auto-generating sphinx docs

pytorch · Jan 30, 2025 · 55526bf · 55526bf
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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
 =================
-**02:26.786** total execution time for 11 files **from all galleries**:
+**02:27.970** 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``)
-     - 01:00.274
+     - 01:01.045
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:55.386
+     - 00:55.772
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:16.974
+     - 00:16.960
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
      - 00:11.022
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.621
+     - 00:01.655
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
-     - 00:01.479
+     - 00:01.485
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``reference/generated/tutorials/tensordict_keys.py``)
      - 00:00.009

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: 2.300501 [    0/60000]
-    loss: 2.291670 [ 6400/60000]
-    loss: 2.268115 [12800/60000]
-    loss: 2.268539 [19200/60000]
-    loss: 2.255385 [25600/60000]
-    loss: 2.210246 [32000/60000]
-    loss: 2.232203 [38400/60000]
-    loss: 2.196187 [44800/60000]
-    loss: 2.187124 [51200/60000]
-    loss: 2.158474 [57600/60000]
+    loss: 2.290899 [    0/60000]
+    loss: 2.283380 [ 6400/60000]
+    loss: 2.263645 [12800/60000]
+    loss: 2.267015 [19200/60000]
+    loss: 2.253845 [25600/60000]
+    loss: 2.216980 [32000/60000]
+    loss: 2.231251 [38400/60000]
+    loss: 2.188987 [44800/60000]
+    loss: 2.188446 [51200/60000]
+    loss: 2.161084 [57600/60000]
     Test Error: 
-     Accuracy: 44.8%, Avg loss: 2.152634 
+     Accuracy: 48.2%, Avg loss: 2.151983 
 
     Epoch 2
     -------------------------
-    loss: 2.159653 [    0/60000]
-    loss: 2.151260 [ 6400/60000]
-    loss: 2.090704 [12800/60000]
-    loss: 2.115746 [19200/60000]
-    loss: 2.058061 [25600/60000]
-    loss: 1.994468 [32000/60000]
-    loss: 2.035158 [38400/60000]
-    loss: 1.954756 [44800/60000]
-    loss: 1.952061 [51200/60000]
-    loss: 1.881502 [57600/60000]
+    loss: 2.158232 [    0/60000]
+    loss: 2.152648 [ 6400/60000]
+    loss: 2.088418 [12800/60000]
+    loss: 2.110807 [19200/60000]
+    loss: 2.069241 [25600/60000]
+    loss: 2.004707 [32000/60000]
+    loss: 2.041099 [38400/60000]
+    loss: 1.952711 [44800/60000]
+    loss: 1.963982 [51200/60000]
+    loss: 1.896268 [57600/60000]
     Test Error: 
-     Accuracy: 56.8%, Avg loss: 1.881396 
+     Accuracy: 51.4%, Avg loss: 1.887250 
 
     Epoch 3
     -------------------------
-    loss: 1.910321 [    0/60000]
-    loss: 1.877284 [ 6400/60000]
-    loss: 1.761520 [12800/60000]
-    loss: 1.811921 [19200/60000]
-    loss: 1.689319 [25600/60000]
-    loss: 1.642401 [32000/60000]
-    loss: 1.684761 [38400/60000]
-    loss: 1.582971 [44800/60000]
-    loss: 1.599584 [51200/60000]
-    loss: 1.496778 [57600/60000]
+    loss: 1.923242 [    0/60000]
+    loss: 1.897610 [ 6400/60000]
+    loss: 1.771249 [12800/60000]
+    loss: 1.817789 [19200/60000]
+    loss: 1.722580 [25600/60000]
+    loss: 1.669959 [32000/60000]
+    loss: 1.705798 [38400/60000]
+    loss: 1.592349 [44800/60000]
+    loss: 1.623200 [51200/60000]
+    loss: 1.527352 [57600/60000]
     Test Error: 
-     Accuracy: 62.2%, Avg loss: 1.515457 
+     Accuracy: 59.8%, Avg loss: 1.532986 
 
     Epoch 4
     -------------------------
-    loss: 1.577228 [    0/60000]
-    loss: 1.540342 [ 6400/60000]
-    loss: 1.393612 [12800/60000]
-    loss: 1.471445 [19200/60000]
-    loss: 1.345142 [25600/60000]
-    loss: 1.337163 [32000/60000]
-    loss: 1.378728 [38400/60000]
-    loss: 1.296360 [44800/60000]
-    loss: 1.323254 [51200/60000]
-    loss: 1.226359 [57600/60000]
+    loss: 1.599365 [    0/60000]
+    loss: 1.570937 [ 6400/60000]
+    loss: 1.412728 [12800/60000]
+    loss: 1.491609 [19200/60000]
+    loss: 1.381384 [25600/60000]
+    loss: 1.368748 [32000/60000]
+    loss: 1.397359 [38400/60000]
+    loss: 1.303051 [44800/60000]
+    loss: 1.338716 [51200/60000]
+    loss: 1.252320 [57600/60000]
     Test Error: 
-     Accuracy: 64.1%, Avg loss: 1.252268 
+     Accuracy: 62.9%, Avg loss: 1.268660 
 
     Epoch 5
     -------------------------
-    loss: 1.322633 [    0/60000]
-    loss: 1.304030 [ 6400/60000]
-    loss: 1.142413 [12800/60000]
-    loss: 1.247943 [19200/60000]
-    loss: 1.122089 [25600/60000]
-    loss: 1.137669 [32000/60000]
-    loss: 1.187212 [38400/60000]
-    loss: 1.116194 [44800/60000]
-    loss: 1.149542 [51200/60000]
-    loss: 1.065090 [57600/60000]
+    loss: 1.339098 [    0/60000]
+    loss: 1.331423 [ 6400/60000]
+    loss: 1.159905 [12800/60000]
+    loss: 1.271557 [19200/60000]
+    loss: 1.152297 [25600/60000]
+    loss: 1.166887 [32000/60000]
+    loss: 1.200246 [38400/60000]
+    loss: 1.119432 [44800/60000]
+    loss: 1.158361 [51200/60000]
+    loss: 1.086429 [57600/60000]
     Test Error: 
-     Accuracy: 64.9%, Avg loss: 1.087330 
+     Accuracy: 64.7%, Avg loss: 1.100924 
 
-    TensorDict training done! time:  8.8829 s
+    TensorDict training done! time:  8.7571 s
     Epoch 1
     -------------------------
-    loss: 2.314956 [    0/60000]
-    loss: 2.293715 [ 6400/60000]
-    loss: 2.269283 [12800/60000]
-    loss: 2.265157 [19200/60000]
-    loss: 2.240936 [25600/60000]
-    loss: 2.217607 [32000/60000]
-    loss: 2.226448 [38400/60000]
-    loss: 2.191822 [44800/60000]
-    loss: 2.192636 [51200/60000]
-    loss: 2.156208 [57600/60000]
+    loss: 2.296923 [    0/60000]
+    loss: 2.295177 [ 6400/60000]
+    loss: 2.279087 [12800/60000]
+    loss: 2.275843 [19200/60000]
+    loss: 2.253629 [25600/60000]
+    loss: 2.232588 [32000/60000]
+    loss: 2.234448 [38400/60000]
+    loss: 2.205310 [44800/60000]
+    loss: 2.203568 [51200/60000]
+    loss: 2.165645 [57600/60000]
     Test Error: 
-     Accuracy: 48.5%, Avg loss: 2.146947 
+     Accuracy: 49.6%, Avg loss: 2.167537 
 
     Epoch 2
     -------------------------
-    loss: 2.163539 [    0/60000]
-    loss: 2.148267 [ 6400/60000]
-    loss: 2.081108 [12800/60000]
-    loss: 2.105221 [19200/60000]
-    loss: 2.039117 [25600/60000]
-    loss: 1.986468 [32000/60000]
-    loss: 2.020276 [38400/60000]
-    loss: 1.935750 [44800/60000]
-    loss: 1.944526 [51200/60000]
-    loss: 1.866489 [57600/60000]
+    loss: 2.174862 [    0/60000]
+    loss: 2.172786 [ 6400/60000]
+    loss: 2.117940 [12800/60000]
+    loss: 2.128313 [19200/60000]
+    loss: 2.083431 [25600/60000]
+    loss: 2.024095 [32000/60000]
+    loss: 2.051723 [38400/60000]
+    loss: 1.976189 [44800/60000]
+    loss: 1.986143 [51200/60000]
+    loss: 1.899558 [57600/60000]
     Test Error: 
-     Accuracy: 52.9%, Avg loss: 1.861864 
+     Accuracy: 55.4%, Avg loss: 1.907887 
 
     Epoch 3
     -------------------------
-    loss: 1.901781 [    0/60000]
-    loss: 1.869791 [ 6400/60000]
-    loss: 1.740497 [12800/60000]
-    loss: 1.790594 [19200/60000]
-    loss: 1.664503 [25600/60000]
-    loss: 1.630339 [32000/60000]
-    loss: 1.652570 [38400/60000]
-    loss: 1.557054 [44800/60000]
-    loss: 1.582744 [51200/60000]
-    loss: 1.474415 [57600/60000]
+    loss: 1.938452 [    0/60000]
+    loss: 1.913136 [ 6400/60000]
+    loss: 1.796396 [12800/60000]
+    loss: 1.833235 [19200/60000]
+    loss: 1.733113 [25600/60000]
+    loss: 1.676963 [32000/60000]
+    loss: 1.700666 [38400/60000]
+    loss: 1.598404 [44800/60000]
+    loss: 1.630065 [51200/60000]
+    loss: 1.516547 [57600/60000]
     Test Error: 
-     Accuracy: 60.4%, Avg loss: 1.494496 
+     Accuracy: 60.6%, Avg loss: 1.538323 
 
