# dockerRML

**Repository Path**: misaka032/dockerRML

## Basic Information

- **Project Name**: dockerRML
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-11
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# RadioML Docker Image

A docker image provided by https://radioml.com/ which provides many of the primitives needed for radio machine learning experimentation.

## Docker Image Contents

 - **Base:**   Ubuntu 16.04 Xenial Xerus
 - **Remote:** ssh-server, x2go server + xfce4, ipython/jupyter notebook
 - **Misc:**   screen, tmux, vim, emacs, git, meld
 - **DL:**     Theano, TensorFlow, Keras, OpenAI Gym, KeRLym
 - **ML:**     Scikit-learn, OpenCV, PyOpenPNL, Pandas
 - **SDR:**    GNU Radio + several useful out-of-tree gr-modules

## Quickstart: Downloading and Running Pre-Built Docker-Hub Images

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
```

## Building the Container

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

## Running the Container

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

## Using the Image

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
```

## Notes

 - **GPU Support:** To build with GPU support for use with nvidia-docker, use dockerRML/full-GPU/Dockerfile 
 - **Image Size:** Current sizes are Full: 10.3GB, Full-GPU: 10.5GB, MinimalML: 4.0GB, MinimalSDR: 8.3GB
 - **Build Time:** Building Full on an 8 core i7-5930K within an RHEL 7.2 KVM instance on a non-SSD raid takes just over 2 hours, YMMV
 - **Docker BaseSize:** default docker basesize is 10GB, you must increase this to 20GB or 50GB by adding ' --storage-opt dm.basesize=50G ' to DOCKER_OPTS in /etc/default/docker or /etc/sysconfig/docker and restarting the docker daeming (**This must be done before starting the build**)