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May 2, 2018
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it's -> its #32

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6 changes: 3 additions & 3 deletions _posts/2018-05-02-road-to-1.0.md
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Expand Up @@ -11,9 +11,9 @@ We would like to give you a preview of the roadmap for PyTorch 1.0 , the next re

However, 1.0 isn't just about stability of the interface.

One of PyTorch's biggest strengths is it's first-class Python integration, it's imperative style, it's simplicity in API and options. These are aspects that make PyTorch good for research and hackability.
One of PyTorch's biggest strengths is its first-class Python integration, imperative style, simplicity of the API and options. These are aspects that make PyTorch good for research and hackability.

One of it's biggest downsides has been production-support. What we mean by production-support is the countless things one has to do to models to run them efficiently at massive scale:
One of its biggest downsides has been production-support. What we mean by production-support is the countless things one has to do to models to run them efficiently at massive scale:

- exporting to C++-only runtimes for use in larger projects
- optimizing mobile systems on iPhone, Android, Qualcomm and other systems
Expand All @@ -35,7 +35,7 @@ To pull this off, we are introducing `torch.jit`, a just-in-time (JIT) compiler

> **In 1.0, your code continues to work as-is, we're not making any big changes to the existing API.**

Making your model production-ready is an opt-in annotation, which uses the `torch.jit` compiler to export your model to a Python-less environment, and improving it's performance. Let's walk through the JIT compiler in detail.
Making your model production-ready is an opt-in annotation, which uses the `torch.jit` compiler to export your model to a Python-less environment, and improving its performance. Let's walk through the JIT compiler in detail.

## `torch.jit`: A JIT-compiler for your models

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