[Tutorial] Fix formatting, grammar, dead link (apache#9281)

* tutorial: preprocess.py: Fix leading whitespace This fixes the indentation of metadata in `preprocess.py` in the TVMC tutorial, removing the leading whitespaces in the HTML rendering[^1]. [^1] https://tvm.apache.org/docs/tutorial/tvmc_command_line_driver.html#preprocess-py * tutorial: Add missing code block escapes * tutorial: Grammar fixup * README.md: Fix link to introduction Co-authored-by: Martin Kröning <[email protected]>
Lucien0 · Oct 23, 2021 · bb5e653 · bb5e653
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Showing 4 changed files with 13 additions and 10 deletions.
diff --git a/README.md b/README.md
@@ -36,7 +36,7 @@ TVM is licensed under the [Apache-2.0](LICENSE) license.
 Getting Started
 ---------------
 Check out the [TVM Documentation](https://tvm.apache.org/docs/) site for installation instructions, tutorials, examples, and more.
-The [Getting Started with TVM](https://tvm.apache.org/docs/tutorials/get_started/introduction.html) tutorial is a great
+The [Getting Started with TVM](https://tvm.apache.org/docs/tutorial/introduction.html) tutorial is a great
 place to start.
 
 Contribute to TVM

diff --git a/gallery/tutorial/autotvm_relay_x86.py b/gallery/tutorial/autotvm_relay_x86.py
@@ -106,7 +106,7 @@
 # TVMC has adopted NumPy's ``.npz`` format for both input and output data.
 #
 # As input for this tutorial, we will use the image of a cat, but you can feel
-# free to substitute image for any of your choosing.
+# free to substitute this image for any of your choosing.
 #
 # .. image:: https://s3.amazonaws.com/model-server/inputs/kitten.jpg
 #    :height: 224px
@@ -278,6 +278,7 @@
 from tvm.autotvm.tuner import XGBTuner
 from tvm import autotvm
 
+################################################################################
 # Set up some basic parameters for the runner. The runner takes compiled code
 # that is generated with a specific set of parameters and measures the
 # performance of it. ``number`` specifies the number of different
@@ -303,6 +304,7 @@
     enable_cpu_cache_flush=True,
 )
 
+################################################################################
 # Create a simple structure for holding tuning options. We use an XGBoost
 # algorithim for guiding the search. For a production job, you will want to set
 # the number of trials to be larger than the value of 10 used here. For CPU we
@@ -426,6 +428,7 @@
 for rank in ranks[0:5]:
     print("class='%s' with probability=%f" % (labels[rank], scores[rank]))
 
+################################################################################
 # Verifying that the predictions are the same:
 #
 # .. code-block:: bash

diff --git a/gallery/tutorial/tensor_expr_get_started.py b/gallery/tutorial/tensor_expr_get_started.py
@@ -133,7 +133,7 @@
 
 ################################################################################
 # Let's run the function, and compare the output to the same computation in
-# numpy. The compiled TVM function is exposes a concise C API that can be invoked
+# numpy. The compiled TVM function exposes a concise C API that can be invoked
 # from any language. We begin by creating a device, which is a device (CPU in this
 # example) that TVM can compile the schedule to. In this case the device is an
 # LLVM CPU target. We can then initialize the tensors in our device and
@@ -258,8 +258,8 @@ def evaluate_addition(func, target, optimization, log):
 print(tvm.lower(s, [A, B, C], simple_mode=True))
 
 ################################################################################
-# Comparing the Diferent Schedules
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Comparing the Different Schedules
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 # We can now compare the different schedules
 
 baseline = log[0][1]
@@ -347,7 +347,7 @@ def evaluate_addition(func, target, optimization, log):
     fadd = tvm.build(s, [A, B, C], target=tgt_gpu, name="myadd")
 
     ################################################################################
-    # The compiled TVM function is exposes a concise C API that can be invoked from
+    # The compiled TVM function exposes a concise C API that can be invoked from
     # any language.
     #
     # We provide a minimal array API in python to aid quick testing and prototyping.

diff --git a/gallery/tutorial/tvmc_command_line_driver.py b/gallery/tutorial/tvmc_command_line_driver.py
@@ -174,10 +174,10 @@
 # data types. For this reason, most models require some pre and
 # post-processing, to ensure the input is valid and to interpret the output.
 # TVMC has adopted NumPy's ``.npz`` format for both input and output data. This
-# is a well-supported NumPy format to serialize multiple arrays into a file
+# is a well-supported NumPy format to serialize multiple arrays into a file.
 #
 # As input for this tutorial, we will use the image of a cat, but you can feel
-# free to substitute image for any of your choosing.
+# free to substitute this image for any of your choosing.
 #
 # .. image:: https://s3.amazonaws.com/model-server/inputs/kitten.jpg
 #    :height: 224px
@@ -197,8 +197,8 @@
 # requirement for the script.
 #
 # .. code-block:: python
-#    :caption: preprocess.py
-#    :name: preprocess.py
+#     :caption: preprocess.py
+#     :name: preprocess.py
 #
 #     #!python ./preprocess.py
 #     from tvm.contrib.download import download_testdata