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.
• Advanced
  • Concurrent Processing provides an explanation on how to execute the RBLN Runtime asynchronously.
• Model Serving
  • Resnet50(Triton Inference Server) explains how to use the RBLN SDK with Nvidia Triton Inference Server.
  • Llama2-7B with Continuous Batching explains how to serve LLM efficiently using vLLM.

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:

• Model Zoo > PyTorch
• Model Zoo > TensorFlow

We also support HuggingFace transformers and diffusers models on single- and multi-device with optimum-rbln.

• HuggingFace Model Support > optimum-rbln