auto-generating sphinx docs

pytorch · Nov 27, 2024 · 73b1f9a · 73b1f9a
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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
 =================
-**02:31.429** total execution time for 11 files **from all galleries**:
+**02:31.531** 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``)
-     - 01:01.012
+     - 01:00.973
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:55.965
+     - 00:55.931
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:19.933
+     - 00:20.083
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
-     - 00:11.021
+     - 00:11.020
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
-     - 00:01.762
+     - 00:01.766
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.707
+     - 00:01.728
      - 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: 2.298371 [    0/60000]
-    loss: 2.293004 [ 6400/60000]
-    loss: 2.270642 [12800/60000]
-    loss: 2.267181 [19200/60000]
-    loss: 2.255021 [25600/60000]
-    loss: 2.223969 [32000/60000]
-    loss: 2.227633 [38400/60000]
-    loss: 2.201384 [44800/60000]
-    loss: 2.196239 [51200/60000]
-    loss: 2.163501 [57600/60000]
+    loss: 2.294849 [    0/60000]
+    loss: 2.291428 [ 6400/60000]
+    loss: 2.272142 [12800/60000]
+    loss: 2.271817 [19200/60000]
+    loss: 2.244780 [25600/60000]
+    loss: 2.217207 [32000/60000]
+    loss: 2.229968 [38400/60000]
+    loss: 2.196187 [44800/60000]
+    loss: 2.195210 [51200/60000]
+    loss: 2.160927 [57600/60000]
     Test Error: 
-     Accuracy: 56.4%, Avg loss: 2.156874 
+     Accuracy: 39.7%, Avg loss: 2.157932 
 
     Epoch 2
     -------------------------
-    loss: 2.164263 [    0/60000]
-    loss: 2.160585 [ 6400/60000]
-    loss: 2.096131 [12800/60000]
-    loss: 2.112279 [19200/60000]
-    loss: 2.079748 [25600/60000]
-    loss: 2.011120 [32000/60000]
-    loss: 2.036626 [38400/60000]
-    loss: 1.965001 [44800/60000]
-    loss: 1.961576 [51200/60000]
-    loss: 1.895948 [57600/60000]
+    loss: 2.160273 [    0/60000]
+    loss: 2.162984 [ 6400/60000]
+    loss: 2.104043 [12800/60000]
+    loss: 2.125551 [19200/60000]
+    loss: 2.065496 [25600/60000]
+    loss: 2.004499 [32000/60000]
+    loss: 2.042349 [38400/60000]
+    loss: 1.959963 [44800/60000]
+    loss: 1.967184 [51200/60000]
+    loss: 1.897277 [57600/60000]
     Test Error: 
-     Accuracy: 60.0%, Avg loss: 1.889390 
+     Accuracy: 54.7%, Avg loss: 1.895548 
 
     Epoch 3
     -------------------------
-    loss: 1.918543 [    0/60000]
-    loss: 1.898324 [ 6400/60000]
-    loss: 1.769424 [12800/60000]
-    loss: 1.809989 [19200/60000]
-    loss: 1.733438 [25600/60000]
-    loss: 1.666118 [32000/60000]
-    loss: 1.685677 [38400/60000]
-    loss: 1.594908 [44800/60000]
-    loss: 1.613509 [51200/60000]
-    loss: 1.509140 [57600/60000]
+    loss: 1.914613 [    0/60000]
+    loss: 1.903345 [ 6400/60000]
+    loss: 1.783549 [12800/60000]
+    loss: 1.835761 [19200/60000]
+    loss: 1.710514 [25600/60000]
+    loss: 1.659563 [32000/60000]
+    loss: 1.694180 [38400/60000]
+    loss: 1.587287 [44800/60000]
+    loss: 1.614228 [51200/60000]
+    loss: 1.509302 [57600/60000]
     Test Error: 
-     Accuracy: 62.3%, Avg loss: 1.523576 
+     Accuracy: 59.1%, Avg loss: 1.530074 
 
     Epoch 4
     -------------------------
-    loss: 1.583820 [    0/60000]
-    loss: 1.560539 [ 6400/60000]
-    loss: 1.398170 [12800/60000]
-    loss: 1.475622 [19200/60000]
-    loss: 1.382453 [25600/60000]
-    loss: 1.352976 [32000/60000]
-    loss: 1.370766 [38400/60000]
-    loss: 1.300876 [44800/60000]
-    loss: 1.335431 [51200/60000]
-    loss: 1.232636 [57600/60000]
+    loss: 1.582662 [    0/60000]
+    loss: 1.564424 [ 6400/60000]
+    loss: 1.413574 [12800/60000]
+    loss: 1.496169 [19200/60000]
+    loss: 1.359147 [25600/60000]
+    loss: 1.359175 [32000/60000]
+    loss: 1.375688 [38400/60000]
+    loss: 1.298281 [44800/60000]
+    loss: 1.334571 [51200/60000]
+    loss: 1.227484 [57600/60000]
     Test Error: 
-     Accuracy: 63.9%, Avg loss: 1.257980 
+     Accuracy: 61.8%, Avg loss: 1.264602 
 
     Epoch 5
     -------------------------
-    loss: 1.326404 [    0/60000]
-    loss: 1.320783 [ 6400/60000]
-    loss: 1.145609 [12800/60000]
-    loss: 1.257305 [19200/60000]
-    loss: 1.150275 [25600/60000]
-    loss: 1.152700 [32000/60000]
-    loss: 1.177740 [38400/60000]
-    loss: 1.121601 [44800/60000]
-    loss: 1.159318 [51200/60000]
-    loss: 1.073041 [57600/60000]
+    loss: 1.335226 [    0/60000]
+    loss: 1.326946 [ 6400/60000]
+    loss: 1.162699 [12800/60000]
+    loss: 1.273690 [19200/60000]
+    loss: 1.134897 [25600/60000]
+    loss: 1.168307 [32000/60000]
+    loss: 1.183381 [38400/60000]
+    loss: 1.122061 [44800/60000]
+    loss: 1.160998 [51200/60000]
+    loss: 1.068695 [57600/60000]
     Test Error: 
-     Accuracy: 65.2%, Avg loss: 1.093486 
+     Accuracy: 63.7%, Avg loss: 1.101542 
 
-    TensorDict training done! time:  8.7920 s
+    TensorDict training done! time:  8.7042 s
     Epoch 1
     -------------------------
-    loss: 2.308129 [    0/60000]
-    loss: 2.288357 [ 6400/60000]
-    loss: 2.277756 [12800/60000]
-    loss: 2.272954 [19200/60000]
-    loss: 2.245050 [25600/60000]
-    loss: 2.233544 [32000/60000]
-    loss: 2.237998 [38400/60000]
-    loss: 2.209743 [44800/60000]
-    loss: 2.195169 [51200/60000]
-    loss: 2.177731 [57600/60000]
+    loss: 2.300797 [    0/60000]
+    loss: 2.289280 [ 6400/60000]
+    loss: 2.263709 [12800/60000]
+    loss: 2.264033 [19200/60000]
+    loss: 2.253232 [25600/60000]
+    loss: 2.204027 [32000/60000]
+    loss: 2.233626 [38400/60000]
+    loss: 2.183805 [44800/60000]
+    loss: 2.192999 [51200/60000]
+    loss: 2.152655 [57600/60000]
     Test Error: 
-     Accuracy: 44.0%, Avg loss: 2.163968 
+     Accuracy: 33.1%, Avg loss: 2.147580 
 
     Epoch 2
     -------------------------
-    loss: 2.167829 [    0/60000]
-    loss: 2.157455 [ 6400/60000]
-    loss: 2.107416 [12800/60000]
-    loss: 2.128080 [19200/60000]
-    loss: 2.068879 [25600/60000]
-    loss: 2.023590 [32000/60000]
-    loss: 2.052728 [38400/60000]
-    loss: 1.976492 [44800/60000]
-    loss: 1.965756 [51200/60000]
-    loss: 1.917540 [57600/60000]
+    loss: 2.159569 [    0/60000]
+    loss: 2.147161 [ 6400/60000]
+    loss: 2.080415 [12800/60000]
+    loss: 2.099682 [19200/60000]
+    loss: 2.062009 [25600/60000]
+    loss: 1.983664 [32000/60000]
+    loss: 2.025923 [38400/60000]
+    loss: 1.931799 [44800/60000]
+    loss: 1.951796 [51200/60000]
+    loss: 1.867469 [57600/60000]
     Test Error: 
-     Accuracy: 49.9%, Avg loss: 1.904248 
+     Accuracy: 56.7%, Avg loss: 1.868980 
 
     Epoch 3
     -------------------------
-    loss: 1.926219 [    0/60000]
-    loss: 1.902835 [ 6400/60000]
-    loss: 1.792676 [12800/60000]
-    loss: 1.839694 [19200/60000]
-    loss: 1.721052 [25600/60000]
-    loss: 1.676916 [32000/60000]
-    loss: 1.706605 [38400/60000]
-    loss: 1.602834 [44800/60000]
-    loss: 1.614985 [51200/60000]
-    loss: 1.530983 [57600/60000]
+    loss: 1.907197 [    0/60000]
+    loss: 1.871519 [ 6400/60000]
+    loss: 1.746847 [12800/60000]
+    loss: 1.787513 [19200/60000]
+    loss: 1.700836 [25600/60000]
+    loss: 1.635293 [32000/60000]
+    loss: 1.662028 [38400/60000]
+    loss: 1.552634 [44800/60000]
+    loss: 1.594174 [51200/60000]
+    loss: 1.477982 [57600/60000]
     Test Error: 
-     Accuracy: 59.4%, Avg loss: 1.538064 
+     Accuracy: 61.0%, Avg loss: 1.504373 
 
     Epoch 4
     -------------------------
-    loss: 1.596175 [    0/60000]
-    loss: 1.563667 [ 6400/60000]
-    loss: 1.420959 [12800/60000]
-    loss: 1.497433 [19200/60000]
-    loss: 1.374490 [25600/60000]
-    loss: 1.366379 [32000/60000]
-    loss: 1.385959 [38400/60000]
-    loss: 1.306147 [44800/60000]
-    loss: 1.331376 [51200/60000]
-    loss: 1.245275 [57600/60000]
+    loss: 1.576218 [    0/60000]
+    loss: 1.539247 [ 6400/60000]
+    loss: 1.385672 [12800/60000]
+    loss: 1.456073 [19200/60000]
+    loss: 1.356970 [25600/60000]
+    loss: 1.336630 [32000/60000]
+    loss: 1.353327 [38400/60000]
+    loss: 1.268900 [44800/60000]
+    loss: 1.317347 [51200/60000]
+    loss: 1.213876 [57600/60000]
     Test Error: 
-     Accuracy: 63.0%, Avg loss: 1.267536 
+     Accuracy: 63.1%, Avg loss: 1.245665 
 
     Epoch 5
     -------------------------
-    loss: 1.341612 [    0/60000]
-    loss: 1.319718 [ 6400/60000]
-    loss: 1.164089 [12800/60000]
-    loss: 1.268837 [19200/60000]
-    loss: 1.153158 [25600/60000]
-    loss: 1.165563 [32000/60000]
-    loss: 1.189975 [38400/60000]
-    loss: 1.124084 [44800/60000]
-    loss: 1.152565 [51200/60000]
-    loss: 1.080202 [57600/60000]
+    loss: 1.323622 [    0/60000]
+    loss: 1.303716 [ 6400/60000]
+    loss: 1.136152 [12800/60000]
+    loss: 1.242420 [19200/60000]
+    loss: 1.131702 [25600/60000]
+    loss: 1.142098 [32000/60000]
+    loss: 1.165405 [38400/60000]
+    loss: 1.094209 [44800/60000]
+    loss: 1.145590 [51200/60000]
+    loss: 1.058907 [57600/60000]
     Test Error: 
-     Accuracy: 64.4%, Avg loss: 1.099794 
+     Accuracy: 64.5%, Avg loss: 1.084633 
 
-    Training done! time:  34.8077 s
+    Training done! time:  34.9424 s
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 55.965 seconds)
+   **Total running time of the script:** (0 minutes 55.931 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([[0.1570, 0.0000, 0.0000, 0.0540]], grad_fn=<ReluBackward0>), tensor([[-0.3942,  0.4940, -0.5228, -0.2410]], grad_fn=<AddmmBackward0>), tensor([[-0.3942,  0.4940]], grad_fn=<SplitBackward0>), tensor([[0.7045, 0.8554]], grad_fn=<ClampMinBackward0>), tensor([[-0.3942,  0.4940]], grad_fn=<SplitBackward0>))
+    (tensor([[0.8152, 0.0000, 0.5324, 0.8970]], grad_fn=<ReluBackward0>), tensor([[ 0.1957,  0.6690,  0.1367, -0.9989]], grad_fn=<AddmmBackward0>), tensor([[0.1957, 0.6690]], grad_fn=<SplitBackward0>), tensor([[1.0881, 0.4942]], grad_fn=<ClampMinBackward0>), tensor([[0.1957, 0.6690]], 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:  536.20 micro-seconds
-    Time for exported module:  348.57 micro-seconds
+    Time for TDModule:  543.12 micro-seconds
+    Time for exported module:  352.14 micro-seconds
 
 
 
@@ -450,7 +450,7 @@ distribution:
 
  .. code-block:: none
 
-    tensor([[-0.3942,  0.4940]], grad_fn=<SplitBackward0>)
+    tensor([[0.1957, 0.6690]], 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 1.762 seconds)
+   **Total running time of the script:** (0 minutes 1.766 seconds)
 
 
 .. _sphx_glr_download_tutorials_export.py: