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Meta AI Uses this Framework to Write Scalable ML
Tensor Comprehensions powers the scalable infrastructure for many machine learning solutions at Facebook.
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Machine learning projects in the real world are regularly vulnerable to the friction between data scientists and developers. That friction is mostly due to the challenges in translating a machine learning model expressed mostly in mathematical terms into code that can scale across several GPUs or CPUs. How many times have we experience the following scenario:
1. A data scientist or researcher write a machine learning algorithm using a highly mathematical notation like PyTorch(which is becoming a favorite of researchers).
