In this talk, we will demo a simple machine learning accelerator deployed on a commodity FPGA and developed using a Python-based toolchain. The FPGA platform is based on an entry level Xilinx FPGA, with a total cost of materials <$200. The toolchain uses a combination of open source software, including PyTorch and ONNX for modeling, and Migen and LiteX for the construction of the System-on-chip. We will also survey the wide array of both open source and proprietary vendor tools necessary to build this project, and discuss the broader open source silicon landscape.