This page contains Google Cloud glossary terms. For all glossary terms, click here.
A category of specialized hardware components designed to perform key computations needed for deep learning algorithms.
Accelerator chips (or just accelerators, for short) can significantly increase the speed and efficiency of training and inference tasks compared to a general-purpose CPU. They are ideal for training neural networks and similar computationally intensive tasks.
Examples of accelerator chips include:
- Google's Tensor Processing Units (TPUs) with dedicated hardware for deep learning.
- NVIDIA's GPUs which, though initially designed for graphics processing, are designed to enable parallel processing, which can significantly increase processing speed.
Batch inference can leverage the parallelization features of accelerator chips. That is, multiple accelerators can simultaneously infer predictions on different batches of unlabeled examples, dramatically increasing the number of inferences per second.
A specialized hardware accelerator designed to speed up machine learning workloads on Google Cloud Platform.
An overloaded term with the following two possible definitions:
- A category of hardware that can run a TensorFlow session, including CPUs, GPUs, and TPUs.
- When training an ML model on accelerator chips (GPUs or TPUs), the part of the system that actually manipulates tensors and embeddings. The device runs on accelerator chips. In contrast, the host typically runs on a CPU.
- The overall flow of the code.
- The extraction and transformation of the input pipeline.
In ML parallel programming, a term associated with assigning the data and model to TPU chips, and defining how these values will be sharded or replicated.
Mesh is an overloaded term that can mean either of the following:
- A physical layout of TPU chips.
- An abstract logical construct for mapping the data and model to the TPU chips.
In either case, a mesh is specified as a shape.
A logical division of the training set or the model. Typically, some process creates shards by dividing the examples or parameters into (usually) equal-sized chunks. Each shard is then assigned to a different machine.
Tensor Processing Unit (TPU)
Abbreviation for Tensor Processing Unit.
A programmable linear algebra accelerator with on-chip high bandwidth memory that is optimized for machine learning workloads. Multiple TPU chips are deployed on a TPU device.
A printed circuit board (PCB) with multiple TPU chips, high bandwidth network interfaces, and system cooling hardware.
The central coordination process running on a host machine that sends and receives data, results, programs, performance, and system health information to the TPU workers. The TPU master also manages the setup and shutdown of TPU devices.
A specific configuration of TPU devices in a Google data center. All of the devices in a TPU Pod are connected to one another over a dedicated high-speed network. A TPU Pod is the largest configuration of TPU devices available for a specific TPU version.
A configuration of one or more TPU devices with a specific
TPU hardware version. You select a TPU type when you create
a TPU node on Google Cloud Platform. For example, a
TPU type is a single TPU v2 device with 8 cores. A
v3-2048 TPU type has 256
networked TPU v3 devices and a total of 2048 cores. TPU types are a resource
defined in the
Cloud TPU API.
A process that runs on a host machine and executes machine learning programs on TPU devices.