curl https://cernbox.cern.ch/remote.php/dav/public-files/9T2zAjpvL2ee9jZ/baler.zip --output baler.zip tar -xf baler.zip cd baler docker run --mount type=bind,source=%cd%/projects/,target=/baler-root/projects --mount type=bind,source=%cd%/data/,target=/baler-root/data pekman/baler:latest --project=example_CFD --mode=train
- You must have Docker installed. See this guide
First download tutorial data and project directories.
curl https://cernbox.cern.ch/remote.php/dav/public-files/9T2zAjpvL2ee9jZ/baler.zip --output baler.zip
Unzip the files
tar -xf baler.zip
Enter the root directory of baler
cd baler
This process has created the following directory tree:
.
├── data
│ ├── example_CFD
│ │ └── example_CFD.npz
│ └── example_CMS
│ └── example_CMS.npz
└── projects
├── example_CFD
│ ├── compressed_output
│ ├── decompressed_output
│ ├── example_CFD_config.py
│ ├── model
│ ├── plotting
│ └── training
└── example_CMS
├── compressed_output
├── decompressed_output
├── example_CMS_analysis.py
├── example_CMS_config.py
├── example_CMS_preprocessing.py
├── model
├── plotting
└── training
For the tutorial example, we want to compress the data called example_CFD.npz
. The configuration file for this, including the compression ratio, number of training epochs, input data path etc is defined in projects/example_CFD/example_CFD_config.py
. the output of the compressed file is projects/example_CFD/compressed_output/
.
Here is the command to start training the network on the example_CFD data:
docker run \
-u ${UID}:${GID} \
--mount type=bind,source=${PWD}/projects/,target=/baler-root/projects \
--mount type=bind,source=${PWD}/data/,target=/baler-root/data \
pekman/baler:latest \
--project=example_CFD \
--mode=train
In this command, the "fixed" lines are:
docker run
invokes docker and specifies the running of a container-u ${UID}:${GID}
tells the container to use your username to create files--mount type=bind,source=${PWD}/projects/,target=/baler-root/projects
mounts the local (host) directory./projects
to the container at/projects
--mount type=bind,source=${PWD}/data/,target=/baler-root/data
mounts the local (host) directory./data
to the container at/data
pekman/baler:latest
specifies the container to run
And the user defined lines are:
--project=example_CFD
specifies the current "project", i.e. the directory for the config file and the output--mode=train
specifies the current running mode of Baler. We start by training the network on the data
To compress and decompress the data use --mode=compress
docker run \
-u ${UID}:${GID} \
--mount type=bind,source=${PWD}/projects/,target=/baler-root/projects \
--mount type=bind,source=${PWD}/data/,target=/baler-root/data \
pekman/baler:latest \
--project=example_CFD \
--mode=compress
To compress and decompress the data use --mode=decompress
docker run \
-u ${UID}:${GID} \
--mount type=bind,source=${PWD}/projects/,target=/baler-root/projects \
--mount type=bind,source=${PWD}/data/,target=/baler-root/data \
pekman/baler:latest \
--project=example_CFD \
--mode=decompress
After that training, compression, and decompression you can plot the performance of the procedure by using --mode=plot
. In this tutorial example, the performance plot is found in projects/exmaple_CFD/plotting/comparison.jpg
To compress and decompress the data use --mode=decompress
docker run \
-u ${UID}:${GID} \
--mount type=bind,source=${PWD}/projects/,target=/baler-root/projects \
--mount type=bind,source=${PWD}/data/,target=/baler-root/data \
pekman/baler:latest \
--project=example_CFD \
--mode=plot
Baler can be run with GPU acceleration, to allow the Docker image access to the system GPU you need to add --gpus all
right after docker run
in the run command:
docker run \
--gpus all \
-u ${UID}:${GID} \
--mount type=bind,source=${PWD}/projects/,target=/baler-root/projects \
--mount type=bind,source=${PWD}/data/,target=/baler-root/data \
pekman/baler:latest \
--project=example_CFD \
--mode=plot
If you would prefer not to use the Docker image provided by us, you may build the image yourself. This is achieved with:
docker build --rm -t myBaler:latest .
