auto-generating sphinx docs

pytorch · Dec 19, 2024 · 48b3ddd · 48b3ddd
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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
 =================
-**02:29.308** total execution time for 11 files **from all galleries**:
+**02:28.336** 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``)
-     - 01:00.787
+     - 01:00.357
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:55.623
+     - 00:55.345
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:18.477
+     - 00:18.265
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
-     - 00:11.021
+     - 00:11.019
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
-     - 00:01.699
+     - 00:01.695
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.673
+     - 00:01.627
      - 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``)
-     - 00:00.004
+     - 00:00.005
      - 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: 2.297034 [    0/60000]
-    loss: 2.293723 [ 6400/60000]
-    loss: 2.269874 [12800/60000]
-    loss: 2.265041 [19200/60000]
-    loss: 2.247218 [25600/60000]
-    loss: 2.226938 [32000/60000]
-    loss: 2.226363 [38400/60000]
-    loss: 2.194768 [44800/60000]
-    loss: 2.195367 [51200/60000]
-    loss: 2.161224 [57600/60000]
+    loss: 2.302697 [    0/60000]
+    loss: 2.292857 [ 6400/60000]
+    loss: 2.279685 [12800/60000]
+    loss: 2.273993 [19200/60000]
+    loss: 2.254303 [25600/60000]
+    loss: 2.239390 [32000/60000]
+    loss: 2.228393 [38400/60000]
+    loss: 2.205550 [44800/60000]
+    loss: 2.199420 [51200/60000]
+    loss: 2.171928 [57600/60000]
     Test Error: 
-     Accuracy: 43.4%, Avg loss: 2.156612 
+     Accuracy: 42.6%, Avg loss: 2.167952 
 
     Epoch 2
     -------------------------
-    loss: 2.160449 [    0/60000]
-    loss: 2.160634 [ 6400/60000]
-    loss: 2.090734 [12800/60000]
-    loss: 2.112868 [19200/60000]
-    loss: 2.055511 [25600/60000]
-    loss: 2.001153 [32000/60000]
-    loss: 2.027912 [38400/60000]
-    loss: 1.945694 [44800/60000]
-    loss: 1.961530 [51200/60000]
-    loss: 1.883855 [57600/60000]
+    loss: 2.172394 [    0/60000]
+    loss: 2.167315 [ 6400/60000]
+    loss: 2.115781 [12800/60000]
+    loss: 2.130166 [19200/60000]
+    loss: 2.083519 [25600/60000]
+    loss: 2.035105 [32000/60000]
+    loss: 2.041870 [38400/60000]
+    loss: 1.974485 [44800/60000]
+    loss: 1.972314 [51200/60000]
+    loss: 1.901936 [57600/60000]
     Test Error: 
-     Accuracy: 53.6%, Avg loss: 1.881469 
+     Accuracy: 60.6%, Avg loss: 1.905931 
 
     Epoch 3
     -------------------------
-    loss: 1.905408 [    0/60000]
-    loss: 1.890264 [ 6400/60000]
-    loss: 1.755859 [12800/60000]
-    loss: 1.807360 [19200/60000]
-    loss: 1.687582 [25600/60000]
-    loss: 1.644098 [32000/60000]
-    loss: 1.667204 [38400/60000]
-    loss: 1.568138 [44800/60000]
-    loss: 1.598023 [51200/60000]
-    loss: 1.490839 [57600/60000]
+    loss: 1.933946 [    0/60000]
+    loss: 1.907204 [ 6400/60000]
+    loss: 1.797352 [12800/60000]
+    loss: 1.831105 [19200/60000]
+    loss: 1.727809 [25600/60000]
+    loss: 1.687854 [32000/60000]
+    loss: 1.687592 [38400/60000]
+    loss: 1.601162 [44800/60000]
+    loss: 1.617153 [51200/60000]
+    loss: 1.510162 [57600/60000]
     Test Error: 
-     Accuracy: 61.1%, Avg loss: 1.508179 
+     Accuracy: 60.6%, Avg loss: 1.533949 
 
     Epoch 4
     -------------------------
-    loss: 1.564770 [    0/60000]
-    loss: 1.547288 [ 6400/60000]
-    loss: 1.384363 [12800/60000]
-    loss: 1.462441 [19200/60000]
-    loss: 1.338688 [25600/60000]
-    loss: 1.338367 [32000/60000]
-    loss: 1.350146 [38400/60000]
-    loss: 1.280336 [44800/60000]
-    loss: 1.319443 [51200/60000]
-    loss: 1.217629 [57600/60000]
+    loss: 1.596050 [    0/60000]
+    loss: 1.562335 [ 6400/60000]
+    loss: 1.421380 [12800/60000]
+    loss: 1.489787 [19200/60000]
+    loss: 1.370520 [25600/60000]
+    loss: 1.372296 [32000/60000]
+    loss: 1.371046 [38400/60000]
+    loss: 1.302831 [44800/60000]
+    loss: 1.339446 [51200/60000]
+    loss: 1.235467 [57600/60000]
     Test Error: 
-     Accuracy: 64.0%, Avg loss: 1.243834 
+     Accuracy: 62.7%, Avg loss: 1.263216 
 
     Epoch 5
     -------------------------
-    loss: 1.309603 [    0/60000]
-    loss: 1.309187 [ 6400/60000]
-    loss: 1.132445 [12800/60000]
-    loss: 1.242703 [19200/60000]
-    loss: 1.116270 [25600/60000]
-    loss: 1.142112 [32000/60000]
-    loss: 1.159446 [38400/60000]
-    loss: 1.102855 [44800/60000]
-    loss: 1.148942 [51200/60000]
-    loss: 1.061529 [57600/60000]
+    loss: 1.335270 [    0/60000]
+    loss: 1.317931 [ 6400/60000]
+    loss: 1.161858 [12800/60000]
+    loss: 1.268943 [19200/60000]
+    loss: 1.137833 [25600/60000]
+    loss: 1.168195 [32000/60000]
+    loss: 1.179101 [38400/60000]
+    loss: 1.118268 [44800/60000]
+    loss: 1.166247 [51200/60000]
+    loss: 1.073762 [57600/60000]
     Test Error: 
-     Accuracy: 65.6%, Avg loss: 1.082501 
+     Accuracy: 64.2%, Avg loss: 1.095857 
 
-    TensorDict training done! time:  8.6850 s
+    TensorDict training done! time:  8.4417 s
     Epoch 1
     -------------------------
-    loss: 2.303568 [    0/60000]
-    loss: 2.292461 [ 6400/60000]
-    loss: 2.262461 [12800/60000]
-    loss: 2.258972 [19200/60000]
-    loss: 2.241117 [25600/60000]
-    loss: 2.201943 [32000/60000]
-    loss: 2.217089 [38400/60000]
-    loss: 2.180319 [44800/60000]
-    loss: 2.187558 [51200/60000]
-    loss: 2.131742 [57600/60000]
+    loss: 2.307300 [    0/60000]
+    loss: 2.286183 [ 6400/60000]
+    loss: 2.266116 [12800/60000]
+    loss: 2.265162 [19200/60000]
+    loss: 2.244730 [25600/60000]
+    loss: 2.212550 [32000/60000]
+    loss: 2.219563 [38400/60000]
+    loss: 2.179386 [44800/60000]
+    loss: 2.180704 [51200/60000]
+    loss: 2.147648 [57600/60000]
     Test Error: 
-     Accuracy: 31.7%, Avg loss: 2.140259 
+     Accuracy: 48.7%, Avg loss: 2.139623 
 
     Epoch 2
     -------------------------
-    loss: 2.164973 [    0/60000]
-    loss: 2.150137 [ 6400/60000]
-    loss: 2.077849 [12800/60000]
-    loss: 2.093443 [19200/60000]
-    loss: 2.038481 [25600/60000]
-    loss: 1.973073 [32000/60000]
-    loss: 1.998879 [38400/60000]
-    loss: 1.921673 [44800/60000]
-    loss: 1.933168 [51200/60000]
-    loss: 1.831749 [57600/60000]
+    loss: 2.153053 [    0/60000]
+    loss: 2.136107 [ 6400/60000]
+    loss: 2.066880 [12800/60000]
+    loss: 2.090652 [19200/60000]
+    loss: 2.039071 [25600/60000]
+    loss: 1.971350 [32000/60000]
+    loss: 2.004218 [38400/60000]
+    loss: 1.914858 [44800/60000]
+    loss: 1.922656 [51200/60000]
+    loss: 1.854018 [57600/60000]
     Test Error: 
-     Accuracy: 48.7%, Avg loss: 1.849003 
+     Accuracy: 55.6%, Avg loss: 1.848790 
 
