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tensorrtx reviews and mentions
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A Three-pronged Approach to Bringing ML Models Into Production
In terms of the latter, this is quite common when employing non-standard SOTA models. You may discover a variety of TensorRT implementations on the web if you want to use popular models—for example, in the project where we needed to train an object-detection algorithm on Rutorch and deploy it on Triton, we used many cases of PyTorch -> TensorRT -> Triton. The implementation of the model on TensoRT was taken from here. You may also be interested in this repository, as it contains many current implementations supported by developers.
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Dall-E 2
I'll try them out. I have an RTX 2070, which apparently supports fp16. But it only has 8GB RAM.
I used the instructions here to check: https://github.com/wang-xinyu/tensorrtx/blob/master/tutorial...
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Increasing usb cam FPS with Yolov5 on a Jetson Xavier NX?
Optimize your model using TensorRT. There is a good implementation here: https://github.com/wang-xinyu/tensorrtx/tree/master/yolov5
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A note from our sponsor - WorkOS
workos.com | 18 Apr 2024
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wang-xinyu/tensorrtx is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of tensorrtx is C++.