This image may be run using by specifying the image myBaler:latest
instead of our pekman/baler:latest
in the above base command.
Docker presents some obstacles to live development, if you wish changes to be made to a Docker container it must be rebuilt. This slows development and can be frustrating.
An alternative is to use Docker volumes (mounts between local and container file systems) to shadow the source files in the container.
An example command is given here:
docker run \
-u ${UID}:${GID} \
--mount type=bind,source=${PWD}/projects/,target=/baler-root/projects \
--mount type=bind,source=${PWD}/data/,target=/baler-root/data \
--mount type=bind,source=${PWD}/baler/modules,target=/baler-root/baler/modules \
--mount type=bind,source=${PWD}/baler/baler.py,target=/baler-root/baler/baler.py \
pekman/baler:latest \
--project=example_CFD \
--mode=train
Where:
--mount type=bind,source=${PWD}/baler/modules,target=/baler-root/baler/modules
mounts the local source code directory shadowing the source files built into the container--mount type=bind,source=${PWD}/baler/baler.py,target=/baler-root/baler/baler.py
mounts the main baler source file shadowing that in the container
Please note, this mounting does not permanently change the behavior of the container, for this the container must be rebuilt.
Docker is not available on all platforms, particularly high-performance or shared environments prefer not to use Docker due to security concerns. In these environments, Apptainer (formerly Singularity) is generally preferred and available.
In order to run Baler on a managed platform may require additional options to work with the system wide Apptainer configuration and respect good practice such as writing to appropriate storage areas, preferably not in on a shared storage space.
Create and enter workspace directory:
mkdir workspace
cd workspace
Download and unzip the example datasets:
wget https://cernbox.cern.ch/remote.php/dav/public-files/9T2zAjpvL2ee9jZ/baler.zip
unzip baler.zip
By default, Apptainer/singularity will write to your home area, this is not desirable on most remote environments. To control this:
export APPTAINER_CACHEDIR=${PWD}
export SINGULARITY_CACHEDIR=${PWD}
To build an Apptainer sandbox, a container completely constrained within a specified local directory, the following command can be run:
apptainer build --sandbox baler-sandbox docker://pekman/baler:latest
Where:
apptainer build
specifies the building of an Apptainer image--sandbox baler-sandbox/
specifies the output directory for the sandboxed containerdocker://ghcr.io/uomresearchit/baler:latest
specifies that a the Baler Docker image should be targeted
Now that the sandbox has been created, we can run the container.
apptainer run \
--no-home \
--no-mount bind-paths \
--pwd /baler-root \
--nv \
--bind ${PWD}/baler/projects/:/baler-root/projects \
--bind ${PWD}/baler/data:/baler-root/data \
baler-sandbox/ \
--project=example_CFD \
--mode=train
Where:
-no-home
specifies to not mount the user's home directory (small, networked storage on Blackett)--no-mount bind-paths
specifies to not mount the directories specified in the global Apptainer config--pwd /baler-root
sets the working directory for the container runtime--nv
allows the use of Nvidia graphics cards
apptainer run \
--no-home \
--no-mount bind-paths \
--pwd /baler-root \
--nv \
--bind ${PWD}/baler/projects/:/baler-root/projects \
--bind ${PWD}/baler/data:/baler-root/data \
baler-sandbox/ \
--project=example_CFD \
--mode=compress
apptainer run \
--no-home \
--no-mount bind-paths \
--pwd /baler-root \
--nv \
--bind ${PWD}/baler/projects/:/baler-root/projects \
--bind ${PWD}/baler/data:/baler-root/data \
baler-sandbox/ \
--project=example_CFD \
--mode=decompress
apptainer run \
--no-home \
--no-mount bind-paths \
--pwd /baler-root \
--nv \
--bind ${PWD}/baler/projects/:/baler-root/projects \
--bind ${PWD}/baler/data:/baler-root/data \
baler-sandbox/ \
--project=example_CFD \
--mode=plot