auto-generating sphinx docs

pytorch · Feb 21, 2025 · b13e426 · b13e426
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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
 =================
-**02:25.381** total execution time for 11 files **from all galleries**:
+**02:25.550** 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``)
-     - 00:59.723
+     - 00:59.876
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:54.416
+     - 00:54.500
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:17.009
+     - 00:16.938
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
      - 00:11.021
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.638
+     - 00:01.639
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
-     - 00:01.545
+     - 00:01.548
      - 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: 2.304261 [    0/60000]
-    loss: 2.293595 [ 6400/60000]
-    loss: 2.280523 [12800/60000]
-    loss: 2.277021 [19200/60000]
-    loss: 2.249677 [25600/60000]
-    loss: 2.232656 [32000/60000]
-    loss: 2.238108 [38400/60000]
-    loss: 2.212889 [44800/60000]
-    loss: 2.210897 [51200/60000]
-    loss: 2.177374 [57600/60000]
+    loss: 2.302957 [    0/60000]
+    loss: 2.287807 [ 6400/60000]
+    loss: 2.260365 [12800/60000]
+    loss: 2.261931 [19200/60000]
+    loss: 2.241335 [25600/60000]
+    loss: 2.211900 [32000/60000]
+    loss: 2.215636 [38400/60000]
+    loss: 2.180986 [44800/60000]
+    loss: 2.188308 [51200/60000]
+    loss: 2.151452 [57600/60000]
     Test Error: 
-     Accuracy: 43.4%, Avg loss: 2.180119 
+     Accuracy: 47.8%, Avg loss: 2.138880 
 
     Epoch 2
     -------------------------
-    loss: 2.187361 [    0/60000]
-    loss: 2.180007 [ 6400/60000]
-    loss: 2.133474 [12800/60000]
-    loss: 2.149908 [19200/60000]
-    loss: 2.101164 [25600/60000]
-    loss: 2.052519 [32000/60000]
-    loss: 2.073738 [38400/60000]
-    loss: 2.009618 [44800/60000]
-    loss: 2.016874 [51200/60000]
-    loss: 1.951048 [57600/60000]
+    loss: 2.149573 [    0/60000]
+    loss: 2.136443 [ 6400/60000]
+    loss: 2.070373 [12800/60000]
+    loss: 2.098220 [19200/60000]
+    loss: 2.035692 [25600/60000]
+    loss: 1.978322 [32000/60000]
+    loss: 2.000681 [38400/60000]
+    loss: 1.920627 [44800/60000]
+    loss: 1.934727 [51200/60000]
+    loss: 1.862901 [57600/60000]
     Test Error: 
-     Accuracy: 58.9%, Avg loss: 1.949780 
+     Accuracy: 55.8%, Avg loss: 1.850576 
 
     Epoch 3
     -------------------------
-    loss: 1.974948 [    0/60000]
-    loss: 1.951905 [ 6400/60000]
-    loss: 1.844734 [12800/60000]
-    loss: 1.881521 [19200/60000]
-    loss: 1.782008 [25600/60000]
-    loss: 1.726826 [32000/60000]
-    loss: 1.741400 [38400/60000]
-    loss: 1.645395 [44800/60000]
-    loss: 1.666677 [51200/60000]
-    loss: 1.561634 [57600/60000]
+    loss: 1.888434 [    0/60000]
+    loss: 1.852615 [ 6400/60000]
+    loss: 1.727759 [12800/60000]
+    loss: 1.780767 [19200/60000]
+    loss: 1.661708 [25600/60000]
+    loss: 1.624064 [32000/60000]
+    loss: 1.645153 [38400/60000]
+    loss: 1.556782 [44800/60000]
+    loss: 1.589450 [51200/60000]
+    loss: 1.486076 [57600/60000]
     Test Error: 
-     Accuracy: 61.0%, Avg loss: 1.575072 
+     Accuracy: 60.6%, Avg loss: 1.495976 
 
     Epoch 4
     -------------------------
-    loss: 1.632278 [    0/60000]
-    loss: 1.600525 [ 6400/60000]
-    loss: 1.444873 [12800/60000]
-    loss: 1.515376 [19200/60000]
-    loss: 1.398325 [25600/60000]
-    loss: 1.384120 [32000/60000]
-    loss: 1.391036 [38400/60000]
-    loss: 1.317859 [44800/60000]
-    loss: 1.352497 [51200/60000]
-    loss: 1.249868 [57600/60000]
+    loss: 1.573276 [    0/60000]
+    loss: 1.531617 [ 6400/60000]
+    loss: 1.379419 [12800/60000]
+    loss: 1.455525 [19200/60000]
+    loss: 1.334508 [25600/60000]
+    loss: 1.338935 [32000/60000]
+    loss: 1.350554 [38400/60000]
+    loss: 1.290698 [44800/60000]
+    loss: 1.326515 [51200/60000]
+    loss: 1.226378 [57600/60000]
     Test Error: 
-     Accuracy: 62.7%, Avg loss: 1.276833 
+     Accuracy: 63.1%, Avg loss: 1.248114 
 
