Deep learning technologies, particularly deep neural networks (DNNs), have demonstrated significant success across many domains. This success has been accompanied by substantial advancements and innovations in the algorithms behind the operations required by DNNs. These enhanced algorithms hold the potential to greatly increase the performance of DNNs. However, discovering the best performing algorithm for a DNN and altering the DNN to use such algorithm is a difficult and time consuming task. To address this, we introduce an open source framework which provides easy to use fine grain algorithmic control for DNNs, enabling algorithmic exploration and selection. Along with built-in high performance implementations of common deep learning operations, the framework enables users to implement and select their own algorithms to be utilized by the DNN. The framework's built-in accelerated implementations are shown to yield outputs equivalent to and exhibit similar performance as implementations in PyTorch, a popular DNN framework. Moreover, the framework incurs no additional performance overhead, meaning that performance depends solely on the algorithms chosen by the user.