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Overview

Remote Python Script Snap has the ability to execute a Python script natively on local/remote Python executors. Remote Python Executor (RPE) can be installed either on the same node as the Snaplex or a remote node that is accessible by the Snaplex.

Steps

1. Establish an SSH connection to the node.

2. Install Docker CE.

3. Create and start the RPE container.

SSH to the Node

Establish an SSH connection to the node where you want to install the RPE. Root access may be required for the latter steps.

Install Docker

Follow the instructions on the official Docker site to install Docker CE on your machine. Click here for Ubuntu, here for CentOS, and here for Windows. If using Amazon Linux AMI, click here.

Limitation

The Remote Python Execute can be installed only on Groundplexes and not on Cloudplexes.

Start RPE

Docker Images

A Docker container is an instance created from a Docker image. We provide RPE as Docker images which you can download from here. Currently, there are four tags (types) available.

1. min: the minimal version for CPU instances.

2. ds: the data science version for CPU instances containing recommended libraries for data science.

3. min-cuda9-cudnn7: the minimal version for GPU instances.

4. ds-cuda9-cudnn7: the data science version for GPU instances containing recommended libraries for data science.

Below is the list of Python libraries in the ds and ds-cuda9-cudnn7 tags. 

CPU Instance

Execute the following command to start the RPE container data science version:

sudo docker pull snaplogic/rpe:ds
sudo docker run --memory-swap="-1" --restart=always -dti -p 5301:5301 -e "REMOTE_PYTHON_EXECUTOR_TOKEN=" -v /opt/remote_python_executor_log/:/opt/remote_python_executor_log/ --name=rpe snaplogic/rpe:ds

Execute the following command to start the RPE container minimal version:

sudo docker pull snaplogic/rpe:min
sudo docker run --memory-swap="-1" --restart=always -dti -p 5301:5301 -e "REMOTE_PYTHON_EXECUTOR_TOKEN=" -v /opt/remote_python_executor_log/:/opt/remote_python_executor_log/ --name=rpe snaplogic/rpe:min 

GPU Instance

Execute the following command to start the RPE container data science version for GPU instance:

sudo docker pull snaplogic/rpe:ds-cuda9-cudnn7
sudo docker run --runtime=nvidia --memory-swap="-1" --restart=always -dti -p 5301:5301 -e "REMOTE_PYTHON_EXECUTOR_TOKEN=" -v /opt/remote_python_executor_log/:/opt/remote_python_executor_log/ --name=rpe snaplogic/rpe:ds-cuda9-cudnn7 

Execute the following command to start the RPE container minimal version for GPU instance:

sudo docker pull snaplogic/rpe:min-cuda9-cudnn7
sudo docker run --runtime=nvidia --memory-swap="-1" --restart=always -dti -p 5301:5301 -e "REMOTE_PYTHON_EXECUTOR_TOKEN=" -v /opt/remote_python_executor_log/:/opt/remote_python_executor_log/ --name=rpe snaplogic/rpe:min-cuda9-cudnn7   

Options

The following table describes each option:

Manage RPE

Since the RPE starts with the container, use the following commands to start, stop, restart and remove the RPE.

sudo docker start rpe
sudo docker stop rpe
sudo docker restart rpe
sudo docker rm rpe

Custom Image

Download the custom RPE package. This package contains Dockerfile and others. You can modify the Dockerfile and add required libraries in requirements.txt. Then, run the following command to build the Docker image.

Tip: Make sure to cd into the directory containing the Dockerfile.

  sudo docker build --no-cache -t snaplogic_custom_rpe .

Once the image is built, execute the following command to create and start the custom RPE container.

sudo docker run --memory-swap="-1" --restart=always -dti -p 5301:5301 -e "REMOTE_PYTHON_EXECUTOR_TOKEN=" -v /opt/remote_python_executor_log/:/opt/remote_python_executor_log/ --name=rpe snaplogic_custom_rpe

Related content

• Docker installation:

  • Ubuntu

  • CentOS

  • Windows

• RPE Docker image

• RPE package download