     Epoch 3
     -------------------------
-    loss: 1.898297 [    0/60000]
-    loss: 1.863893 [ 6400/60000]
-    loss: 1.733816 [12800/60000]
-    loss: 1.774636 [19200/60000]
-    loss: 1.670297 [25600/60000]
-    loss: 1.623713 [32000/60000]
-    loss: 1.639363 [38400/60000]
-    loss: 1.557734 [44800/60000]
-    loss: 1.581697 [51200/60000]
-    loss: 1.458045 [57600/60000]
+    loss: 1.881976 [    0/60000]
+    loss: 1.850410 [ 6400/60000]
+    loss: 1.717640 [12800/60000]
+    loss: 1.775696 [19200/60000]
+    loss: 1.666987 [25600/60000]
+    loss: 1.615968 [32000/60000]
+    loss: 1.642830 [38400/60000]
+    loss: 1.542044 [44800/60000]
+    loss: 1.571446 [51200/60000]
+    loss: 1.474978 [57600/60000]
     Test Error: 
-     Accuracy: 59.3%, Avg loss: 1.494883 
+     Accuracy: 60.0%, Avg loss: 1.492682 
 
     Epoch 4
     -------------------------
-    loss: 1.569354 [    0/60000]
-    loss: 1.539536 [ 6400/60000]
-    loss: 1.385659 [12800/60000]
-    loss: 1.456576 [19200/60000]
-    loss: 1.343820 [25600/60000]
-    loss: 1.342496 [32000/60000]
-    loss: 1.348758 [38400/60000]
-    loss: 1.292623 [44800/60000]
-    loss: 1.323011 [51200/60000]
-    loss: 1.209712 [57600/60000]
+    loss: 1.555621 [    0/60000]
+    loss: 1.525777 [ 6400/60000]
+    loss: 1.369483 [12800/60000]
+    loss: 1.458440 [19200/60000]
+    loss: 1.341782 [25600/60000]
+    loss: 1.335871 [32000/60000]
+    loss: 1.351265 [38400/60000]
+    loss: 1.275028 [44800/60000]
+    loss: 1.313100 [51200/60000]
+    loss: 1.220522 [57600/60000]
     Test Error: 
-     Accuracy: 63.5%, Avg loss: 1.249484 
+     Accuracy: 62.7%, Avg loss: 1.248171 
 
     Epoch 5
     -------------------------
-    loss: 1.326457 [    0/60000]
-    loss: 1.315519 [ 6400/60000]
-    loss: 1.147514 [12800/60000]
-    loss: 1.252299 [19200/60000]
-    loss: 1.127516 [25600/60000]
-    loss: 1.154889 [32000/60000]
-    loss: 1.166634 [38400/60000]
-    loss: 1.122377 [44800/60000]
-    loss: 1.159742 [51200/60000]
-    loss: 1.059098 [57600/60000]
+    loss: 1.321405 [    0/60000]
+    loss: 1.305536 [ 6400/60000]
+    loss: 1.137689 [12800/60000]
+    loss: 1.254935 [19200/60000]
+    loss: 1.132553 [25600/60000]
+    loss: 1.153863 [32000/60000]
+    loss: 1.173263 [38400/60000]
+    loss: 1.107450 [44800/60000]
+    loss: 1.148060 [51200/60000]
+    loss: 1.069191 [57600/60000]
     Test Error: 
-     Accuracy: 65.0%, Avg loss: 1.092335 
+     Accuracy: 64.3%, Avg loss: 1.093540 
 
-    Training done! time:  34.4519 s
+    Training done! time:  34.4242 s
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 55.623 seconds)
+   **Total running time of the script:** (0 minutes 55.345 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, 0.1837, 1.0734, 0.8747]], grad_fn=<ReluBackward0>), tensor([[-0.4912,  0.6789,  0.5227,  0.0275]], grad_fn=<AddmmBackward0>), tensor([[-0.4912,  0.6789]], grad_fn=<SplitBackward0>), tensor([[1.3587, 1.0174]], grad_fn=<ClampMinBackward0>), tensor([[-0.4912,  0.6789]], grad_fn=<SplitBackward0>))
+    (tensor([[0.5728, 0.0000, 0.8152, 0.3047]], grad_fn=<ReluBackward0>), tensor([[ 0.0695, -0.1837, -0.1363, -0.3182]], grad_fn=<AddmmBackward0>), tensor([[ 0.0695, -0.1837]], grad_fn=<SplitBackward0>), tensor([[0.9166, 0.8121]], grad_fn=<ClampMinBackward0>), tensor([[ 0.0695, -0.1837]], 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:  677.82 micro-seconds
-    Time for exported module:  369.31 micro-seconds
+    Time for TDModule:  679.73 micro-seconds
+    Time for exported module:  375.27 micro-seconds
 
 
 
@@ -450,7 +450,7 @@ distribution:
 
  .. code-block:: none
 
-    tensor([[-0.4912,  0.6789]], grad_fn=<SplitBackward0>)
+    tensor([[ 0.0695, -0.1837]], 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 1.699 seconds)
+   **Total running time of the script:** (0 minutes 1.695 seconds)
 
 
 .. _sphx_glr_download_tutorials_export.py: