tum-dlr-automl-for-eo

Description

Towards a NAS Benchmark for Classification in Earth Observation

Quickstart

Installation

• Create the pipeline environment and install the tum_dlr_automl_for_eo package
• Before using the template, one needs to install the project as a package.

• First, create a virtual environment.

You can either do it with conda (preferred) or venv.

• Then, activate the environment
• Install the Naslib with the command below:

pip install -e git+https://github.com/emreds/NASLib.git#egg=naslib

• Then cd into the project's folder:

cd tum-dlr-automl-for-eo

• Finally, install the rest of the dependencies Run:

pip install -e .

How to Use?

• Main functions to trigger are under the ./scripts folder.
• There are many scripts, including helper functions like cluster_archs.py which is not necessary for the main functionality.
• nb101_dict_creator.py reads the pickle containing NB101 architectures and converts them into json dict format.
• path_sampler.py reads the NB101 dict and also the list of previously trained architectures from NB101(if any) and samples the new architures using random walk sampling.
• bash_slurm folder contains the bash scripts to submit training jobs to slurm using bash script. Every training job is submitted separately the have a certain level of fault tolerancy during the training.
• batch_train_submit.py submits the training jobs using bash scripts in batch.