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Recommend using latest (#7233)

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Add a sentence to DeepCompile blog to recommend using the latest
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Signed-off-by: Masahiro Tanaka <mtanaka@microsoft.com>
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Masahiro Tanaka
2025-04-18 09:35:49 -07:00
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@ -17,7 +17,7 @@ Existing distributed training frameworks such as DeepSpeed and FSDP have made la
DeepCompile addresses this gap by enabling compiler-level optimizations for distributed training. It takes a standard single-GPU model implementation and transforms it into an optimized multi-GPU training graph without requiring changes to the model code. Unlike existing approaches, DeepCompile automatically applies parameter sharding, communication scheduling, and memory-aware execution at the compiler IR level, enabling global analysis and optimization that are difficult to express in traditional frameworks. Furthermore, during training, DeepCompile employs profile-guided optimization techniques to dynamically tune these parallelization strategies and improve training performance.
Our evaluation demonstrates that DeepCompile improves training performance over ZeRO-3 baselines, achieving up to 1.5x speedup when sufficient GPU resources are available, and up to 7x speedup in GPU-constrained settings that require offloading. DeepCompile is available in DeepSpeed versions >= [0.16.6](https://github.com/deepspeedai/DeepSpeed/releases/tag/v0.16.6).
Our evaluation demonstrates that DeepCompile improves training performance over ZeRO-3 baselines, achieving up to 1.5x speedup when sufficient GPU resources are available, and up to 7x speedup in GPU-constrained settings that require offloading. DeepCompile is available in DeepSpeed versions >= [0.16.6](https://github.com/deepspeedai/DeepSpeed/releases/tag/v0.16.6). As it is under active development, we recommend using the latest version of DeepSpeed or installing from source to access the most recent updates and bug fixes.
# Design Overview