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
Nvidia runtime for Docker
One or more GPUs
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.
Then run the container
Then we start the Dask scheduler
This also creates a tmux session named dask
Then we start the Dask workers
Each Dask worker will bind to a specific GPU
Finally we run the 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
You can run bash in the container by running