A mini Tensor framework for tensor operations on GPU.
Project description
DeepOps
A Mini Deep learning library, accelerated on GPUs with PyCuda.
No no no.. I havent wrote this library on tensorflow or torch, it is a standalone machine. :P
Implemented backpropogations using reverse traversal, Support gradients and GPU operations, You can make a mini neural network (FNN) and use the in-house optimizer to train it on a dataset (e.g MNIST).
Tip: always give your tensor a funny name! :)
Note: Only for Educational Usage.
Installation.
pip install deepops
Tensor.
a = Tensor([1,2,3,4,5])
# deepop tensor
Attach to a cuda device.
a = Tensor([1,2,3,4,5])
a.device("gpu:0") # attach to gpu device.
Check the Device.
a.where
# 'cpu'
Addition.
a = Tensor([1.0,2.0])
print(a + a)
# GPU Operation
Multiplication.
a = Tensor([1.0, 2.0])
print(a.mul(a))
print(a * a)
Calculate Gradients.
Tensor = dp.Tensor
a1 = Tensor([1.0, 3.0, 1.0])
b1 = Tensor([7.0, 3.0, 5.0])
a2 = Tensor([4.0, 3.0, 1.0])
a3 = Tensor([3.0, 3.0, 1.0])
a4 = Tensor([7.0, 1.0, 6.0])
b2 = Tensor([1.0, 21.0, 12.0])
c = a1 * b1 + a3
d = a2 * b2 + a4
out = c * d
# backward
out.backward()
print(out.grad)
print(a1.grad)
Run Tests.
python -m pytest -s
Contribution is highly appreciated.
Please contribute to my work.
TODOs
- write more tests...
- need a optimizer.
- support more operations.
License
MIT
