UiPath Document Understanding

UiPath Document Understanding

Hardware Requirements

Running the Document Understanding ML Packages on a GPU includes an optimization meant to accelerate the training process.

Compatibility Matrix

As a result, training on GPU is five times faster than on CPU (previously it was 10-20 times faster). This also makes it possible to train models on CPU with up to 5000 pages (previously it was 500 maximum).

Please be aware that training Document Understanding models on GPU requires a GPU with at least 11GB of video RAM to run successfully.

Use the below table to check the compatibility between the ML Packages, CUDA version, and GPU driver version.

ML Packages versionCUDA versioncudDNN versionNVIDIA driver (lowest compatible version)Hardware Generation
2022.4CUDA 11.3cuDNN 8.2.0R450.36.06Ampere, Turing, Volta, Pascal, Maxwell, Kepler

CUDA is backward compatible, meaning that existing CUDA applications can continue to be used with newer CUDA versions.
More information about compatibility can be found here.

CPU and GPU Usage

You can use the Document Understanding framework for reading text using an OCR engine, classifying the documents, and extracting information from the documents. While classification and extraction tasks are run on CPU, the OCR is recommended to be run on GPU (through a CPU version is also provided in case a GPU is not available).
The On-premises deployment is done using Automation Suite and its hardware requirements.

You can use the same type of VM for both extractors and classifiers, the only difference being the infrastructure size. We recommend using the OCR engine with a GPU VM. The compatibility between the ML Packages, CUDA version, and GPU driver version are described in the Compatibility Matrix section.

Let's take an actual example for better understanding the hardware requirements.

ServiceHardware requirementCapability
ExtractorUse a VM with minimum 2 CPU cores and 8 GB RAMCan process 25,000 pages/day or 5 million pages/year, assuming perfectly constant traffic (no spikes).
ClassifierUse a VM with minimum 2 CPU cores and 8 GB RAMCan process 40,000 documents/day or 8 million documents/year, assuming perfectly constant traffic (no spikes).
OCRRecommended to be used with GPU VMCan process 50,000 pages/day.

Example : If you process 10 million pages/year, then you need a VM with 4 CPU cores, 16 GB RAM for the extractor, another one for the classifier, and a third VM with an NVidia GPU core for the OCR engine.
You can also choose to use only one VM for both extractor and classifier, meaning that you need a single VM with 8 CPU cores and 32 GB RAM.



You can always use more more powerful CPU/GPU VMs for increasing the number of processed documents/day.

Updated 2 months ago

Hardware Requirements

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