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auto-generating sphinx docs
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pytorchbot committed Jan 7, 2025
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Showing 36 changed files with 574 additions and 575 deletions.
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18 changes: 9 additions & 9 deletions main/_sources/sg_execution_times.rst.txt
Original file line number Diff line number Diff line change
@@ -6,7 +6,7 @@

Computation times
=================
**02:26.123** total execution time for 11 files **from all galleries**:
**02:24.361** total execution time for 11 files **from all galleries**:

.. container::

@@ -33,31 +33,31 @@ Computation times
- Time
- Mem (MB)
* - :ref:`sphx_glr_tutorials_tensorclass_fashion.py` (``reference/generated/tutorials/tensorclass_fashion.py``)
- 01:00.128
- 00:59.208
- 0.0
* - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
- 00:54.931
- 00:54.079
- 0.0
* - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
- 00:16.937
- 00:16.979
- 0.0
* - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
- 00:11.022
- 00:11.020
- 0.0
* - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
- 00:01.638
- 00:01.625
- 0.0
* - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
- 00:01.437
- 00:01.420
- 0.0
* - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``reference/generated/tutorials/tensordict_keys.py``)
- 00:00.010
- 00:00.009
- 0.0
* - :ref:`sphx_glr_tutorials_tensordict_shapes.py` (``reference/generated/tutorials/tensordict_shapes.py``)
- 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
226 changes: 113 additions & 113 deletions main/_sources/tutorials/data_fashion.rst.txt
Original file line number Diff line number Diff line change
@@ -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: 2.309888 [ 0/60000]
loss: 2.295121 [ 6400/60000]
loss: 2.280686 [12800/60000]
loss: 2.271210 [19200/60000]
loss: 2.259902 [25600/60000]
loss: 2.231734 [32000/60000]
loss: 2.239176 [38400/60000]
loss: 2.206609 [44800/60000]
loss: 2.211201 [51200/60000]
loss: 2.176013 [57600/60000]
loss: 2.315767 [ 0/60000]
loss: 2.292673 [ 6400/60000]
loss: 2.269821 [12800/60000]
loss: 2.265822 [19200/60000]
loss: 2.257568 [25600/60000]
loss: 2.217916 [32000/60000]
loss: 2.232420 [38400/60000]
loss: 2.195806 [44800/60000]
loss: 2.196768 [51200/60000]
loss: 2.159376 [57600/60000]
Test Error:
Accuracy: 36.6%, Avg loss: 2.171962
Accuracy: 37.0%, Avg loss: 2.157578
Epoch 2
-------------------------
loss: 2.184973 [ 0/60000]
loss: 2.174263 [ 6400/60000]
loss: 2.128072 [12800/60000]
loss: 2.130871 [19200/60000]
loss: 2.096814 [25600/60000]
loss: 2.035709 [32000/60000]
loss: 2.053072 [38400/60000]
loss: 1.985246 [44800/60000]
loss: 1.994782 [51200/60000]
loss: 1.903500 [57600/60000]
loss: 2.172309 [ 0/60000]
loss: 2.152880 [ 6400/60000]
loss: 2.088961 [12800/60000]
loss: 2.112477 [19200/60000]
loss: 2.070258 [25600/60000]
loss: 1.999463 [32000/60000]
loss: 2.040056 [38400/60000]
loss: 1.952431 [44800/60000]
loss: 1.963655 [51200/60000]
loss: 1.892861 [57600/60000]
Test Error:
Accuracy: 55.9%, Avg loss: 1.920622
Accuracy: 54.9%, Avg loss: 1.887992
Epoch 3
-------------------------
loss: 1.956147 [ 0/60000]
loss: 1.926316 [ 6400/60000]
loss: 1.830256 [12800/60000]
loss: 1.843623 [19200/60000]
loss: 1.754887 [25600/60000]
loss: 1.705938 [32000/60000]
loss: 1.705119 [38400/60000]
loss: 1.623518 [44800/60000]
loss: 1.646965 [51200/60000]
loss: 1.522858 [57600/60000]
loss: 1.924237 [ 0/60000]
loss: 1.883972 [ 6400/60000]
loss: 1.762340 [12800/60000]
loss: 1.812946 [19200/60000]
loss: 1.714830 [25600/60000]
loss: 1.659969 [32000/60000]
loss: 1.693677 [38400/60000]
loss: 1.586629 [44800/60000]
loss: 1.620106 [51200/60000]
loss: 1.518462 [57600/60000]
Test Error:
Accuracy: 58.5%, Avg loss: 1.560956
Accuracy: 61.0%, Avg loss: 1.530891
Epoch 4
-------------------------
loss: 1.629779 [ 0/60000]
loss: 1.591552 [ 6400/60000]
loss: 1.466318 [12800/60000]
loss: 1.505903 [19200/60000]
loss: 1.403274 [25600/60000]
loss: 1.402906 [32000/60000]
loss: 1.391027 [38400/60000]
loss: 1.327004 [44800/60000]
loss: 1.359146 [51200/60000]
loss: 1.254223 [57600/60000]
loss: 1.599155 [ 0/60000]
loss: 1.559146 [ 6400/60000]
loss: 1.405361 [12800/60000]
loss: 1.480620 [19200/60000]
loss: 1.377105 [25600/60000]
loss: 1.365985 [32000/60000]
loss: 1.384322 [38400/60000]
loss: 1.303597 [44800/60000]
loss: 1.341573 [51200/60000]
loss: 1.244097 [57600/60000]
Test Error:
Accuracy: 62.0%, Avg loss: 1.288574
Accuracy: 63.1%, Avg loss: 1.268229
Epoch 5
-------------------------
loss: 1.367239 [ 0/60000]
loss: 1.344848 [ 6400/60000]
loss: 1.199537 [12800/60000]
loss: 1.279345 [19200/60000]
loss: 1.166658 [25600/60000]
loss: 1.194966 [32000/60000]
loss: 1.193302 [38400/60000]
loss: 1.135209 [44800/60000]
loss: 1.173753 [51200/60000]
loss: 1.091729 [57600/60000]
loss: 1.344266 [ 0/60000]
loss: 1.324413 [ 6400/60000]
loss: 1.155209 [12800/60000]
loss: 1.258924 [19200/60000]
loss: 1.150252 [25600/60000]
loss: 1.168796 [32000/60000]
loss: 1.188404 [38400/60000]
loss: 1.122490 [44800/60000]
loss: 1.163347 [51200/60000]
loss: 1.078930 [57600/60000]
Test Error:
Accuracy: 64.1%, Avg loss: 1.115064
Accuracy: 64.4%, Avg loss: 1.100910
TensorDict training done! time: 8.6569 s
TensorDict training done! time: 8.3431 s
Epoch 1
-------------------------
loss: 2.319256 [ 0/60000]
loss: 2.300828 [ 6400/60000]
loss: 2.273302 [12800/60000]
loss: 2.264598 [19200/60000]
loss: 2.246600 [25600/60000]
loss: 2.218545 [32000/60000]
loss: 2.228676 [38400/60000]
loss: 2.191392 [44800/60000]
loss: 2.187474 [51200/60000]
loss: 2.147722 [57600/60000]
loss: 2.305927 [ 0/60000]
loss: 2.284379 [ 6400/60000]
loss: 2.270463 [12800/60000]
loss: 2.267353 [19200/60000]
loss: 2.230858 [25600/60000]
loss: 2.213165 [32000/60000]
loss: 2.213929 [38400/60000]
loss: 2.178227 [44800/60000]
loss: 2.177415 [51200/60000]
loss: 2.143059 [57600/60000]
Test Error:
Accuracy: 39.4%, Avg loss: 2.152461
Accuracy: 44.1%, Avg loss: 2.141761
Epoch 2
-------------------------
loss: 2.167552 [ 0/60000]
loss: 2.161367 [ 6400/60000]
loss: 2.093919 [12800/60000]
loss: 2.111467 [19200/60000]
loss: 2.065855 [25600/60000]
loss: 1.999444 [32000/60000]
loss: 2.031877 [38400/60000]
loss: 1.943139 [44800/60000]
loss: 1.948649 [51200/60000]
loss: 1.876375 [57600/60000]
loss: 2.150444 [ 0/60000]
loss: 2.137104 [ 6400/60000]
loss: 2.081196 [12800/60000]
loss: 2.106140 [19200/60000]
loss: 2.030934 [25600/60000]
loss: 1.976243 [32000/60000]
loss: 2.003132 [38400/60000]
loss: 1.922070 [44800/60000]
loss: 1.931738 [51200/60000]
loss: 1.848063 [57600/60000]
Test Error:
Accuracy: 55.2%, Avg loss: 1.882336
Accuracy: 55.8%, Avg loss: 1.859430
Epoch 3
-------------------------
loss: 1.915303 [ 0/60000]
loss: 1.894418 [ 6400/60000]
loss: 1.764845 [12800/60000]
loss: 1.809904 [19200/60000]
loss: 1.706668 [25600/60000]
loss: 1.653618 [32000/60000]
loss: 1.677387 [38400/60000]
loss: 1.567994 [44800/60000]
loss: 1.598126 [51200/60000]
loss: 1.495569 [57600/60000]
loss: 1.893246 [ 0/60000]
loss: 1.861535 [ 6400/60000]
loss: 1.748274 [12800/60000]
loss: 1.795070 [19200/60000]
loss: 1.663789 [25600/60000]
loss: 1.628693 [32000/60000]
loss: 1.646140 [38400/60000]
loss: 1.557471 [44800/60000]
loss: 1.582310 [51200/60000]
loss: 1.469498 [57600/60000]
Test Error:
Accuracy: 60.2%, Avg loss: 1.518754
Accuracy: 61.3%, Avg loss: 1.498710
Epoch 4
-------------------------
loss: 1.585963 [ 0/60000]
loss: 1.560733 [ 6400/60000]
loss: 1.400808 [12800/60000]
loss: 1.473205 [19200/60000]
loss: 1.359597 [25600/60000]
loss: 1.354113 [32000/60000]
loss: 1.368542 [38400/60000]
loss: 1.283031 [44800/60000]
loss: 1.324119 [51200/60000]
loss: 1.228730 [57600/60000]
loss: 1.567009 [ 0/60000]
loss: 1.538322 [ 6400/60000]
loss: 1.388656 [12800/60000]
loss: 1.458963 [19200/60000]
loss: 1.326037 [25600/60000]
loss: 1.335291 [32000/60000]
loss: 1.346583 [38400/60000]
loss: 1.275556 [44800/60000]
loss: 1.307435 [51200/60000]
loss: 1.212606 [57600/60000]
Test Error:
Accuracy: 63.0%, Avg loss: 1.257389
Accuracy: 63.1%, Avg loss: 1.238631
Epoch 5
-------------------------
loss: 1.335658 [ 0/60000]
loss: 1.323170 [ 6400/60000]
loss: 1.151214 [12800/60000]
loss: 1.254571 [19200/60000]
loss: 1.131282 [25600/60000]
loss: 1.157894 [32000/60000]
loss: 1.179484 [38400/60000]
loss: 1.106296 [44800/60000]
loss: 1.151046 [51200/60000]
loss: 1.071718 [57600/60000]
loss: 1.312395 [ 0/60000]
loss: 1.308002 [ 6400/60000]
loss: 1.135946 [12800/60000]
loss: 1.241152 [19200/60000]
loss: 1.103871 [25600/60000]
loss: 1.137554 [32000/60000]
loss: 1.163266 [38400/60000]
loss: 1.096367 [44800/60000]
loss: 1.135108 [51200/60000]
loss: 1.061703 [57600/60000]
Test Error:
Accuracy: 64.5%, Avg loss: 1.093438
Accuracy: 64.5%, Avg loss: 1.077826
Training done! time: 33.7727 s
Training done! time: 33.2653 s
.. rst-class:: sphx-glr-timing

**Total running time of the script:** (0 minutes 54.931 seconds)
**Total running time of the script:** (0 minutes 54.079 seconds)


.. _sphx_glr_download_tutorials_data_fashion.py:
10 changes: 5 additions & 5 deletions main/_sources/tutorials/export.rst.txt
Original file line number Diff line number Diff line change
@@ -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([[-0.2311, 0.3673, 0.3495, -0.3369]], grad_fn=<AddmmBackward0>), tensor([[-0.2311, 0.3673]], grad_fn=<SplitBackward0>), tensor([[1.2334, 0.8018]], grad_fn=<ClampMinBackward0>), tensor([[-0.2311, 0.3673]], grad_fn=<SplitBackward0>))
(tensor([[0.6344, 0.0000, 0.0000, 0.0000]], grad_fn=<ReluBackward0>), tensor([[-0.1209, -0.5358, -0.0135, 0.1727]], grad_fn=<AddmmBackward0>), tensor([[-0.1209, -0.5358]], grad_fn=<SplitBackward0>), tensor([[0.9916, 1.1120]], grad_fn=<ClampMinBackward0>), tensor([[-0.1209, -0.5358]], 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: 712.63 micro-seconds
Time for exported module: 353.10 micro-seconds
Time for TDModule: 699.76 micro-seconds
Time for exported module: 360.49 micro-seconds
@@ -450,7 +450,7 @@ distribution:

.. code-block:: none
tensor([[-0.2311, 0.3673]], grad_fn=<SplitBackward0>)
tensor([[-0.1209, -0.5358]], 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 1.437 seconds)
**Total running time of the script:** (0 minutes 1.420 seconds)


.. _sphx_glr_download_tutorials_export.py:
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