Integrate TVM into Your Project
===============================

TVM's runtime is designed to be lightweight and portable.
There are several ways you can integrate TVM into your project.
If you are looking for minimum deployment of a compiled module, take a look at [deployment guide](deploy.md)

This article introduces possible ways to integrate TVM
as a JIT compiler to generate functions on your system.


## DLPack Support

TVM's generated function follows the PackedFunc convention.
It is a function that can take positional arguments including
standard types such as float, integer, string.
The PackedFunc takes DLTensor pointer in [dlpack](https://github.com/dmlc/dlpack) convention.
So the only thing you need to solve is to create a corresponding DLTensor object.



## Integrate User Defined C++ Array

The only thing we have to do in C++ is to convert your array to DLTensor and pass in its address as
```DLTensor*``` to the generated function.


## Integrate User Defined Python Array

Assume you have a python object ```MyArray```. There are three things that you need to do

- Add ```_tvm_tcode``` field to your array which returns ```tvm.TypeCode.ARRAY_HANDLE```
- Support ```_tvm_handle``` property in your object, which returns the address of DLTensor in python integer
- Register this class by ```tvm.register_extension```

```python
# Example code
import tvm

class MyArray(object):
    _tvm_tcode = tvm.TypeCode.ARRAY_HANDLE

    @property
    def _tvm_handle(self):
        dltensor_addr = self.get_dltensor_addr()
        return dltensor_addr

# You can put registration step in a separate file mypkg.tvm.py
# and only optionally import that if you only want optional dependency.
tvm.register_extension(MyArray)
```
