Building and Running Custom Python Container

Introduction

In this tutorial example, we will walk you through building your own docker container and running the container on the bacalhau network.

Prerequisites

To get started, you need to install the Bacalhau client, see more information here

Sample Recommendation Dataset

We will using a simple recommendation script that when given a movie ID will recommend other movies based on user ratings. Assuming you want if recommendations for the movie Toy Story (1995) it will recommend movies from similar categories:

Recommendations for Toy Story (1995):
:  Toy Story (1995)
:  Postino, Il (The Postman) (1994)
:  Fantasia 2000 (1999)
:  I Like It Like That (1994)
:  Carmen Miranda: Bananas Is My Business (1994)
:  Two Much (1996)
:  Pocahontas (1995)
:  Boys, The (1997)
:  Citizen's Band (a.k.a. Handle with Care) (1977)
:  Operation Dumbo Drop (1995)

Downloading the dataset

Download Movielens1M dataset from this link https://files.grouplens.org/datasets/movielens/ml-1m.zip

!wget https://files.grouplens.org/datasets/movielens/ml-1m.zip

In this example we’ll be using 2 files from the MovieLens 1M dataset: ratings.dat and movies.dat. After the dataset is downloaded extract the zip and place ratings.dat and movies.dat into a folder called input

The structure of input directory should be

input
├── movies.dat
└── ratings.dat

# Extracting the downloaded zip file
!unzip ml-1m.zip

#moving  ratings.dat and movies.dat into a folder called 'input'
!mkdir input; mv ml-1m/movies.dat ml-1m/ratings.dat input/

Installing Dependencies

Create a requirements.txt for the Python libraries we’ll be using:

%%writefile requirements.txt
numpy
pandas

To install the dependencies run the command

%%bash
pip install -r requirements.txt

Writing the Script

Create a new file called similar-movies.py and in it paste the following script

%%writefile similar-movies.py
# Imports
import numpy as np
import pandas as pd
import argparse
from distutils.dir_util import mkpath
import warnings
warnings.filterwarnings("ignore")
# Read the files with pandas
data = pd.io.parsers.read_csv('input/ratings.dat',
names=['user_id', 'movie_id', 'rating', 'time'],
engine='python', delimiter='::', encoding='latin-1')
movie_data = pd.io.parsers.read_csv('input/movies.dat',
names=['movie_id', 'title', 'genre'],
engine='python', delimiter='::', encoding='latin-1')

# Create the ratings matrix of shape (m×u) with rows as movies and columns as users

ratings_mat = np.ndarray(
shape=((np.max(data.movie_id.values)), np.max(data.user_id.values)),
dtype=np.uint8)
ratings_mat[data.movie_id.values-1, data.user_id.values-1] = data.rating.values

# Normalise matrix (subtract mean off)

normalised_mat = ratings_mat - np.asarray([(np.mean(ratings_mat, 1))]).T

# Compute SVD

normalised_mat = ratings_mat - np.matrix(np.mean(ratings_mat, 1)).T
cov_mat = np.cov(normalised_mat)
evals, evecs = np.linalg.eig(cov_mat)

# Calculate cosine similarity, sort by most similar and return the top N.

def top_cosine_similarity(data, movie_id, top_n=10):
   
index = movie_id - 1
# Movie id starts from 1
   
movie_row = data[index, :]
magnitude = np.sqrt(np.einsum('ij, ij -> i', data, data))
similarity = np.dot(movie_row, data.T) / (magnitude[index] * magnitude)
sort_indexes = np.argsort(-similarity)
return sort_indexes[:top_n]

# Helper function to print top N similar movies
def print_similar_movies(movie_data, movie_id, top_indexes):
print('Recommendations for {0}: \n'.format(
movie_data[movie_data.movie_id == movie_id].title.values[0]))
for id in top_indexes + 1:
print(str(id),' : ',movie_data[movie_data.movie_id == id].title.values[0])


parser = argparse.ArgumentParser(description='Personal information')
parser.add_argument('--k', dest='k', type=int, help='principal components to represent the movies',default=50)
parser.add_argument('--id', dest='id', type=int, help='Id of the movie',default=1)
parser.add_argument('--n', dest='n', type=int, help='No of recommendations',default=10)

args = parser.parse_args()
k = args.k
movie_id = args.id # Grab an id from movies.dat
top_n = args.n

# k = 50
# # Grab an id from movies.dat
# movie_id = 1
# top_n = 10

sliced = evecs[:, :k] # representative data
top_indexes = top_cosine_similarity(sliced, movie_id, top_n)
print_similar_movies(movie_data, movie_id, top_indexes)

What the similar-movies.py script does

Read the files with pandas
Create the ratings matrix of shape (m×u) with rows as movies and columns as user
Normalise matrix (subtract mean off)
Compute SVD
Calculate cosine similarity, sort by most similar and return the top N.
Select k principal components to represent the movies, a movie_id to find recommendations and print the top_n results.

