Quick Links¶
Congratulations on successful setup of the RBLN SDK! You are now ready to run your PyTorch and TensorFlow models on RBLN NPU.
Here is a comprehensive list of useful resources that can help you gain a better understanding of the RBLN SDK through examples.
Tutorials¶
We recommend that you explore the following tutorials for a better understanding of how to use the RBLN SDK:
- Basic
- PyTorch (Vision) provides instructions on how to use the TorchVision library with RBLN SDK through
ResNet50
example. - PyTorch (NLP) provides instructions on how to use PyTorch with RBLN SDK through
BERT-base
example. - TensorFlow (Vision) provides instructions on how to use TF Keras Applications library with RBLN SDK through
EfficientNet-B0
example. - TensorFlow (NLP) provides instructions on how to use TensorFlow with RBLN SDK through
BERT-base
example.
- PyTorch (Vision) provides instructions on how to use the TorchVision library with RBLN SDK through
- Advanced
- Concurrent Processing provides an explanation on how to execute the RBLN runtime asynchronously.
- LLM Serving explanins how to use the RBLN SDK with Nvidia Triton Inference Server to serve Llama2-7B.
Model Zoo¶
Check out the following resources to learn more about RBLN model zoo, which offers an in-depth review of the RBLN SDK by covering various TensorFlow and PyTorch models:
We also support HuggingFace transformers
and diffusers
models on single- and multi-device with optimum-rbln
.