     Epoch 5
     -------------------------
-    loss: 1.348793 [    0/60000]
-    loss: 1.334695 [ 6400/60000]
-    loss: 1.161684 [12800/60000]
-    loss: 1.269059 [19200/60000]
-    loss: 1.145733 [25600/60000]
-    loss: 1.164551 [32000/60000]
-    loss: 1.178636 [38400/60000]
-    loss: 1.121588 [44800/60000]
-    loss: 1.160211 [51200/60000]
-    loss: 1.077813 [57600/60000]
+    loss: 1.334279 [    0/60000]
+    loss: 1.309238 [ 6400/60000]
+    loss: 1.143550 [12800/60000]
+    loss: 1.248187 [19200/60000]
+    loss: 1.121105 [25600/60000]
+    loss: 1.151895 [32000/60000]
+    loss: 1.169105 [38400/60000]
+    loss: 1.122838 [44800/60000]
+    loss: 1.160763 [51200/60000]
+    loss: 1.072355 [57600/60000]
     Test Error: 
-     Accuracy: 64.2%, Avg loss: 1.099435 
+     Accuracy: 64.5%, Avg loss: 1.091594 
 
-    TensorDict training done! time:  8.4028 s
+    TensorDict training done! time:  8.3702 s
     Epoch 1
     -------------------------
-    loss: 2.312976 [    0/60000]
-    loss: 2.298801 [ 6400/60000]
-    loss: 2.273588 [12800/60000]
-    loss: 2.268426 [19200/60000]
-    loss: 2.253394 [25600/60000]
-    loss: 2.217758 [32000/60000]
-    loss: 2.231827 [38400/60000]
-    loss: 2.195425 [44800/60000]
-    loss: 2.195154 [51200/60000]
-    loss: 2.156736 [57600/60000]
+    loss: 2.298803 [    0/60000]
+    loss: 2.286026 [ 6400/60000]
+    loss: 2.270411 [12800/60000]
+    loss: 2.269202 [19200/60000]
+    loss: 2.235025 [25600/60000]
+    loss: 2.224410 [32000/60000]
+    loss: 2.217039 [38400/60000]
+    loss: 2.188777 [44800/60000]
+    loss: 2.189151 [51200/60000]
+    loss: 2.162184 [57600/60000]
     Test Error: 
-     Accuracy: 33.8%, Avg loss: 2.154119 
+     Accuracy: 47.3%, Avg loss: 2.149291 
 
     Epoch 2
     -------------------------
-    loss: 2.169655 [    0/60000]
-    loss: 2.160613 [ 6400/60000]
-    loss: 2.096575 [12800/60000]
-    loss: 2.116937 [19200/60000]
-    loss: 2.064738 [25600/60000]
-    loss: 1.987226 [32000/60000]
-    loss: 2.028071 [38400/60000]
-    loss: 1.941487 [44800/60000]
-    loss: 1.951808 [51200/60000]
-    loss: 1.873447 [57600/60000]
+    loss: 2.154761 [    0/60000]
+    loss: 2.147109 [ 6400/60000]
+    loss: 2.096130 [12800/60000]
+    loss: 2.121600 [19200/60000]
+    loss: 2.054765 [25600/60000]
+    loss: 2.010749 [32000/60000]
+    loss: 2.024581 [38400/60000]
+    loss: 1.953214 [44800/60000]
+    loss: 1.957816 [51200/60000]
+    loss: 1.895921 [57600/60000]
     Test Error: 
-     Accuracy: 57.3%, Avg loss: 1.876765 
+     Accuracy: 57.6%, Avg loss: 1.884277 
 
     Epoch 3
     -------------------------
-    loss: 1.916889 [    0/60000]
-    loss: 1.888280 [ 6400/60000]
-    loss: 1.766488 [12800/60000]
-    loss: 1.810617 [19200/60000]
-    loss: 1.702317 [25600/60000]
-    loss: 1.638729 [32000/60000]
-    loss: 1.670147 [38400/60000]
-    loss: 1.571424 [44800/60000]
-    loss: 1.600017 [51200/60000]
-    loss: 1.493437 [57600/60000]
+    loss: 1.913606 [    0/60000]
+    loss: 1.882402 [ 6400/60000]
+    loss: 1.776751 [12800/60000]
+    loss: 1.823550 [19200/60000]
+    loss: 1.698094 [25600/60000]
+    loss: 1.663047 [32000/60000]
+    loss: 1.670777 [38400/60000]
+    loss: 1.577076 [44800/60000]
+    loss: 1.599474 [51200/60000]
+    loss: 1.500665 [57600/60000]
     Test Error: 
-     Accuracy: 61.8%, Avg loss: 1.512841 
+     Accuracy: 59.2%, Avg loss: 1.511643 
 
     Epoch 4
     -------------------------
-    loss: 1.585416 [    0/60000]
-    loss: 1.555381 [ 6400/60000]
-    loss: 1.400376 [12800/60000]
-    loss: 1.474395 [19200/60000]
-    loss: 1.353361 [25600/60000]
-    loss: 1.339754 [32000/60000]
-    loss: 1.360496 [38400/60000]
-    loss: 1.286862 [44800/60000]
-    loss: 1.320621 [51200/60000]
-    loss: 1.227082 [57600/60000]
+    loss: 1.578410 [    0/60000]
+    loss: 1.538728 [ 6400/60000]
+    loss: 1.399516 [12800/60000]
+    loss: 1.477490 [19200/60000]
+    loss: 1.346635 [25600/60000]
+    loss: 1.351274 [32000/60000]
+    loss: 1.353770 [38400/60000]
+    loss: 1.281492 [44800/60000]
+    loss: 1.321404 [51200/60000]
+    loss: 1.223507 [57600/60000]
     Test Error: 
-     Accuracy: 63.9%, Avg loss: 1.247487 
+     Accuracy: 61.8%, Avg loss: 1.245935 
 
     Epoch 5
     -------------------------
-    loss: 1.325089 [    0/60000]
-    loss: 1.316720 [ 6400/60000]
-    loss: 1.141077 [12800/60000]
-    loss: 1.251515 [19200/60000]
-    loss: 1.118180 [25600/60000]
-    loss: 1.137443 [32000/60000]
-    loss: 1.164653 [38400/60000]
-    loss: 1.105882 [44800/60000]
-    loss: 1.141964 [51200/60000]
-    loss: 1.063180 [57600/60000]
+    loss: 1.325139 [    0/60000]
+    loss: 1.300074 [ 6400/60000]
+    loss: 1.144771 [12800/60000]
+    loss: 1.255317 [19200/60000]
+    loss: 1.125511 [25600/60000]
+    loss: 1.154963 [32000/60000]
+    loss: 1.163410 [38400/60000]
+    loss: 1.102962 [44800/60000]
+    loss: 1.151665 [51200/60000]
+    loss: 1.065594 [57600/60000]
     Test Error: 
-     Accuracy: 65.3%, Avg loss: 1.078922 
+     Accuracy: 63.8%, Avg loss: 1.084004 
 
-    Training done! time:  33.6515 s
+    Training done! time:  34.0545 s
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 54.416 seconds)
+   **Total running time of the script:** (0 minutes 54.500 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, 0.7382, 0.0000, 0.0000]], grad_fn=<ReluBackward0>), tensor([[ 0.6164, -0.1399, -0.5988, -0.4106]], grad_fn=<AddmmBackward0>), tensor([[ 0.6164, -0.1399]], grad_fn=<SplitBackward0>), tensor([[0.6672, 0.7622]], grad_fn=<ClampMinBackward0>), tensor([[ 0.6164, -0.1399]], grad_fn=<SplitBackward0>))
+    (tensor([[0.3311, 0.2989, 0.0000, 0.0000]], grad_fn=<ReluBackward0>), tensor([[-0.2948, -0.0984, -0.0903,  0.1684]], grad_fn=<AddmmBackward0>), tensor([[-0.2948, -0.0984]], grad_fn=<SplitBackward0>), tensor([[0.9442, 1.1091]], grad_fn=<ClampMinBackward0>), tensor([[-0.2948, -0.0984]], 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:  538.35 micro-seconds
-    Time for exported module:  355.96 micro-seconds
+    Time for TDModule:  538.83 micro-seconds
+    Time for exported module:  350.24 micro-seconds
 
 
 
@@ -450,7 +450,7 @@ distribution:
 
  .. code-block:: none
 
-    tensor([[ 0.6164, -0.1399]], grad_fn=<SplitBackward0>)
+    tensor([[-0.2948, -0.0984]], 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 1.545 seconds)
+   **Total running time of the script:** (0 minutes 1.548 seconds)
 
 
 .. _sphx_glr_download_tutorials_export.py: