Update: Our team previously had a tradition of naming projects with terms or characters from the Harry Potter series, but we are disappointed by J.K. Rowling's persistent transphobic comments. In response, we have renamed the creevey library "wildebeest." There are endless resources for someone who wants to learn to train a deep learning model, but running a successful deep learning project requires managing many additional moving parts that are much less discussed. This talk contributes to filling that gap in our deep learning education resources. Deep learning projects require managing large datasets, heavy-duty dependencies, complex experiments, and large amounts of code.

There are endless resources for someone who wants to learn to train a deep learning model, but running a successful deep learning project requires managing many additional moving parts that are much less discussed. This talk contributes to filling that gap in our deep learning education resources. Deep learning projects require managing large datasets, heavy-duty dependencies, complex experiments, and large amounts of code. This talk provides best practices for accomplishing these tasks efficiently and reproducibly. Tools that are covered include the Creevey library for processing large collections of files; pip-tools and nvidia-docker for managing dependencies; and MLflow Tracking for tracking experiments.