@@ -51,9 +51,9 @@ We use a simple example that uses the low level TVM API directly. The example is
::
n = 1024
A = tvm.placeholder((n,), name='A')
B = tvm.placeholder((n,), name='B')
C = tvm.compute(A.shape, lambda i: A[i] + B[i], name="C")
A = tvm.te.placeholder((n,), name='A')
B = tvm.te.placeholder((n,), name='B')
C = tvm.te.compute(A.shape, lambda i: A[i] + B[i], name="C")
Here, types of ``A``, ``B``, ``C`` are ``tvm.tensor.Tensor``, defined in ``python/tvm/te/tensor.py``. The Python ``Tensor`` is backed by C++ ``Tensor``, implemented in ``include/tvm/te/tensor.h`` and ``src/te/tensor.cc``. All Python types in TVM can be thought of as a handle to the underlying C++ type with the same name. If you look at the definition of Python ``Tensor`` type below, you can see it is a subclass of ``Object``.
@@ -175,4 +176,4 @@ Server server = new Server(proxyHost, proxyPort, "key");
server.start();
```
You can also use `StandaloneServerProcessor` and `ConnectProxyServerProcessor` to build your own RPC server. Refer to [Android RPC Server](https://github.com/apache/incubator-tvm/blob/master/apps/android_rpc/app/src/main/java/org/apache/tvm/tvmrpc/RPCProcessor.java) for more details.
\ No newline at end of file
You can also use `StandaloneServerProcessor` and `ConnectProxyServerProcessor` to build your own RPC server. Refer to [Android RPC Server](https://github.com/apache/incubator-tvm/blob/master/apps/android_rpc/app/src/main/java/org/apache/tvm/tvmrpc/RPCProcessor.java) for more details.