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If you want to actually deploy AI models, TFLite is still your best option. And for exporting there, you first need things to run well inside regular TF. Also,
by fxtentacle 4y ago
If you want to actually deploy AI models, TFLite is still your best option. And for exporting there, you first need things to run well inside regular TF.
Also, I often find myself using the TF data pre-processing pipeline even from inside PyTorch, because it's just so much easier to get excellent processing performance with TF. Not sure why, but PyTorch is in many cases "unnecessarily" single-threaded.
- learndeeply 4y ago> If you want to actually deploy AI models On mobile*. > I often find myself using the TF data pre-processing pipeline even from inside PyTorch the tf.data pipeline is quite nice, has some neat auto-tuning features.
- hustwindmaple1 4y agoThere are a ton of mobile deployment options that support PyTorch+TF models. It's hard to argue TFLite is the best. https://github.com/alibaba/MNN https://github.com/alibaba/MNN https://github.com/Tencent/ncnn https://github.com/Tencent/ncnn https://github.com/Tencent/tnn https://github.com/Tencent/tnn https://github.com/XiaoMi/mace https://github.com/XiaoMi/mace Plus, each chip vendor has their own SDK (e.g., Qualcomm has SNPE, MediaTek has NeuroPilot)