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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.
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To install Docker on specific operating systems use the following links:
| Info |
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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 a Docker images image, which you can download from here. Currently, there are four tags (types) available.This is the type available: min: the minimal version for CPU instances.
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CPU
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min-cuda9-cudnn7: the minimal version for GPU instances.
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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.
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Library
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Version
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simplejson
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3.16.0
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requests
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2.20.0
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jsonpickle
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1.0
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python-dateutil
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2.7.4
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more-itertools
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4.3.0
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pydub
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0.23.0
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numpy
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1.15.3
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scipy
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1.1.0
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scikit-learn
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0.20.0
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xgboost
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0.80
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lightgbm
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2.2.1
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pillow
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5.3.0
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bokeh
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0.13.0
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pandas
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0.23.4
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tensorflow
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1.5.0
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keras
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2.2.4
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nltk
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3.3
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textblob
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0.15.1
| Note |
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ds-cuda9-cudnn7 has tensorflow-gpu==1.11.0 instead of tensorflow==1.5.0. |
CPU Instance
Execute the following command to start the RPE container data science version:
| Code Block |
|---|
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:
| Code Block |
|---|
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:
| Code Block |
|---|
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:
| Code Block |
|---|
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:
Option | Format | Description |
|---|---|---|
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| The container can use unlimited space for memory swapping. See Memory Swap for more information. "–restart=always" automatically restarts the docker container if the machine restarts. |
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| The RPE is accessible from the host port which is 5301 by default. You may change the host port to support multiple containers on the same node. |
|
| The default token is empty. A strong token is recommended. |
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| The log is mounted to /opt/remote_python_executor_log/by default. This location can be changed. |
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| The container’s name can be changed in case of multiple containers on the same node. |
Manage RPE
Since the RPE starts with the container, use the following commands to start, stop, restart, and remove the RPE.
| Code Block |
|---|
sudo docker start rpe sudo docker stop rpe sudo docker restart rpe sudo docker rm rpe |
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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:
| Tip |
|---|
Make sure |
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to run cd into the directory containing the Dockerfile. |
| Code Block |
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sudo docker build --no-cache -t snaplogic_custom_rpe . |
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