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examples/curb-detection/lifelong_learning_bench/README.md
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# Quick Start | ||
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Welcome to Ianvs! Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, | ||
in order to facilitate more efficient and effective development. Quick start helps you to test your algorithm on Ianvs | ||
with a simple example of industrial defect detection. You can reduce manual procedures to just a few steps so that you can | ||
build and start your distributed synergy AI solution development within minutes. | ||
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Before using Ianvs, you might want to have the device ready: | ||
- One machine is all you need, i.e., a laptop or a virtual machine is sufficient and a cluster is not necessary | ||
- 2 CPUs or more | ||
- 4GB+ free memory, depends on algorithm and simulation setting | ||
- 10GB+ free disk space | ||
- Internet connection for GitHub and pip, etc | ||
- Python 3.6+ installed | ||
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In this example, we are using the Linux platform with Python 3.6.9. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. | ||
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## Step 1. Ianvs Preparation | ||
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First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git` | ||
as: | ||
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``` shell | ||
mkdir /ianvs | ||
cd /ianvs #One might use another path preferred | ||
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mkdir project | ||
cd project | ||
git clone https://github.com/kubeedge/ianvs.git | ||
``` | ||
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Then, we install third-party dependencies for ianvs. | ||
``` shell | ||
sudo apt-get update | ||
sudo apt-get install libgl1-mesa-glx -y | ||
python -m pip install --upgrade pip | ||
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cd ianvs | ||
python -m pip install ./examples/resources/third_party/* | ||
python -m pip install -r requirements.txt | ||
``` | ||
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We are now ready to install Ianvs. | ||
``` shell | ||
python setup.py install | ||
``` | ||
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## Step 2. Dataset Preparation | ||
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Datasets and models can be large. To avoid over-size projects in the Github repository of Ianvs, the Ianvs code base does | ||
not include origin datasets. Then developers do not need to download non-necessary datasets for a quick start. | ||
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``` shell | ||
cd /ianvs #One might use another path preferred | ||
mkdir dataset | ||
cd dataset | ||
wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/ianvs/curb-detection/curb-detection.zip | ||
unzip dataset.zip | ||
``` | ||
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The URL address of this dataset then should be filled in the configuration file ``testenv.yaml``. In this quick start, | ||
we have done that for you and the interested readers can refer to [testenv.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. | ||
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<!-- Please put the downloaded dataset on the above dataset path, e.g., `/ianvs/dataset`. One can transfer the dataset to the path, e.g., on a remote Linux system using [XFTP]. --> | ||
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Related algorithm is also ready in this quick start. | ||
``` shell | ||
export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet | ||
``` | ||
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The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick | ||
start, we have done that for you and the interested readers can refer to [algorithm.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. | ||
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## Step 3. Ianvs Execution and Presentation | ||
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We are now ready to run the ianvs for benchmarking. | ||
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``` shell | ||
cd /ianvs/project | ||
ianvs -f examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml | ||
``` | ||
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Finally, the user can check the result of benchmarking on the console and also in the output path( | ||
e.g. `/ianvs/lifelong_learning_bench/workspace`) defined in the benchmarking config file ( | ||
e.g. `benchmarkingjob.yaml`). In this quick start, we have done all configurations for you and the interested readers | ||
can refer to [benchmarkingJob.yaml](https://ianvs.readthedocs.io/en/latest/guides/how-to-test-algorithms.html#step-1-test-environment-preparation) for more details. | ||
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The final output might look like this: | ||
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|rank |algorithm |accuracy |samples_transfer_ratio|paradigm |basemodel |task_definition |task_allocation |basemodel-learning_rate |task_definition-origins|task_allocation-origins | | ||
|:----:|:-----------------------:|:--------:|:--------------------:|:------------------:|:---------:|:--------------------:|:---------------------:|:-----------------------:|:----------------------|:-----------------------| | ||
|1 |rfnet_lifelong_learning | 0.2123 |0.4649 |lifelonglearning | BaseModel |TaskDefinitionByOrigin| TaskAllocationByOrigin|0.0001 |['real', 'sim'] |['real', 'sim'] | | ||
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This ends the quick start experiment. | ||
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# What is next | ||
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If any problems happen, the user can refer to [the issue page on Github](https://github.com/kubeedge/ianvs/issues) for help and are also welcome to raise any new issue. | ||
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Enjoy your journey on Ianvs! |