From 6d81466892cd4192405d761eda3b783f6ba65834 Mon Sep 17 00:00:00 2001 From: SamueleSoraggi Date: Mon, 24 Jun 2024 11:32:11 +0200 Subject: [PATCH] corrected wrong genomedksteps --- access/genomedk.qmd | 57 +++++++++++++++++++++++++-------------------- 1 file changed, 32 insertions(+), 25 deletions(-) diff --git a/access/genomedk.qmd b/access/genomedk.qmd index 188ecab..f3ca176 100644 --- a/access/genomedk.qmd +++ b/access/genomedk.qmd @@ -37,29 +37,29 @@ cd MYPROJECT/ngsSummerSchool ``` -**3.** Use `singularity` to download the container of the course. This will take some time, and at the end a file called `course.sif` is created into the folder. +:::{.callout-warning title="NGS summer school 2024"} + +Get instead into the folder for the course: ```{.bash} -singularity pull course.sif docker://hdssandbox/ngssummerschool:2024.07 +cd NGS_summer_school/USERNAME ``` -**4.** Now we need to run a configuration script, which will setup jupyterlab so that the packages are detected correctly. This is downloaded from the internet and runs immediately, downloading also the necessary data. If a folder called `Data` exists, it will not download the data again (also meaning that you can use our container with your own data folder for your own analysis in future) - -```{.bash} +where you substitute `USERNAME` with your own user id. -wget -qO- https://raw.githubusercontent.com/hds-sandbox/NGS_summer_course_Aarhus/docker/scripts/courseMaterial.sh | bash +::: -``` +**3.** Use `singularity` to download the container of the course. This will take some time and show a lot of text, and at the end a file called `course.sif` is created into the folder. -:::{.callout-warning} +```{.bash} -You need to create the file `course.dif` only once. Next time, you only need the configuration script. +singularity pull course.sif docker://hdssandbox/ngssummerschool:2024.07 -::: +``` -**5.** Now it's time to get a few resources to run all the material. We suggest one CPU and 32GB of RAM for the first three modules, and 2 CPUs and 64GB of RAM for the single-cell analysis. For the first configuration suggested, you get resources using +**4.** Now it's time to get a few resources to run all the material. We suggest one CPU and 32GB of RAM for the first three modules, and 2 CPUs and 64GB of RAM for the single-cell analysis. For the first configuration suggested, you get resources using ```{.bash} @@ -75,12 +75,12 @@ Note you need your project name, and you can also choose for how long you want t ::: -**6.** Once resources are assigned, note down the node name. This is on the left side of the command line: for example, in the figure below, the node is `s21n33` +**5.** Once resources are assigned, note down the node name. This is on the left side of the command line: for example, in the figure below, the node is `s21n33` ![](../images/genomedkNode.png){fig-align="center" width="400px"} -**7.** execute the container with +**6.** execute the container with ```{.bash} singularity exec course.sif /bin/bash @@ -88,8 +88,21 @@ singularity exec course.sif /bin/bash Note that the command line shows now `Apptainer>` on its left. We are *inside* the container and the tools we need are now available into it. +**7.** Now we need to run a configuration script, which will setup jupyterlab so that the packages are detected correctly. This is downloaded from the internet and runs immediately, downloading also the necessary data. If a folder called `Data` exists, it will not download the data again (also meaning that you can use our container with your own data folder for your own analysis in future) + +```{.bash} + +wget -qO- https://raw.githubusercontent.com/hds-sandbox/NGS_summer_course_Aarhus/docker/scripts/courseMaterial.sh | bash + +``` + +:::{.callout-warning} -**7.** We are ready to go. Activate the environment and start jupyterLab with the following: +You need to create the file `course.dif` only once. Next time, you only need the configuration script. + +::: + +**8.** We are ready to go. Activate the environment and start jupyterLab with the following: ```{.bash} conda activate /opt/conda/envs/NGS_aarhus_py @@ -100,16 +113,16 @@ you will see a lot of messages, which is normal. You need also to create a tunne ```{.bash} -ssh -L6835:NODENAME:6835 samuele@login.genome.au.dk +ssh -L6835:NODENAME:6835 USERNAME@login.genome.au.dk ``` -where you substitute `NODENAME` with the correct depiction. +where you substitute `NODENAME` with the correct depiction,and USERNAME with your own user id. -**8.** Open your browser and go to the address [http://127.0.0.1:6835/lab](http://127.0.0.1:6835/lab). Jupyterlab opens +**9.** Open your browser and go to the address [http://127.0.0.1:6835/lab](http://127.0.0.1:6835/lab). Jupyterlab opens -**9.** Now you are ready to use JupyterLab for coding. Use the file browser (on the left-side) to find the folder `Notebooks`. Select one of the four tutorials of the course. You will see that the notebook opens on the right-side pane. Read the text of the tutorial and execute each code cell starting from the first. You will see results showing up directly on the notebook! +**10.** Now you are ready to use JupyterLab for coding. Use the file browser (on the left-side) to find the folder `Notebooks`. Select one of the four tutorials of the course. You will see that the notebook opens on the right-side pane. Read the text of the tutorial and execute each code cell starting from the first. You will see results showing up directly on the notebook! ![](../images/startNotebook.gif) @@ -119,7 +132,7 @@ Right click on a notebook or a saved results file, and use the download option t ::: -**10.** At the end of your session, it is a good idea to empty the cache of `singularity`. This will fill up your home folder very quickly (size limit is 100GB). Simply run these two commands: +**11.** At the end of your session, it is a good idea to empty the cache of `singularity`. This will fill up your home folder very quickly (size limit is 100GB). Simply run these two commands: ```{.bash} @@ -134,9 +147,3 @@ Everything is saved in the folder you are working in. Next time, follow the whol - - - - - -