* Update greeting
* Cleanup README
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* Update Requirements with PyTorch CUDA
Added --extra-index-url https://download.pytorch.org/whl/cu116 URL to requirements file for ease of creating venv with CUDA enabled PyTorch. Otherwise CPU PyTorch is installed an unable to use local GPUs.
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* Update requirements.txt
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* Update requirements.txt
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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.
* added triton client to requirements
* fixed support for TFSavedModels in Triton
* reverted change
* Test CoreML update
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* Update ci-testing.yml
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* Use pathlib
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* Refacto DetectMultiBackend to directly accept triton url as --weights http://...
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* Deploy category
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* Update detect.py
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* Update common.py
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* Update common.py
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* Update predict.py
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* Update predict.py
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* Update predict.py
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* Update triton.py
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* Update triton.py
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* Add printout and requirements check
* Cleanup
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* triton fixes
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* fixed triton model query over grpc
* Update check_requirements('tritonclient[all]')
* group imports
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* Fix likely remote URL bug
* update comment
* Update is_url()
* Fix 2x download attempt on http://path/to/model.pt
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* TensorFlow macOS AutoUpdate
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The low package version is causing conflicts among other dependencies, commenting it causes no ill effects in CI so this should be fine.
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* assert torch!=1.12.0 for DDP training
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* Add `psutil` and `ipython` to requirements.txt
Lightweight packages used by YOLOv5 for system utilization (psutil) and interactive notebooks (IPython)
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* Requirements updated
1. Requests added to requirements.txt. That might not be included in all docker base images, adding it to the requirements is safer.
2. Added a minimum version to Pandas. It's a good practice to have versions for all dependencies.
* Sort alphabetically
* Update requirements.txt
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