Fixing some details for the tutorials to run.

docs/src/tutorials/pytorch-mnist.rst

@@ -55,7 +55,7 @@ be called only once because Oríon only looks at 1 ``'objective'`` value per run
 
 .. code-block:: python
 
-        test_error_rate = test(args, model, device, test_loader)
+        test_error_rate = test(model, device, test_loader)
 
     report_objective(test_error_rate)
 

docs/src/tutorials/scikit-learn.rst

@@ -19,7 +19,7 @@ Sample script
 
 .. literalinclude:: /../../examples/scikitlearn-iris/main.py
    :language: python
-   :lines: 1-2, 5-9, 13-30
+   :lines: 1-9, 13-30
 
 This very basic script takes in parameter one positional argument for the hyper-parameter *epsilon*
 which control the loss in the script.

examples/tutorials/code_1_python_api.py

@@ -7,6 +7,9 @@
 1-d ``rosenbrock`` function with random search and TPE and visualize the regret curve to compare
 the algorithms.
 
+Note for macos users : You will need to either run this page as a jupyter notebook in order for it to compile, or 
+encapsulate the code in a main function and running it under ``if __name__ == '__main__'``.
+
 We first import the only function needed, :func:`build experiment <orion.client.build_experiment>`.
 """
 from orion.client import build_experiment

examples/tutorials/code_2_hyperband_checkpoint.py

@@ -35,6 +35,9 @@
 checkpoints.
 We will demonstrate below how this can be done with PyTorch, but using Oríon's Python API.
 
+Note for macos users : You will need to either run this page as a jupyter notebook in order for it to compile, or 
+encapsulate the code in a main function and running it under ``if __name__ == '__main__'``.
+
 Training code
 -------------
 
@@ -122,7 +125,7 @@ def build_data_loaders(batch_size, split_seed=1):
 # Next, we write the function to save checkpoints. It is important to include
 # not only the model in the checkpoint, but also the optimizer and the learning rate
 # schedule when using one. In this example we will use the exponential learning rate schedule,
-# so we checkpoint it. We save the current epoch as well so that we now where we resume from.
+# so we checkpoint it. We save the current epoch as well so that we know where we resume from.
 
 
 def save_checkpoint(checkpoint, model, optimizer, lr_scheduler, epoch):