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After you have installed tinygrad, try the [MNIST tutorial](mnist.md)
We also have [developer docs](developer/developer.md), and Di Zhu has created a [bunch of tutorials](https://mesozoic-egg.github.io/tinygrad-notes/) to help understand how tinygrad works. Additionally, if you are new to tensor libraries, learn how they work by solving puzzles from [tinygrad-tensor-puzzles](https://github.com/obadakhalili/tinygrad-tensor-puzzles), adapted by Obada Khalili from [Sasha's Tensor Puzzles](https://github.com/srush/Tensor-Puzzles).
We also have [developer docs](developer/developer.md), and Di Zhu has created a [bunch of tutorials](https://mesozoic-egg.github.io/tinygrad-notes/) to help understand how tinygrad works. Additionally, if you are new to tensor libraries, learn how to use them by solving puzzles from [tinygrad-tensor-puzzles](https://github.com/obadakhalili/tinygrad-tensor-puzzles), adapted by Obada Khalili from [Sasha's Tensor Puzzles](https://github.com/srush/Tensor-Puzzles).
## tinygrad Usage
The main class you will interact with is [Tensor](tensor/index.md). It functions very similarly to PyTorch, but has a bit more of a functional style. tinygrad supports [many datatypes](dtypes.md). All operations in tinygrad are lazy, meaning they won't do anything until you realize.
The main class you will interact with is [Tensor](tensor/index.md). It functions very similarly to PyTorch, but has a bit more of a functional style. tinygrad supports [many datatypes](dtypes.md). All operations in tinygrad are lazy, meaning they won't do anything until you realize.
* tinygrad has a built in [neural network library](nn.md) with some classes, optimizers, and load/save state management.
* tinygrad has a JIT to make things fast. Decorate your pure function with `TinyJit`
* tinygrad has amazing support for multiple GPUs, allowing you to shard your Tensors with `Tensor.shard`
- tinygrad has a built in [neural network library](nn.md) with some classes, optimizers, and load/save state management.
- tinygrad has a JIT to make things fast. Decorate your pure function with `TinyJit`
- tinygrad has amazing support for multiple GPUs, allowing you to shard your Tensors with `Tensor.shard`
To understand what training looks like in tinygrad, you should read `beautiful_mnist.py`