Automated tutorials push

Author: pytorchbot

_downloads/13cdb386a4b0dc48c626f32e6cf8681d/amp_recipe.ipynb

@@ -261,7 +261,7 @@
     "# The same ``GradScaler`` instance should be used for the entire convergence run.\n",
     "# If you perform multiple convergence runs in the same script, each run should use\n",
     "# a dedicated fresh ``GradScaler`` instance. ``GradScaler`` instances are lightweight.\n",
-    "scaler = torch.cuda.amp.GradScaler()\n",
+    "scaler = torch.amp.GradScaler(\"cuda\")\n",
     "\n",
     "for epoch in range(0): # 0 epochs, this section is for illustration only\n",
     "    for input, target in zip(data, targets):\n",
@@ -308,7 +308,7 @@
     "\n",
     "net = make_model(in_size, out_size, num_layers)\n",
     "opt = torch.optim.SGD(net.parameters(), lr=0.001)\n",
-    "scaler = torch.cuda.amp.GradScaler(enabled=use_amp)\n",
+    "scaler = torch.amp.GradScaler(\"cuda\" ,enabled=use_amp)\n",
     "\n",
     "start_timer()\n",
     "for epoch in range(epochs):\n",

_downloads/cadb3a57e7a6d7c149b5ae377caf36a8/amp_recipe.py

@@ -150,7 +150,7 @@ def make_model(in_size, out_size, num_layers):
 # The same ``GradScaler`` instance should be used for the entire convergence run.
 # If you perform multiple convergence runs in the same script, each run should use
 # a dedicated fresh ``GradScaler`` instance. ``GradScaler`` instances are lightweight.
-scaler = torch.cuda.amp.GradScaler()
+scaler = torch.amp.GradScaler("cuda")
 
 for epoch in range(0): # 0 epochs, this section is for illustration only
     for input, target in zip(data, targets):
@@ -182,7 +182,7 @@ def make_model(in_size, out_size, num_layers):
 
 net = make_model(in_size, out_size, num_layers)
 opt = torch.optim.SGD(net.parameters(), lr=0.001)
-scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
+scaler = torch.amp.GradScaler("cuda" ,enabled=use_amp)
 
 start_timer()
 for epoch in range(epochs):

_images/sphx_glr_char_rnn_classification_tutorial_001.png

@@ -1634,26 +1634,26 @@ modules we need.
 
 
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-    reward: -2.34 (r0 = -2.00), reward eval: reward: -0.00, reward normalized=-2.59/6.17, grad norm= 147.92, loss_value= 295.62, loss_actor= 13.96, target value: -15.91:  56%|#####6    | 5600/10000 [00:06<00:03, 1208.77it/s]
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-    reward: -4.93 (r0 = -2.00), reward eval: reward: -5.98, reward normalized=-3.38/3.91, grad norm= 120.25, loss_value= 129.44, loss_actor= 15.23, target value: -23.98: : 10400it [00:18, 364.88it/s]
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+    reward: -2.38 (r0 = -3.75), reward eval: reward: -0.01, reward normalized=-2.90/6.27, grad norm= 184.34, loss_value= 346.66, loss_actor= 15.34, target value: -18.57:  64%|######4   | 6400/10000 [00:07<00:04, 876.74it/s] 
+    reward: -0.14 (r0 = -3.75), reward eval: reward: -0.01, reward normalized=-2.44/5.85, grad norm= 49.14, loss_value= 239.65, loss_actor= 14.06, target value: -14.04:  64%|######4   | 6400/10000 [00:07<00:04, 876.74it/s] 
+    reward: -0.14 (r0 = -3.75), reward eval: reward: -0.01, reward normalized=-2.44/5.85, grad norm= 49.14, loss_value= 239.65, loss_actor= 14.06, target value: -14.04:  72%|#######2  | 7200/10000 [00:08<00:03, 713.38it/s]
+    reward: -2.36 (r0 = -3.75), reward eval: reward: -0.01, reward normalized=-2.26/5.79, grad norm= 128.31, loss_value= 254.09, loss_actor= 12.59, target value: -14.58:  72%|#######2  | 7200/10000 [00:09<00:03, 713.38it/s]
+    reward: -2.36 (r0 = -3.75), reward eval: reward: -0.01, reward normalized=-2.26/5.79, grad norm= 128.31, loss_value= 254.09, loss_actor= 12.59, target value: -14.58:  80%|########  | 8000/10000 [00:10<00:03, 627.39it/s]
+    reward: -4.64 (r0 = -3.75), reward eval: reward: -0.01, reward normalized=-3.11/5.26, grad norm= 115.52, loss_value= 239.16, loss_actor= 18.21, target value: -19.89:  80%|########  | 8000/10000 [00:11<00:03, 627.39it/s]
+    reward: -4.64 (r0 = -3.75), reward eval: reward: -0.01, reward normalized=-3.11/5.26, grad norm= 115.52, loss_value= 239.16, loss_actor= 18.21, target value: -19.89:  88%|########8 | 8800/10000 [00:11<00:02, 582.30it/s]
+    reward: -4.99 (r0 = -3.75), reward eval: reward: -5.59, reward normalized=-3.11/5.35, grad norm= 68.47, loss_value= 219.28, loss_actor= 19.75, target value: -19.78:  88%|########8 | 8800/10000 [00:14<00:02, 582.30it/s] 
+    reward: -4.99 (r0 = -3.75), reward eval: reward: -5.59, reward normalized=-3.11/5.35, grad norm= 68.47, loss_value= 219.28, loss_actor= 19.75, target value: -19.78:  96%|#########6| 9600/10000 [00:15<00:00, 404.39it/s]
+    reward: -5.39 (r0 = -3.75), reward eval: reward: -5.59, reward normalized=-3.21/5.31, grad norm= 204.79, loss_value= 298.20, loss_actor= 19.41, target value: -22.63:  96%|#########6| 9600/10000 [00:15<00:00, 404.39it/s]
+    reward: -5.39 (r0 = -3.75), reward eval: reward: -5.59, reward normalized=-3.21/5.31, grad norm= 204.79, loss_value= 298.20, loss_actor= 19.41, target value: -22.63: : 10400it [00:17, 366.15it/s]                        
+    reward: -4.68 (r0 = -3.75), reward eval: reward: -5.59, reward normalized=-3.57/4.27, grad norm= 74.57, loss_value= 203.88, loss_actor= 23.34, target value: -24.98: : 10400it [00:18, 366.15it/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  29.005 seconds)
+   **Total running time of the script:** ( 0 minutes  28.797 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): 208.2
+    elapsed time (seconds): 202.0
     loss: 5.168
-    elapsed time (seconds): 115.8
+    elapsed time (seconds): 113.8
 
 
 
@@ -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  34.880 seconds)
+   **Total running time of the script:** ( 5 minutes  26.458 seconds)
 
 
 .. _sphx_glr_download_advanced_dynamic_quantization_tutorial.py:

_images/sphx_glr_coding_ddpg_001.png

@@ -410,32 +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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@@ -756,22 +757,22 @@ Finally, we can run the algorithm.
 
     Optimizing..
     run [50]:
-    Style Loss : 4.243999 Content Loss: 4.230177
+    Style Loss : 4.124988 Content Loss: 4.180835
 
     run [100]:
-    Style Loss : 1.153091 Content Loss: 3.027826
+    Style Loss : 1.149009 Content Loss: 3.025290
 
     run [150]:
-    Style Loss : 0.714814 Content Loss: 2.653670
+    Style Loss : 0.722513 Content Loss: 2.658751
 
     run [200]:
-    Style Loss : 0.479303 Content Loss: 2.491420
+    Style Loss : 0.484897 Content Loss: 2.496019
 
     run [250]:
-    Style Loss : 0.347053 Content Loss: 2.402259
+    Style Loss : 0.348076 Content Loss: 2.404442
 
     run [300]:
-    Style Loss : 0.262986 Content Loss: 2.348989
+    Style Loss : 0.265312 Content Loss: 2.350962
 
 
 
@@ -780,7 +781,7 @@ Finally, we can run the algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  38.572 seconds)
+   **Total running time of the script:** ( 0 minutes  38.498 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  0.602 seconds)
+   **Total running time of the script:** ( 0 minutes  0.568 seconds)
 
 
 .. _sphx_glr_download_advanced_numpy_extensions_tutorial.py: