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Torch2trt Alternatives
Similar projects and alternatives to torch2trt
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Pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
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onnxruntime
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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nn
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
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TensorRT
PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT (by pytorch)
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functorch
functorch is JAX-like composable function transforms for PyTorch.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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tensorrt_demos
TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet
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transformer-deploy
Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better torch2trt alternative or higher similarity.
torch2trt reviews and mentions
Posts with mentions or reviews of torch2trt.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-10-27.
- [D] How you deploy your ML model?
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PyTorch 1.10
Main thing you want for server inference is auto batching. It's a feature that's included in onnxruntime, torchserve, nvidia triton inference server and ray serve.
If you have a lot of preprocessing and post logic in your model it can be hard to export it for onnxruntime or triton so I usually recommend starting with Ray Serve (https://docs.ray.io/en/latest/serve/index.html) and using an actor that runs inference with a quantized model or optimized with tensorrt (https://github.com/NVIDIA-AI-IOT/torch2trt)
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Jetson Nano: TensorFlow model. Possibly I should use PyTorch instead?
https://github.com/NVIDIA-AI-IOT/torch2trt <- pretty straightforward https://github.com/jkjung-avt/tensorrt_demos <- this helped me a lot
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How to get TensorFlow model to run on Jetson Nano?
I find Pytorch easier to work with generally. Nvidia has a Pytorch --> TensorRT converter which yields some significant speedups and has a simple Python API. Convert the Pytorch model on the Nano.
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A note from our sponsor - WorkOS
workos.com | 18 Apr 2024
Stats
Basic torch2trt repo stats
5
4,376
3.1
24 days ago
NVIDIA-AI-IOT/torch2trt is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of torch2trt is Python.
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