Originally published at: https://developer.nvidia.com/blog/deploying-diverse-ai-model-categories-from-public-model-zoo-using-nvidia-triton-inference-server/
Nowadays, a huge number of implementations of state-of-the-art (SOTA) models and modeling solutions are present for different frameworks like TensorFlow, ONNX, PyTorch, Keras, MXNet, and so on. These models can be used for out-of-the-box inference if you are interested in categories already in the datasets, or they can be embedded to custom business scenarios with…
We hope you find this post a helpful starting point for AI model inference. If you have any questions or comments, let us know
Great blog post! I wanted to get the example code, but the link in the post appears to be broken. How can I get the code?
Thank you for the feedback. The code should be accessible now; please check it again.
@encouver, I just confirmed that the link to Arslan’s code is still working as listed in the post. Could you try some of the other troubleshooting ideas and let me know what you find out? Thanks!