Fusion and astrophysical plasmas are often modeled as charged fluids. To understand their dynamical behavior, the Euler partial-differential-equations for a charged fluid can be solved as an initial value problem or as an externally driven system. However, the fluid equations do not always capture the full richness of the plasma dynamics, for example, in scenarios where microphysics governs macroscopic behavior. Here, we present ADEPT, an Automatic Differentiation Enabled Plasma Transport code written in JAX that has been tested to reproduce known physics. ADEPT provides the user with the ability to train deep models for missing microphysics that improves the solvers ability to reproduce experimental data and/or first-principles simulations. Other applications include the ability to learn improved numerical methods, to perform parameter estimation and parameter discovery [1], and to perform sensitivity analyses. The GitHub repo includes the source code, installation and testing instructions, and an ab-initio simulation generated dataset on which we have trained a microphysics model [2]. [1] - A. S. Joglekar and A. G. R. Thomas - Unsupervised Discovery of Nonlinear Plasma Physics using Differentiable Kinetic Simulations - Journal of Plasma Physics - Dec 2022 [2] - A. S. Joglekar and A. G. R. Thomas - IoP Machine Learning Science & Technology - In Preparation