Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d138a7e2",
+   "id": "aa160397",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c99a9b4c",
+   "id": "190f9950",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "71f07c09",
+   "id": "48d2c3c7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89a9c73d",
+   "id": "21d0685d",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "31e0c762",
+   "id": "c1aebcb0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95ad8926",
+   "id": "0e2aaa9c",
    "metadata": {},
    "source": [
     "\n",

_downloads/6b019e0b5f84b568fcca1120bd28e230/torch_compile_tutorial.py

@@ -30,6 +30,10 @@
 # - ``numpy``
 # - ``scipy``
 # - ``tabulate``
+#
+# **System Requirements**
+# - A C++ compiler, such as ``g++``
+# - Python development package (``python-devel``/``python-dev``)
 
 ######################################################################
 # NOTE: a modern NVIDIA GPU (H100, A100, or V100) is recommended for this tutorial in

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c0fa3ab2",
+   "id": "c38505ba",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3be9147",
+   "id": "78c5656f",
    "metadata": {},
    "source": [
     "\n",

_downloads/96ad88eb476f41a5403dcdade086afb8/torch_compile_tutorial.ipynb

@@ -48,7 +48,10 @@
     "-   `torchvision`\n",
     "-   `numpy`\n",
     "-   `scipy`\n",
-    "-   `tabulate`\n"
+    "-   `tabulate`\n",
+    "\n",
+    "**System Requirements** - A C++ compiler, such as `g++` - Python\n",
+    "development package (`python-devel`/`python-dev`)\n"
    ]
   },
   {

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "06a5e4e8",
+   "id": "6a17d7ee",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1ad12c3",
+   "id": "1a17e29f",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "43435335",
+   "id": "84ecc7cc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8314615",
+   "id": "580590a2",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0d66ae34",
+   "id": "c18fa265",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90e25f71",
+   "id": "77d9b723",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "89c30003",
+   "id": "277d217e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96d0efc3",
+   "id": "c51520d0",
    "metadata": {},
    "source": [
     "\n",

_images/sphx_glr_char_rnn_classification_tutorial_001.png

@@ -1634,26 +1634,26 @@ modules we need.
 
 
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-    reward: -2.10 (r0 = -2.77), reward eval: reward: -0.00, reward normalized=-2.03/6.49, grad norm= 118.91, loss_value= 421.56, loss_actor= 13.52, target value: -12.78:  56%|#####6    | 5600/10000 [00:07<00:03, 1188.73it/s]
-    reward: -2.10 (r0 = -2.77), reward eval: reward: -0.00, reward normalized=-2.03/6.49, grad norm= 118.91, loss_value= 421.56, loss_actor= 13.52, target value: -12.78:  64%|######4   | 6400/10000 [00:07<00:04, 818.74it/s] 
-    reward: -0.18 (r0 = -2.77), reward eval: reward: -0.00, reward normalized=-1.85/5.98, grad norm= 64.71, loss_value= 317.24, loss_actor= 12.01, target value: -11.96:  64%|######4   | 6400/10000 [00:08<00:04, 818.74it/s] 
-    reward: -0.18 (r0 = -2.77), reward eval: reward: -0.00, reward normalized=-1.85/5.98, grad norm= 64.71, loss_value= 317.24, loss_actor= 12.01, target value: -11.96:  72%|#######2  | 7200/10000 [00:09<00:04, 685.27it/s]
-    reward: -1.72 (r0 = -2.77), reward eval: reward: -0.00, reward normalized=-1.86/5.98, grad norm= 67.66, loss_value= 282.10, loss_actor= 10.29, target value: -11.38:  72%|#######2  | 7200/10000 [00:10<00:04, 685.27it/s]
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-    reward: -4.75 (r0 = -2.77), reward eval: reward: -0.00, reward normalized=-2.61/4.96, grad norm= 89.98, loss_value= 182.59, loss_actor= 17.27, target value: -17.35:  80%|########  | 8000/10000 [00:11<00:03, 614.48it/s]
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-    reward: -5.26 (r0 = -2.77), reward eval: reward: -5.32, reward normalized=-2.15/5.30, grad norm= 81.40, loss_value= 181.11, loss_actor= 13.41, target value: -14.11:  96%|#########6| 9600/10000 [00:16<00:01, 368.51it/s]
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-    reward: -4.34 (r0 = -2.77), reward eval: reward: -5.32, reward normalized=-3.35/4.36, grad norm= 87.44, loss_value= 220.61, loss_actor= 17.77, target value: -24.09: : 10400it [00:20, 346.55it/s] 
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+    reward: -2.48 (r0 = -2.61), reward eval: reward: -0.01, reward normalized=-2.54/6.46, grad norm= 94.40, loss_value= 408.21, loss_actor= 14.01, target value: -15.93:  64%|######4   | 6400/10000 [00:07<00:04, 813.08it/s] 
+    reward: -0.12 (r0 = -2.61), reward eval: reward: -0.01, reward normalized=-2.23/5.55, grad norm= 59.75, loss_value= 249.44, loss_actor= 13.52, target value: -14.12:  64%|######4   | 6400/10000 [00:08<00:04, 813.08it/s]
+    reward: -0.12 (r0 = -2.61), reward eval: reward: -0.01, reward normalized=-2.23/5.55, grad norm= 59.75, loss_value= 249.44, loss_actor= 13.52, target value: -14.12:  72%|#######2  | 7200/10000 [00:09<00:04, 678.33it/s]
+    reward: -3.47 (r0 = -2.61), reward eval: reward: -0.01, reward normalized=-2.69/6.34, grad norm= 119.62, loss_value= 349.01, loss_actor= 15.25, target value: -16.23:  72%|#######2  | 7200/10000 [00:10<00:04, 678.33it/s]
+    reward: -3.47 (r0 = -2.61), reward eval: reward: -0.01, reward normalized=-2.69/6.34, grad norm= 119.62, loss_value= 349.01, loss_actor= 15.25, target value: -16.23:  80%|########  | 8000/10000 [00:11<00:03, 606.90it/s]
+    reward: -4.16 (r0 = -2.61), reward eval: reward: -0.01, reward normalized=-2.91/5.23, grad norm= 265.29, loss_value= 256.97, loss_actor= 13.80, target value: -19.01:  80%|########  | 8000/10000 [00:11<00:03, 606.90it/s]
+    reward: -4.16 (r0 = -2.61), reward eval: reward: -0.01, reward normalized=-2.91/5.23, grad norm= 265.29, loss_value= 256.97, loss_actor= 13.80, target value: -19.01:  88%|########8 | 8800/10000 [00:12<00:02, 564.64it/s]
+    reward: -4.98 (r0 = -2.61), reward eval: reward: -5.96, reward normalized=-2.89/5.09, grad norm= 36.41, loss_value= 230.77, loss_actor= 17.59, target value: -19.00:  88%|########8 | 8800/10000 [00:15<00:02, 564.64it/s] 
+    reward: -4.98 (r0 = -2.61), reward eval: reward: -5.96, reward normalized=-2.89/5.09, grad norm= 36.41, loss_value= 230.77, loss_actor= 17.59, target value: -19.00:  96%|#########6| 9600/10000 [00:16<00:01, 365.72it/s]
+    reward: -4.28 (r0 = -2.61), reward eval: reward: -5.96, reward normalized=-2.86/5.27, grad norm= 124.44, loss_value= 230.24, loss_actor= 17.83, target value: -20.33:  96%|#########6| 9600/10000 [00:17<00:01, 365.72it/s]
+    reward: -4.28 (r0 = -2.61), reward eval: reward: -5.96, reward normalized=-2.86/5.27, grad norm= 124.44, loss_value= 230.24, loss_actor= 17.83, target value: -20.33: : 10400it [00:19, 343.33it/s]                        
+    reward: -4.70 (r0 = -2.61), reward eval: reward: -5.96, reward normalized=-3.60/4.12, grad norm= 92.27, loss_value= 170.00, loss_actor= 22.34, target value: -25.35: : 10400it [00:20, 343.33it/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  31.426 seconds)
+   **Total running time of the script:** ( 0 minutes  31.807 seconds)
 
