-    loss: 1.884605 [51200/60000]
-    loss: 1.803872 [57600/60000]
+    loss: 2.149040 [    0/60000]
+    loss: 2.142530 [ 6400/60000]
+    loss: 2.075416 [12800/60000]
+    loss: 2.099092 [19200/60000]
+    loss: 2.033061 [25600/60000]
+    loss: 1.982826 [32000/60000]
+    loss: 1.996649 [38400/60000]
+    loss: 1.917044 [44800/60000]
+    loss: 1.925382 [51200/60000]
+    loss: 1.846381 [57600/60000]
     Test Error: 
-     Accuracy: 54.2%, Avg loss: 1.804730 
+     Accuracy: 51.5%, Avg loss: 1.846959 
 
     Epoch 3
     -------------------------
-    loss: 1.841818 [    0/60000]
-    loss: 1.806565 [ 6400/60000]
-    loss: 1.681915 [12800/60000]
-    loss: 1.733117 [19200/60000]
-    loss: 1.620957 [25600/60000]
-    loss: 1.582071 [32000/60000]
-    loss: 1.604616 [38400/60000]
-    loss: 1.510135 [44800/60000]
-    loss: 1.538761 [51200/60000]
-    loss: 1.432629 [57600/60000]
+    loss: 1.878904 [    0/60000]
+    loss: 1.854604 [ 6400/60000]
+    loss: 1.725572 [12800/60000]
+    loss: 1.773118 [19200/60000]
+    loss: 1.660257 [25600/60000]
+    loss: 1.622359 [32000/60000]
+    loss: 1.631321 [38400/60000]
+    loss: 1.536023 [44800/60000]
+    loss: 1.566329 [51200/60000]
+    loss: 1.465487 [57600/60000]
     Test Error: 
-     Accuracy: 61.9%, Avg loss: 1.458440 
+     Accuracy: 59.7%, Avg loss: 1.482586 
 
     Epoch 4
     -------------------------
-    loss: 1.522330 [    0/60000]
-    loss: 1.496469 [ 6400/60000]
-    loss: 1.344924 [12800/60000]
-    loss: 1.425967 [19200/60000]
-    loss: 1.315338 [25600/60000]
-    loss: 1.313046 [32000/60000]
-    loss: 1.329174 [38400/60000]
-    loss: 1.260167 [44800/60000]
-    loss: 1.293201 [51200/60000]
-    loss: 1.194556 [57600/60000]
+    loss: 1.546394 [    0/60000]
+    loss: 1.523421 [ 6400/60000]
+    loss: 1.364943 [12800/60000]
+    loss: 1.442173 [19200/60000]
+    loss: 1.334812 [25600/60000]
+    loss: 1.330345 [32000/60000]
+    loss: 1.337560 [38400/60000]
+    loss: 1.261183 [44800/60000]
+    loss: 1.298624 [51200/60000]
+    loss: 1.215231 [57600/60000]
     Test Error: 
-     Accuracy: 63.9%, Avg loss: 1.224321 
+     Accuracy: 63.4%, Avg loss: 1.232315 
 
     Epoch 5
     -------------------------
-    loss: 1.293618 [    0/60000]
-    loss: 1.286654 [ 6400/60000]
-    loss: 1.117417 [12800/60000]
-    loss: 1.227480 [19200/60000]
-    loss: 1.110482 [25600/60000]
-    loss: 1.132054 [32000/60000]
-    loss: 1.156976 [38400/60000]
-    loss: 1.098149 [44800/60000]
-    loss: 1.134253 [51200/60000]
-    loss: 1.047433 [57600/60000]
+    loss: 1.300956 [    0/60000]
+    loss: 1.294875 [ 6400/60000]
+    loss: 1.123130 [12800/60000]
+    loss: 1.234035 [19200/60000]
+    loss: 1.123085 [25600/60000]
+    loss: 1.139949 [32000/60000]
+    loss: 1.157534 [38400/60000]
+    loss: 1.090237 [44800/60000]
+    loss: 1.131326 [51200/60000]
+    loss: 1.065410 [57600/60000]
     Test Error: 
-     Accuracy: 65.2%, Avg loss: 1.072687 
+     Accuracy: 65.1%, Avg loss: 1.075023 
 
-    Training done! time:  34.2947 s
+    Training done! time:  33.5233 s
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 55.435 seconds)
+   **Total running time of the script:** (0 minutes 54.475 seconds)
 
 
 .. _sphx_glr_download_tutorials_data_fashion.py:

diff --git a/main/_sources/tutorials/export.rst.txt b/main/_sources/tutorials/export.rst.txt
@@ -141,7 +141,7 @@ Let us run this model and see what the output looks like:
 
  .. code-block:: none
 
-    (tensor([[0., 0., 0., 0.]], grad_fn=<ReluBackward0>), tensor([[ 0.2379,  0.2794, -0.0173,  0.1769]], grad_fn=<AddmmBackward0>), tensor([[0.2379, 0.2794]], grad_fn=<SplitBackward0>), tensor([[0.9892, 1.1147]], grad_fn=<ClampMinBackward0>), tensor([[0.2379, 0.2794]], grad_fn=<SplitBackward0>))
+    (tensor([[0.0000, 0.0961, 0.0000, 0.0000]], grad_fn=<ReluBackward0>), tensor([[ 0.4136, -0.0047,  0.4026,  0.1457]], grad_fn=<AddmmBackward0>), tensor([[ 0.4136, -0.0047]], grad_fn=<SplitBackward0>), tensor([[1.2712, 1.0940]], grad_fn=<ClampMinBackward0>), tensor([[ 0.4136, -0.0047]], grad_fn=<SplitBackward0>))
 
 
 
@@ -266,8 +266,8 @@ This module can be run exactly like our original module (with a lower overhead):
 
  .. code-block:: none
 
-    Time for TDModule:  725.03 micro-seconds
-    Time for exported module:  351.19 micro-seconds
+    Time for TDModule:  700.47 micro-seconds
+    Time for exported module:  357.15 micro-seconds
 
 
 
@@ -450,7 +450,7 @@ distribution:
 
  .. code-block:: none
 
-    tensor([[0.2379, 0.2794]], grad_fn=<SplitBackward0>)
+    tensor([[ 0.4136, -0.0047]], grad_fn=<SplitBackward0>)
 
 
 
@@ -657,7 +657,7 @@ Next steps and further reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 1.429 seconds)
+   **Total running time of the script:** (0 minutes 1.425 seconds)
 
 
 .. _sphx_glr_download_tutorials_export.py: