A docker image provided by https://radioml.com/ which provides many of the primitives needed for radio machine learning experimentation.
The easiest way to use this image is to pull a pre-built version directly from docker hub
# Get the docker image from the whale cloud
docker pull radioml/full
# Run it (or use various running recipes below)
docker run -i -t radioml/full /bin/bash
Please note: your docker image max size must be >10GB for this build, please see Notes section.
git clone https://github.com/radioML/dockerRML.git rml
cd rml && sudo docker build -t radioml/radioml .
This will take a while to build, so find something to do for an hour
To launch in foreground terminal
docker run -i -t radioml/radioml /bin/bash
To launch in background with ssh up (needed before x2go)
docker run -d -P --name test_rml radioml/radioml
docker port test_rml 22
docker port test_rml 8888
Connect with CLI
sudo docker exec -i -t test_rml /bin/bash
or
ssh root@`docker port test_rml 22`
# use password radioml
Connect with x2go (good way to run GRC)
docker port test_rml 22
x2goclient
# set ssh ip and port from docker, login with root/radioml, use xfce as window manager
Connect with iPython Notebook (good way to run python experiments)
sudo docker exec -i -t test_rml /bin/bash
screen
cd /root/src/notebooks/
ipython notebook
now open http://docker_ip:8888 in the host browser
Launching GNU Radio Companion
gnuradio-companion
Running Keras Examples
cd /root/src/keras/examples
python mnist_mlp.py
Running KeRLym Examples
cd /root/src/kerlym/examples
KERAS_BACKEND='tensorflow' ./run_breakout.sh
Running PyOpenPNL Examples
cd /root/src/PyOpenPNL/examples
./simple_bnet.py