Spiking Neural Networks (SNNs) is a promising paradigm for efficient event-driven processing of spatio-temporally sparse data streams. Spiking Neural Networks (SNNs) have inspired the design of and can take advantage of the emerging class of neuromorphic processors like Intel Loihi. These novel hardware architectures expose a variety of constraints that affect firmware, compiler, and algorithm development alike. To enable rapid and flexible development of SNN algorithms on Loihi, we developed NxTF: a programming interface derived from Keras and compiler optimized for mapping deep convolutional SNNs to the multi-core Intel Loihi architecture. We evaluate NxTF on Deep Neural Networks (DNNs) trained directly on spikes as well as models converted from traditional DNNs, processing both sparse event-based and dense frame-based datasets. Further, we assess the effectiveness of the compiler to distribute models across a large number of cores and to compress models by exploiting Loihi’s weight-sharing features. Finally, we evaluate model accuracy, energy, and time-to-solution compared to other architectures. The compiler achieves near-optimal resource utilization of 80% across 16 Loihi chips for a 28-layer, 4M parameter MobileNet model with input size 128×128. In addition, we report the lowest error rate of 8.52% for the CIFAR-10 dataset on neuromorphic hardware, using an off-the-shelf MobileNet.