- Overview
- Document Understanding Process
- Quickstart Tutorials
- Framework Components
- ML Packages
- About ML Packages
- Hardware Requirements
- OCR Configuration
- Pipelines
- Document Manager
- OCR Services
- Document Understanding deployed in Automation Suite
- Document Understanding deployed in AI Center standalone
- Deep Learning
- Licensing
- References
- UiPath.Abbyy.Activities
- UiPath.AbbyyEmbedded.Activities
- UiPath.DocumentUnderstanding.ML.Activities
- UiPath.DocumentUnderstanding.OCR.LocalServer.Activities
- UiPath.IntelligentOCR.Activities
- UiPath.OCR.Activities
- UiPath.OCR.Contracts
- UiPath.DocumentProcessing.Contracts
- UiPath.OmniPage.Activities
- UiPath.PDF.Activities
Hardware Requirements
Running the Document Understanding ML Packages on a GPU includes an optimization meant to accelerate the training process.
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 version |
CUDA version |
cudDNN version |
NVIDIA driver (lowest compatible version) |
Hardware Generation |
|---|---|---|---|---|
|
2022.4 |
CUDA 11.3 |
cuDNN 8.2.0 |
R450.80.02 |
Ampere, 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
