Weaver aims at providing a streamlined yet flexible machine learning R&D framework for high energy physics (HEP) applications. It puts particular emphases on:

• handling common HEP dataset formats (ROOT, HDF5, awkward array) efficiently, up to terabyte-level
• providing a simple way to perform input processing on-the-fly (e.g., sample selections, new variable definition, inputs transformation/standardization, sample reweighting, etc.)
• bridging the gap between development and production: neural networks are trained with PyTorch and exported to the ONNX format for fast inference (e.g., using ONNXRuntime)

Compared to its predecessor NNTools, Weaver simplifies the data processing pipeline by running all the pre-processing on-the-fly, without the necessity of creating intermediate transformed dataset (though it still supports that). The neural network training now uses the more widely adopted PyTorch instead of Apache MXNet.

• Weaver
  • Set up your environment
    • Install Miniconda (if you don't already have it)
    • Set up a conda environment and install the required packages
  • Prepare your configuration files
    • Data configuration file
    • Model configuration file
  • Start running!
    • Training
    • Prediction/Inference
    • Model exportation
  • More about data loading and processing
  • Performance consideration

The Weaver package requires Python 3.7+ and a number of packages like numpy, scikit-learn, PyTorch, etc. To run the neural network training, a Nvidia GPU with CUDA support is needed.

To manage the Python environment, we recommend using Miniconda. Below are the instructions to set up Miniconda and install the required packages. This needs to be done only once.

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
# Follow the instructions to finish the installation

# Make sure to choose `yes` for the following one to let the installer initialize Miniconda3
# > Do you wish the installer to initialize Miniconda3
# > by running conda init? [yes|no]

# disable auto activation of the base environment
conda config --set auto_activate_base false

Verify the installation is successful by running conda info and check if the paths are pointing to your Miniconda installation.

If you cannot run conda command, check if you need to add the conda path to your PATH variable in your bashrc/zshrc file, e.g.,

export PATH="$HOME/miniconda3/bin:$PATH"

# create a new conda environment
conda create -n weaver python=3.7

# activate the environment
conda activate weaver

# install the necessary python packages
pip install numpy pandas scikit-learn scipy matplotlib tqdm PyYAML

# install uproot for reading/writing ROOT files
pip install uproot lz4 xxhash

# install PyTables if using HDF5 files
pip install tables

# install onnxruntime if needs to run inference w/ ONNX models
pip install onnxruntime-gpu

# install pytorch, follow instructions for your OS/CUDA version at:
# https://pytorch.org/get-started
# pip install torch

To train a neural network using Weaver, you need to prepare:

• A YAML data configuration file describing how to process the input data.
• A python model configuration file providing the neural network module and the loss function.

The data configuration file is a YAML format file describing how to process the input data. It needs the following sections:

• selection (optional): event selection
• new_variables (optional): new variable definition
• inputs (required): input groups, variables for each group, variable transformation (mean/scale/min/max for standardization, length/pad values for padding/clipping, etc.)
• labels (required): label definition
• observers (optional): additional variables that are not used in the training, but will be added to the output files when running prediction
• weights (optional): instance weight information for sampling the instances during the training

An example of the data configuration file is data/ak15_points_pf_sv.yaml. For more details, check utils/data/config.py and utils/dataset.py.

The model configuration file specifies the neural network model and the loss function. It needs to implement the get_model function (required) and the get_loss fucntion (optional, default is torch.nn.CrossEntropyLoss()) in the following signatures:

def get_model(data_config, **kwargs):
    model = ... # instance of PyTorch Module
    model_info = {
        'input_names':...,
        'input_shapes':...,
        'output_names':...,
        'dynamic_axes':...,
        }
    return model, model_info

# `get_loss` is optional
# if not provided, fallback to use `torch.nn.CrossEntropyLoss()`
def get_loss(data_config, **kwargs):
    loss_func = ...
    return loss_func

An example of the model configuration file is networks/particle_net_pf_sv.py.

The train.py script is the top-level script to run for training a neural net, getting prediction from trained models, and exporting trained models to ONNX for production. To check all the command-line options for train.py, run python train.py -h. Examples for training, inference and model exportation are shown below:

python train.py --data-train '/path/to/train_files/*/*/*/*/output_*.root' \
 --data-config data/ak15_points_pf_sv.yaml \
 --network-config networks/particle_net_pf_sv.py \
 --model-prefix /path/to/models/prefix \
 --num-workers 3 --gpus 0,1,2,3 --batch-size 512 --start-lr 5e-3 --num-epochs 20 --optimizer ranger \
 | tee logs/train.log

Note:

• --data-train supports providing multiple entries, e.g., --data-train /path/to/A /path/to/B /path/to/C, and each entry also supports wildcards (*, ?, etc. -- The python glob package is used to parse the paths).
• for training, --model-prefix sets the prefix part of the paths to save model snapshots. At the end of each epoch, the model parameters will be saved to /path/to/models/prefix_epoch-%d_state.pt, and the optimizer states will be saved to /path/to/models/prefix_epoch-%d_optimizer.pt in case the training is interrupted and needed to be resumed from a certain epoch.

Once you have a trained model, you can load it to run prediction and test its performance, e.g.,

python train.py --predict --data-test '/path/to/test_files/*/*/*/*/output_*.root' \
 --data-config data/ak15_points_pf_sv_forTesting.yaml \ 
 --network-config networks/particle_net_pf_sv.py \
 --model-prefix /path/to/models/prefix_epoch-19_state.pt \
 --num-workers 3 --gpus 0,1,2,3 --batch-size 512 \
 --predict-output /path/to/output.root

Note:

• --data-test supports providing multiple entries, e.g., --data-test /path/to/A /path/to/B /path/to/C, and each entry also supports wildcards (*, ?, etc. -- The python glob package is used to parse the paths).
• for inference, one can use a data configuration file with different selection / observers sections, but the inputs and labels sections must remain unchanged.
• for inference, one can specify the full path of the model parameters in --model-prefix.
• --predict-output sets the path for the output file. Currently support saving to ROOT files (use .root extension) or awkward arrays (use .awkd extension).

When you are satisfied with the trained model, you could export it from PyTorch to ONNX format for inference (e.g., using ONNXRuntime):

python train.py -c data/ak15_points_pf_sv.yaml -n networks/particle_net_pf_sv.py -m /path/to/models/prefix_epoch-19_state.pt --export-onnx model.onnx  

To come...

Check also utils/dataset.py.

Loading data from disk can often become a bottleneck. Here are a few tips to get better data loading performance:

• When using ROOT files as inputs, prepare the files w/ LZ4 compression:

f.SetCompressionAlgorithm(ROOT::kLZ4); 
f.SetCompressionLevel(4);

and use larger buffer sizes for the TTree branches:

tree->Branch("x", &var, "x/F", /*bufsize=32000*/1024000);

(e.g., here the buffer size is set to 1024000 instead of the default 32000).

• Copy files to faster disk (e.g., SSD) if possible.
• Enable multiprocessing for data loading. Setting --num-workers to 2 or 3 generally gives a good performance. Setting this value too high could overload the disk and degrade the performance.
  • Note that the memory usage also increases with the number of workers. So if you are getting any memory-related errors, try reducing --num-workers.
  • Note that the workload splitting is file-based, so make sure the number of input files is not too small (i.e., make sure each worker is able to load several files to get samples from all classes).