Comments (3)
I think the best solution is to separate Nvidia dependencies from ONNX Runtime ones and for those needing TensorRT, the documentation would mention the possibility to just use Docker image.
Are you planing to use TensorRT deployment?
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Does pip3 install ".[GPU]" -f https://download.pytorch.org/whl/cu113/torch_stable.html --extra-index-url https://pypi.ngc.nvidia.com
works out of the box for you?
from transformer-deploy.
Thanks, @pommedeterresautee. I am not experiencing the issue with the latest version of the code.
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Related Issues (20)
- ViT serving
- How to deploy TrCOR HOT 2
- Can we support facebook/m2m100_418M model?
- sharing the code for the triton models.
- Occasional "CUDA error cudaErrorInvalidConfiguration:invalid configuration argument" error HOT 1
- I faced with Stub process is unhealthy and it will be restarted.
- Marginal Improvement Between INT8 and FP16
- Torch 2.0 HOT 3
- Llama support HOT 2
- Tokenizer path
- Failed to load 'transformer_onnx_model' HOT 4
- convert_model command not found HOT 3
- transformer-deploy on triton server 22.08 HOT 1
- No supported GPU(s) detected to run this container
- unable to do tensorrt inference
- GPTModelWrapper' object has no attribute 'can_generate' HOT 1
- unable to host .onnx model on triton server
- Unable To use batching HOT 1
- the performance of onxx fp16 seems to be even worse than that of onnx fp32
- Encounter Error: ValueError: Message onnx.ModelProto exceeds maximum protobuf size of 2GB
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