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merged 2 commits into from
Apr 10, 2025
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Add examples for CPP demo app #10022

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Add examples for CPP demo app
mergennachin Apr 9, 2025
725f2aa
Merge branch 'main' into fix_getting_started_cpp_example
mergennachin Apr 10, 2025
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8 changes: 6 additions & 2 deletions docs/source/getting-started.md
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Expand Up @@ -99,6 +99,7 @@ print("Comparing against original PyTorch module")
print(torch.allclose(output[0], eager_reference_output, rtol=1e-3, atol=1e-5))
```

For complete examples of exporting and running the model, please refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/python).

<hr/>

Expand Down Expand Up @@ -178,6 +179,7 @@ target_link_libraries(
xnnpack_backend)
```


#### Runtime APIs
Both high-level and low-level C++ APIs are provided. The low-level APIs are platform independent, do not dynamically allocate memory, and are most suitable for resource-constrained embedded systems. The high-level APIs are provided as a convenience wrapper around the lower-level APIs, and make use of dynamic memory allocation and standard library constructs to reduce verbosity.

Expand All @@ -194,8 +196,8 @@ using namespace ::executorch::extension;
Module module("/path/to/model.pte");

// Create an input tensor.
float input[1 * 3 * 256 * 256];
auto tensor = from_blob(input, {1, 3, 256, 256});
float input[1 * 3 * 224 * 224];
auto tensor = from_blob(input, {1, 3, 224, 224});

// Perform an inference.
const auto result = module.forward(tensor);
Expand All @@ -208,6 +210,8 @@ if (result.ok()) {

For more information on the C++ APIs, see [Running an ExecuTorch Model Using the Module Extension in C++](extension-module.md) and [Managing Tensor Memory in C++](extension-tensor.md).

For complete examples of building and running C++ application, please refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/cpp).

<hr/>

## Next Steps
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