Merge pull request #30 from JimmyYang20/feature-lifelong-n

Add readme for lifelong learning example

examples/curb-detection/lifelong_learning_bench/README.md

@@ -0,0 +1,103 @@
+# 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 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. 
+
+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
+
+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. 
+
+## Step 1. Ianvs Preparation
+
+First, we download the code of Ianvs. Assuming that we are using `/ianvs` as workspace, Ianvs can be cloned with `Git`
+as:
+
+``` shell
+mkdir /ianvs
+cd /ianvs #One might use another path preferred
+
+mkdir project
+cd project
+git clone https://github.com/kubeedge/ianvs.git   
+```
+
+
+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
+
+cd ianvs 
+python -m pip install ./examples/resources/third_party/*
+python -m pip install -r requirements.txt
+```
+
+We are now ready to install Ianvs. 
+``` shell
+python setup.py install  
+```
+
+## Step 2. Dataset Preparation
+
+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.
+
+``` 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
+```
+
+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.
+
+<!-- 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].  -->
+
+
+Related algorithm is also ready in this quick start. 
+``` shell
+export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet
+```
+
+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.
+
+## Step 3. Ianvs Execution and Presentation
+
+We are now ready to run the ianvs for benchmarking. 
+
+``` shell
+cd /ianvs/project
+ianvs -f examples/curb-detection/lifelong_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/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.
+
+The final output might look like this:   
+
+|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']         |
+
+
+This ends the quick start experiment.
+
+# What is next
+
+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. 
+
+Enjoy your journey on Ianvs!