C++11 -> C++14

_sources/frontend.rst.txt

@@ -1,7 +1,7 @@
 The C++ Frontend
 ================
 
-The PyTorch C++ frontend is a C++11 library for CPU and GPU
+The PyTorch C++ frontend is a C++14 library for CPU and GPU
 tensor computation, with automatic differentiation and high level building
 blocks for state of the art machine learning applications.
 

_sources/installing.rst.txt

@@ -38,7 +38,7 @@ this:
 
   add_executable(example-app example-app.cpp)
   target_link_libraries(example-app "${TORCH_LIBRARIES}")
-  set_property(TARGET example-app PROPERTY CXX_STANDARD 11)
+  set_property(TARGET example-app PROPERTY CXX_STANDARD 14)
 
   # The following code block is suggested to be used on Windows.
   # According to https://github.com/pytorch/pytorch/issues/25457,

_sources/notes/tensor_basics.rst.txt

@@ -2,7 +2,7 @@ Tensor Basics
 =============
 
 The ATen tensor library backing PyTorch is a simple tensor library thats exposes
-the Tensor operations in Torch directly in C++11. ATen's API is auto-generated
+the Tensor operations in Torch directly in C++14. ATen's API is auto-generated
 from the same declarations PyTorch uses so the two APIs will track each other
 over time.
 

frontend.html

@@ -269,7 +269,7 @@
 
   <div class="section" id="the-c-frontend">
 <h1>The C++ Frontend<a class="headerlink" href="#the-c-frontend" title="Permalink to this headline">¶</a></h1>
-<p>The PyTorch C++ frontend is a C++11 library for CPU and GPU
+<p>The PyTorch C++ frontend is a C++14 library for CPU and GPU
 tensor computation, with automatic differentiation and high level building
 blocks for state of the art machine learning applications.</p>
 <div class="section" id="description">
@@ -703,4 +703,4 @@ <h2>Resources</h2>
     })
   </script>
 </body>
-</html>
+</html>

installing.html

@@ -298,7 +298,7 @@ <h2>Minimal Example<a class="headerlink" href="#minimal-example" title="Permalin
 
 <span class="nb">add_executable</span><span class="p">(</span><span class="s">example-app</span> <span class="s">example-app.cpp</span><span class="p">)</span>
 <span class="nb">target_link_libraries</span><span class="p">(</span><span class="s">example-app</span> <span class="s2">&quot;${TORCH_LIBRARIES}&quot;</span><span class="p">)</span>
-<span class="nb">set_property</span><span class="p">(</span><span class="s">TARGET</span> <span class="s">example-app</span> <span class="s">PROPERTY</span> <span class="s">CXX_STANDARD</span> <span class="s">11</span><span class="p">)</span>
+<span class="nb">set_property</span><span class="p">(</span><span class="s">TARGET</span> <span class="s">example-app</span> <span class="s">PROPERTY</span> <span class="s">CXX_STANDARD</span> <span class="s">14</span><span class="p">)</span>
 
 <span class="c"># The following code block is suggested to be used on Windows.</span>
 <span class="c"># According to https://github.com/pytorch/pytorch/issues/25457,</span>
@@ -675,4 +675,4 @@ <h2>Resources</h2>
     })
   </script>
 </body>
-</html>
+</html>

notes/tensor_basics.html

@@ -270,7 +270,7 @@
   <div class="section" id="tensor-basics">
 <h1>Tensor Basics<a class="headerlink" href="#tensor-basics" title="Permalink to this headline">¶</a></h1>
 <p>The ATen tensor library backing PyTorch is a simple tensor library thats exposes
-the Tensor operations in Torch directly in C++11. ATen’s API is auto-generated
+the Tensor operations in Torch directly in C++14. ATen’s API is auto-generated
 from the same declarations PyTorch uses so the two APIs will track each other
 over time.</p>
 <p>Tensor types are resolved dynamically, such that the API is generic and does not
@@ -679,4 +679,4 @@ <h2>Resources</h2>
     })
   </script>
 </body>
-</html>
+</html>