trendingtechnology / daskmaskrcnn

Running Mask-RCNN on Dask with PyTorch

Using Dask with MaskRCNN

In this repository is a demo on how to use Dask with MaskRCNN in PyTorch.

All needed commands are in the Makefile

Requirements

Ubuntu PC/VM
Docker
Nvidia runtime for Docker
One or more GPUs

Getting Started

Before you do anything you will need to modify the makefile.

• First edit data_volume and replace /mnt/pipelines with a location on your computer where you will read the data from and write the data to. This will be mapped to /data inside the container.
• Next edit filepath. This is the location as it appears inside the docker container. As it is set by default inside the makefile the location is /data/people. The location people contains a number of files which will be processed by the model. /data/people will actually match to /mnt/pipelines/people outside the container.
• Edit output_path. This should be where there results will be written to.
• Place files you want to run MaskRCNN against in the folder you are mapping from in filepath. This is by default /mnt/pipelines/people

Then you must build the container in which we will execute everything.

make build

Then run the container

make run

Then we start the Dask scheduler

make start-scheduler

This also creates a tmux session named dask

Then we start the Dask workers

make start-workers

Each Dask worker will bind to a specific GPU

Finally we run the pipeline:

make run-pipeline

You should be able to view the Dask dashboard if you point your browser to port 8787 of your VM/PC.

You can then stop everything by simply running

make stop

Extra Commands

You can run bash in the container by running

make bash

Notes

This serves as a demo, performance is not optimal. The adding of annotations takes a long time and needs to be improved.

By default this demo is set up to use 4 GPUs, make sure you edit the makefile and adjust to your setup