/quantlab

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QuantLab

QuantLab is a tool to train, compare and deploy quantized neural networks (QNNs). It was developed on top of the PyTorch deep learning framework, and it is a purely-command-line-based tool.

QuantLab consists of two main components:

  • a library of quantization tools (quantlib);
  • organising software to manage machine learning (ML) experiments (systems and manager packages, as well as the main.py façade script).

Installation and usage

At the moment, QuantLab can run only on UNIX systems. Therefore, we will conventionally use the UNIX separator / when writing filesystem paths and the dollar sign $ to denote the command line prompt. Analogously, we will denote your home folder on the machine on which you are working by ~.

Create an Anaconda environment and install quantlib

After cloning QuantLab, navigate to the repository and initialise the quantlib sub-module:

$ cd ~
$ git clone [...]
$ cd ~/QuantLab
$ git submodule update --init

We call ~/QuantLab the QuantLab home.

Then, use Anaconda to install QuantLab's prerequisites

$ conda env create -f quantlab.yml

Note: at the moment of writing, the PyTorch Anaconda channel does not provide a distribution package for the combination Python 3.8/PyTorch 1.9 supporting a CUDA version lower than 10.2. Therefore, if you plan to install QuantLab with GPU support, ensure that your CUDA version is not lower than 10.2.

To use new PyTorch versions use

# PyTorch 1.13.1 (Recommended)
$> conda create --name pytorch-1.13
$> conda activate pytorch-1.13
$> conda config --env --add channels conda-forge
$> conda config --env --add channels pytorch 
$> conda install python=3.8 pytorch=1.13.1 pytorch-gpu torchvision=0.14.1 torchtext=0.14.1 torchaudio=0.13.1 cudatoolkit=11.6 -c pytorch -c conda-forge
$> conda install ipython packaging parse setuptools tensorboard tqdm networkx python-graphviz scipy pandas ipdb onnx onnxruntime einops yapf tabulate
$> pip install setuptools==59.5.0 torchsummary parse coloredlogs netron
# PyTorch 1.12.1
$> conda create --name pytorch-1.12
$> conda activate pytorch-1.12
$> conda config --env --add channels conda-forge
$> conda config --env --add channels pytorch 
$> conda install python=3.8 pytorch=1.12.1 torchvision=0.13.1 torchtext=0.13.1 torchaudio=0.12.1 -c pytorch -c conda-forge
$> conda install ipython packaging parse setuptools tensorboard tqdm networkx python-graphviz scipy pandas ipdb onnx onnxruntime einops yapf tabulate
$> pip install setuptools==59.5.0 torchsummary parse coloredlogs netron

After creating the Anaconda environment, it can make your life easier to install the quantlib quantization library in your Anaconda environment:

$ conda activate quantlab
(quantlab) $ pip install -e quantlib

Also, this installation is mandatory in case you intend to run some of the Jupyter notebooks contained in the examples folder.

Configure storage drives

QuantLab was originally conceived to accelerate and organise experiments on workstations and (some) supercomputers. We conceived a configuration mechanism to optimise execution on such systems, which must be set properly before using QuantLab. You might find this part of the installation process slightly annoying if you have cloned and installed QuantLab to your personal computer. We plan to make this step more transparent in the future, but for the moment there is no workaround: we are sorry.

An ML experiment takes as input a machine learning system and produces logs as outputs. The machine learning system itself consists of three main parts:

  • the data set;
  • the hypothesis space (usually a space of parametric functions);
  • the learning algorithm (in the case of deep neural networks this is an iterative application of both the backpropagation algorithm and a variant of stochastic gradient descent).

The logs include:

  • checkpoints of the state of the system during the training run;
  • statistics collected during the training run.

If you are used to reasoning in abstract terms, you'd probably never had to think that the access time to whatever resource you need is greater than zero, or that you can not store infinite amounts of information. In practice, whereas the hypothesis space and the learning algorithm of an ML system occupy little more storage capacity than what is needed by their defining code, data and logs can take up gigabytes of storage space and quickly fill up the most capable storage devices. Moreover, when training a machine learning model, you'd like to have high access speed to your data, since you don't want that the process which runs your program wastes time into waiting for data to be moved from/to storage to/from the computer's main memory. Instead, you usually care less about spending a longer time storing the checkpoints and statistics about your training run: you will need them later for analysis, and you just care that whatever information you want to keep, you have sufficiently much space to store it.

Modern workstations usually include storage devices of two types that allow to trade access speed for storage capacity:

  • hard disk drives (HDD), with very large capacity but slow access;
  • solid state drives (SSD), which have smaller capacity than a typical HDD, but also provide much faster access.

Imagine that your machine has exactly one HDD and exactly one SSD. Also, suppose that your operating system has a filesystem on which the devices are mounted to /dev/sda1 and /dev/sdb1, respectively. Finally, suppose that your filesystem exposes two folders:

  • /scratcha mounted on /dev/sda1 (i.e., physically being the HDD);
  • /scratchb mounted on /dev/sdb1 (i.e., physically being the SSD).

If you

(quantlab) $ ls QuantLab/configure

you will find a JSON file and three BASH scripts:

  • storage_cfg.json;
  • storage.sh;
  • problem.sh;
  • topology.sh.

