Quick Start¶
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 on industrial defect detection. You can reduce manual procedures to just a few steps so that you can building and start your distributed synergy AI solution development within minutes.
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 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
In this example, we are using 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.
Step 1. Ianvs Preparation¶
First, we download the code of Ianvs. Assuming that we are using /home/ianvs-qs as workspace, Ianvs can be cloned with Git as:
/home$ cd /home/ianvs-qs #One might use other path preferred
/home/ianvs-qs$ mkdir -p ./project/
/home/ianvs-qs$ cd ./project/
/home/ianvs-qs/project$ git clone https://github.com/kubeedge/ianvs.git
Then, we install third-party dependencies for ianvs.
/home/ianvs-qs/project$ cd ./ianvs
/home/ianvs-qs/project/ianvs$ apt update
/home/ianvs-qs/project/ianvs$ apt install libgl1-mesa-glx -y
/home/ianvs-qs/project/ianvs$ python -m pip install --upgrade pip
/home/ianvs-qs/project/ianvs$ python -m pip install third_party/*
/home/ianvs-qs/project/ianvs$ python -m pip install -r requirements.txt
We are now ready to install Ianvs.
/home/ianvs-qs/project/ianvs$ python setup.py install
Step 2. Dataset and Model Preparation¶
Datasets and models can be large. To avoid over-size projects in the Github repository of Ianvs, the Ianvs code base do not include origin datasets and models. Then developers do not need to download non-necessary datasets and models for a quick start.
First, the user need to prepare the dataset according to the targeted scenario, from source links (e.g., from Cloud Service or Kaggle) provided by Ianvs. All scenarios with dataset are available Links of scenarios. As an example in this document, we are using the PCB-AoI Public Dataset released by KubeEdge SIG AI members on Kaggle. See Details of PCB-AoI dataset for more information of this dataset.
/home/ianvs-qs/project/ianvs$ cd /home/ianvs-qs #One might use other path preferred
/home/ianvs-qs$ mkdir -p ./dataset/
/home/ianvs-qs$ cd ./dataset
/home/ianvs-qs/dataset$ wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com:443/ianvs/pcb-aoi/dataset.zip
/home/ianvs-qs/dataset$ unzip dataset.zip
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 for more details.
Then we may Develop the targeted algorithm as usual. In this quick start, Ianvs has prepared an initial model for benchmarking. One can find the model at FPN-model.
/home/ianvs-qs/dataset$ cd /home/ianvs-qs #One might use other path preferred
/home/ianvs-qs$ mkdir -p ./initial_model
/home/ianvs-qs$ cd /ianvs/initial_model/
/home/ianvs-qs/initial_model$ wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com:443/ianvs/pcb-aoi/model.zip
Related algorithm is also ready as a wheel in this quick start.
/home/ianvs-qs/initial_model$ cd /home/ianvs-qs #One might use other path preferred
/home/ianvs-qs$ cd ./project/ianvs/
/home/ianvs-qs/project/ianvs$ python -m pip install examples/resources/algorithms/FPN_TensorFlow-0.1-py3-none-any.whl
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 for more details.
Step 3. Ianvs Execution and Presentation¶
We are now ready to run the ianvs for benchmarking on PCB-AoI dataset.
/home/ianvs-qs/project/ianvs$ ianvs -f examples/pcb-aoi/singletask_learning_bench/benchmarkingjob.yaml
Finally, the user can check the result of benchmarking on the console and also in the output path(e.g. /ianvs/singletask_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 for more details.
The final output might look like:
rank |
algorithm |
f1_score |
paradigm |
basemodel |
learning_rate |
momentum |
time |
url |
|---|---|---|---|---|---|---|---|---|
1 |
fpn_singletask_learning |
0.7177014191939565 |
singletasklearning |
estimator |
0.1 |
0.7 |
2022-06-16 15:02:59 |
/ianvs/pcb-aoi/workspace/benchmarkingjob/fpn_singletask_learning/b3a84564-ed41-11ec-83c3-53ead20896e4 |
2 |
fpn_singletask_learning |
0.7157613691075415 |
singletasklearning |
estimator |
0.1 |
0.5 |
2022-06-16 15:02:59 |
/ianvs/pcb-aoi/workspace/benchmarkingjob/fpn_singletask_learning/b3a84564-ed41-11ec-83c3-53ead20896e4 |
What is next¶
Now the reader shall be ready to exlore more on Ianvs: