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GTC ON-DEMAND

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Abstract:
Until recently, deep learning on GPUs has been characterized by non-exact results. Training and inference have been approximate due to non-deterministic effects beyond pseudo-random choices such as mini-batch processing used to implement stochastic gradient descent. This non-determinism has created insurmountable challenges in traceability, debugging, experimentation, and regression testing. We'll discuss our work to eliminate non-determinism from deep learning when using TensorFlow. We'll explain how we were motivated by a need to make our processes reproducible with a primary focus on auditing and traceability in safety-critical applications. Beneficial side effects are simplified debugging, more effective experimentation, and the ability to accurately regression-test changes. Our talk summarizes discoveries and solutions in this area.
Until recently, deep learning on GPUs has been characterized by non-exact results. Training and inference have been approximate due to non-deterministic effects beyond pseudo-random choices such as mini-batch processing used to implement stochastic gradient descent. This non-determinism has created insurmountable challenges in traceability, debugging, experimentation, and regression testing. We'll discuss our work to eliminate non-determinism from deep learning when using TensorFlow. We'll explain how we were motivated by a need to make our processes reproducible with a primary focus on auditing and traceability in safety-critical applications. Beneficial side effects are simplified debugging, more effective experimentation, and the ability to accurately regression-test changes. Our talk summarizes discoveries and solutions in this area.  Back
 
Topics:
Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9911
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