auto-generating sphinx docs

pytorch · Dec 9, 2024 · a73fe94 · a73fe94
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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
 =================
-**02:31.166** total execution time for 11 files **from all galleries**:
+**02:29.990** total execution time for 11 files **from all galleries**:
 
 .. container::
 
@@ -33,25 +33,25 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_tutorials_tensorclass_fashion.py` (``reference/generated/tutorials/tensorclass_fashion.py``)
-     - 01:00.839
+     - 01:00.862
      - 0.0
    * - :ref:`sphx_glr_tutorials_data_fashion.py` (``reference/generated/tutorials/data_fashion.py``)
-     - 00:55.706
+     - 00:54.911
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_module.py` (``reference/generated/tutorials/tensordict_module.py``)
-     - 00:20.006
+     - 00:19.686
      - 0.0
    * - :ref:`sphx_glr_tutorials_streamed_tensordict.py` (``reference/generated/tutorials/streamed_tensordict.py``)
-     - 00:11.020
+     - 00:11.022
      - 0.0
    * - :ref:`sphx_glr_tutorials_export.py` (``reference/generated/tutorials/export.py``)
-     - 00:01.864
+     - 00:01.824
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensorclass_imagenet.py` (``reference/generated/tutorials/tensorclass_imagenet.py``)
-     - 00:01.699
+     - 00:01.657
      - 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
@@ -60,7 +60,7 @@ Computation times
      - 00:00.005
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_memory.py` (``reference/generated/tutorials/tensordict_memory.py``)
-     - 00:00.005
+     - 00:00.004
      - 0.0
    * - :ref:`sphx_glr_tutorials_tensordict_preallocation.py` (``reference/generated/tutorials/tensordict_preallocation.py``)
      - 00:00.003

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: 2.308235 [    0/60000]
-    loss: 2.290428 [ 6400/60000]
-    loss: 2.272404 [12800/60000]
-    loss: 2.270692 [19200/60000]
-    loss: 2.250867 [25600/60000]
-    loss: 2.219301 [32000/60000]
-    loss: 2.237770 [38400/60000]
-    loss: 2.193873 [44800/60000]
-    loss: 2.195773 [51200/60000]
-    loss: 2.177800 [57600/60000]
+    loss: 2.300076 [    0/60000]
+    loss: 2.286131 [ 6400/60000]
+    loss: 2.270285 [12800/60000]
+    loss: 2.269111 [19200/60000]
+    loss: 2.246381 [25600/60000]
+    loss: 2.232410 [32000/60000]
+    loss: 2.226862 [38400/60000]
+    loss: 2.200187 [44800/60000]
+    loss: 2.191414 [51200/60000]
+    loss: 2.166425 [57600/60000]
     Test Error: 
-     Accuracy: 41.6%, Avg loss: 2.155380 
+     Accuracy: 49.6%, Avg loss: 2.163558 
 
     Epoch 2
     -------------------------
-    loss: 2.165997 [    0/60000]
-    loss: 2.152457 [ 6400/60000]
-    loss: 2.089101 [12800/60000]
-    loss: 2.111734 [19200/60000]
-    loss: 2.067156 [25600/60000]
-    loss: 1.996914 [32000/60000]
-    loss: 2.044302 [38400/60000]
-    loss: 1.953458 [44800/60000]
-    loss: 1.964826 [51200/60000]
-    loss: 1.911599 [57600/60000]
+    loss: 2.169617 [    0/60000]
+    loss: 2.159865 [ 6400/60000]
+    loss: 2.108187 [12800/60000]
+    loss: 2.129137 [19200/60000]
+    loss: 2.074021 [25600/60000]
+    loss: 2.023518 [32000/60000]
+    loss: 2.047665 [38400/60000]
+    loss: 1.969611 [44800/60000]
+    loss: 1.970435 [51200/60000]
+    loss: 1.909339 [57600/60000]
     Test Error: 
-     Accuracy: 52.9%, Avg loss: 1.885347 
+     Accuracy: 55.3%, Avg loss: 1.904233 
 
     Epoch 3
     -------------------------
-    loss: 1.917414 [    0/60000]
-    loss: 1.880982 [ 6400/60000]
-    loss: 1.754690 [12800/60000]
-    loss: 1.811048 [19200/60000]
-    loss: 1.710644 [25600/60000]
-    loss: 1.653802 [32000/60000]
-    loss: 1.698502 [38400/60000]
-    loss: 1.586629 [44800/60000]
-    loss: 1.619078 [51200/60000]
-    loss: 1.530940 [57600/60000]
+    loss: 1.931548 [    0/60000]
+    loss: 1.902627 [ 6400/60000]
+    loss: 1.787215 [12800/60000]
+    loss: 1.838000 [19200/60000]
+    loss: 1.722751 [25600/60000]
+    loss: 1.671043 [32000/60000]
+    loss: 1.698132 [38400/60000]
+    loss: 1.589851 [44800/60000]
+    loss: 1.611929 [51200/60000]
+    loss: 1.516776 [57600/60000]
     Test Error: 
-     Accuracy: 61.0%, Avg loss: 1.523789 
+     Accuracy: 61.2%, Avg loss: 1.529558 
 
     Epoch 4
     -------------------------
-    loss: 1.587635 [    0/60000]
-    loss: 1.547149 [ 6400/60000]
-    loss: 1.394430 [12800/60000]
-    loss: 1.478471 [19200/60000]
-    loss: 1.364701 [25600/60000]
-    loss: 1.353640 [32000/60000]
-    loss: 1.377746 [38400/60000]
-    loss: 1.296400 [44800/60000]
-    loss: 1.337154 [51200/60000]
-    loss: 1.241225 [57600/60000]
+    loss: 1.591249 [    0/60000]
+    loss: 1.554227 [ 6400/60000]
+    loss: 1.404630 [12800/60000]
+    loss: 1.492260 [19200/60000]
+    loss: 1.367048 [25600/60000]
+    loss: 1.355335 [32000/60000]
+    loss: 1.376330 [38400/60000]
+    loss: 1.291993 [44800/60000]
+    loss: 1.328009 [51200/60000]
+    loss: 1.234482 [57600/60000]
     Test Error: 
-     Accuracy: 63.8%, Avg loss: 1.256419 
+     Accuracy: 64.1%, Avg loss: 1.258872 
 
     Epoch 5
     -------------------------
-    loss: 1.330760 [    0/60000]
-    loss: 1.307394 [ 6400/60000]
-    loss: 1.146088 [12800/60000]
-    loss: 1.255699 [19200/60000]
-    loss: 1.136910 [25600/60000]
-    loss: 1.152944 [32000/60000]
-    loss: 1.175349 [38400/60000]
-    loss: 1.112468 [44800/60000]
-    loss: 1.159765 [51200/60000]
-    loss: 1.070423 [57600/60000]
+    loss: 1.335057 [    0/60000]
+    loss: 1.311481 [ 6400/60000]
+    loss: 1.146769 [12800/60000]
+    loss: 1.267080 [19200/60000]
+    loss: 1.139563 [25600/60000]
+    loss: 1.155214 [32000/60000]
+    loss: 1.180817 [38400/60000]
+    loss: 1.110676 [44800/60000]
+    loss: 1.153375 [51200/60000]
+    loss: 1.072037 [57600/60000]
     Test Error: 
-     Accuracy: 65.2%, Avg loss: 1.087738 
+     Accuracy: 65.1%, Avg loss: 1.092873 
 
-    TensorDict training done! time:  8.5392 s
+    TensorDict training done! time:  8.7976 s
     Epoch 1
     -------------------------
-    loss: 2.301747 [    0/60000]
-    loss: 2.295032 [ 6400/60000]
-    loss: 2.279933 [12800/60000]
-    loss: 2.278636 [19200/60000]
-    loss: 2.268745 [25600/60000]
-    loss: 2.236384 [32000/60000]
-    loss: 2.248804 [38400/60000]
-    loss: 2.216845 [44800/60000]
-    loss: 2.219797 [51200/60000]
-    loss: 2.192523 [57600/60000]
+    loss: 2.296688 [    0/60000]
+    loss: 2.288965 [ 6400/60000]
+    loss: 2.276427 [12800/60000]
+    loss: 2.270163 [19200/60000]
+    loss: 2.243812 [25600/60000]
+    loss: 2.231423 [32000/60000]
+    loss: 2.236040 [38400/60000]
+    loss: 2.204681 [44800/60000]
+    loss: 2.199894 [51200/60000]
+    loss: 2.183506 [57600/60000]
     Test Error: 
-     Accuracy: 46.4%, Avg loss: 2.188948 
+     Accuracy: 54.0%, Avg loss: 2.171313 
 
     Epoch 2
     -------------------------
-    loss: 2.192766 [    0/60000]
-    loss: 2.184700 [ 6400/60000]
-    loss: 2.142058 [12800/60000]
-    loss: 2.161197 [19200/60000]
-    loss: 2.117076 [25600/60000]
-    loss: 2.063707 [32000/60000]
-    loss: 2.095457 [38400/60000]
-    loss: 2.023851 [44800/60000]
-    loss: 2.038857 [51200/60000]
-    loss: 1.978099 [57600/60000]
+    loss: 2.178011 [    0/60000]
+    loss: 2.167310 [ 6400/60000]
+    loss: 2.121355 [12800/60000]
+    loss: 2.132777 [19200/60000]
+    loss: 2.077321 [25600/60000]
+    loss: 2.037216 [32000/60000]
+    loss: 2.065279 [38400/60000]
+    loss: 1.991604 [44800/60000]
+    loss: 1.993688 [51200/60000]
+    loss: 1.945264 [57600/60000]
     Test Error: 
-     Accuracy: 52.3%, Avg loss: 1.970957 
+     Accuracy: 58.0%, Avg loss: 1.931493 
 
     Epoch 3
     -------------------------
-    loss: 1.990906 [    0/60000]
-    loss: 1.967228 [ 6400/60000]
-    loss: 1.868619 [12800/60000]
-    loss: 1.912106 [19200/60000]
-    loss: 1.808444 [25600/60000]
-    loss: 1.746715 [32000/60000]
-    loss: 1.782484 [38400/60000]
-    loss: 1.673189 [44800/60000]
-    loss: 1.709751 [51200/60000]
-    loss: 1.609759 [57600/60000]
+    loss: 1.958883 [    0/60000]
+    loss: 1.928166 [ 6400/60000]
+    loss: 1.826211 [12800/60000]
+    loss: 1.859600 [19200/60000]
+    loss: 1.752121 [25600/60000]
+    loss: 1.713980 [32000/60000]
+    loss: 1.735593 [38400/60000]
+    loss: 1.639609 [44800/60000]
+    loss: 1.658125 [51200/60000]
+    loss: 1.568059 [57600/60000]
     Test Error: 
-     Accuracy: 60.1%, Avg loss: 1.616188 
+     Accuracy: 59.8%, Avg loss: 1.578069 
 
     Epoch 4
     -------------------------
-    loss: 1.667624 [    0/60000]
-    loss: 1.636496 [ 6400/60000]
-    loss: 1.492325 [12800/60000]
-    loss: 1.563238 [19200/60000]
-    loss: 1.448025 [25600/60000]
-    loss: 1.421502 [32000/60000]
-    loss: 1.442929 [38400/60000]
-    loss: 1.355865 [44800/60000]
-    loss: 1.400344 [51200/60000]
-    loss: 1.292719 [57600/60000]
+    loss: 1.641037 [    0/60000]
+    loss: 1.601733 [ 6400/60000]
+    loss: 1.462240 [12800/60000]
+    loss: 1.520895 [19200/60000]
+    loss: 1.408690 [25600/60000]
+    loss: 1.401289 [32000/60000]
+    loss: 1.408055 [38400/60000]
+    loss: 1.339955 [44800/60000]
+    loss: 1.367329 [51200/60000]
+    loss: 1.270362 [57600/60000]
     Test Error: 
-     Accuracy: 62.6%, Avg loss: 1.317928 
+     Accuracy: 62.0%, Avg loss: 1.300243 
 
     Epoch 5
     -------------------------
-    loss: 1.387819 [    0/60000]
-    loss: 1.372476 [ 6400/60000]
-    loss: 1.209445 [12800/60000]
-    loss: 1.308686 [19200/60000]
-    loss: 1.190643 [25600/60000]
-    loss: 1.199597 [32000/60000]
-    loss: 1.219788 [38400/60000]
-    loss: 1.151322 [44800/60000]
-    loss: 1.197672 [51200/60000]
-    loss: 1.104183 [57600/60000]
+    loss: 1.380182 [    0/60000]
+    loss: 1.355997 [ 6400/60000]
+    loss: 1.197436 [12800/60000]
+    loss: 1.285367 [19200/60000]
+    loss: 1.172652 [25600/60000]
+    loss: 1.192038 [32000/60000]
+    loss: 1.199795 [38400/60000]
+    loss: 1.150505 [44800/60000]
+    loss: 1.181850 [51200/60000]
+    loss: 1.097848 [57600/60000]
     Test Error: 
-     Accuracy: 64.4%, Avg loss: 1.128394 
+     Accuracy: 64.0%, Avg loss: 1.124975 
 
-    Training done! time:  34.7385 s
+    Training done! time:  34.0093 s
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (0 minutes 55.706 seconds)
+   **Total running time of the script:** (0 minutes 54.911 seconds)
 
 
 .. _sphx_glr_download_tutorials_data_fashion.py: