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Most differentiable programming frameworks work by constructing a graph containing the control flow and [[data structures]] in the program.<ref name="flux">{{cite arxiv|last=Innes|first=Michael|last2=Saba|first2=Elliot|last3=Fischer|first3=Keno|last4=Gandhi|first4=Dhairya|last5=Rudilosso|first5=Marco Concetto|last6=Joy|first6=Neethu Mariya|last7=Karmali|first7=Tejan|last8=Pal|first8=Avik|last9=Shah|first9=Viral|date=2018-10-31|title=Fashionable Modelling with Flux|eprint=1811.01457|class=cs.PL}}</ref> Earlier attempts generally fall into two groups:
* ''' Static, [[compiled]] graph''' based approaches such as [[TensorFlow]], [[Theano (software)|Theano]], and [[MXNet]]. They tend to allow for good [[compiler optimization]] and easier scaling to large systems, but their static nature limits interactivity and the types of programs that can be created easily (e.g. those involving [[loop (computing)|loops]] or [[recursion]]), as well as making it harder for users to reason effectively about their programs.<ref name="flux" /><ref name="myia1">{{Cite web|url=https://
* '''[[Operator overloading]], dynamic graph''' based approaches such as [[PyTorch]] and [[AutoGrad (NumPy)|AutoGrad]]. Their dynamic and interactive nature lets most programs be written and reasoned about more easily. However, they lead to [[interpreter (computing)|interpreter]] overhead (particularly when composing many small operations), poorer scalability, and cannot gain benefit from compiler optimization.<ref name="myia1" /><ref name="pytorchtut" />
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