Copyright (c) 2021 Antmicro
This repository contains tasks for laboratories for the "Optimization of Deep Learning applications for IoT devices" course.
Each of the l<number>_<topic> directories in the dl_in_iot_course module contain a separate README with the list of tasks.
Please follow the links to go to the list of tasks:
NOTE: Git LFS tool is required to pull the large files, such as models.
Install it before cloning the repository.
To clone the repository with all models, run:
git clone --recursive https://github.com/antmicro/dl-in-iot-course.git
cd dl-in-iot-course/models
git lfs pull
cd ..
To provide a consistent environment for running tasks, the Sylabs Singularity image definitions are available in the environments directory.
To get started with the Singularity environment, check the Quick Start guide for installation and running steps.
To build the SIF files from image definitions, run:
cd environments/
mkdir tmp
env SINGULARITY_TMPDIR=(pwd)/tmp sudo -E singularity build development-environment.sif development-environment.def
cd ..
NOTE: Use development-environment-gpu.def definition for the GPU-enabled version of the image.
To start working in the container, run:
singularity shell environments/development-environment.sif
To use the GPU-enabled container (only for NVIDIA with CUDA), run:
singularity shell --nv environments/development-environment-gpu.sif
Singularity by default enables using GUI, makes all of the devices available from the container, and mounts the /home directory by default.
In order to handle submodules easily, all of the executable scripts should be started from the root of the repository, i.e.:
python3 -m dl_in_iot_course.l02_quantization.quantization_experiments -h