DSC is a PyTorch-compatible tensor library and inference framework for machine learning models. It features a C-compatible low-level API that is wrapped in a modern Python API very similar to NumPy / PyTorch but with some nice usability improvements.
Some key features of DSC include:
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Intuitive API: DSC Python API closely resembles NumPy / PyTorch.
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Built-in neural networks support: DSC comes with
nn.Modulebuilt-in. Porting a model from PyTorch to DSC is trivial (check out the examples). -
Multiple backends: DSC supports both CPU and CUDA with other backends being worked on. Programs written using DSC can seamlessly switch between backends by simply adding a
dsc.set_default_device('...')instruction, no changes needed. -
Minimal external dependencies: DSC doesn't require external libraries to be efficient. On CPU the core operations are written from scratch in portable C++, this makes code written using DSC extremely portable.
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No runtime allocations: DSC has it's own custom memory allocator, memory is pre-allocated only once so no extra calls to
malloc()orfree()are required. It's also possible to switch to a linear allocator to remove the (minimal) overhead introduced by a general purpose allocator.
Getting started with DSC is very simple. The only requirements are:
- A compiler with good support for C++20
- GNU Make for building
On a Linux-based system these can be obtained with:
sudo apt update
sudo apt install build-essentialThe recommended way to install DSC is from source:
git clone git@github.com:nirw4nna/dsc.git
cd dsc/
python3 -m venv venv
source venv/bin/activate
python3 -m pip install -e .To build the C++ library:
make clean; make shared DSC_FAST=1This will compile DSC without any debug information, you can specify different options to enable/disable specific features:
| Option | Description |
|---|---|
| DSC_LOG_LEVEL | Configure the logging level (values: [0-3] with 0 meaning everything on) |
| DSC_FAST | Turn off logging (level=2) and compile with the highest optimisation level |
| DSC_CUDA | Enable CUDA backend |
| DSC_MAX_OBJS | Max number of DSC tensors that can be used at the same time (default=1K) |
To verify that everything worked out as expected try a simple operation:
python3 -c "import dsc; x = dsc.arange(10); print(x)"This provides GPU acceleration on NVIDIA GPUs. To get started make sure to have the CUDA Toolkit installed.
To build the C++ library with CUDA enabled:
make clean; make shared DSC_FAST=1 DSC_CUDA=1Note: if you see errors when compiling with CUDA support make sure that the CUDA installation path is /usr/local/cuda
(default on Ubuntu).
If that is not the case you have to manually update the Makefile or set the CUDA environment variable before calling make.
To verify that the CUDA backend is working try a simple GPU operation:
python3 -c "import dsc; x = dsc.arange(10, device='cuda'); print(x)"DSC uses pytest to run unit tests against NumPy which is the reference for correctness.
To run all the tests simple do:
cd python/tests/
pytest -s test_ops.py --no-header --no-summary -qNote: there are quite a few tests so to run them it's better to compile DSC with DSC_FAST=1.
BSD-3-Clause