mirror of https://github.com/commaai/tinygrad.git
98 lines
2.2 KiB
Markdown
98 lines
2.2 KiB
Markdown
<p align="center">
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<img src="https://raw.githubusercontent.com/geohot/tinygrad/master/docs/logo.png">
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</p>
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--------------------------------------------------------------------
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For something in between a [pytorch](https://github.com/pytorch/pytorch) and a [karpathy/micrograd](https://github.com/karpathy/micrograd)
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This may not be the best deep learning framework, but it is a deep learning framework.
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The Tensor class is a wrapper around a numpy array, except it does Tensor things.
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### Installation
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```bash
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pip3 install tinygrad
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```
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### Example
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```python
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from tinygrad.tensor import Tensor
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x = Tensor.eye(3)
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y = Tensor([[2.0,0,-2.0]])
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z = y.matmul(x).sum()
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z.backward()
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print(x.grad) # dz/dx
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print(y.grad) # dz/dy
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```
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### Same example in torch
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```python
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import torch
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x = torch.eye(3, requires_grad=True)
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y = torch.tensor([[2.0,0,-2.0]], requires_grad=True)
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z = y.matmul(x).sum()
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z.backward()
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print(x.grad) # dz/dx
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print(y.grad) # dz/dy
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```
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### Neural networks?
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It turns out, a decent autograd tensor library is 90% of what you need for neural networks. Add an optimizer (SGD, RMSprop, and Adam implemented) from tinygrad.optim, write some boilerplate minibatching code, and you have all you need.
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### Neural network example (from test/test_mnist.py)
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```python
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from tinygrad.tensor import Tensor
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import tinygrad.optim as optim
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from tinygrad.utils import layer_init_uniform
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class TinyBobNet:
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def __init__(self):
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self.l1 = Tensor(layer_init_uniform(784, 128))
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self.l2 = Tensor(layer_init_uniform(128, 10))
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def forward(self, x):
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return x.dot(self.l1).relu().dot(self.l2).logsoftmax()
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model = TinyBobNet()
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optim = optim.SGD([model.l1, model.l2], lr=0.001)
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# ... and complete like pytorch, with (x,y) data
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out = model.forward(x)
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loss = out.mul(y).mean()
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loss.backward()
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optim.step()
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```
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### The promise of small
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tinygrad, with tests, will always be below 1000 lines. If it isn't, we will revert commits until tinygrad becomes smaller.
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### Running tests
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```bash
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python -m pytest
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```
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### TODO
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* Train an EfficientNet
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* EfficientNet backward pass
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* Tensors on GPU (GPU support, must support Mac)
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* Reduce code
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* Increase speed
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* Add features
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