SageMaker Training Compiler accelerates training jobs by converting DL models from their high-level language representation to hardware-optimized instructions that train faster than jobs with the native frameworks. More specifically, SageMaker Training Compiler uses graph-level optimization (operator fusion, memory planning, and algebraic simplification), data flow-level optimizations (layout transformation, common sub-expression elimination), and backend optimizations (memory latency hiding, loop oriented optimizations) to produce an optimized model training job that more efficiently uses hardware resources and, as a result, trains faster.