tensorQTL 1.0.10-foss-2023a-CUDA-12.1.1

tensorQTL is a GPU-enabled QTL mapper, achieving ~200-300 fold faster cis- and trans-QTL mapping compared to CPU-based implementations.

Accessing tensorQTL 1.0.10-foss-2023a-CUDA-12.1.1

To load the module for tensorQTL 1.0.10-foss-2023a-CUDA-12.1.1 please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load bear-apps/2023a
module load tensorQTL/1.0.10-foss-2023a-CUDA-12.1.1

There is a CPU version of this module: tensorQTL 1.0.10-foss-2023a

BEAR Apps Version

2023a

Architectures

EL8-icelake (GPUs: NVIDIA A100, NVIDIA A30)

The listed architectures consist of two parts: OS-CPU. The OS used is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.

Extensions

• deprecated 1.2.18
• pandas_plink 2.2.9
• Pgenlib 0.90.2
• qtl 0.1.10
• tensorqtl 1.0.10
• wrapt 1.16.0
• zstandard 0.23.0

More Information

For more information visit the tensorQTL website.

Dependencies

This version of tensorQTL has a direct dependency on: bx-python/0.10.0-foss-2023a CUDA/12.1.1 dask/2023.9.2-foss-2023a foss/2023a matplotlib/3.7.2-gfbf-2023a pyBigWig/0.3.22-gfbf-2023a Python/3.11.3-GCCcore-12.3.0 PyTorch/2.1.2-foss-2023a-CUDA-12.1.1 SciPy-bundle/2023.07-gfbf-2023a Seaborn/0.13.2-gfbf-2023a tqdm/4.66.1-GCCcore-12.3.0 xarray/2023.9.0-gfbf-2023a

Other Versions

These versions of tensorQTL are available on the BEAR systems (BlueBEAR and BEAR Cloud VMs). These will be retained in accordance with our Applications Support and Retention Policy.

Last modified on 4th April 2025