A machine learning tool for fusion simulation validation.
Project description
Icarus
Machine learning for fusion simulation validation. Named after the Icarus of greek mythology - because to reach for the stars, you risk a little sunburn.
The purpose of this package is to provide a set of machine learning tools that engineers can use to assess the agreement between an experiment and simulation; that is, to validate the simulation with experimental data. When the experiment does not agree with the simulation the tools should provide the engineer with a probable reason for the mismatch to allow further investigation and diagnosis.
Installation
Standard Installation (PyPI)
You can install icarus from PyPi as follows:
pip install icarus
Developer Installation
Clone icarus
to your local system and cd
to the root directory of icarus
. Ensure that your virtual environment is activated and run from the icarus
root directory:
pip install -e .
PyTorch
Icarus requires the latest stable version of PyTorch. The installation process varies depending on your hardware and operating system. Please follow the appropriate instructions below:
CPU Installation:
If you do not have access to NVIDIA GPUs, install the CPU version of PyTorch. Use the following commands based on your operating system:
- Windows/macOS:
pip3 install torch
- Linux:
pip3 install torch --index-url https://download.pytorch.org/whl/cpu
GPU Installation (NVIDIA CUDA):
If you have access to NVIDIA GPUs and want to leverage CUDA for faster computation, use these commands (note: CUDA is not available on MacOS):
- Linux:
pip3 install torch
- Windows:
pip3 install torch --index-url https://download.pytorch.org/whl/cu121
Note: The CUDA version (cu121
in this example) may change. Always check the official PyTorch website for the most up-to-date installation instructions and CUDA version compatibility.
Verifying Installation:
After installation, you can verify that PyTorch is installed correctly by running:
import torch
print(torch.__version__)
print(torch.cuda.is_available()) # returns True if CUDA available and properly installed
Getting Started
The examples folder includes a sequence of examples using icarus
: to generate the dataset and train an ml model from the suite available on the generated data.
Contributors
- Arjav Poudel, UK Atomic Energy Authority, (arjavp-ukaea)
- Baris Cavusoglu, UK Atomic Energy Authority, (barisc-ukaea)
- Luke Humphrey, UK Atomic Energy Authority, (lukethehuman)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for icarus_fusion-0.1.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 80519993d71aba997d51f11511b78104a5ac56fe12d73f4acffd03ef92ad55a9 |
|
MD5 | 2f7c0b862851a69dfa05083b776df06e |
|
BLAKE2b-256 | ec58e6a1bfdb2b0fa851b36c3f5f9fd3313958ef006903ad220475ba52c3ceda |