     Epoch 4
     -------------------------
-    loss: 1.565853 [    0/60000]
-    loss: 1.537299 [ 6400/60000]
-    loss: 1.378601 [12800/60000]
-    loss: 1.453936 [19200/60000]
-    loss: 1.327626 [25600/60000]
-    loss: 1.338252 [32000/60000]
-    loss: 1.342915 [38400/60000]
-    loss: 1.276868 [44800/60000]
-    loss: 1.309620 [51200/60000]
-    loss: 1.205595 [57600/60000]
+    loss: 1.596945 [    0/60000]
+    loss: 1.566176 [ 6400/60000]
+    loss: 1.416750 [12800/60000]
+    loss: 1.488615 [19200/60000]
+    loss: 1.383947 [25600/60000]
+    loss: 1.369466 [32000/60000]
+    loss: 1.382822 [38400/60000]
+    loss: 1.299737 [44800/60000]
+    loss: 1.343412 [51200/60000]
+    loss: 1.242868 [57600/60000]
     Test Error: 
-     Accuracy: 63.1%, Avg loss: 1.237788 
+     Accuracy: 63.3%, Avg loss: 1.267165 
 
     Epoch 5
     -------------------------
-    loss: 1.318985 [    0/60000]
-    loss: 1.307475 [ 6400/60000]
-    loss: 1.134210 [12800/60000]
-    loss: 1.241522 [19200/60000]
-    loss: 1.111890 [25600/60000]
-    loss: 1.148203 [32000/60000]
-    loss: 1.157786 [38400/60000]
-    loss: 1.103595 [44800/60000]
-    loss: 1.142417 [51200/60000]
-    loss: 1.051960 [57600/60000]
+    loss: 1.334766 [    0/60000]
+    loss: 1.319309 [ 6400/60000]
+    loss: 1.156104 [12800/60000]
+    loss: 1.260619 [19200/60000]
+    loss: 1.152369 [25600/60000]
+    loss: 1.167890 [32000/60000]
+    loss: 1.184418 [38400/60000]
+    loss: 1.113907 [44800/60000]
+    loss: 1.161320 [51200/60000]
+    loss: 1.078406 [57600/60000]
     Test Error: 
-     Accuracy: 64.6%, Avg loss: 1.080538 
+     Accuracy: 64.4%, Avg loss: 1.096670 
 
-    Training done! time:  33.9325 s
+    Training done! time:  34.1557 s
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 55.386 seconds)
+   **Total running time of the script:** (0 minutes 55.772 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([[0.0000, 1.1659, 0.4612, 0.0000]], grad_fn=<ReluBackward0>), tensor([[-0.1092, -0.0661, -0.5568, -0.2937]], grad_fn=<AddmmBackward0>), tensor([[-0.1092, -0.0661]], grad_fn=<SplitBackward0>), tensor([[0.6876, 0.8257]], grad_fn=<ClampMinBackward0>), tensor([[-0.1092, -0.0661]], grad_fn=<SplitBackward0>))
+    (tensor([[0.5058, 0.1084, 0.0000, 0.0000]], grad_fn=<ReluBackward0>), tensor([[ 0.0516, -0.0173,  0.0108, -0.3756]], grad_fn=<AddmmBackward0>), tensor([[ 0.0516, -0.0173]], grad_fn=<SplitBackward0>), tensor([[1.0068, 0.7809]], grad_fn=<ClampMinBackward0>), tensor([[ 0.0516, -0.0173]], 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:  504.97 micro-seconds
-    Time for exported module:  353.57 micro-seconds
+    Time for TDModule:  532.15 micro-seconds
+    Time for exported module:  359.30 micro-seconds
 
 
 
@@ -450,7 +450,7 @@ distribution:
 
  .. code-block:: none
 
-    tensor([[-0.1092, -0.0661]], grad_fn=<SplitBackward0>)
+    tensor([[ 0.0516, -0.0173]], 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 1.479 seconds)
+   **Total running time of the script:** (0 minutes 1.485 seconds)
 
 
 .. _sphx_glr_download_tutorials_export.py:

diff --git a/main/_sources/tutorials/sg_execution_times.rst.txt b/main/_sources/tutorials/sg_execution_times.rst.txt
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:26.786** total execution time for 11 files **from tutorials**:
+**02:27.970** total execution time for 11 files **from tutorials**:
 
 .. container::
 
@@ -33,22 +33,22 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_tutorials_tensorclass_fashion.py` (``tensorclass_fashion.py``)
-     - 01:00.274
+     - 01:01.045
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``data_fashion.py``)
-     - 00:55.386
+     - 00:55.772
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``tensordict_module.py``)
-     - 00:16.974
+     - 00:16.960
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``streamed_tensordict.py``)
      - 00:11.022
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``tensorclass_imagenet.py``)
-     - 00:01.621
+     - 00:01.655
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``export.py``)
-     - 00:01.479
+     - 00:01.485
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``tensordict_keys.py``)
      - 00:00.009