auto-generating sphinx docs

pytorch · Jan 9, 2025 · d7eda4b · d7eda4b
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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
 =================
-**02:27.506** total execution time for 11 files **from all galleries**:
+**02:27.862** 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``)
-     - 01:01.061
+     - 01:00.830
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:55.391
+     - 00:56.045
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:16.990
+     - 00:16.872
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
-     - 00:11.020
+     - 00:11.021
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.600
+     - 00:01.635
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
-     - 00:01.415
+     - 00:01.429
      - 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: 2.306025 [    0/60000]
-    loss: 2.282991 [ 6400/60000]
-    loss: 2.265514 [12800/60000]
-    loss: 2.261661 [19200/60000]
-    loss: 2.249508 [25600/60000]
-    loss: 2.218925 [32000/60000]
-    loss: 2.228790 [38400/60000]
-    loss: 2.188310 [44800/60000]
-    loss: 2.181474 [51200/60000]
-    loss: 2.153430 [57600/60000]
+    loss: 2.312200 [    0/60000]
+    loss: 2.296130 [ 6400/60000]
+    loss: 2.277030 [12800/60000]
+    loss: 2.271615 [19200/60000]
+    loss: 2.259232 [25600/60000]
+    loss: 2.228248 [32000/60000]
+    loss: 2.233325 [38400/60000]
+    loss: 2.201997 [44800/60000]
+    loss: 2.201436 [51200/60000]
+    loss: 2.170677 [57600/60000]
     Test Error: 
-     Accuracy: 43.5%, Avg loss: 2.147885 
+     Accuracy: 41.4%, Avg loss: 2.166564 
 
     Epoch 2
     -------------------------
-    loss: 2.160659 [    0/60000]
-    loss: 2.141886 [ 6400/60000]
-    loss: 2.084566 [12800/60000]
-    loss: 2.100719 [19200/60000]
-    loss: 2.052186 [25600/60000]
-    loss: 1.985862 [32000/60000]
-    loss: 2.015748 [38400/60000]
-    loss: 1.927163 [44800/60000]
-    loss: 1.938641 [51200/60000]
-    loss: 1.856640 [57600/60000]
+    loss: 2.179688 [    0/60000]
+    loss: 2.167929 [ 6400/60000]
+    loss: 2.118076 [12800/60000]
+    loss: 2.136425 [19200/60000]
+    loss: 2.095579 [25600/60000]
+    loss: 2.028844 [32000/60000]
+    loss: 2.059120 [38400/60000]
+    loss: 1.985047 [44800/60000]
+    loss: 1.989834 [51200/60000]
+    loss: 1.927613 [57600/60000]
     Test Error: 
-     Accuracy: 52.6%, Avg loss: 1.863404 
+     Accuracy: 56.3%, Avg loss: 1.925085 
 
     Epoch 3
     -------------------------
-    loss: 1.902221 [    0/60000]
-    loss: 1.861729 [ 6400/60000]
-    loss: 1.746640 [12800/60000]
-    loss: 1.788350 [19200/60000]
-    loss: 1.687310 [25600/60000]
-    loss: 1.632831 [32000/60000]
-    loss: 1.653028 [38400/60000]
-    loss: 1.554172 [44800/60000]
-    loss: 1.588989 [51200/60000]
-    loss: 1.471380 [57600/60000]
+    loss: 1.957312 [    0/60000]
+    loss: 1.926498 [ 6400/60000]
+    loss: 1.821919 [12800/60000]
+    loss: 1.863346 [19200/60000]
+    loss: 1.766098 [25600/60000]
+    loss: 1.701897 [32000/60000]
+    loss: 1.725719 [38400/60000]
+    loss: 1.626440 [44800/60000]
+    loss: 1.646036 [51200/60000]
+    loss: 1.541970 [57600/60000]
     Test Error: 
-     Accuracy: 60.4%, Avg loss: 1.502399 
+     Accuracy: 61.3%, Avg loss: 1.557087 
 
     Epoch 4
     -------------------------
-    loss: 1.573000 [    0/60000]
-    loss: 1.530745 [ 6400/60000]
-    loss: 1.388597 [12800/60000]
-    loss: 1.460475 [19200/60000]
-    loss: 1.356817 [25600/60000]
-    loss: 1.343242 [32000/60000]
-    loss: 1.353971 [38400/60000]
-    loss: 1.279595 [44800/60000]
-    loss: 1.320497 [51200/60000]
-    loss: 1.212627 [57600/60000]
+    loss: 1.623195 [    0/60000]
+    loss: 1.578914 [ 6400/60000]
+    loss: 1.433959 [12800/60000]
+    loss: 1.508353 [19200/60000]
+    loss: 1.392073 [25600/60000]
+    loss: 1.367655 [32000/60000]
+    loss: 1.386392 [38400/60000]
+    loss: 1.308929 [44800/60000]
+    loss: 1.342425 [51200/60000]
+    loss: 1.239829 [57600/60000]
     Test Error: 
-     Accuracy: 62.9%, Avg loss: 1.248055 
+     Accuracy: 63.5%, Avg loss: 1.268540 
 
     Epoch 5
     -------------------------
-    loss: 1.327363 [    0/60000]
-    loss: 1.300071 [ 6400/60000]
-    loss: 1.141670 [12800/60000]
-    loss: 1.248802 [19200/60000]
-    loss: 1.137496 [25600/60000]
-    loss: 1.149047 [32000/60000]
-    loss: 1.166963 [38400/60000]
-    loss: 1.104461 [44800/60000]
-    loss: 1.147796 [51200/60000]
-    loss: 1.056699 [57600/60000]
+    loss: 1.346081 [    0/60000]
+    loss: 1.319188 [ 6400/60000]
+    loss: 1.157992 [12800/60000]
+    loss: 1.268466 [19200/60000]
+    loss: 1.145921 [25600/60000]
+    loss: 1.154696 [32000/60000]
+    loss: 1.179433 [38400/60000]
+    loss: 1.117282 [44800/60000]
+    loss: 1.154837 [51200/60000]
+    loss: 1.072019 [57600/60000]
     Test Error: 
-     Accuracy: 64.4%, Avg loss: 1.086629 
+     Accuracy: 64.7%, Avg loss: 1.095184 
 
-    TensorDict training done! time:  8.7073 s
+    TensorDict training done! time:  8.3897 s
     Epoch 1
     -------------------------
-    loss: 2.327713 [    0/60000]
-    loss: 2.304104 [ 6400/60000]
-    loss: 2.284567 [12800/60000]
-    loss: 2.270504 [19200/60000]
-    loss: 2.255109 [25600/60000]
-    loss: 2.239622 [32000/60000]
-    loss: 2.238759 [38400/60000]
-    loss: 2.213040 [44800/60000]
-    loss: 2.211779 [51200/60000]
-    loss: 2.166598 [57600/60000]
+    loss: 2.305997 [    0/60000]
+    loss: 2.296665 [ 6400/60000]
+    loss: 2.270539 [12800/60000]
+    loss: 2.263849 [19200/60000]
+    loss: 2.252926 [25600/60000]
+    loss: 2.218652 [32000/60000]
+    loss: 2.228376 [38400/60000]
+    loss: 2.189555 [44800/60000]
+    loss: 2.201430 [51200/60000]
+    loss: 2.151739 [57600/60000]
     Test Error: 
-     Accuracy: 41.0%, Avg loss: 2.172854 
+     Accuracy: 41.6%, Avg loss: 2.152146 
 
     Epoch 2
     -------------------------
-    loss: 2.193644 [    0/60000]
-    loss: 2.174910 [ 6400/60000]
-    loss: 2.125861 [12800/60000]
-    loss: 2.136044 [19200/60000]
-    loss: 2.091737 [25600/60000]
-    loss: 2.044018 [32000/60000]
-    loss: 2.056765 [38400/60000]
-    loss: 1.985916 [44800/60000]
-    loss: 1.992527 [51200/60000]
-    loss: 1.915244 [57600/60000]
+    loss: 2.171188 [    0/60000]
+    loss: 2.158051 [ 6400/60000]
+    loss: 2.091060 [12800/60000]
+    loss: 2.101427 [19200/60000]
+    loss: 2.061359 [25600/60000]
+    loss: 1.998915 [32000/60000]
+    loss: 2.026133 [38400/60000]
+    loss: 1.941369 [44800/60000]
+    loss: 1.963244 [51200/60000]
+    loss: 1.868522 [57600/60000]
     Test Error: 
-     Accuracy: 58.1%, Avg loss: 1.926432 
+     Accuracy: 54.5%, Avg loss: 1.872824 
 
     Epoch 3
     -------------------------
-    loss: 1.961756 [    0/60000]
-    loss: 1.927878 [ 6400/60000]
-    loss: 1.823750 [12800/60000]
-    loss: 1.858918 [19200/60000]
-    loss: 1.762407 [25600/60000]
-    loss: 1.706371 [32000/60000]
-    loss: 1.713295 [38400/60000]
-    loss: 1.612265 [44800/60000]
-    loss: 1.638020 [51200/60000]
-    loss: 1.526685 [57600/60000]
+    loss: 1.918673 [    0/60000]
+    loss: 1.883101 [ 6400/60000]
+    loss: 1.758963 [12800/60000]
+    loss: 1.790021 [19200/60000]
+    loss: 1.696515 [25600/60000]
+    loss: 1.642287 [32000/60000]
+    loss: 1.668709 [38400/60000]
+    loss: 1.561718 [44800/60000]
+    loss: 1.600756 [51200/60000]
+    loss: 1.481319 [57600/60000]
     Test Error: 
-     Accuracy: 61.0%, Avg loss: 1.559980 
+     Accuracy: 61.3%, Avg loss: 1.501997 
 
     Epoch 4
     -------------------------
-    loss: 1.624006 [    0/60000]
-    loss: 1.583191 [ 6400/60000]
-    loss: 1.442377 [12800/60000]
-    loss: 1.512647 [19200/60000]
-    loss: 1.405243 [25600/60000]
-    loss: 1.384283 [32000/60000]
-    loss: 1.390174 [38400/60000]
-    loss: 1.302800 [44800/60000]
-    loss: 1.345479 [51200/60000]
-    loss: 1.242111 [57600/60000]
+    loss: 1.578914 [    0/60000]
+    loss: 1.543213 [ 6400/60000]
+    loss: 1.386595 [12800/60000]
+    loss: 1.451409 [19200/60000]
+    loss: 1.344465 [25600/60000]
+    loss: 1.338440 [32000/60000]
+    loss: 1.366079 [38400/60000]
+    loss: 1.276151 [44800/60000]
+    loss: 1.320195 [51200/60000]
+    loss: 1.219761 [57600/60000]
     Test Error: 
-     Accuracy: 63.3%, Avg loss: 1.280965 
+     Accuracy: 64.1%, Avg loss: 1.240759 
 
     Epoch 5
     -------------------------
-    loss: 1.355206 [    0/60000]
-    loss: 1.329616 [ 6400/60000]
-    loss: 1.171600 [12800/60000]
-    loss: 1.278760 [19200/60000]
-    loss: 1.166483 [25600/60000]
-    loss: 1.172769 [32000/60000]
-    loss: 1.190366 [38400/60000]
-    loss: 1.111315 [44800/60000]
-    loss: 1.161075 [51200/60000]
-    loss: 1.075265 [57600/60000]
+    loss: 1.320014 [    0/60000]
+    loss: 1.307106 [ 6400/60000]
+    loss: 1.134614 [12800/60000]
+    loss: 1.237779 [19200/60000]
+    loss: 1.115975 [25600/60000]
+    loss: 1.142946 [32000/60000]
+    loss: 1.182056 [38400/60000]
+    loss: 1.099508 [44800/60000]
+    loss: 1.147290 [51200/60000]
+    loss: 1.065523 [57600/60000]
     Test Error: 
-     Accuracy: 64.4%, Avg loss: 1.106414 
+     Accuracy: 65.3%, Avg loss: 1.079886 
 
-    Training done! time:  34.6573 s
+    Training done! time:  34.7662 s
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 55.391 seconds)
+   **Total running time of the script:** (0 minutes 56.045 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([[0.3251, 0.0000, 0.0000, 0.0000]], grad_fn=<ReluBackward0>), tensor([[ 0.1503, -0.4470,  0.1971, -0.2783]], grad_fn=<AddmmBackward0>), tensor([[ 0.1503, -0.4470]], grad_fn=<SplitBackward0>), tensor([[1.1283, 0.8344]], grad_fn=<ClampMinBackward0>), tensor([[ 0.1503, -0.4470]], grad_fn=<SplitBackward0>))
+    (tensor([[0., 0., 0., 0.]], grad_fn=<ReluBackward0>), tensor([[-0.3200, -0.1250,  0.3932,  0.3409]], grad_fn=<AddmmBackward0>), tensor([[-0.3200, -0.1250]], grad_fn=<SplitBackward0>), tensor([[1.2644, 1.2273]], grad_fn=<ClampMinBackward0>), tensor([[-0.3200, -0.1250]], 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:  684.98 micro-seconds
-    Time for exported module:  344.28 micro-seconds
+    Time for TDModule:  697.37 micro-seconds
+    Time for exported module:  361.20 micro-seconds
 
 
 
@@ -450,7 +450,7 @@ distribution:
 
  .. code-block:: none
 
-    tensor([[ 0.1503, -0.4470]], grad_fn=<SplitBackward0>)
+    tensor([[-0.3200, -0.1250]], 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 1.415 seconds)
+   **Total running time of the script:** (0 minutes 1.429 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
 =================
-**02:27.506** total execution time for 11 files **from tutorials**:
+**02:27.862** 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``)
-     - 01:01.061
+     - 01:00.830
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``data_fashion.py``)
-     - 00:55.391
+     - 00:56.045
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``tensordict_module.py``)
-     - 00:16.990
+     - 00:16.872
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``streamed_tensordict.py``)
-     - 00:11.020
+     - 00:11.021
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``tensorclass_imagenet.py``)
-     - 00:01.600
+     - 00:01.635
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``export.py``)
-     - 00:01.415
+     - 00:01.429
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_keys.py` (``tensordict_keys.py``)
      - 00:00.009