Submarine Python SDK
Submarine Python SDK can runs on any machine and it will talk to Submarine Server via REST API. So you can install Submarine Python SDK on your laptop, a gateway machine, your favorite IDE (like PyCharm/Jupyter, etc.).
Furthermore, Submarine supports an extensible package of CTR models based on TensorFlow and PyTorch along with lots of core components layers that can be used to easily build custom models. You can train any model with model.train() and model.predict().
Prepare Python Environment to run Submarine SDKβ
Submarine SDK requires Python3.7+.
It's better to use a new Python environment created by Anoconda or Python virtualenv to try this to avoid trouble to existing Python environment.
A sample Python virtual env can be setup like this:
wget https://files.pythonhosted.org/packages/33/bc/fa0b5347139cd9564f0d44ebd2b147ac97c36b2403943dbee8a25fd74012/virtualenv-16.0.0.tar.gz
tar xf virtualenv-16.0.0.tar.gz
# Make sure to install using Python 3
python3 virtualenv-16.0.0/virtualenv.py venv
. venv/bin/activate
Install Submarine SDKβ
Install SDK from pypi.org (recommended)β
Starting from 0.4.0, Submarine provides Python SDK. Please change it to a proper version needed.
More detail: https://pypi.org/project/apache-submarine/
# Install latest stable version
pip install apache-submarine
# Install specific version
pip install apache-submarine==<REPLACE_VERSION>
Install SDK from source codeβ
Please first clone code from github or go to http://submarine.apache.org/download.html to download released source code.
git clone https://github.com/apache/submarine.git
# (optional) chackout specific branch or release
git checkout <correct release tag/branch>
cd submarine/submarine-sdk/pysubmarine
pip install .
Manage Submarine Experimentβ
Assuming you've installed submarine on K8s and forward the traefik service to localhost, now you can open a Python shell, Jupyter notebook or any tools with Submarine SDK installed.
Follow SDK experiment example to run an experiment.
Training a DeepFM modelβ
The Submarine also supports users to train an easy-to-use CTR model with a few lines of code and a configuration file, so they donβt need to reimplement the model by themself. In addition, they can train the model on both local on distributed systems, such as Hadoop or Kubernetes.
Follow SDK DeepFM example to try the model.