Adam Optimizer
An adaptive learning rate gradient descent optimization algorithm that combines the advantages of momentum methods and RMSProp. It adaptively adjusts the learning rate for each parameter by estimating the first and second moments of the gradients, and is one of the most widely used optimizers in deep learning training.
Timeline (last 90 days)
During training, the Adam optimizer additionally stores gradient momentum at approximately 2–3 times the weight size, meaning the full training memory requirement for a 70B model can easily exceed 400 GB.
The training configuration uses a batch size of 4, the Adam optimizer, and a learning rate of 2e-4.
The core features planned for the project include global image alignment, fully fused forward and backward passes, the Adam optimizer, adaptive control and densification, and a stable and flexible C API.
The Adam optimizer was proposed by Kingma and Ba in 2014, achieving adaptive learning rate adjustment by maintaining exponential moving averages of the first and second moments of gradients.
Mixed-precision training with the Adam optimizer requires approximately 14 bytes per parameter, meaning full-parameter training of a 70-billion-parameter model may require over 1TB of memory.
All Facts (5)
During training, the Adam optimizer additionally stores gradient momentum at approximately 2–3 times the weight size, meaning the full training memory requirement for a 70B model can easily exceed 400 GB.
50%UnverifiedThe training configuration uses a batch size of 4, the Adam optimizer, and a learning rate of 2e-4.
50%UnverifiedThe core features planned for the project include global image alignment, fully fused forward and backward passes, the Adam optimizer, adaptive control and densification, and a stable and flexible C API.
50%UnverifiedThe Adam optimizer was proposed by Kingma and Ba in 2014, achieving adaptive learning rate adjustment by maintaining exponential moving averages of the first and second moments of gradients.
50%UnverifiedMixed-precision training with the Adam optimizer requires approximately 14 bytes per parameter, meaning full-parameter training of a 70-billion-parameter model may require over 1TB of memory.
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