[documentation] swapped the order of pytriton and triton-c tutorial in README.md
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@@ -29,8 +29,8 @@ python dot.py
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## Tutorials
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## Tutorials
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- [The Triton-C language](https://github.com/ptillet/triton/blob/master/docs/triton-c.md)
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- [The PyTriton API](https://github.com/ptillet/triton/blob/master/docs/pytriton.md)
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- [The PyTriton API](https://github.com/ptillet/triton/blob/master/docs/pytriton.md)
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- [The Triton-C language](https://github.com/ptillet/triton/blob/master/docs/triton-c.md)
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- The Triton-IR representation (coming soon...)
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- The Triton-IR representation (coming soon...)
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- The Triton-JIT compiler (coming soon...)
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- The Triton-JIT compiler (coming soon...)
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@@ -3,8 +3,6 @@
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## <span style="color:darkred"> Table of Contents </span>
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## <span style="color:darkred"> Table of Contents </span>
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This tutorial is the continuation of the [Triton-C tutorial](https://github.com/ptillet/triton/blob/master/docs/triton-c.md), so check it out if you have not already!
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1. [Motivations](#motivations)
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1. [Motivations](#motivations)
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2. [Triton Functions](#pytriton-function)
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2. [Triton Functions](#pytriton-function)
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1. [Creation of Triton Kernels](#creation-triton-kernels)
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1. [Creation of Triton Kernels](#creation-triton-kernels)
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@@ -232,4 +230,4 @@ Still like for PyTorch, a callable operation can be created using the `apply` me
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```python
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```python
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dot = _dot.apply
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dot = _dot.apply
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```
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```
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And that's it! Our custom op is now created and ready to be used with both PyTorch and Tensorflow.
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And that's it! Our custom op is now created and ready to be used with both PyTorch and Tensorflow.
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