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# Prepare nnpackage
## Convert tensorflow pb file to nnpackage
Follow the [compiler guide](https://github.sec.samsung.net/STAR/nnfw/blob/master/docs/nncc/Release_2019/tutorial.md) to generate nnpackge from tensorflow pb file
## Convert tflite file to nnpackage
Please see [model2nnpkg](https://github.sec.samsung.net/STAR/nnfw/tree/master/tools/nnpackage_tool/model2nnpkg) for converting from tflite model file.
# Build app with nnfw API
Here are basic steps to build app with [nnfw C API](https://github.sec.samsung.net/STAR/nnfw/blob/master/runtime/neurun/api/include/nnfw.h)
1) Initialize nnfw_session
``` c
nnfw_session *session = nullptr;
nnfw_create_session(&session);
```
2) Load nnpackage
``` c
nnfw_load_model_from_file(session, nnpackage_path);
```
3) (Optional) Assign a specific backend to operations
``` c
// Use acl_neon backend for CONV_2D and acl_cl for otherwise.
// Note that defalut backend is acl_cl
nnfw_set_op_backend(session, "CONV_2D", "acl_neon");
```
4) Compilation
``` c
// Compile model
nnfw_prepare(session);
```
5) Prepare Input/Output
``` c
// Prepare input. Here we just allocate dummy input arrays.
std::vector<float> input;
nnfw_tensorinfo ti;
nnfw_input_tensorinfo(session, 0, &ti); // get first input's info
uint32_t input_elements = num_elems(&ti);
input.resize(input_elements);
// TODO: Please add initialization for your input.
nnfw_set_input(session, 0, ti.dtype, input.data(), sizeof(float) * input_elements);
// Prepare output
std::vector<float> output;
nnfw_output_tensorinfo(session, 0, &ti); // get first output's info
uint32_t output_elements = num_elems(&ti);
output.resize(output_elements);
nnfw_set_output(session, 0, ti.dtype, output.data(), sizeof(float) * output_elements);
```
6) Inference
``` c
// Do inference
nnfw_run(session);
```
## Run Inference with app on the target devices
reference app : [minimal app](https://github.sec.samsung.net/STAR/nnfw/blob/master/runtime/neurun/sample/minimal)
```
$ ./minimal path_to_nnpackage_directory
```
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