To configure QuantLab, you need to tell it where the data for and logs of your experiments should be physically stored. You do this by editing storage_cfg.json. Continuing the example above, you would set

(quantlab) $ vim QuantLab/configure/storage_cfg.json
$ 
{
    'data': '/scratchb',
    'logs': '/scratcha'
}
:wq
(quantlab) $

so that the tool will fetch your data from the SSD drive and write logs to the HDD drive. Note that you must specify absolute (not relative) paths. Running the storage.sh script creates mock-up QuantLab homes under both folders:

(quantlab) $ bash configure/storage.sh
(quantlab) $ ls /scratchb/QuantLab  # this folder now exists
(quantlab) $ ls /scratcha/QuantLab  # this folder now exists

QuantLab is shipped with example problem packages (CIFAR10, ILSVRC12), each of which contains one or more topology sub-packages (e.g., VGG). When running an experiment in QuantLab, its abstractions will look for data in a data sub-folder under the chosen problem package, independently of the chosen network topology. Instead, they will log results in a logs sub-folder under the chosen topology sub-package. The problem.sh and topology.sh scripts actually create such folders on the devices specified at configuration time, and then create links to these folders under the problem and topology sub-package:

(quantlab) $ bash configure/problem.sh CIFAR10       # ~/QuantLab/systems/CIFAR10/data     -- now points to -> /scratchb/QuantLab/systems/CIFAR10/data
(quantlab) $ bash configure/topology.sh CIFAR10 VGG  # ~/QuantLab/systems/CIFAR10/VGG/logs -- now points to -> /scratcha/QuantLab/systems/CIFAR10/VGG/logs

Run a QuantLab experiment

You can now run your first QuantLab experiment! Configure the experiment

(quantlab) $ python main.py --problem=CIFAR10 --topology=VGG configure
[QuantLab] Experimental unit #0.
[QuantLab] Experimental unit's logs folder created at <~/QuantLab/systems/CIFAR10/VGG/logs/exp0000>.

and train the system

(quantlab) $ python main.py --problem=CIFAR10 --topology=VGG train --exp_id=0
[QuantLab] Experimental unit #0.
[QuantLab] No checkpoint found at <~/QuantLab/systems/CIFAR10/VGG/logs/exp0000/fold0/saves>.
...

QuantLab depends on TensorBoard for enabling useful analysis and data visualisations. After a training run has reached completion, you can inspect the logged statistics by issuing the following command:

(quantlab) $ tensorboard --log_dir=~/QuantLab/systems/CIFAR10/VGG/logs/exp0000 --port=6006

Adding problems and topologies

Whenever you want to start working on a new data set, you should invoke the configuration script problem.sh. Assuming that your data set is codenamed XYZ, issue the following commands:

(quantlab) $ cd ~/QuantLab
(quantlab) $ bash configure/problem.sh XYZ
[QuantLab] Remember to prepare the data for problem XYZ at </scratchb/QuantLab/systems/XYZ/data>."
(quantlab) $ ls ~/QuantLab/systems/XYZ  # this folder now exists

In particular, the warning [QuantLab] Remember to prepare the data for problem XYZ at </scratchb/QuantLab/systems/XYZ/data>. is raised to the user because QuantLab has no way to know how to prepare the files representing the data points of your data set. In some cases, the raw-but-prepared data points might already be available on the computing systems where you installed QuantLab; for example, this is the case for many laboratory workstations that are shared amongst many people. In such cases, assuming that the data set is stored in some folder /scratchc/ml_datasets/XYZ, we suggest the following workaround:

(quantlab) $ cd /scratchb/QuantLab/systems/XYZ
(quantlab) $ rm -r data
(quantlab) $ ln -s /scratchc/ml_datasets/XYZ data

In other words: you should manually replace the data folder automatically created by problem.sh inside /scratchb/QuantLab/systems/XYZ with a soft-link to the folder containing the raw-but-prepared data points.

To prepare the (empty) working files for a new network topology ABC that you want to apply to the problem XYZ, invoke the configuration script topology.sh:

(quantlab) $ cd ~/QuantLab
(quantlab) $ bash configure/topology.sh XYZ ABC
(quantlab) $ ls ~/QuantLab/systems/XYZ/ABC  # this folder now exists

Accelerating training experiments with Horovod

Apart from CPU and GPU training, QuantLab supports multi-GPU training by wrapping PyTorch networks inside nn.DataParallel constructs. If you need even more training speed, QuantLab also supports multi-process (and therefore potentially multi-node) training runs via Horovod.

Horovod is a software tool developed to simplify the execution of distributed training runs for deep learning models. It is compatible with the most popular deep learning frameworks, including PyTorch and TensorFlow. In case you want to benefit from this performance boost, you need to install Horovod according to the official guide.

Assuming that you installed Horovod inside the quantlab Anaconda environment, you can try the following to verify that everything works correctly:

(quantlab) $ cd ~/QuantLab
(quantlab) $ python main.py --problem=XYZ --topology=ABC configure
[QuantLab] Experimental unit #0.
[QuantLab] Experimental unit's logs folder created at <~/QuantLab/systems/CIFAR10/VGG/logs/exp0000>.
(quantlab) $ horovodrun -np 1 -H localhost:1 python main.py --problem=XYZ --topology=ABC train --exp_id=0
[QuantLab] Experimental unit #0.
[QuantLab] No checkpoint found at <~/QuantLab/systems/CIFAR10/VGG/logs/exp0000/fold0/saves>.

Notice

Licensing information

Both QuantLab and quantlib are distributed under the Apache 2.0 license. QuantLab and quantlib have been developed at the Integrated Systems Laboratory (IIS) of ETH Zürich, which is the copyright owner of both tools.

In case you are planning to use QuantLab and quantlib in your projects, you might also want to consider the licenses under which the packages on which they depend are distributed:

Authors