Aufgrund einer Störung des s3 Storage, könnten in nächster Zeit folgende GitLab Funktionen nicht zur Verfügung stehen: LFS, Container Registry, Job Artifacs, Uploads (Wiki, Bilder, Projekt-Exporte). Wir bitten um Verständnis. Es wird mit Hochdruck an der Behebung des Problems gearbeitet. Weitere Informationen zur Störung des Object Storage finden Sie hier: https://maintenance.itc.rwth-aachen.de/ticket/status/messages/59-object-storage-pilot

Commit 304a8532 authored by laochailan's avatar laochailan
Browse files

updated outdated information in the README

parent 738dd515
...@@ -13,20 +13,19 @@ Dependencies ...@@ -13,20 +13,19 @@ Dependencies
^^^^^^^^^^^^ ^^^^^^^^^^^^
- MPI - MPI
- HDF5 >=1.10.1 - HDF5 >=1.10.1 (fallback provided)
- yaml-cpp >= 0.6 (fallback provided) - nlohmann_json (fallback provided)
- fmt (fallback provided) - fmt (fallback provided)
The python package requires The python package requires
- pyyaml
- h5py - h5py
- numpy - numpy
Building loadleveller Building loadleveller
^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^^^^^
If you don’t have meson installed install it from your distributions package manager or If you don’t have meson installed install it from your distributions package manager or
:: ::
pip3 install --user meson ninja pip3 install --user meson ninja
...@@ -54,9 +53,9 @@ How it works ...@@ -54,9 +53,9 @@ How it works
For details on how to implement and use your MC simulation with loadleveller see the `example project <https://git.rwth-aachen.de/lukas.weber2/ising>`_. After you build it and get an executable. You need to create a job file containing the set of parameters you want to calculate. This is conveniently done with the ``loadleveller.taskmaker`` module. For details on how to implement and use your MC simulation with loadleveller see the `example project <https://git.rwth-aachen.de/lukas.weber2/ising>`_. After you build it and get an executable. You need to create a job file containing the set of parameters you want to calculate. This is conveniently done with the ``loadleveller.taskmaker`` module.
You can start it (either on a cluster batch system or locally) using the ``yrun JOBFILE`` command from the python package. It will calculate all the tasks defined in the jobfile in parallel. Measurements and checkpoints will be saved in the ``JOBFILE.data`` directory. After everything is done, measurements will be averaged and merged into the human-readable ``JOBFILE.results.yml`` file. You may use the ``loadleveller.mcextract`` module to conveniently extract results from it for further processing. You can start it (either on a cluster batch system or locally) using the ``loadl run JOBFILE`` command from the python package. It will calculate all the tasks defined in the jobfile in parallel. Measurements and checkpoints will be saved in the ``JOBFILE.data`` directory. After everything is done, measurements will be averaged and merged into the human-readable ``JOBFILE.results.json`` file. You may use the ``loadleveller.mcextract`` module to conveniently extract results from it for further processing.
Use the ``ydelete`` tool to delete all job data for a fresh start after you changed something. ``ystatus`` gives you information about the job progress. You do not have to wait until completion to get the result file. ``yrun -m JOBFILE`` merges whatever results are already done. Use the ``loadl delete`` tool to delete all job data for a fresh start after you changed something. ``loadl status`` gives you information about the job progress. You do not have to wait until completion to get the result file. ``loadl merge JOBFILE`` merges whatever results are already done.
Hidden features Hidden features
--------------- ---------------
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment