If you're a jQuery user, they have something similar called Deferreds. Although promise implementations follow a standardized behaviour, their overall APIs differ. Here's how you create a promise: Do something within the callback, perhaps async, then call resolve if everything worked, otherwise call reject.
Abstract This TensorRT 5. It shows how you can take an existing model built with a deep learning framework and use that to build a TensorRT engine using the provided parsers.
Lastly, a section on every sample included in the package is also provided. It focuses specifically on running an already trained network quickly and efficiently on a GPU for the purpose of generating a result a process that is referred to in various places as scoring, detecting, regression, or inference.
Some training frameworks such as TensorFlow have integrated TensorRT so that it can be used to accelerate inference within the framework. Alternatively, TensorRT can be used as a library within a user application.
TensorRT is a high performance neural network inference optimizer and runtime engine for production deployment. TensorRT optimizes the network by combining layers and optimizing kernel selection for improved latency, throughput, custom paper size google docs efficiency and memory consumption.
If the application specifies, it will additionally optimize the network to run in lower precision, further increasing performance and reducing memory requirements. The following figure shows TensorRT defined as part high-performance inference optimizer and part runtime engine.
It can take in neural networks trained on these popular frameworks, optimize the neural network computation, generate a light-weight runtime engine which is the only thing you need to deploy to your production environmentand it will then maximize the throughput, latency, and performance on these GPU platforms.
TensorRT is a programmable inference accelerator. For more information about the layers, see TensorRT Layers. Benefits Of TensorRT After the neural network is trained, TensorRT enables the network to be compressed, optimized and deployed as a runtime without the overhead of a framework.
TensorRT combines layers, optimizes kernel selection, and also performs normalization and conversion to optimized matrix math depending on the specified precision FP32, FP16 or INT8 for improved latency, throughput, and efficiency. For deep learning inference, there are 5 critical factors that are used to measure software: Throughput The volume of output within a given period.
Efficiency is another key factor to cost effective data center scaling, since servers, server racks and entire data centers must operate within fixed power budgets. Latency Time to execute an inference, usually measured in milliseconds. Low latency is critical to delivering rapidly growing, real-time inference-based services.
For image classification based usages, the critical metric is expressed as a top-5 or top-1 percentage. Memory usage The host and device memory that need to be reserved to do inference on a network depends on the algorithms used.
This constrains what networks and what combinations of networks can run on a given inference platform. This is particularly important for systems where multiple networks are needed and memory resources are limited - such as cascading multi-class detection networks used in intelligent video analytics and multi-camera, multi-network autonomous driving systems.
Alternatives to using TensorRT include: Using the training framework itself to perform inference. Writing a custom application that is designed specifically to execute the network using low level libraries and math operations.
Using the training framework to perform inference is easy, but tends to result in much lower performance on a given GPU than would be possible with an optimized solution like TensorRT. Training frameworks tend to implement more general purpose code which stress generality and when they are optimized the optimizations tend to focus on efficient training.
Higher efficiency can be obtained by writing a custom application just to execute a neural network, however it can be quite labor intensive and require quite a bit of specialized knowledge to reach a high level of performance on a modern GPU. Furthermore, optimizations that work on one GPU may not translate fully to other GPUs in the same family and each generation of GPU may introduce new capabilities that can only be leveraged by writing new code.
TensorRT solves these problems by combining an API with a high level of abstraction from the specific hardware details and an implementation which is developed and optimized specifically for high throughput, low latency, and low device memory footprint inference.
Who Can Benefit From TensorRT TensorRT is intended for use by engineers who are responsible for building features and applications based on new or existing deep learning models or deploying models into production environments.
These deployments might be into servers in a datacenter or cloud, in an embedded device, robot or vehicle, or application software which will run on users workstations. TensorRT has been used successfully across a wide range of scenarios, including: Robots Companies sell robots using TensorRT to run various kinds of computer vision models to autonomously guide an unmanned aerial system flying in dynamic environments.Google Docs only allows you to create documents from a set of standard page sizes; it does not allow you to create a document of a non-standard page size (custom page size) of your choosing.
Fortunately, there is a workaround that will allow you to create a document of any page size. Build, innovate, and scale with Google Cloud Platform. Collaborate and be more productive with G Suite.
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Google has many special features to help you find exactly what you're looking for. Google Docs does not, as of this writing, have a custom paper size option. I could have modified the rulers on an x 11 doc, but seeing as I wanted to make a template, this could get messed up too easily by other users playing with the settings.
Creating a Word document in one paper size doesn't mean you are limited to that size paper and presentation when you print it out. Microsoft Word makes it easy to change the paper size when it's time to print.
You can make the size change for just a single printing, or you can save the new size . The Google company was officially launched in by Larry Page and Sergey Brin to market Google Search, which has become the most widely used web-based search rutadeltambor.com and Brin, students at Stanford University in California, developed a search algorithm – at first known as "BackRub" – in The search engine soon proved successful and the expanding company moved several times.