Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fd7b9d2d",
+   "id": "07ca7101",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46c9b1d1",
+   "id": "5c80c64e",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbdc2894",
+   "id": "65cd1d93",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6876e37",
+   "id": "355262c2",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "077f87e9",
+   "id": "c15d3934",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "552e9b60",
+   "id": "edc64c5d",
    "metadata": {},
    "source": [
     "\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9a3fe3ca",
+   "id": "416301d9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb3a0ab0",
+   "id": "f8d96351",
    "metadata": {},
    "source": [
     "\n",

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9416f772",
+   "id": "1f4610df",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fcf8bbd",
+   "id": "73c65f06",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a88c8a4e",
+   "id": "bd2b8139",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95a37240",
+   "id": "4fc6d23a",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "07b323fb",
+   "id": "4fdbbc06",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "def99fa8",
+   "id": "59a4103a",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bd934e01",
+   "id": "f252b13b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32100206",
+   "id": "9f07beb5",
    "metadata": {},
    "source": [
     "\n",

_images/sphx_glr_char_rnn_classification_tutorial_001.png

@@ -1634,26 +1634,26 @@ modules we need.
 
 
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-    reward: -3.22 (r0 = -2.19), reward eval: reward: -3.03, reward normalized=-3.20/4.04, grad norm= 242.17, loss_value= 150.76, loss_actor= 15.33, target value: -22.04: : 10400it [00:18, 364.44it/s]
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+    reward: -1.14 (r0 = -2.30), reward eval: reward:  0.01, reward normalized=-2.23/5.64, grad norm= 53.64, loss_value= 203.78, loss_actor= 10.83, target value: -13.65:  80%|########  | 8000/10000 [00:10<00:03, 645.26it/s]
+    reward: -4.46 (r0 = -2.30), reward eval: reward:  0.01, reward normalized=-2.25/5.01, grad norm= 107.66, loss_value= 201.47, loss_actor= 14.83, target value: -15.88:  80%|########  | 8000/10000 [00:10<00:03, 645.26it/s]
+    reward: -4.46 (r0 = -2.30), reward eval: reward:  0.01, reward normalized=-2.25/5.01, grad norm= 107.66, loss_value= 201.47, loss_actor= 14.83, target value: -15.88:  88%|########8 | 8800/10000 [00:11<00:02, 595.57it/s]
+    reward: -5.10 (r0 = -2.30), reward eval: reward: -4.21, reward normalized=-2.79/5.24, grad norm= 125.92, loss_value= 211.97, loss_actor= 16.07, target value: -19.41:  88%|########8 | 8800/10000 [00:14<00:02, 595.57it/s]
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+    reward: -4.24 (r0 = -2.30), reward eval: reward: -4.21, reward normalized=-3.06/4.72, grad norm= 93.86, loss_value= 195.94, loss_actor= 17.67, target value: -22.37: : 10400it [00:18, 364.77it/s] 
 
 
 
@@ -1723,7 +1723,7 @@ To iterate further on this loss module we might consider:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  28.623 seconds)
+   **Total running time of the script:** ( 0 minutes  28.439 seconds)
 
 
 .. _sphx_glr_download_advanced_coding_ddpg.py:

_images/sphx_glr_char_rnn_classification_tutorial_002.png

@@ -1207,3 +1207,5 @@ examples displayed in this note `here
 <https://github.com/pytorch/extension-cpp>`_. If you have questions, please use
 `the forums <https://discuss.pytorch.org>`_. Also be sure to check our `FAQ
 <https://pytorch.org/cppdocs/notes/faq.html>`_ in case you run into any issues.
+A blog on writing extensions for AMD ROCm can be found `here
+<https://rocm.blogs.amd.com/artificial-intelligence/cpp-extn/readme.html>`_.

_images/sphx_glr_coding_ddpg_001.png

@@ -517,9 +517,9 @@ models run single threaded.
  .. code-block:: none
 
     loss: 5.167
-    elapsed time (seconds): 214.3
+    elapsed time (seconds): 207.7
     loss: 5.168
-    elapsed time (seconds): 119.1
+    elapsed time (seconds): 118.3
 
 
 
@@ -541,7 +541,7 @@ Thanks for reading! As always, we welcome any feedback, so please create an issu
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  42.296 seconds)
+   **Total running time of the script:** ( 5 minutes  34.400 seconds)
 
 
 .. _sphx_glr_download_advanced_dynamic_quantization_tutorial.py:

_images/sphx_glr_dqn_with_rnn_tutorial_001.png

@@ -410,37 +410,33 @@ network to evaluation mode using ``.eval()``.
     Downloading: "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth
 
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@@ -761,22 +757,22 @@ Finally, we can run the algorithm.
 
     Optimizing..
     run [50]:
-    Style Loss : 4.123869 Content Loss: 4.182580
+    Style Loss : 4.168906 Content Loss: 4.182115
 
     run [100]:
-    Style Loss : 1.127630 Content Loss: 3.023808
+    Style Loss : 1.157379 Content Loss: 3.045664
 
     run [150]:
-    Style Loss : 0.704597 Content Loss: 2.645709
+    Style Loss : 0.716242 Content Loss: 2.652578
 
     run [200]:
-    Style Loss : 0.479943 Content Loss: 2.490248
+    Style Loss : 0.478957 Content Loss: 2.489498
 
     run [250]:
-    Style Loss : 0.346584 Content Loss: 2.404827
+    Style Loss : 0.346494 Content Loss: 2.404430
 
     run [300]:
-    Style Loss : 0.265733 Content Loss: 2.350008
+    Style Loss : 0.264193 Content Loss: 2.346598
 
 
 
@@ -785,7 +781,7 @@ Finally, we can run the algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  36.658 seconds)
+   **Total running time of the script:** ( 0 minutes  36.240 seconds)
 
 
 .. _sphx_glr_download_advanced_neural_style_tutorial.py:

_images/sphx_glr_neural_style_tutorial_004.png

@@ -303,7 +303,7 @@ The backward pass computes the gradient ``wrt`` the input and the gradient ``wrt
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.597 seconds)
+   **Total running time of the script:** ( 0 minutes  0.589 seconds)
 
 
 .. _sphx_glr_download_advanced_numpy_extensions_tutorial.py: