Update Tensorboard use

tutorials/tutorial1/tutorial.ipynb

@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "fcac93e4",
    "metadata": {},
    "outputs": [
@@ -546,10 +546,8 @@
     }
    ],
    "source": [
-    "# Load the TensorBoard extension\n",
-    "%load_ext tensorboard\n",
-    "# Show saved losses\n",
-    "%tensorboard --logdir 'tutorial_logs'"
+    "print('\\nTo load TensorBoard run load_ext tensorboard on your terminal')\n",
+    "print(\"To visualize the loss you can run tensorboard --logdir 'tutorial_logs' on your terminal\\n\")"
    ]
   },
   {

tutorials/tutorial1/tutorial.py

@@ -261,13 +261,11 @@ plt.legend()
 
 # The solution is overlapped with the actual one, and they are barely indistinguishable. We can also take a look at the loss using `TensorBoard`:
 
-# In[10]:
+# In[ ]:
 
 
-# Load the TensorBoard extension
-get_ipython().run_line_magic('load_ext', 'tensorboard')
-# Show saved losses
-get_ipython().run_line_magic('tensorboard', "--logdir 'tutorial_logs'")
+print('\nTo load TensorBoard run load_ext tensorboard on your terminal')
+print("To visualize the loss you can run tensorboard --logdir 'tutorial_logs' on your terminal\n")
 
 
 # As we can see the loss has not reached a minimum, suggesting that we could train for longer! Alternatively, we can also take look at the loss using callbacks. Here we use `MetricTracker` from `pina.callback`:

tutorials/tutorial2/tutorial.py

@@ -311,12 +311,11 @@ trainer_learn.train()
 
 # Let us compare the training losses for the various types of training
 
-# In[10]:
+# In[ ]:
 
 
-# Load the TensorBoard extension
-get_ipython().run_line_magic('load_ext', 'tensorboard')
-get_ipython().run_line_magic('tensorboard', "--logdir 'tutorial_logs'")
+print('To load TensorBoard run load_ext tensorboard on your terminal')
+print("To visualize the loss you can run tensorboard --logdir 'tutorial_logs' on your terminal")
 
 
 # ## What's next?

tutorials/tutorial3/tutorial.py

@@ -194,12 +194,11 @@ trainer.train()
 
 # Let's now plot the logging to see how the losses vary during training. For this, we will use `TensorBoard`.
 
-# In[5]:
+# In[ ]:
 
 
-# Load the TensorBoard extension
-get_ipython().run_line_magic('load_ext', 'tensorboard')
-get_ipython().run_line_magic('tensorboard', "--logdir 'tutorial_logs'")
+print('\nTo load TensorBoard run load_ext tensorboard on your terminal')
+print("To visualize the loss you can run tensorboard --logdir 'tutorial_logs' on your terminal\n")
 
 
 # Notice that the loss on the boundaries of the spatial domain is exactly zero, as expected! After the training is completed one can now plot some results using the `matplotlib`. We plot the predicted output on the left side, the true solution at the center and the difference on the right side using the `plot_solution` function.
@@ -335,12 +334,12 @@ plt.figure(figsize=(12, 6))
 plot_solution(solver=pinn, time=1)
 
 
-# We can see now that the results are way better! This is due to the fact that previously the network was  not learning correctly the initial conditon, leading to a poor solution when time evolved. By imposing the initial condition the network is able to correctly solve the problem. We can also see using Tensorboard how the two losses decreased:
+# We can see now that the results are way better! This is due to the fact that previously the network was  not learning correctly the initial conditon, leading to a poor solution when time evolved. By imposing the initial condition the network is able to correctly solve the problem. We can also see how the two losses decreased using Tensorboard.
 
-# In[11]:
+# In[ ]:
 
 
-get_ipython().run_line_magic('tensorboard', "--logdir 'tutorial_logs'")
+print("To visualize the loss you can run tensorboard --logdir 'tutorial_logs' on your terminal")
 
 
 # ## What's next?