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Today, there is a rising interest to introduce this flexible modeling to solve real-world problems. A major challenge when moving from research to application is the strict constraints on computational resources<jats:italic>(memory and time)<\/jats:italic>. It is difficult to determine and contain the resource requirements of differential models, especially during the early training and hyperparameter exploration stages. In this article, we address this challenge by introducing<jats:sc>C<\/jats:sc>alc<jats:sc>G<\/jats:sc>raph, a model abstraction of differentiable programming layers.<jats:sc>C<\/jats:sc>alc<jats:sc>G<\/jats:sc>raph allows to model the computational resources that should be used and then<jats:sc>C<\/jats:sc>alc<jats:sc>G<\/jats:sc>raph\u2019s model interpreter can automatically schedule the execution respecting the specifications made. We propose a novel way to efficiently switch models from storage to preallocated memory zones and vice versa to maximize the number of model executions given the available resources. 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