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AIMET Installation and Usage

This page provides instructions to build, install and use the AIMET software for model optimizations. Please follow the instructions in the order provided, unless specified otherwise.

Requirements

The AIMET package requires the following host platform setup:

To use the GPU accelerated training modules an Nvidia CUDA enabled GPU with a minimum Nvidia driver version of 361+ is required. Using the latest driver is always recommended, especially if using a newer GPU. Both CUDA and cuDNN (the more advanced CUDA interface) enabled GPUs are supported.

Recommended host system hardware requirements:

  • Intel i7 multicore CPU w/hyperthreading
  • GPU: Nvidia GeForce GTX 1080 or Tesla K80
  • 16+ GB RAM
  • 500GB+ SSD hard drive

While these are not minimum requirements, they are recommended for good performance when training large networks.

Get the code

To obtain the code, first define a workspace and follow these instructions:

WORKSPACE="<absolute_path_to_workspace>"
mkdir $WORKSPACE && cd $WORKSPACE
git clone https://github.com/quic/aimet.git

Setup the environment

In order to build and run AIMET code, several dependencies are required (such as python, cmake, tensorflow, pytorch, etc). A docker file with all prerequisites and dependencies is available here. Either install the dependencies on your machine using this Dockerfile as a guide, or just build and launch the docker using the instructions here.

Build code and install

Follow these instructions to build the AIMET code:

NOTE: If you are inside the docker, set WORKSPACE="<absolute_path_to_workspace>" again.

cd $WORKSPACE 
mkdir build && cd build
cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON ../aimet && make -j8

After a successful build, AIMET package can be installed using the following instructions:

cd $WORKSPACE/build
make install

Set package and library paths

Once the installation step is complete, AIMET package would be available at $WORKSPACE/build/staging/lib/, which should get reflected in some environment variables:

export PYTHONPATH=$WORKSPACE/build/staging/lib/x86_64-linux-gnu:$WORKSPACE/build/staging/lib/python:$PYTHONPATH
export LD_LIBRARY_PATH=$WORKSPACE/build/staging/lib/x86_64-linux-gnu:$WORKSPACE/build/staging/lib/python:$LD_LIBRARY_PATH

At this point, we are all set to use AIMET!

Usage examples and documentation

The following steps would generate AIMET documentation including the user guide, examples and API documentation at $WORKSPACE/build/staging/Docs:

cd $WORKSPACE/build
make doc

To begin navigating the documentation, open the page $WORKSPACE/build/staging/Docs/user_guide/index.html on any browser.

Docker information

Code may optionally be developed inside a development docker container. This section describes how to build a docker image and launch a container using the provided Dockerfile.

Build docker image manually

Follow these instructions to build the docker:

WORKSPACE="<absolute_path_to_workspace>"
docker_image_name="aimet-dev-docker:<any_tag>"
docker_container_name="aimet-dev-<any_name>"
docker build -t ${docker_image_name} -f $WORKSPACE/aimet/Jenkins/Dockerfile .

NOTE: Feel free to modify the docker_image_name and docker_container_name as needed.

Start docker container manually

Ensure that a docker named $docker_container_name is not already running; otherwise remove the existing container and then start a new container as follows:

docker ps -a | grep ${docker_container_name} && docker kill ${docker_container_name}

docker run --rm -it -u $(id -u ${USER}):$(id -g ${USER}) \
  -v /etc/passwd:/etc/passwd:ro -v /etc/group:/etc/group:ro \
  -v ${HOME}:${HOME} -v ${WORKSPACE}:${WORKSPACE} \
  -v "/local/mnt/workspace":"/local/mnt/workspace" \
  --entrypoint /bin/bash -w ${WORKSPACE} --hostname aimet-dev ${docker_image_name}

NOTE

  • Feel free to modify the above docker run command based on the environment and filesystem on your host machine.
  • If nvidia-docker 2.0 is installed, then add --gpus all to the docker run commands in order to enable GPU access inside the docker container.
  • If nvidia-docker 1.0 is installed, then replace docker run with nvidia-docker run in order to enable GPU access inside the docker container.

Build and launch docker using script

The development docker may also be built and launched in interactive mode using the provided script as follows:

cd aimet
./buildntest.sh -i

If additional directories need to be mounted, use -m option with list of targeted directories separated by space surrounded by double quotes ""

cd aimet
./buildntest.sh -i -m "sample_dir_1 sample_dir2"

To help construct user-specific docker commands, the dry-run option (-n) can be used with the above script which prints out the equivalent docker command(s):

cd aimet
./buildntest.sh -i -n
# OR
./buildntest.sh -i -n -m "sample_dir_1 sample_dir2"