From e34c1a922533cbaf7deef413f1dd3d310245c9e7 Mon Sep 17 00:00:00 2001 From: Martin Schroschk <martin.schroschk@tu-dresden.de> Date: Thu, 5 Aug 2021 08:34:12 +0200 Subject: [PATCH] Synatx for ordered lists and minor typos --- .../docs/software/big_data_frameworks.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/doc.zih.tu-dresden.de/docs/software/big_data_frameworks.md b/doc.zih.tu-dresden.de/docs/software/big_data_frameworks.md index bd63b29ba..472637e3a 100644 --- a/doc.zih.tu-dresden.de/docs/software/big_data_frameworks.md +++ b/doc.zih.tu-dresden.de/docs/software/big_data_frameworks.md @@ -29,9 +29,9 @@ started. The steps are: 1. Load the Spark software module -2. Configure the Spark cluster -3. Start a Spark cluster -4. Start the Spark application +1. Configure the Spark cluster +1. Start a Spark cluster +1. Start the Spark application Apache Spark can be used in [interactive](#interactive-jobs) and [batch](#batch-jobs) jobs as well as via [Jupyter notebook](#jupyter-notebook). All three ways are outlined in the following. @@ -47,7 +47,7 @@ as via [Jupyter notebook](#jupyter-notebook). All three ways are outlined in the ### Default Configuration The Spark module is available for both `scs5` and `ml` partitions. -Thus, Spark can be executed using different CPU architectures, e. g., Haswell and Power9. +Thus, Spark can be executed using different CPU architectures, e.g., Haswell and Power9. Let us assume that two nodes should be used for the computation. Use a `srun` command similar to the following to start an interactive session @@ -155,7 +155,7 @@ Please use a [batch job](../jobs_and_resources/slurm.md) similar to ## Jupyter Notebook There are two general options on how to work with Jupyter notebooks: -There is [jupyterhub](../access/jupyterhub.md), where you can simply +There is [JupyterHub](../access/jupyterhub.md), where you can simply run your Jupyter notebook on HPC nodes (the preferable way). Also, you can run a remote Jupyter server manually within a GPU job using the modules and packages you need. You can find the manual server @@ -203,7 +203,7 @@ Assuming that you have prepared everything as described above, you can go to [https://taurus.hrsk.tu-dresden.de/jupyter](https://taurus.hrsk.tu-dresden.de/jupyter). In the tab "Advanced", go to the field "Preload modules" and select one of the Spark modules. -When your jupyter instance is started, check whether the kernel that +When your Jupyter instance is started, check whether the kernel that you created in the preparation phase (see above) is shown in the top right corner of the notebook. If it is not already selected, select the kernel `haswell-py3.6-spark`. Then, you can set up Spark. Since the setup -- GitLab