The below table should be used as reference to understand which frameworks (and their respective versions) are currently supported in the Platform when targeting specific hardware choices.

If you have a use case with a specific hardware/framework requirement that is not supported, please communicate this to your Customer Success representative so our team is aware and works diligently for your use case needs!

Hardware

ONNX

TFLite

TVM

Tensorflow

TensorRT x Cuda (via ONNX-RT) on GPU

Edge: RPi 32bit

Coming Soon

Yes for all FP32 and INT8 models uploaded in TFLite format (TF 2.0-2.6)

Yes for all FP32 and INT8 models

Not Available due to TF not being well-supported on 32-bit devices

N/A

Edge: Jetson family GPUs

Coming Soon!

Not Available due to TFLite not supporting NVIDIA GPUs

Yes for all FP32 and INT8 models

Coming Soon!

Yes for all FP32 and INT8 models - TensorRT 7.0 x CUDA 10.2 (A version upgrade to TensorRT 7.2 x CUDA 11.1 is coming soon)

Cloud: NVIDIA GPUs

Yes for all FP32 and INT8 models - (ONNX 1.8)

Not Available due to TFLite not supporting NVIDIA GPUs

Yes for all FP32 and INT8 models

Yes for FP32 and INT8 models uploaded in TF format - TF 2.0-2.6

Yes for all FP32 and INT8 models - TensorRT 7.0 x CUDA 10.2 (A version upgrade to TensorRT 7.2 x CUDA 11.1 is coming soon)

Cloud: x86

Yes for all FP32 and INT8 models (ONNX 1.8)

Yes for FP32 and INT8 models uploaded in TFLite format

(TF 2.0-2.6)

Yes for all FP32 and INT8 models

Yes for FP32 and INT8 models uploaded in TF format - TF 2.0-2.6

N/A

Edge: RPi 64bit

Yes for all FP32 and INT8 models (ONNX 1.8)

Yes for FP32 and INT8 models uploaded in TFLite format

(TF 2.0-2.6)

Yes for all FP32 and INT8 models

Yes for FP32 and INT8 models uploaded in TF format - TF 2.0-2.6

N/A

Did this answer your question?