RepDL: Bit-level Reproducible Deep Learning Training and Inference
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912640569180160 |
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| author | Xie, Peichen Zhang, Xian Chen, Shuo |
| author_facet | Xie, Peichen Zhang, Xian Chen, Shuo |
| contents | Non-determinism and non-reproducibility present significant challenges in deep learning, leading to inconsistent results across runs and platforms. These issues stem from two origins: random number generation and floating-point computation. While randomness can be controlled through deterministic configurations, floating-point inconsistencies remain largely unresolved. To address this, we introduce RepDL, an open-source library that ensures deterministic and bitwise-reproducible deep learning training and inference across diverse computing environments. RepDL achieves this by enforcing correct rounding and order invariance in floating-point computation. The source code is available at https://github.com/microsoft/RepDL . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09180 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RepDL: Bit-level Reproducible Deep Learning Training and Inference Xie, Peichen Zhang, Xian Chen, Shuo Machine Learning Software Engineering Non-determinism and non-reproducibility present significant challenges in deep learning, leading to inconsistent results across runs and platforms. These issues stem from two origins: random number generation and floating-point computation. While randomness can be controlled through deterministic configurations, floating-point inconsistencies remain largely unresolved. To address this, we introduce RepDL, an open-source library that ensures deterministic and bitwise-reproducible deep learning training and inference across diverse computing environments. RepDL achieves this by enforcing correct rounding and order invariance in floating-point computation. The source code is available at https://github.com/microsoft/RepDL . |
| title | RepDL: Bit-level Reproducible Deep Learning Training and Inference |
| topic | Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2510.09180 |