Coreset is a data subsetting method. Since the uncompressed datasets can get very large when compressed, it becomes much harder to train them as training time increases with the dataset size. To reduce training time and cut costs, we employ the coreset method; the coreset method can also be applied to other datasets. In this case, we use the coreset method which can lead to a fast speed in solving the k-means problem among the big data with high accuracy in the meantime.
We construct a small coreset for arbitrary shapes of numerical data with a decent time cost. The implementation was mainly based on the coreset construction algorithm that was proposed by Braverman et al. (SODA 2021).
{% hint style="info" %}
For a deeper understanding of the core concepts, it's recommended to explore:
1. Coresets for Ordered Weighted Clustering
2. Efficient Implementation of Coreset-based K-Means Methods
{% endhint %}
In this tutorial example, we will run compressed dataset with Bacalhau
To get started, you need to install the Bacalhau client, see more information here
Clone the repo which contains the code
git clone https://github.com/js-ts/CoresetTo download the dataset you should open Street Map, which is a public repository that aims to generate and distribute accessible geographic data for the whole world. Basically, it supplies detailed position information, including the longitude and latitude of the places around the world.
The dataset is a osm.pbf (compressed format for .osm file), the file can be downloaded from Geofabrik Download Server
wget https://download.geofabrik.de/europe/monaco-latest.osm.pbfThe following command is installing Linux dependencies:
sudo apt-get -y update \
sudo apt-get -y install osmium-tool \
sudo apt-get -y install libpq-dev gdal-bin libgdal-dev libxml2-dev libxslt-devEnsure that the requirements.txt file contains the following dependencies:
# requirements.txt
certifi==2020.12.5
chardet==4.0.0
cycler==0.10.0
idna==2.10
joblib
kiwisolver==1.3.1
lxml==4.6.2
matplotlib==3.3.3
numpy==1.19.4
overpy==0.4
pandas==1.1.4
Pillow==8.0.1
pyparsing==2.4.7
python-dateutil==2.8.1
pytz==2020.4
requests==2.25.1
scikit-learn
scipy
six==1.15.0
threadpoolctl
tqdm==4.56.0
urllib3==1.26.2
geopandasThe following command is installing Python dependencies:
pip3 install -r Coreset/requirements.txtTo run coreset locally, you need to convert from compressed pbf format to geojson format:
osmium export monaco-latest.osm.pbf -o monaco-latest.geojsonThe following command is to run the Python script to generate the coreset:
python Coreset/python/coreset.py -f monaco-latest.geojson{% hint style="info" %}
coreset.py contains the following script here
{% endhint %}
To build your own docker container, create a Dockerfile, which contains instructions on how the image will be built, and what extra requirements will be included.
FROM python:3.8
RUN apt-get -y update && apt-get -y install osmium-tool && apt update && apt-get -y install libpq-dev gdal-bin libgdal-dev libxml2-dev libxslt-dev
ADD Coreset Coreset
ADD monaco-latest.geojson .
RUN cd Coreset && pip3 install -r requirements.txt
We will use the python:3.8 image, we run the same commands for installing dependencies that we used locally.
{% hint style="info" %} See more information on how to containerize your script/app here {% endhint %}
We will run docker build command to build the container:
docker build -t <hub-user>/<repo-name>:<tag> .
Before running the command replace:
hub-user with your docker hub username, If you don’t have a docker hub account follow these instructions to create docker account, and use the username of the account you created
repo-name with the name of the container, you can name it anything you want
tag this is not required but you can use the latest tag
In our case:
docker build -t jsace/coresetNext, upload the image to the registry. This can be done by using the Docker hub username, repo name or tag.
docker push <hub-user>/<repo-name>:<tag>In our case:
docker push jsace/coresetAfter the repo image has been pushed to Docker Hub, we can now use the container for running on Bacalhau. We've already converted the monaco-latest.osm.pbf file from compressed pbf format to geojson format here. To submit a job, run the following Bacalhau command:
bacalhau docker run \
--input https://github.com/js-ts/Coreset/blob/master/monaco-latest.geojson \
jsace/coreset \
-- /bin/bash -c 'python Coreset/python/coreset.py -f monaco-latest.geojson -o outputs'Let's look closely at the command above:
bacalhau docker run: call to bacalhau--input https://github.com/js-ts/Coreset/blob/master/monaco-latest.geojson: mount themonaco-latest.geojsonfile inside the container so it can be used by the scriptjsace/coreset: the name of the docker image we are usingpython Coreset/python/coreset.py -f monaco-latest.geojson -o outputs: the script initializes cluster centers, creates a coreset using these centers, and saves the results to the specified folder.
-k: amount of initialized centers (default=5)
-n: size of coreset (default=50)
-o: the output folder
When a job is submitted, Bacalhau prints out the related job_id. We store that in an environment variable so that we can reuse it later on.
The same job can be presented in the declarative format. In this case, the description will look like this:
name: Coresets On Bacalhau
type: batch
count: 1
tasks:
- name: My main task
Engine:
type: docker
params:
Image: "jsace/coreset"
Entrypoint:
- /bin/bash
Parameters:
- -c
- "osmium export input/liechtenstein-latest.osm.pbf -o /liechtenstein-latest.geojson;python Coreset/python/coreset.py -f /liechtenstein-latest.geojson -o /outputs"
Publisher:
Type: ipfs
ResultPaths:
- Name: outputs
Path: /outputs
InputSources:
- Source:
Type: "s3"
Params:
Bucket: "coreset"
Key: "*"
Region: "us-east-1"
Target: "/input" The job description should be saved in .yaml format, e.g. coreset.yaml, and then run with the command:
bacalhau job run coreset.yamlJob status: You can check the status of the job using bacalhau job list.
bacalhau job list --id-filter ${JOB_ID} --wideWhen it says Published or Completed, that means the job is done, and we can get the results.
Job information: You can find out more information about your job by using bacalhau job describe.
bacalhau job describe ${JOB_ID}Job download: You can download your job results directly by using bacalhau job get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory (results) and downloaded our job output to be stored in that directory.
rm -rf results && mkdir -p results
bacalhau job get $JOB_ID --output-dir resultsTo view the file, run the following command:
ls results/outputs
centers.csv coreset-weights-monaco-latest.csv
coreset-values-monaco-latest.csv ids.csvTo view the output as a CSV file, run:
cat results/outputs/centers.csv | head -n 10
lat,long
7.423843975787508,43.730621154072196
7.4252607,43.7399135
7.411026970571964,43.72937671121925
7.459404485446199,43.62065587026715
7.429551373022234,43.74042043301333cat results/outputs/coreset-values-monaco-latest.csv | head -n 10
7.418849799999999384e+00,4.372759140000000144e+01
7.416779063194204547e+00,4.373053835217195484e+01
7.422073648233502574e+00,4.374059957604499971e+01
7.434173206469590234e+00,4.374591689556921636e+01
7.417540100000000081e+00,4.372501400000000160e+01
7.427359010538406636e+00,4.374324133692341121e+01
7.427839200000001085e+00,4.374025220000000758e+01
7.418834173612560257e+00,4.372760402368248833e+01
7.416381731248183229e+00,4.373708812663696932e+01
7.412050699999999992e+00,4.372842109999999849e+01cat results/outputs/coreset-weights-monaco-latest.csv | head -n 10
7.704359156916230233e+01
2.090893934427382987e+02
1.560611140982714744e+02
2.516557569411126281e+02
7.714605094768158722e+01
2.640808776415075840e+02
2.326085291610944523e+02
7.704841021255269595e+01
2.089705263763523249e+02
1.728105655128551632e+02If you have questions or need support or guidance, please reach out to the Bacalhau team via Slack (#general channel).