Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine)

After almost a year of Cloud ML Engine release, Google has finally announced the use of Cloud TPU for faster training and running of machine learning models on Cloud ML Engine. This beta release allow customers of Cloud ML and Google Cloud Platform to use the revolutionary TPUs and accelerate the TensorFlow based machine learning models.

Key features of Cloud TPU:

• High-level Performance - Each Cloud TPU offers a potential of up to 180 teraflops of computing performance and 64 gigabytes of ultra-high bandwidth memory.
• Availability of Reference Models - Solve challenges faced in image classification and object detection applications on Cloud TPUs with access to models like RetinaNet and ResNet 50.
• Access to Custom Machine Types - Get an an advantage of balancing processor speeds, memory, storage resources by connecting to Cloud TPU from various custom Virtual Machine types.

Key Benefits:

• Speed Up Machine Learning Workloads - The newly innovated Cloud TPUs are designed to help in accelerating machine learning workloads with TensorFlow. Each of the Cloud TPU are buckled up with 180 teraflops of computational power for the cutting-edge machine learning models. Such large amounts of processing speed can help you create the next research breakthrough across Machine Learning and AI technology.
• On-Demand Machine Learning Supercomputing - You can access to powerful and high-performance machine learning accelerators on demand with absolute zero capital investment.
• Easy Ramping on Google Cloud - Knowing that TensorFlow is open-source, you can simply push your machine learning workloads of TensorFlow on Cloud TPUs.You can use TensorFlow high-level APIs and move your machine learning models to CPUs, GPUs, and TPUs with few line of codes. The Cloud TPU also offers models and training environment which can easily suffice your image classification and machine translation needs.

Read more about Cloud TPU features at the official  CLOUD TPU page.

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