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These are all repos with at least 300 stars, maybe there are some credibility with these repos, and if so, is it possible to implement them and add them to the toolkit?
Here are benchmarks done by …
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I use docker with TensorRT (nvcr.io/nvidia/19.10-py3), Tensorflow version is 2.2.0
First, I export savedmodel with TensorRT:
`python3 model_inspect.py --runmode=saved_model --model_name=efficientd…
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is it OpenML platform dose not record the experiment(like one run) time?
for example if I want to see this run_id = 9029596 as the flowing site, how should I do?
https://www.openml.org/r/9029596…
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Some of the links lead to 404 - Page not found:
Features
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- **New AutoML systems can be added** as Docker images
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Installation:
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- The Python libraries listed in **requirements.txt…
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System environments:
TF 2.2.0
CUDA 10.1
CUDNN 7.6.5
TensorRT 6.0.1.5
GPU RTX2080Ti
I tried this command: `python model_inspect.py --runmode=bm --model_name=efficientdet-d…
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This is the draft plan for the upcoming iteration in July, it's a 4 weeks iteration, and it is targeted to release on early August.
## Release Plan
- Release manager: @ultmaster
- Feature freeze…
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Hi @zylo117, I think that the speed test comparison with the official implementation in the README is no longer valid, they improved model latency, see:
https://github.com/google/automl/tree/master/e…
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https://github.com/google/automl/blob/5804815148b872c6afc63ccd234d27fa3f20c9c6/efficientdet/main.py#L138
Eval could run benchmark without val_json_file.
https://github.com/google/automl/blob/5804815…
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https://github.com/awslabs/autogluon
Last stable version seems to be v0.0.6.
@Innixma do you have any concern if we want to make Autogluon publicly available in the benchmark application?
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I'm working on AutoML package [mljar-supervised](https://github.com/mljar/mljar-supervised) and I would like to compare my results with AutoGluon on OpenML datasets.
What metric is reported in Tabl…