Comments (2)
kindly provide the following information:
Description
A clear and concise description of what the bug is.
Triton Information
What version of Triton are you using?
Are you using the Triton container or did you build it yourself?
To Reproduce
Steps to reproduce the behavior.
Describe the models (framework, inputs, outputs), ideally include the model configuration file (if using an ensemble include the model configuration file for that as well).
Expected behavior
A clear and concise description of what you expected to happen.
from server.
I found out that the reason was a bug in my custom backend
from server.
Related Issues (20)
- A fluctuating result is obtained when perf_analyze is run for a pressure test HOT 2
- When will the latest version be released? HOT 2
- [New] Discord channel for triton-inference-server, tensorrt
- Not able to perform multiple inferences on my ASR model HOT 1
- Unable to find backend library for backend 'onnxruntime' HOT 2
- Is inferencing natively with C++ natively supported in Triton For Windows version 2.47 and ONNX backend? (Without GRPC and HTTPs calls. HOT 2
- Triton considers max_batch_size as a number of channels for a given input image HOT 1
- Metric Endpoint not working for custom backend. HOT 8
- Tensorflow 2.16 / Keras 3 support
- Add Triton Backend: MindSpore
- Streaming output is not working with the GRPC client HOT 1
- TRITON with Pytorch CPU only build not working HOT 13
- Issue while loading the model using TIS (Triton Inference Server) : For the model to support batching the shape should have at least 1 dimension and the first dimension must be -1 HOT 2
- support auto padding for tensorflow_backend HOT 1
- Triton crashes with SIGSEGV (signal 11) HOT 2
- Provide native support for server-side tokenization HOT 1
- Reduce a docker image size. HOT 1
- Exllamav2 inference with EXL Quants HOT 1
- Add field for default value in config.pbtxt when using optional input HOT 1
- nv_inference_count no longer includes gpu_uuid? HOT 3
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from server.