For further reading on how the script works, go to Simple Movie Recommender Using SVD | Alyssa

Running the Script

Running the script similar-movies.py using the default values you can also use other flags to set your own values

! python similar-movies.py

Setting Up Docker

We will create a Dockerfile and add the desired configuration to the file. These commands specify how the image will be built, and what extra requirements will be included.

%%writefile Dockerfile
FROM python:3.8
ADD similar-movies.py .
ADD /input input
COPY ./requirements.txt /requirements.txt
RUN pip install -r requirements.txt

We will use the python:3.8 docker image and add our script similar-movies.py to copy the script to the docker image, similarly we also add the dataset directory and also the requirements, after that run the command to install the dependencies in the image

The final folder structure will look like this:

├── Dockerfile
├── input
│   ├── movies.dat
│   └── ratings.dat
├── requirements.txt
└── similar-movies.py

info

See more information on how to containerize your script/app here

Build the container

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/python-similar-movies

Push the container

Next, 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/python-similar-movies

Running a Bacalhau Job

After the repo image has been pushed to docker hub, we can now use the container for running on Bacalhau. You can submit a Bacalhau job using by running your container on bacalhau with default or custom parameters

Running the Container with Default Parameters

To submit a Bacalhau job by running your container on bacalhau with default parameters, run the following Bacalhau command:

%%bash --out job_id
bacalhau docker run \
--id-only \
--wait \
jsace/python-similar-movies \
-- python similar-movies.py

Structure of the command

Let's look closely at the command above:

bacalhau docker run: call to bacalhau
jsace/python-similar-movies: the name and the tag of the docker image we are using
-- python similar-movies.py: execute the python script

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.

Running the Container with Custom Parameters

To submit a Bacalhau job by running your container on bacalhau with custom parameters, run the following Bacalhau command:

bacalhau docker run \
jsace/python-similar-movies \
-- python similar-movies.py --k 50 --id 10 --n 10

Structure of the command

Let's look closely at the command above:

bacalhau docker run: call to bacalhau
jsace/python-similar-movies: the name and the tag of the docker image we are using
-- python similar-movies.py --k 50 --id 10 --n 10: execute the python script

Checking the State of your Jobs

Job status: You can check the status of the job using bacalhau list.

%%bash
bacalhau list --id-filter ${JOB_ID}

When 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 describe.

%%bash
bacalhau describe ${JOB_ID}

Job download: You can download your job results directly by using bacalhau get. Alternatively, you can choose to create a directory to store your results. In the command below, we created a directory and downloaded our job output to be stored in that directory.

%%bash
rm -rf results && mkdir -p results
bacalhau get $JOB_ID --output-dir results

Viewing your Job Output

Each job creates 3 subfolders: the combined_results,per_shard files, and the raw directory. To view the file, run the following command:

!cat results/combined_results/stdout # displays the contents of the file

Recommendations for GoldenEye (1995): 

:  GoldenEye (1995)
:  Casino (1995)
:  Sabrina (1995)
:  Rock, The (1996)
:  Mission: Impossible (1996)
:  Ghost and the Darkness, The (1996)
:  Balto (1995)
:  Crow: City of Angels, The (1996)
:  Moonlight Murder (1936)
:  Clear and Present Danger (1994)

Building and Running Custom Python Container

Introduction​

Prerequisites​

Sample Recommendation Dataset​

Downloading the dataset​

Installing Dependencies​

Writing the Script​

Running the Script​

Setting Up Docker​

Build the container​

Push the container​

Running a Bacalhau Job​

Running the Container with Default Parameters​

Structure of the command​

Running the Container with Custom Parameters​

Structure of the command​

Checking the State of your Jobs​

Viewing your Job Output​

Introduction

Prerequisites

Sample Recommendation Dataset

Downloading the dataset

Installing Dependencies

Writing the Script

Running the Script

Setting Up Docker

Build the container

Push the container

Running a Bacalhau Job

Running the Container with Default Parameters

Structure of the command

Running the Container with Custom Parameters

Structure of the command

Checking the State of your Jobs

Viewing your Job Output