A match made in heaven
The idea behind Docker is that the developer creates a container with all dependencies that he wants, make sure that everything works and done. The staff responsible for software deployment does not need to know what is inside the container, it just needs to be able to run container on the server. While this feature could be achieved with virtual machines, they ended up coming with much more than necessary, so the VM files are too large and the host system becomes very slow.
On the other hand, docker does not use a full OS, it shares the same host kernel (yes, it needs to run on Linux) but is a completely isolated environment. So run a docker containers is much lighter than run a virtual machine. Docker features do not stop there, it also allows a kind of versioning and has a kind of github for containers, the Docker Hub, where the user can download and use ready images for various software, such as MySQL, Postgres , LAMP, WordPress, RStudio, among others - Flavio Barros
NOTE: The docker image (as a .tar) won't be included as it is over 1GB in size
__author__ = 'Alistair Rogers'With the stereotypical iris example...
import flask
import pandas as pd
from sklearn import datasets
from sklearn import svmiris = datasets.load_iris()svm = svm.SVC()X, y = iris.data, iris.targetX = pd.DataFrame(X)
X.columns = ['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width']y = pd.DataFrame(y)
y.columns = ['Species']print(X.head()) Sepal_Length Sepal_Width Petal_Length Petal_Width
0 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 4.7 3.2 1.3 0.2
3 4.6 3.1 1.5 0.2
4 5.0 3.6 1.4 0.2
svm.fit(X, y)/Users/arogers2/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
y = column_or_1d(y, warn=True)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
import pickle
pickle.dump(svm, open('svm_iris.pkl', 'wb'))Save the following as app.py
You can also create an empty init.py if you'd like
%%bash
cat app.pyfrom flask import Flask
from flask import request
from flask import jsonify
from sklearn import svm
import pickle
import numpy as np
import pandas as pd
app = Flask(__name__)
# Create a test method
@app.route('/isalive')
def index():
return "This API is Alive"
@app.route('/prediction', methods=['POST', 'GET'])
def get_prediction():
# GET the JSONified Pandas dataframe
print('Requesting...')
json = request.args.get('data')
# Transform JSON into Pandas DataFrame
print('dataframing...')
df = pd.read_json(json)
df = df.reset_index(drop=True)
# Read the serialised model
print('reading model')
modelname = 'svm_iris.pkl'
print('Loading %s' % modelname)
loaded_model = pickle.load(open(modelname, 'rb'), encoding='latin1')
# Get predictions
print('predicting')
prediction = loaded_model.predict(df)
prediction_df = pd.DataFrame(prediction)
prediction_df.columns = ['Species']
prediction_df.reset_index(drop=True)
# OPTIONAL: Concatenate Predictions with original Dataframe
df_with_preds = pd.concat([df, prediction_df], axis=1)
return df_with_preds.to_json()
if __name__ == '__main__':
app.run(port=5000,host='0.0.0.0')
#app.run(debug=True)
Open a terminal window and navigate to the directory that you are working from. Then run python app.py
You should get something like this (while debug mode is on..):
- Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
We will also create a json to test the api endpoint. We will create this with the first 10 rows of the data that was used to train the model
X.iloc[1:10, :].to_json()'{"Sepal_Length":{"1":4.9,"2":4.7,"3":4.6,"4":5.0,"5":5.4,"6":4.6,"7":5.0,"8":4.4,"9":4.9},"Sepal_Width":{"1":3.0,"2":3.2,"3":3.1,"4":3.6,"5":3.9,"6":3.4,"7":3.4,"8":2.9,"9":3.1},"Petal_Length":{"1":1.4,"2":1.3,"3":1.5,"4":1.4,"5":1.7,"6":1.4,"7":1.5,"8":1.4,"9":1.5},"Petal_Width":{"1":0.2,"2":0.2,"3":0.2,"4":0.2,"5":0.4,"6":0.3,"7":0.2,"8":0.2,"9":0.1}}'
import requestsLet's try the testing isalive method first
local_test = requests.get('http://0.0.0.0:5000/isalive')print(local_test.content)b'This API is Alive'
Now let's see what happens when we feed some data in.
data = X.iloc[1:10, :].to_json()local_prediction = requests.get('http://0.0.0.0:5000/prediction?data='+data)local_prediction.text'{"Petal_Length":{"0":1.4,"1":1.3,"2":1.5,"3":1.4,"4":1.7,"5":1.4,"6":1.5,"7":1.4,"8":1.5},"Petal_Width":{"0":0.2,"1":0.2,"2":0.2,"3":0.2,"4":0.4,"5":0.3,"6":0.2,"7":0.2,"8":0.1},"Sepal_Length":{"0":4.9,"1":4.7,"2":4.6,"3":5.0,"4":5.4,"5":4.6,"6":5.0,"7":4.4,"8":4.9},"Sepal_Width":{"0":3.0,"1":3.2,"2":3.1,"3":3.6,"4":3.9,"5":3.4,"6":3.4,"7":2.9,"8":3.1},"Species":{"0":2,"1":2,"2":2,"3":2,"4":2,"5":2,"6":2,"7":2,"8":2}}'
In your terminal, you should see something like this:
Requesting...
dataframing...
reading model
Loading svm_iris.pkl
predicting
127.0.0.1 - - [05/Jun/2018 09:28:32] "GET /prediction?data=%7B%22Sepal_Length%22:%7B%221%22:4.9,%222%22:4.7,%223%22:4.6,%224%22:
5.0,%225%22:5.4,%226%22:4.6,%227%22:5.0,%228%22:4.4,%229%22:4.9%7D,%22
Sepal_Width%22:%7B%221%22:3.0,%222%22:3.2,%223%22:3.1,%224%22:3.6,%225
%22:3.9,%226%22:3.4,%227%22:3.4,%228%22:2.9,%229%22:3.1%7D,%22Petal_Le
ngth%22:%7B%221%22:1.4,%222%22:1.3,%223%22:1.5,%224%22:1.4,%225%22:1.7
,%226%22:1.4,%227%22:1.5,%228%22:1.4,%229%22:1.5%7D,%22Petal_Width%22:
%7B%221%22:0.2,%222%22:0.2,%223%22:0.2,%224%22:0.2,%225%22:0.4,%226%22
:0.3,%227%22:0.2,%228%22:0.2,%229%22:0.1%7D%7D HTTP/1.1" 200
print(pd.read_json(local_prediction.text)) Petal_Length Petal_Width Sepal_Length Sepal_Width Species
0 1.4 0.2 4.9 3.0 2
1 1.3 0.2 4.7 3.2 2
2 1.5 0.2 4.6 3.1 2
3 1.4 0.2 5.0 3.6 2
4 1.7 0.4 5.4 3.9 2
5 1.4 0.3 4.6 3.4 2
6 1.5 0.2 5.0 3.4 2
7 1.4 0.2 4.4 2.9 2
8 1.5 0.1 4.9 3.1 2
Well that all works, fantastic! Close your connection with a CTRL + C
There are two things you need to build a Docker image (besides Docker installed on your machine).
A Dockerfile
- Specifies the commands to run and sources dependencies.
A requirements file
- A list of all the dependencies you require, in this case it will be the python packages used
%%bash
cat DockerfileFROM python:3.5.3
MAINTAINER Alistair Rogers
# Create a directory to work from
WORKDIR /app/
# Place the requirements file specifying all of the dependencies in that file
COPY requirements.txt /app/
RUN pip install -r ./requirements.txt
# Place the Flask application and pickled model file into the directory
COPY app.py __init__.py /app/
COPY svm_iris.pkl /app/
# Expose the app on port 5000
EXPOSE 5000
ENTRYPOINT python ./app.py
%%bash
cat requirements.txtnumpy==1.13
scipy==0.19.1
Flask==0.12.2
scikit_learn==0.18.1
pandas==0.18.1
You can do this with the following command:
docker build . -t [NAME]
We will call this image iris_svm
!docker build . -t iris_svmSending build context to Docker daemon 1.193GB
Step 1/9 : FROM python:3.5.3
---> 56b15234ac1d
Step 2/9 : MAINTAINER Alistair Rogers
---> Using cache
---> e8948cd02846
Step 3/9 : WORKDIR /app/
---> Using cache
---> 2aa2e410bb64
Step 4/9 : COPY requirements.txt /app/
---> Using cache
---> c1b090adbb9d
Step 5/9 : RUN pip install -r ./requirements.txt
---> Using cache
---> cee84e2a220b
Step 6/9 : COPY app.py __init__.py /app/
---> Using cache
---> 159569d5d750
Step 7/9 : COPY svm_iris.pkl /app/
---> Using cache
---> 89dc1650ecee
Step 8/9 : EXPOSE 5000
---> Using cache
---> 4475acdeb6ab
Step 9/9 : ENTRYPOINT python ./app.py
---> Using cache
---> a644e6b2467e
Successfully built a644e6b2467e
Successfully tagged iris_svm:latest
Now we can save this docker image as a .tar file and do some other stuff with it. We could send it to someone else who has docker so they can run it or use a cloud service to expose the model as a service etc
!docker save iris_svm > iris_svm.tarWe can see that the tar file is ready and available
!ls Dockerfile __init__.py requirements.txt
README.md app.py svm_iris.pkl
Sklearn_with_Docker.ipynb iris_svm.tar
In order to run a docker image from a tar file, we must first load it: docker load -i NAME.tar
!docker load -i iris_svm.tarLoaded image: iris_svm:latest
If we try to run the app on port 5000 (as specified in the flask app) then message we get when we run it will say it's listening on port 5000, but if we try to access that on the same host we get an error.
So need to expose the port outside the container that it's running in
Let's run it on ports 5001:5000
Run this on your command line! docker run -p 5001:5000 -it iris_svm We can't run it here because it would continuously run and no other cell could be executed in this notebook
You should get a similar output as before:
Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
request_docker_test = requests.get('http://localhost:5001/isalive')
request_docker_test.text'This API is Alive'
Lets try this with the same data as before
request_docker_pred = requests.get('http://localhost:5001/prediction?data='+data)
request_docker_pred.text'{"Petal_Length":{"0":1.4,"1":1.3,"2":1.5,"3":1.4,"4":1.7,"5":1.4,"6":1.5,"7":1.4,"8":1.5},"Petal_Width":{"0":0.2,"1":0.2,"2":0.2,"3":0.2,"4":0.4,"5":0.3,"6":0.2,"7":0.2,"8":0.1},"Sepal_Length":{"0":4.9,"1":4.7,"2":4.6,"3":5.0,"4":5.4,"5":4.6,"6":5.0,"7":4.4,"8":4.9},"Sepal_Width":{"0":3.0,"1":3.2,"2":3.1,"3":3.6,"4":3.9,"5":3.4,"6":3.4,"7":2.9,"8":3.1},"Species":{"0":2,"1":2,"2":2,"3":2,"4":2,"5":2,"6":2,"7":2,"8":2}}'
print(pd.read_json(request_docker_pred.text)) Petal_Length Petal_Width Sepal_Length Sepal_Width Species
0 1.4 0.2 4.9 3.0 2
1 1.3 0.2 4.7 3.2 2
2 1.5 0.2 4.6 3.1 2
3 1.4 0.2 5.0 3.6 2
4 1.7 0.4 5.4 3.9 2
5 1.4 0.3 4.6 3.4 2
6 1.5 0.2 5.0 3.4 2
7 1.4 0.2 4.4 2.9 2
8 1.5 0.1 4.9 3.1 2
Woohoo!
Now CTRL + C to stop your docker