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* update coco128-seg comments
* Enables detect.py to use Triton for inference
Triton Inference Server is an open source inference serving software
that streamlines AI inferencing.
https://github.com/triton-inference-server/server
The user can now provide a "--triton-url" argument to detect.py to use
a local or remote Triton server for inference.
For e.g., http://localhost:8000 will use http over port 8000
and grpc://localhost:8001 will use grpc over port 8001.
Note, it is not necessary to specify a weights file to use Triton.
A Triton container can be created by first exporting the Yolov5 model
to a Triton supported runtime. Onnx, Torchscript, TensorRT are
supported by both Triton and the export.py script.
The exported model can then be containerized via the OctoML CLI.
See https://github.com/octoml/octo-cli#getting-started for a guide.
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* precommit: yapf
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# Conflicts:
# utils/plots.py
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* Update batch-size in model.warmup() + indentation for logging inference results
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* Add models/tf.py for TensorFlow and TFLite export
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* Export and detect with TensorRT engine file
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```
export.py:310: DeprecationWarning: Use build_serialized_network instead.
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
```
* Revert deprecation warning fix
@imyhxy it seems we can't apply the deprecation warning fix because then export fails, so I'm reverting my previous change here.
* Update export.py
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`check_requirements()` is unreliable for large packages like torch and tensorflow that may have multiple installation routes (i.e. conda, pip, tensorflow-cpu, etc.)
Glenn Jocher
Gaz Iqbal
zombob
Ayush Chaurasia
VELC
Nicholas Zolton
Ryan Hirasaki
Jirka Borovec
RcINS
DavidB
greg2451
Jiacong Fang
Diego Montes
imyhxy
Nam Vu
Jebastin Nadar
