Statements (74)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Library
|
| gptkbp:developed_by |
gptkb:Intel_Corporation
|
| gptkbp:enhances |
training speed
inference performance |
| https://www.w3.org/2000/01/rdf-schema#label |
Intel DNN Library
|
| gptkbp:includes |
gptkb:Performance_Monitoring
pre-trained models |
| gptkbp:integrates_with |
gptkb:Intel_MKL-DNN
gptkb:Intel_one_API |
| gptkbp:is_compatible_with |
gptkb:Tensor_Flow
gptkb:Py_Torch |
| gptkbp:is_optimized_for |
gptkb:Intel_architecture
|
| gptkbp:offers |
multi-threading capabilities
|
| gptkbp:provides |
API for developers
case studies community support debugging tools networking capabilities security features version control performance benchmarks sample applications user forums performance improvements data parallelism documentation and tutorials memory optimization techniques model optimization tools training datasets custom layer support cloud integration tools performance tuning guidelines |
| gptkbp:released_in |
gptkb:2018
|
| gptkbp:supports |
API versioning
data visualization tools user-defined functions various operating systems edge computing quantization data augmentation transfer learning deep learning frameworks batch processing cloud deployment distributed training real-time inference ONNX format data science workflows data privacy features model compression mixed precision training dynamic neural networks CPU and GPU acceleration neural network layers |
| gptkbp:used_for |
gptkb:vehicles
gptkb:robotics financial modeling natural language processing smart cities speech recognition transportation systems image classification video analytics healthcare applications advertising technology social media analytics e-commerce applications telecommunications applications gaming AI |
| gptkbp:used_in |
research and development
machine learning applications |
| gptkbp:written_in |
gptkb:C++
|
| gptkbp:bfsParent |
gptkb:Intel_Performance_Libraries
|
| gptkbp:bfsLayer |
6
|