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#!/usr/bin/env bash | |
# | |
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger | |
# | |
# last updated: 2019-04-24 | |
PIC_BRANCH="dev" | |
# get PIConGPU profile | |
if [ ! -f "$PIC_PROFILE" ]; then |
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## Installing the environment | |
# * Download and install anaconda or miniconda | |
# * Activate the base environment | |
# `conda activate` (which maybe preceeded by `source ~/.bashrc`) | |
# * Install | |
# `conda env create --file picongpu-analysis-environment.yml` | |
# | |
# Alternatively, mamba could be used instead of conda which is supposed | |
# to be faster. | |
# In the base environment |
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#!/usr/bin/env bash | |
# | |
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger, Anton Lebedev | |
# | |
# last updated: 2022-06-13 | |
PIC_BRANCH="dev" | |
# get PIConGPU profile | |
if [ ! -f "$PIC_PROFILE" ]; then |
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#!/usr/bin/env bash | |
# | |
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger | |
# | |
# last updated: 2021-05-26 | |
PIC_BRANCH="dev" | |
# get PIConGPU profile | |
if [ ! -f "$PIC_PROFILE" ]; then |
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# See https://github.com/ComputationalRadiationPhysics/picongpu/issues/3530 | |
# and https://github.com/ComputationalRadiationPhysics/picongpu/issues/3506 | |
TBG_ADIOS2_configuration="'{ \ | |
\"adios2\": { \ | |
\"dataset\": { \ | |
\"operators\": [ { \ | |
\"type\": \"blosc\" \ | |
, \"parameters\": { \ | |
\"clevel\": \"1\" \ |
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#!/usr/bin/env bash | |
# | |
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger | |
# | |
# last updated: 2021-08-18 | |
# It is advisable to compile libraries on an interactive node | |
# > srun -p gpu2-interactive --gres=gpu:0 --ntasks=1 --pty --mem=8G -t 2:00:00 bash # get resources on a node | |
# > ./PIConGPU_0.6.0-dev_taurus-tud.sh | tee lib_inst.out 2>lib_inst.err # compile libraries |
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#!/bin/bash | |
# | |
# Script to transfer data from Juelich Supercomputing Center to HZDR. | |
# Call with | |
# > screen # open a screen session first to be able to logout from the | |
# # data mover system | |
# > exec ssh-agent bash # prepare shell to add ssh key passphrase to ssh-agent | |
# # in order to not type it all the time | |
# > ssh-add ~/.ssh/id_ed25519 # add ssh key passphrase to ssh-agent | |
# > xargs -a dirs.list -n 1 -P 5 ~/bin/data-transfer_judac.sh | tee transfer.out |
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#!/usr/bin/env bash | |
# | |
# Authors: Axel Huebl, Marco Garten, Klaus Steiniger | |
# | |
# last updated: 2022-03-02 | |
PIC_BRANCH="dev" | |
# get PIConGPU profile | |
if [ ! -f "$PIC_PROFILE" ]; then |
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## Installing the environment | |
# 1. Download and install Miniforge, see (https://github.com/conda-forge/miniforge?tab=readme-ov-file#install) | |
# ``` | |
# curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" | |
# bash Miniforge3-$(uname)-$(uname -m).sh | |
# ``` | |
# | |
# 2. Activate the base environment | |
# `conda activate` (which maybe preceeded by `source ~/.bashrc`) | |
# |
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## Install a kernel for the picongpu analysis environment | |
## | |
## Before runnign this script, see JSC documentation at | |
## (https://docs.jupyter-jsc.fz-juelich.de/github/FZJ-JSC/jupyter-jsc-notebooks/blob/documentation/index.ipynb) -> 'Install kernel with conda' | |
## | |
## The steps performed here are only the lsat ones after installing an own conda environment. | |
## | |
CONDA_TARGET_DIR=/p/project/<PROJECTNAME>/<USERNAME>/miniconda3 |
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