[Doc] Make tutos runnable without colab (#1826)

Author: Vincent Moens

torchrl/objectives/ppo.py

@@ -345,41 +345,46 @@ def functional(self):
 
     @property
     def actor(self):
-        logging.warning(
+        warnings.warn(
             f"{self.__class__.__name__}.actor is deprecated, use {self.__class__.__name__}.actor_network instead. This "
-            "link will be removed in v0.4."
+            "link will be removed in v0.4.",
+            category=DeprecationWarning,
         )
         return self.actor_network
 
     @property
     def critic(self):
-        logging.warning(
+        warnings.warn(
             f"{self.__class__.__name__}.critic is deprecated, use {self.__class__.__name__}.critic_network instead. This "
-            "link will be removed in v0.4."
+            "link will be removed in v0.4.",
+            category=DeprecationWarning,
         )
         return self.critic_network
 
     @property
     def actor_params(self):
         logging.warning(
             f"{self.__class__.__name__}.actor_params is deprecated, use {self.__class__.__name__}.actor_network_params instead. This "
-            "link will be removed in v0.4."
+            "link will be removed in v0.4.",
+            category=DeprecationWarning,
         )
         return self.actor_network_params
 
     @property
     def critic_params(self):
-        logging.warning(
+        warnings.warn(
             f"{self.__class__.__name__}.critic_params is deprecated, use {self.__class__.__name__}.critic_network_params instead. This "
-            "link will be removed in v0.4."
+            "link will be removed in v0.4.",
+            category=DeprecationWarning,
         )
         return self.critic_network_params
 
     @property
     def target_critic_params(self):
-        logging.warning(
+        warnings.warn(
             f"{self.__class__.__name__}.target_critic_params is deprecated, use {self.__class__.__name__}.target_critic_network_params instead. This "
-            "link will be removed in v0.4."
+            "link will be removed in v0.4.",
+            category=DeprecationWarning,
         )
         return self.target_critic_network_params
 

tutorials/sphinx-tutorials/coding_ddpg.py

@@ -57,6 +57,16 @@
 from typing import Tuple
 
 warnings.filterwarnings("ignore")
+from torch import multiprocessing
+
+# TorchRL prefers spawn method, that restricts creation of  ``~torchrl.envs.ParallelEnv`` inside
+# `__main__` method call, but for the easy of reading the code switch to fork
+# which is also a default spawn method in Google's Colaboratory
+try:
+    multiprocessing.set_start_method("fork")
+except RuntimeError:
+    assert multiprocessing.get_start_method() == "fork"
+
 # sphinx_gallery_end_ignore
 
 import torch.cuda

tutorials/sphinx-tutorials/coding_dqn.py

@@ -87,6 +87,18 @@
 import warnings
 
 warnings.filterwarnings("ignore")
+
+from torch import multiprocessing
+
+# TorchRL prefers spawn method, that restricts creation of  ``~torchrl.envs.ParallelEnv`` inside
+# `__main__` method call, but for the easy of reading the code switch to fork
+# which is also a default spawn method in Google's Colaboratory
+try:
+    multiprocessing.set_start_method("fork")
+except RuntimeError:
+    assert multiprocessing.get_start_method() == "fork"
+
+
 # sphinx_gallery_end_ignore
 
 import os

tutorials/sphinx-tutorials/coding_ppo.py

@@ -104,6 +104,22 @@
 # description and more about the algorithm itself.
 #
 
+# sphinx_gallery_start_ignore
+import warnings
+
+warnings.filterwarnings("ignore")
+from torch import multiprocessing
+
+# TorchRL prefers spawn method, that restricts creation of  ``~torchrl.envs.ParallelEnv`` inside
+# `__main__` method call, but for the easy of reading the code switch to fork
+# which is also a default spawn method in Google's Colaboratory
+try:
+    multiprocessing.set_start_method("fork")
+except RuntimeError:
+    assert multiprocessing.get_start_method() == "fork"
+
+# sphinx_gallery_end_ignore
+
 from collections import defaultdict
 
