AI News, Build and train machine learning models on our new Google Cloud TPUs

Build and train machine learning models on our new Google Cloud TPUs

We’re excited to announce that our second-generation Tensor Processing Units (TPUs) are coming to Google Cloud to accelerate a wide range of machine learning workloads, including both training and inference.

These breakthroughs required enormous amounts of computation, both to train the underlying machine learning models and to run those models once they’re trained (this is called “inference”).

Training a machine learning model is even more difficult than running it, and days or weeks of computation on the best available CPUs and GPUs are commonly required to reach state-of-the-art levels of accuracy.

However, this wasn’t enough to meet our machine learning needs, so we designed an entirely new machine learning system to eliminate bottlenecks and maximize overall performance.

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Build and train machine learning models on our new Google Cloud TPUs

We’re excited to announce that our second-generation Tensor Processing Units (TPUs) are coming to Google Cloud to accelerate a wide range of machine learning workloads, including both training and inference.

These breakthroughs required enormous amounts of computation, both to train the underlying machine learning models and to run those models once they’re trained (this is called “inference”).

Training a machine learning model is even more difficult than running it, and days or weeks of computation on the best available CPUs and GPUs are commonly required to reach state-of-the-art levels of accuracy.

However, this wasn’t enough to meet our machine learning needs, so we designed an entirely new machine learning system to eliminate bottlenecks and maximize overall performance.

Google brings Cloud TPUs to its Cloud Machine Learning Engine to speed AI training

On Monday, Google announced that customers can now use Cloud TPUs on the Cloud Machine Learning Engine (ML Engine) in beta to speed the training of machine learning models.

'As a managed service, ML Engine handles the infrastructure, compute resources, and job scheduling on your behalf, allowing you to focus on data and modeling,' the post said.

Google first launched Cloud ML Engine in March 2017 as a managed TensorFlow service, allowing customers to scale machine learning workloads with distributed training and GPU acceleration, the post noted.

With support for Cloud TPUs, ML Engine customers can train a number of high-performance, open-source reference models with 'differentiated performance per dollar,' the post said.

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