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https://github.com/khaledsharif/sklearn2docker
Convert your trained scikit-learn classifier to a Docker container with a pre-configured API
https://github.com/khaledsharif/sklearn2docker
api classification docker docker-container machine-learning scikit-learn
Last synced: 2 months ago
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Convert your trained scikit-learn classifier to a Docker container with a pre-configured API
- Host: GitHub
- URL: https://github.com/khaledsharif/sklearn2docker
- Owner: KhaledSharif
- License: lgpl-3.0
- Created: 2018-02-07T15:45:02.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2018-03-05T10:43:02.000Z (almost 7 years ago)
- Last Synced: 2024-10-15T15:24:29.651Z (2 months ago)
- Topics: api, classification, docker, docker-container, machine-learning, scikit-learn
- Language: Python
- Size: 29.3 KB
- Stars: 5
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# sklearn2docker
#### Convert your trained scikit-learn classifier to a Docker container with a pre-configured API
[](http://www.gnu.org/licenses/lgpl-3.0)
## Installation
The easiest way to install `sklearn2docker` with all its dependencies is through `pip`:
```bash
pip install git+git://github.com/KhaledSharif/sklearn2docker.git
```
## Getting started
First, create your `sklearn` classifier. In this example we will use the [Iris dataset](http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html).
```python
from pandas import DataFrame
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
iris = load_iris()
input_df = DataFrame(data=iris['data'], columns=iris['feature_names'])
clf = DecisionTreeClassifier(max_depth=2)
clf.fit(input_df.values, iris['target'])
```
Second, import the `Sklearn2Docker` class and use it to build your container.
```python
from sklearn2docker.constructor import Sklearn2Docker
s2d = Sklearn2Docker(
classifier=clf,
feature_names=iris['feature_names'],
class_names=iris['target_names'].tolist()
)
s2d.save(name="classifier", tag="iris")
```
The name and tag arguments we passed to the `save` function are the name and tag of the Docker container we just built ([see: `docker tag`](https://docs.docker.com/engine/reference/commandline/tag/)). Below is an example of the output of the `s2d.save()` line we executed above.
```
Now attempting to run the command:
[docker build --file /tmp/tmpywbu3_ad/Dockerfile
--tag classifier:iris /tmp/tmpywbu3_ad]
=====================================================================
> Sending build context to Docker daemon
> Step 1/6 : FROM python:3.6
> ---> c1e459c00dc3
... output truncated ...
> Step 6/6 : ENTRYPOINT python /code/api.py
> ---> Running in bd61983358d9
> Removing intermediate container bd61983358d9
> ---> fa2041ac6d60
> Successfully built fa2041ac6d60
> Successfully tagged classifier:iris
=====================================================================
Success! You can now run your Docker container using the following command:
docker run -d -p 5000:5000 classifier:iris
```
You can now test your container by asking it to predict the same Iris dataset and return the predicted probabilities ([see: `predict_proba`](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier.predict_proba)) as a DataFrame.
```python
from os import system
system("docker run -d -p 5000:5000 classifier:iris && sleep 5")
from requests import post
from pandas import read_json
request = post("http://localhost:5000/predict_proba/split", json=input_df.to_json(orient="split"))
result = read_json(request.content.decode(), orient="split")
print(result.head())
```
```
setosa versicolor virginica
0 1 0.0 0.0
1 1 0.0 0.0
2 1 0.0 0.0
3 1 0.0 0.0
4 1 0.0 0.0
```
You can also request regular classification ([see: `predict`](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier.predict)). The format for the URL for your Docker container is as so:
```
http://[a]:[b]/[c]/[d]
a: the hostname of the container, defaults to `localhost`
b: the port of the container, defaults to 5000
c: one of `predict` or `predict_proba`, similar to the scikit-learn api
d: defaults to `split`; orient of the Pandas DataFrame JSON conversion*
```
(*: see [this documentation article](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_json.html) for more information about Pandas orients, and [this Github issue](https://github.com/pandas-dev/pandas/issues/18912#issuecomment-354430046) for a comparison; most of the time, setting the orient to `split` should do just fine)
```python
request = post(
"http://localhost:5000/predict/split",
json=input_df.to_json(orient="split")
)
```
```
prediction
0 setosa
1 setosa
2 setosa
3 setosa
4 setosa
```