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README.md

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```
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## Running the codes in the container
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1. Run a container and enter it.
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### Network training
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1. Example : Network training
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```
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make run
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docker-compose up pytorch-train
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```
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2. Visualization using Tensorboard
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```
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docker-compose up tensorboard
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```
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2. Run a script inside the container
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1. Example : Training with greenhouse
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```
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cd /root/PyTorch-ENet
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python train.py -m train --save-dir save/dir --name [model_name] --dataset greenhouse --dataset-dir dataset/dir --with-unlabeled
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```
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1. Run tensorboard
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```
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cd /root/PyTorch-ENet
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tensorboard --logdir=./runs/
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```
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You can now use `docker-compose` to launch both the training script and `tensorboard`.
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```
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docker-compose up
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### ROS node
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1. Build (`catkin build`)
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```
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docker-compose up build
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```
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2. Launch `roscore`
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```
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docker-compose up pytorch-ros-master
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```
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3. Run a ROS node (`roslaunch pytorch_enet_ros pytorch_enet_ros.launch`)
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```
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docker-compose up pytorch-ros-node
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```

docker-compose.yml

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services:
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pytorch-train:
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container_name: pytorch-train
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image: aisl/pytorch:latest
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image: aisl/pytorch-enet:latest
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runtime: nvidia
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build: .
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command: >
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tensorboard:
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container_name: tensorboard
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image: aisl/pytorch:latest
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image: aisl/pytorch-enet:latest
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command: tensorboard --logdir /root/PyTorch-ENet/runs
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build: .
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ports:

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