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HML ENV

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Introduction

The hml-env provides a development environment for interdisciplinary research between high-energy physics and artificial intelligence. Based on the included software and libraries, it is divided into three environments:

  1. base: Provides the fundamental software for phenomenological research in high-energy physics and can generate simulated collision events.

  2. lite: Builds upon base by adding support for TensorFlow 2.16.

  3. dev: Builds upon lite by adding support for a complete machine learning framework, enabling comprehensive research.

Configurations

base lite dev
Ubuntu 22.04 ✔ ✔ ✔
CUDA 12.2.2 ✔ ✔
cuDNN 8 ✔ ✔
ROOT 6.28.12 ✔ ✔ ✔
Delphes 3.5.0 ✔ ✔ ✔
Madgraph5 (MG5) 3.4.2 ✔ ✔ ✔
HepMC 2.06.09 (by MG5) ✔ ✔ ✔
LHAPDF 6.5.4 (by MG5) ✔ ✔ ✔
PYTHIA 8.311 (by MG5) ✔ ✔ ✔
Tensorflow 2.16.1 (include Keras 3) ✔ ✔
PyTorch 2.3.0 ✔
Jax 0.4.28 ✔

  • lite and dev are built based on the image nvidia/cuda:12.2.2-cudnn8-devel-ubuntu22.04. Before starting the container, you need to install the NVIDIA Container Toolkit. Please refer to here for installation instructions.

  • Delphes is installed separately for performance considerations during compilation. The software installed by MG5 has also undergone some minor modifications to utilize all CPU cores during compilation.

  • All three machine learning frameworks support GPU and can be used as backends for Keras versions 3 and above.

  • The memory growth of TensorFlow and Jax is limited to prevent a single program from occupying the entire GPU memory at once.

Use cases

  1. Launching the Container and Generating Events with Madgraph5:

    docker run -it --rm star9daisy/hml-env:3.0.0-base

    After entering the container, type mg5_aMC to open the Madgraph5 command-line interface.

  2. Starting the Container in the Background and Attaching with VSCode:

    docker run -itd --gpus all star9daisy/hml-env:3.0.0-dev

    After starting, attach VSCode to the running container, allowing you to open the container as if it were a local directory.

  3. The image includes an SSH service, and you can add port mappings and passwords when starting the container:

    docker run -itd --gpus all --publish <port>:22 --env PASSWORD=<password> star9daisy/hml-env:3.0.0-dev

    When this container is running on a remote server, doing so can save a step: changing from local → remote → container to local → container. Use the following command to SSH directly into the container:

    ssh -p <port> root@<remote server ip>