Removing Conda install references.

Author: AlannaBurke

recipes_source/recipes/tensorboard_with_pytorch.py

@@ -1,24 +1,17 @@
 """
 How to use TensorBoard with PyTorch
 ===================================
-TensorBoard is a visualization toolkit for machine learning experimentation. 
-TensorBoard allows tracking and visualizing metrics such as loss and accuracy, 
-visualizing the model graph, viewing histograms, displaying images and much more. 
-In this tutorial we are going to cover TensorBoard installation, 
+TensorBoard is a visualization toolkit for machine learning experimentation.
+TensorBoard allows tracking and visualizing metrics such as loss and accuracy,
+visualizing the model graph, viewing histograms, displaying images and much more.
+In this tutorial we are going to cover TensorBoard installation,
 basic usage with PyTorch, and how to visualize data you logged in TensorBoard UI.
 
 Installation
 ----------------------
-PyTorch should be installed to log models and metrics into TensorBoard log 
-directory. The following command will install PyTorch 1.4+ via 
-Anaconda (recommended):
-
-.. code-block:: sh
-
-   $ conda install pytorch torchvision -c pytorch 
-   
-
-or pip
+PyTorch should be installed to log models and metrics into TensorBoard log
+directory. The following command will install PyTorch 1.4+ via
+pip:
 
 .. code-block:: sh
 
@@ -29,31 +22,32 @@
 ######################################################################
 # Using TensorBoard in PyTorch
 # -----------------------------
-# 
-# Let’s now try using TensorBoard with PyTorch! Before logging anything, 
+#
+# Let’s now try using TensorBoard with PyTorch! Before logging anything,
 # we need to create a ``SummaryWriter`` instance.
-#   
+#
 
 import torch
 from torch.utils.tensorboard import SummaryWriter
+
 writer = SummaryWriter()
 
 ######################################################################
 # Writer will output to ``./runs/`` directory by default.
-# 
+#
 
 
 ######################################################################
 # Log scalars
 # -----------
-# 
-# In machine learning, it’s important to understand key metrics such as 
-# loss and how they change during training. Scalar helps to save 
-# the loss value of each training step, or the accuracy after each epoch. 
-#
-# To log a scalar value, use 
-# ``add_scalar(tag, scalar_value, global_step=None, walltime=None)``. 
-# For example, lets create a simple linear regression training, and 
+#
+# In machine learning, it’s important to understand key metrics such as
+# loss and how they change during training. Scalar helps to save
+# the loss value of each training step, or the accuracy after each epoch.
+#
+# To log a scalar value, use
+# ``add_scalar(tag, scalar_value, global_step=None, walltime=None)``.
+# For example, lets create a simple linear regression training, and
 # log loss value using ``add_scalar``
 #
 
@@ -62,7 +56,8 @@
 
 model = torch.nn.Linear(1, 1)
 criterion = torch.nn.MSELoss()
-optimizer = torch.optim.SGD(model.parameters(), lr = 0.1)
+optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
+
 
 def train_model(iter):
     for epoch in range(iter):
@@ -72,18 +67,19 @@ def train_model(iter):
         optimizer.zero_grad()
         loss.backward()
         optimizer.step()
-        
+
+
 train_model(10)
 writer.flush()
 
 
-###################################################################### 
-# Call ``flush()`` method to make sure that all pending events 
+######################################################################
+# Call ``flush()`` method to make sure that all pending events
 # have been written to disk.
-# 
-# See `torch.utils.tensorboard tutorials <https://pytorch.org/docs/stable/tensorboard.html>`_ 
+#
+# See `torch.utils.tensorboard tutorials <https://pytorch.org/docs/stable/tensorboard.html>`_
 # to find more TensorBoard visualization types you can log.
-# 
+#
 # If you do not need the summary writer anymore, call ``close()`` method.
 #
 
@@ -92,17 +88,17 @@ def train_model(iter):
 ######################################################################
 # Run TensorBoard
 # ----------------
-# 
+#
 # Install TensorBoard through the command line to visualize data you logged
 #
 # .. code-block:: sh
 #
 #    pip install tensorboard
 #
 #
-# Now, start TensorBoard, specifying the root log directory you used above. 
-# Argument ``logdir`` points to directory where TensorBoard will look to find 
-# event files that it can display. TensorBoard will recursively walk 
+# Now, start TensorBoard, specifying the root log directory you used above.
+# Argument ``logdir`` points to directory where TensorBoard will look to find
+# event files that it can display. TensorBoard will recursively walk
 # the directory structure rooted at ``logdir``, looking for ``.*tfevents.*`` files.
 #
 # .. code-block:: sh
@@ -114,17 +110,17 @@ def train_model(iter):
 # .. image:: ../../_static/img/thumbnails/tensorboard_scalars.png
 #    :scale: 40 %
 #
-# This dashboard shows how the loss and accuracy change with every epoch. 
-# You can use it to also track training speed, learning rate, and other 
-# scalar values. It’s helpful to compare these metrics across different 
+# This dashboard shows how the loss and accuracy change with every epoch.
+# You can use it to also track training speed, learning rate, and other
+# scalar values. It’s helpful to compare these metrics across different
 # training runs to improve your model.
 #
 
 
 ########################################################################
 # Learn More
 # ----------------------------
-# 
+#
 # -  `torch.utils.tensorboard <https://pytorch.org/docs/stable/tensorboard.html>`_ docs
 # -  `Visualizing models, data, and training with TensorBoard <https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html>`_ tutorial
 #