 import matplotlib.pyplot as plt

tutorials/sphinx-tutorials/dqn_with_rnn.py

@@ -68,6 +68,22 @@
 # -----
 #
 
+# sphinx_gallery_start_ignore
+import warnings
+
+warnings.filterwarnings("ignore")
+from torch import multiprocessing
+
+# TorchRL prefers spawn method, that restricts creation of  ``~torchrl.envs.ParallelEnv`` inside
+# `__main__` method call, but for the easy of reading the code switch to fork
+# which is also a default spawn method in Google's Colaboratory
+try:
+    multiprocessing.set_start_method("fork")
+except RuntimeError:
+    assert multiprocessing.get_start_method() == "fork"
+
+# sphinx_gallery_end_ignore
+
 import torch
 import tqdm
 from tensordict.nn import TensorDictModule as Mod, TensorDictSequential as Seq

tutorials/sphinx-tutorials/multi_task.py

@@ -13,6 +13,17 @@
 import warnings
 
 warnings.filterwarnings("ignore")
+
+from torch import multiprocessing
+
+# TorchRL prefers spawn method, that restricts creation of  ``~torchrl.envs.ParallelEnv`` inside
+# `__main__` method call, but for the easy of reading the code switch to fork
+# which is also a default spawn method in Google's Colaboratory
+try:
+    multiprocessing.set_start_method("fork")
+except RuntimeError:
+    assert multiprocessing.get_start_method() == "fork"
+
 # sphinx_gallery_end_ignore
 
 import torch

tutorials/sphinx-tutorials/multiagent_ppo.py

@@ -659,8 +659,8 @@
     with torch.no_grad():
         GAE(
             tensordict_data,
-            params=loss_module.critic_params,
-            target_params=loss_module.target_critic_params,
+            params=loss_module.critic_network_params,
+            target_params=loss_module.target_critic_network_params,
         )  # Compute GAE and add it to the data
 
     data_view = tensordict_data.reshape(-1)  # Flatten the batch size to shuffle data

tutorials/sphinx-tutorials/pendulum.py

@@ -73,6 +73,23 @@
 #   simulation graph.
 # * Finally, we will train a simple policy to solve the system we implemented.
 #
+
+# sphinx_gallery_start_ignore
+import warnings
+
+warnings.filterwarnings("ignore")
+from torch import multiprocessing
+
+# TorchRL prefers spawn method, that restricts creation of  ``~torchrl.envs.ParallelEnv`` inside
+# `__main__` method call, but for the easy of reading the code switch to fork
+# which is also a default spawn method in Google's Colaboratory
+try:
+    multiprocessing.set_start_method("fork")
+except RuntimeError:
+    assert multiprocessing.get_start_method() == "fork"
+
+# sphinx_gallery_end_ignore
+
 from collections import defaultdict
 from typing import Optional
 

tutorials/sphinx-tutorials/pretrained_models.py

@@ -13,6 +13,23 @@
 # in one or the other context. In this tutorial, we will be using R3M (https://arxiv.org/abs/2203.12601),
 # but other models (e.g. VIP) will work equally well.
 #
+
+# sphinx_gallery_start_ignore
+import warnings
+
+warnings.filterwarnings("ignore")
+from torch import multiprocessing
+
+# TorchRL prefers spawn method, that restricts creation of  ``~torchrl.envs.ParallelEnv`` inside
+# `__main__` method call, but for the easy of reading the code switch to fork
+# which is also a default spawn method in Google's Colaboratory
+try:
+    multiprocessing.set_start_method("fork")
+except RuntimeError:
+    assert multiprocessing.get_start_method() == "fork"
+
+# sphinx_gallery_end_ignore
+
 import torch.cuda
 from tensordict.nn import TensorDictSequential
 from torch import nn

tutorials/sphinx-tutorials/rb_tutorial.py

@@ -46,6 +46,23 @@
 # replay buffer is a straightforward process, as shown in the following
 # example:
 #
+
+# sphinx_gallery_start_ignore
+import warnings
+
+warnings.filterwarnings("ignore")
+from torch import multiprocessing
+
+# TorchRL prefers spawn method, that restricts creation of  ``~torchrl.envs.ParallelEnv`` inside
+# `__main__` method call, but for the easy of reading the code switch to fork
+# which is also a default spawn method in Google's Colaboratory
+try:
+    multiprocessing.set_start_method("fork")
+except RuntimeError:
+    assert multiprocessing.get_start_method() == "fork"
+
+# sphinx_gallery_end_ignore
+
 import tempfile
 
 from torchrl.data import ReplayBuffer