Deep Learning (DL) workloads have rapidly increased in popularity in enterprise clusters and several new cluster schedulers have been proposed in recent years to support these workloads. With rapidly evolving DL workloads, it is challenging to quickly prototype and compare scheduling policies across workloads. Further, as prior systems target different aspects of scheduling (resource allocation, placement, elasticity etc.), it is also challenging to combine these techniques and understand the overall benefits. To address these challenges we propose Blox, a modular toolkit which allows developers to compose individual components and realize diverse scheduling frameworks. We identify a set of core abstractions for DL scheduling, implement several existing schedulers using these abstractions, and verify the fidelity of these implementations by reproducing results from prior research. We also highlight how we can evaluate and compare existing schedulers in new settings: different workload traces, higher cluster load, change in DNN workloads and deployment characteristics. Finally, we showcase Blox's extensibility by composing policies from different schedulers, and implementing novel policies with minimal code changes. Blox is available at \url{https://github.com/msr-fiddle/blox}.

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