Set of conda environments for KAUST linux machines and clusters at KAUST.
First of all install Miniconda following instructions at: https://github.com/kaust-rccl/ibex-miniconda-install
For each environment, an environment_$ENVNAME.yml
file is provided alongside with a
shell installation script install_$ENVNAME.sh
.
Simply run:
./install_$ENVNAME.sh $PATH_TO_DEV_LIB
where $PATH_TO_DEV_LIB
contains the path of the library to install in developer mode
(if empty, the library will be installed in user mode using pip or conda installers).
-
Scientific: CPU installation of basic scientific stack (customize this to your needs)
-
Azure_CLI: CPU installation of azure-cli (to be able to programmatically download Volve data)
-
MPI4PY: CPU installation of basic scientific stack with MPI4Py
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PyLops: CPU installation of PyLops and its dependencies
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PyLops_36: CPU installation of PyLops and its dependencies
-
PyLops_37: CPU installation of PyLops and its dependencies
-
PyLops_cupy: GPU-enabled installation of PyLops with Cupy (cuda11.1.0)
-
PyLops_cupy_cusignal: GPU-enabled installation of PyLops with Cupy + CuSignal (cuda10.2.89)
-
PyLops_cupy_cusignal_3090: GPU-enabled installation of PyLops with Cupy + CuSignal (cuda11.0 to be used with GeForce RTX 3090)
-
PyLops_cupy_mpi4py_3090: GPU-enabled installation of PyLops with Cupy + PyLops (cuda11.8 to be used with GeForce RTX 3090)
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PyLops_gpu: GPU-enabled installation of PyLops-gpu with PyTorch + Cupy (cuda10.2.89)
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PyLops_gpu_3090: GPU-enabled installation of PyLops-gpu with PyTorch + Cupy (cuda11.0 to be used with GeForce RTX 3090)
-
PyLops_diffusers_3090: GPU-enabled installation of PyLops-gpu with PyTorch + Cupy + Diffusers (cuda11.8 to be used with GeForce RTX 3090)
-
PyLops_dist: CPU installation of PyLops-distributed and its dependencies
-
Devito: CPU installation of Devito
-
EGS: Stanford GPU-enabled FD propagators and PDE-constrained inversion
- Jupyter Extensions: install Jupyter extensions (e.g., TOC)
- Curvelops: install FFWT, Curvelab and Curvelops (works only with PyLops_36 and PyLops_37)