Fixed some links in documentation

hpvm/docs/getting-started.rst

@@ -63,8 +63,8 @@ Create a DNN `module` and load the checkpoint:
 
 Any `torch.nn.Module` can be similarly used,
 as long as they only contain the tensor operators supported in HPVM.
-See "Supported Operators" in :doc:`PyTorch frontend <components/torch2hpvm>`
-and :doc:`Keras frontend <components/keras-frontend>`.
+See "Supported Operators" in :doc:`PyTorch frontend </components/torch2hpvm>`
+and :doc:`Keras frontend </components/keras-frontend>`.
 
 Now we are ready to export the model. The main functioning class of `torch2hpvm` is `ModelExporter`:
 
@@ -183,7 +183,7 @@ After the tuning finishes, the tuner will
 * save the HPVM config format (write-only) at ``./hpvm_confs.txt``.
 
 It is also possible to save the configuration in other formats
-(see the :doc:`predtuner documentation <components/predtuner>`).
+(see the :doc:`predtuner documentation </components/predtuner>`).
 
 Profiling the Configurations
 ----------------------------
@@ -228,4 +228,4 @@ An example of ``configs_profiled.png`` looks like this (proportion of your image
 
 This concludes the whole workflow of HPVM.
 For more detailed usages, check out the documentation of each component listed
-:doc:`here <components/index>`.
+:doc:`here </components/index>`.

hpvm/docs/install.rst

@@ -161,4 +161,4 @@ You can run tests similarly as how ``approxhpvm.py`` is compiled: for example,
 
    make -j<number of threads> check-hpvm-pass
 
-runs ``check-hpvm-pass`` tests. See TODO for details on benchmarks and test cases.
+runs ``check-hpvm-pass`` tests. See :doc:`/tests` for details on benchmarks and test cases.

predtuner @ a149e365

-Subproject commit aa28a41ca12b15af31e1d15b687367e27cde3878
+Subproject commit a149e365170263666db764664ad8ed6b03f258d3

hpvm/test/README.rst

@@ -120,9 +120,3 @@ Currently, there are 20 of them. These are:
 
 ``_cudnn`` suffix indicates the code is generated onto cuDNN functions.
 Otherwise they are generated to ``tensor_runtime`` DNN functions which are hand-written in CUDA.
-
-TODO: figure out how to
-^^^^^^^^^^^^^^^^^^^^^^^
-
-
-#. Auto run Keras and PyTorch tests (generating, compiling and running all DNNs)