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Deep Learning
Accelerate: Democratizing Deep Learning Distributed Training
The need to scale-out model training is higher than ever; don’t worry, you don’t need to change much
The Machine Learning and Deep Learning fields have seen massive growth in the past decade. This is mainly due to hardware advances and the abundance of data that we are now able to produce and collect.
With that, the need to scale out model training to more computational resources is higher than ever. But what does that mean for you? Apart from getting or renting more devices, do you need to learn a new API or unlearn your current habits? Does distributed training require advanced software engineering skills?
Today, the need to scale out model training to more computational resources is higher than ever.
Fortunately, if you are a PyTorch user, you’re in luck; you only need to add/change four lines of code. As a matter of fact, in the end, your code will look much simpler and easier to interpret!
Learning Rate is a newsletter for those who are curious about the world of AI and MLOps. You’ll hear from me every Friday with updates and thoughts on the latest AI news and…