 
 .. _sphx_glr_download_advanced_coding_ddpg.py:

_images/sphx_glr_char_rnn_classification_tutorial_002.png

@@ -517,9 +517,9 @@ models run single threaded.
  .. code-block:: none
 
     loss: 5.167
-    elapsed time (seconds): 210.8
+    elapsed time (seconds): 207.8
     loss: 5.168
-    elapsed time (seconds): 117.2
+    elapsed time (seconds): 118.5
 
 
 
@@ -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  36.731 seconds)
+   **Total running time of the script:** ( 5 minutes  34.789 seconds)
 
 
 .. _sphx_glr_download_advanced_dynamic_quantization_tutorial.py:

_images/sphx_glr_coding_ddpg_001.png

@@ -411,31 +411,31 @@ network to evaluation mode using ``.eval()``.
 
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@@ -756,22 +756,22 @@ Finally, we can run the algorithm.
 
     Optimizing..
     run [50]:
-    Style Loss : 3.818673 Content Loss: 4.082992
+    Style Loss : 4.072629 Content Loss: 4.158342
 
     run [100]:
-    Style Loss : 1.115698 Content Loss: 3.015260
+    Style Loss : 1.157763 Content Loss: 3.050233
 
     run [150]:
-    Style Loss : 0.697345 Content Loss: 2.641728
+    Style Loss : 0.714200 Content Loss: 2.651302
 
     run [200]:
-    Style Loss : 0.465102 Content Loss: 2.482987
+    Style Loss : 0.489686 Content Loss: 2.496655
 
     run [250]:
-    Style Loss : 0.338386 Content Loss: 2.395916
+    Style Loss : 0.354790 Content Loss: 2.407404
 
     run [300]:
-    Style Loss : 0.258478 Content Loss: 2.345674
+    Style Loss : 0.268439 Content Loss: 2.352382
 
 
 
@@ -780,7 +780,7 @@ Finally, we can run the algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  36.060 seconds)
+   **Total running time of the script:** ( 0 minutes  36.042 seconds)
 
 
 .. _sphx_glr_download_advanced_neural_style_tutorial.py:

_images/sphx_glr_dqn_with_rnn_tutorial_001.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  0.613 seconds)
+   **Total running time of the script:** ( 0 minutes  0.591 seconds)
 
 
 .. _sphx_glr_download_advanced_numpy_extensions_tutorial.py: