From bb74e0f8fbc73df9b25b71f032f4b5f8f7ecb438 Mon Sep 17 00:00:00 2001 From: Erik Fasterius Date: Tue, 15 Oct 2024 16:39:43 +0200 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- index.html | 2 +- pages/conda.html | 947 ++++++++++++ pages/containers.html | 1230 +++++++++++++++ pages/images/dag_mrsa.svg | 229 +++ pages/images/jobgraph.svg | 42 + pages/images/jupyter_basic_update.png | Bin 0 -> 299640 bytes pages/images/jupyter_dashboard.png | Bin 0 -> 145358 bytes pages/images/jupyter_empty_nb.png | Bin 0 -> 73638 bytes pages/images/jupyter_widget.png | Bin 0 -> 187150 bytes pages/images/jupyterlab_dashboard.png | Bin 0 -> 390805 bytes pages/images/rulegraph.svg | 28 + pages/images/rulegraph_complex.svg | 1478 ++++++++++++++++++ pages/images/rulegraph_mrsa.svg | 123 ++ pages/jupyter.html | 1438 +++++++++++++++++ pages/nextflow.html | 1848 ++++++++++++++++++++++ pages/quarto.html | 1352 ++++++++++++++++ pages/snakemake.html | 2052 +++++++++++++++++++++++++ search.json | 439 +++++- sitemap.xml | 28 +- 20 files changed, 11228 insertions(+), 10 deletions(-) create mode 100644 pages/conda.html create mode 100644 pages/containers.html create mode 100644 pages/images/dag_mrsa.svg create mode 100644 pages/images/jobgraph.svg create mode 100644 pages/images/jupyter_basic_update.png create mode 100644 pages/images/jupyter_dashboard.png create mode 100644 pages/images/jupyter_empty_nb.png create mode 100644 pages/images/jupyter_widget.png create mode 100644 pages/images/jupyterlab_dashboard.png create mode 100644 pages/images/rulegraph.svg create mode 100644 pages/images/rulegraph_complex.svg create mode 100644 pages/images/rulegraph_mrsa.svg create mode 100644 pages/jupyter.html create mode 100644 pages/nextflow.html create mode 100644 pages/quarto.html create mode 100644 pages/snakemake.html diff --git a/.nojekyll b/.nojekyll index f1e7790a..c31efbe9 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -6e8a44e8 \ No newline at end of file +ea827d25 \ No newline at end of file diff --git a/index.html b/index.html index c274ec3e..d0185398 100644 --- a/index.html +++ b/index.html @@ -143,7 +143,7 @@

How to work reproducibly with control and structuring of project code, environment and workflow management


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Updated: 10-10-2024 at 09:14:09 .

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Updated: 15-10-2024 at 16:39:31 .

diff --git a/pages/conda.html b/pages/conda.html new file mode 100644 index 00000000..c184c8cd --- /dev/null +++ b/pages/conda.html @@ -0,0 +1,947 @@ + + + + + + + + + +Controlling your environment with Conda + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Controlling your environment with Conda

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How to keep track of your software packages and their versions

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Published
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15-Oct-2024

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1 Introduction

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Conda is a package and environment manager. As a package manager it enables you to install a wide range of software and tools using one simple command: conda install. As an environment manager it allows you to create and manage multiple different environments, each with their own set of packages.

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What are the benefits of using an environment manager? Some examples include the ability to easily run different versions of the same package, have different cross-package dependencies that are otherwise incompatible with each other and, last but not least, easy installation of all the software needed for an analysis.

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Environments are of particular relevance when making bioinformatics projects reproducible. Full reproducibility requires the ability to recreate the system that was originally used to generate the results. This can, to a large extent, be accomplished by using Conda to make a project environment with specific versions of the packages that are needed in the project. You can read more about Conda here.

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A Conda package is a compressed tarball (system-level libraries, Python or other modules, executable programs or other components). Conda keeps track of the dependencies between packages and platforms - this means that when installing a given package, all necessary dependencies will also be installed.

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Conda packages are typically hosted and downloaded from remote so-called channels. Some widely used channels for general-purpose and bioinformatics packages are conda-forge and Bioconda, respectively. Both of these are community-driven projects, so if you’re missing some package you can contribute to the channel by adding the package to it. When installing a Conda package you specify the package name, version (optional) and channel to download from.

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A Conda environment is essentially a directory that is added to your PATH and that contains a specific collection of packages that you have installed. Packages are symlinked between environments to avoid unnecessary duplication.

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Different Conda flavours You may come across several flavours of Conda. There’s Miniconda, which is the installer for Conda. The second is Anaconda, which is a distribution of not only Conda, but also over 150 scientific Python packages curated by the company by the same name (Anaconda). It’s generally better to stick with the Miniconda installation rather than installing 3 GB worth of packages you may not even use. Then, lastly, there’s the Miniforge flavour that we’re using here, which is a community-driven version of Conda that’s highly popular within the scientific community.

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The difference between Miniconda and Miniforge is that the former points to points to the default channel by default (which requires an Anaconda license for commercial purposes), while the latter points to the community-maintained conda-forge channel by default. While Conda is created and owned by Anaconda the company, Conda itself is open source - it’s the default channel that is proprietary. The conda-forge and bioconda channels (two of the largest channels outside of default) are community-driven. Confusing? Yes. If you want this information more in-depth you can read this blog post by Anaconda.

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2 The basics

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This tutorial depends on files from the course GitHub repo. Take a look at the setup for instructions on how to set it up if, you haven’t done so already. Then open up a terminal and go to workshop-reproducible-research/tutorials/conda. Instructions below assume that you are standing in workshop-reproducible-research/tutorials/conda/ unless otherwise specified (e.g. if it says “create a file”, it means save it in workshop-reproducible-research/tutorials/conda/).

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Let’s assume that you are just about to start a new exciting research project called Project A.

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2.1 Creating Conda environments

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  • Let’s make our first Conda environment:
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conda create -n project_a -c bioconda fastqc
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This will create an environment called project_a, containing FastQC from the Bioconda channel. Conda will list the packages that will be installed and ask for your confirmation.

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  • Once it is done, you can activate the environment:
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conda activate project_a
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By default, Conda will add information to your prompt telling you which environment that is active.

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  • To see all your environments you can run:
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conda info --envs
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The active environment will be marked with an asterisk.

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  • To see the installed packages and their versions in the active environment, run:
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conda list
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  • To save the installed packages to a file, run:
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conda env export --from-history > environment.yml
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Where --from-history only reports the packages requested to be installed and not additional dependencies. A caveat is that if no version was originally specified, then it is not included in the export file either.

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  • Now, deactivate the environment by running conda deactivate.
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  • List all environments again. Which environment is now marked as active?
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  • Try to run FastQC:
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fastqc --version
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  • Did it work? Activate your project_a environment and run the fastqc --version command again. Does it work now?
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Hopefully the FastQC software was not found in your base environment (unless you had installed it previously), but worked once your environment was activated.

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2.2 Adding more packages

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  • Now, let’s add another package (MultiQC) to our environment using conda install. Make sure that project_a is the active environment first.
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conda install -c bioconda multiqc
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  • If we don’t specify the package version, the latest available version will be installed. What version of MultiQC got installed?
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  • Run the following to see what versions are available:
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conda search -c bioconda multiqc
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  • Now try to install a different version of MultiQC, e.g.:
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conda install -c bioconda multiqc=1.13
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Read the information that Conda displays in the terminal. It probably asks if you want to downgrade the initial MultiQC installation to the one specified here (1.13 in the example). You can only have one version of a given package in a given environment.

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Let’s assume that you will have sequencing data in your Project A, and want to use the latest BBMap software to align your reads.

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  • Find out what versions of BBMap are available in the Bioconda channel using conda search -c bioconda bbmap.
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  • Now install the latest available version of BBMap in your project_a environment.
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Let’s further assume that you have an old project (called Project Old) where you know you used BBMap 37.10. You just got back reviewer comments and they want you to include some alignment statistics. Unfortunately, you haven’t saved that information so you will have to rerun the alignment. Now, it is essential that you use the same version of BBMap that your results are based on, otherwise the alignment statistics will be misleading. Using Conda environments this becomes simple. You can just have a separate environment for your old project where you have an old version of BBMap without interfering with your new Project A where you want the latest version.

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  • Make a new environment for your old project:
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conda create -n project_old -c bioconda bbmap=37.10
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  • List your environments (do you remember the command?).
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  • Activate project_old and check the BBMap version (bbmap.sh --version).
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  • Activate project_a again and check the BBMap version.
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2.3 Removing packages

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Now let’s try to remove an installed package from the active environment:

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conda remove multiqc
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  • Run conda deactivate to exit your active environment.
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  • Now, let’s remove an environment:
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conda env remove -n project_old
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After making a few different environments and installing a bunch of packages, Conda can take up some disk space. You can remove unnecessary files with the command:

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conda clean -a
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This will remove package tar-balls that are left from package installations, unused packages (i.e. those not present in any environments), and cached data.

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+Quick recap +
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In this section we’ve learned:

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  • How to use conda install for installing packages on the fly.
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  • How to create, activate and change between environments.
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  • How to remove packages or environments and clean up.
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3 Working with environments

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We have up until now specified which Conda packages to install directly on the command line using the conda create and conda install commands. For working in projects this is not the recommended way. Instead, for increased control and reproducibility, it is better to use an environment file (in YAML format) that specifies the packages, versions and channels needed to create the environment for a project.

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Throughout these tutorials we will use a case study where we analyse an RNA-seq experiment with the multi-resistant bacteria MRSA (see intro). You will now start to make a Conda YAML file for this MRSA project. The file will contain a list of the software and versions needed to execute the analysis code.

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In this Conda tutorial, all code for the analysis is available in the script code/run_qc.sh. This code will download the raw FASTQ-files and subsequently run quality control on these using the FastQC software.

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3.1 Working with environments

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We will start by making a Conda YAML-file that contains the required packages to perform these two steps. Later in the course, you will update the Conda YAML-file with more packages, as the analysis workflow is expanded.

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  • Let’s get going! Make a YAML file called environment.yml looking like this, and save it in the current directory (which should be workshop-reproducible-research/tutorials/conda):
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channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - fastqc=0.12.1
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  • Now, make a new Conda environment from the YAML file (note that here the command is conda env create as opposed to conda create that we used before):
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conda env create -n project_mrsa -f environment.yml
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You can also specify exactly which channel a package should come from inside the environment file, using the channel::package=version syntax.

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Instead of the -n flag you can use the -p flag to set the full path to where the Conda environment should be installed. In that way you can contain the Conda environment inside the project directory, which does make sense from a reproducibility perspective, and makes it easier to keep track of what environment belongs to what project. If you don’t specify -p the environment will be installed in the envs/ directory inside your Conda installation path.

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  • Activate the environment!

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  • Now we can run the code for the MRSA project found in code/run_qc.sh, either by running bash code/run_qc.sh or by opening the run_qc.sh file and executing each line in the terminal one by one. Do this!

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This should download the project FASTQ files and run FastQC on them (as mentioned above).

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  • Check your directory contents (ls -Rlh, or in your file browser). It should now have the following structure:
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   conda/
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+    |- code/
+    |   |- run_qc.sh
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+    |- data/
+    |   |- SRR935090.fastq.gz
+    |   |- SRR935091.fastq.gz
+    |   |- SRR935092.fastq.gz
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+    |- results/
+    |   |- fastqc/
+    |       |- SRR935090_fastqc.html
+    |       |- SRR935090_fastqc.zip
+    |       |- SRR935091_fastqc.html
+    |       |- SRR935091_fastqc.zip
+    |       |- SRR935092_fastqc.html
+    |       |- SRR935092_fastqc.zip
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+    |- environment.yml
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Note that all that was needed to carry out the analysis and generate these files and results was environment.yml (that we used to create a Conda environment with the required packages) and the analysis code in code/run_qc.sh.

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3.2 Keeping track of dependencies

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Projects can often be quite large and require lots of dependencies; it can feel daunting to try to capture all of that in a single Conda environment, especially when you consider potential incompatibilities that may arise. It can therefore be a good idea to start new projects with an environment file with each package you know that you will need to use, but without specifying exact versions (except for those packages where you know you need a specific version). This will install the latest compatible versions of all the specified software, making the start-up and installation part of new projects easier. You can then add the versions that were installed to your environment file afterwards, ensuring future reproducibility.

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There is one command that can make this easier: conda env export. This allows you to export a list of the packages you’ve already installed, including their specific versions, meaning you can easily add them after the fact to your environment file. If you use the --no-builds flag, you’ll get a list of the packages minus their OS-specific build specifications, which is more useful for making the environment portable across systems. This way, you can start with an environment file with just the packages you need (without version), which will install the most up-to-date version possible, and then add the resulting version back in to the environment file using the export command!

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+Quick recap +
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In this section we’ve learned:

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  • How to define our Conda environment using a YAML-file.
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  • How to use conda env create to make a new environment from a YAML-file.
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  • How to use conda env export to get a list of installed packages.
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  • How to work in a project-like setting.
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4 Extra material

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The following extra material contains some more advanced things you can do with Conda and the command line in general, which is not part of the main course materials. All the essential skills of are covered by the previous section: the material here should be considered tips and tricks from people who use Conda as part of their daily work. You thus don’t need to use these things unless you want to, and you can even skip this part of the lesson if you like!

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4.1 Configuration

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The behaviour of your Conda installation can be changed using an optional configuration file .condarc. On a fresh Conda install no such file is included but it’s created in your home directory as ~/.condarc the first time you run conda config.

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You can edit the .condarc file either using a text editor or by way of the conda config command. To list all config parameters and their settings run:

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conda config --show
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Similar to Conda environment files, the configuration file is in YAML syntax. This means that the config file is structured in the form of key:value pairs where the key is the name of the config parameter (e.g. auto_update_conda) and the value is the parameter setting (e.g. True).

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Adding the name of a config parameter to conda config --show will show only that parameter, e.g. conda config --show channels.

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You can change parameters with the --set, --add, --append and --remove flags to conda config.

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If you for example want to enable the ‘Always yes’ behaviour which makes Conda automatically choose the yes option, such as when installing, you can run:

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conda config --set always_yes True
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To see details about a config parameter you can run conda config --describe parameter. Try running it on the channels parameter:

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conda config --describe channels
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In the beginning of this tutorial we added Conda channels to the .condarc file using conda config --add channels. To remove one of the channels from the configuration file you can run:

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conda config --remove channels conda-forge
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Check your .condarc file to see the change. To add the conda-forge channel back to the top of the channels simply run:

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conda config --add channels conda-forge
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To completely remove a parameter and all its values run:

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conda config --remove-key parameter
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For a list of Conda configuration parameters see the Conda configuration page.

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4.2 Managing Python versions

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With Conda environments it’s possible to keep several different versions of Python on your computer at the same time, and switching between these versions is very easy. However, a single Conda environment can only contain one version of Python.

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4.2.1 Your current Python installation

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The base environment has its own version of Python installed. When you open a terminal (after having installed Conda on your system) this base environment is activated by default (as evidenced by (base) prepended to your prompt). You can check what Python version is installed in this environment by running python --version. To see the exact path to the Python executable type which python.

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In addition to this your computer may already have Python installed in a separate (system-wide) location outside of the Conda installation. To see if that is the case type conda deactivate until your prompt is not prepended with a Conda environment name. Then type which python. If a path was printed to the terminal (e.g. /usr/bin/python) that means some Python version is already installed in that location. Check what version it is by typing python --version.

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Now activate the base environment again by typing conda activate (or the equivalent conda activate base) then check the Python installation path and version using which and python --version as above. See the difference? When you activate an environment your $PATH variable is updated so that when you call python (or any other program) the system first searches the directory of the currently active environment.

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4.2.2 Different Python versions

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When you create a new Conda environment you can choose to install a specific version of Python in that environment as well. As an example, create an environment containing Python version 3.5 by running:

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conda create -n py35 python=3.5
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Here we name the environment py35 but you can choose whatever name you want.

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To activate the environment run:

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conda activate py35
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You now have a completely separate environment with its own Python version.

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Let’s say you instead want an environment with Python version 2.7 installed. You may for instance want to run scripts or packages that were written for Python 2.x and are thus incompatible with Python 3.x. Simply create the new Conda environment with:

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conda create -n py27 python=2.7
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Activate this environment with:

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conda activate py27
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Now, switching between Python versions is as easy as typing conda activate py35 / conda activate py27.

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If you create an environment where none of the packages require Python, and you don’t explicitly install the python package then that new environment will use the Python version installed in your base environment.

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4.3 Decorating your prompt

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By default, the name of the currently activated environment is added to your command line prompt. This is a good thing, as it makes it easier to keep track of what environment and packages you have access to. The way this is done in the default implementation becomes an issue when using absolute paths for environments (specifying conda env create -p path/to/environment, though, as the entire path will be added to the prompt. This can take up a lot of unnecessary space on your screen, but can be solved in a number of ways.

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The most straightforward way to solve this is to change the Conda configuration file, specifically the settings of the env_prompt configuration value which determines how Conda modifies your command line prompt. For more information about this setting you can run conda config --describe env_prompt and to see your current setting you can run conda config --show env_prompt.

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By default env_prompt is set to ({default_env}) which modifies your prompt with the active environment name if it was installed using the -n flag or if the environment folder has a parent folder named envs/. Otherwise the full environment path (i.e. the ‘prefix’) is displayed.

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If you instead set env_prompt to ({name}) Conda will modify your prompt with the folder name of the active environment. You can change the setting by running conda config --set env_prompt '({name}) '

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If you wish to keep the ({default_env}) behaviour, or just don’t want to change your Conda config, an alternative is to keep Conda environment folders within a parent folder called envs/. This will make Conda only add the folder name of the Conda environment to your prompt when you activate it.

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As an example, say you have a project called project_a with the project path ~/myprojects/project_a. You could then install the environment for project_a into a folder ~/myprojects/project_a/envs/project_a_environment. Activating the environment by pointing Conda to it (e.g. conda activate ~/myprojects/project_a/envs/project_a_environment) will only cause your prompt to be modified with project_a_environment.

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4.4 Bash aliases for conda

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Some programmers like to have aliases (i.e. shortcuts) for common commands. Two aliases that might be useful for you are alias coac='conda activate' and alias code='conda deactivate'. Don’t forget to add them to your ~/.bash_profile if you want to use them!

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4.5 Rolling back to an earlier version of the environment

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The history of the changes to an environment are automatically tracked. You can see revisions to an environment by using:

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conda list --revisions
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Which shows each revision (numbered) and what’s installed.

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You can revert back to particular revision using:

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conda install --revision 5
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4.6 Mamba, the drop-in Conda replacement

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There is another piece of software that is built on top of Conda as a drop-in replacement for it: Mamba. The reason for Mamba’s existence is that it used to have a better solver algorithm for the dependency tree than Conda did. These days, however, this algorithm is included in Conda as the default. There is still some minor reasons you might want to use Mamba, however, the first of which being that Mamba re-implements Conda in C++, which runs slightly faster than the Python-based Conda. This only yields a minor speed increase compared to the dependency-tree algorithm, though, so don’t expect major differences in execution time between Conda and Mamba. Another reason is that Mamba colours its output, which is nice if you care about that sort of thing. If you installed Conda as described in the pre-course material you’ll, conveniently, already have installed Mamba as well!

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Controlling your environment with Containers

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How to keep keep track of both your environment and operating system

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Published
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15-Oct-2024

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1 Introduction

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Container-based technologies are designed to make it easier to create, deploy, and run applications by isolating them in self-contained software units (hence their name). The idea is to package software and/or code together with everything it needs (other packages it depends, various environment settings, etc.) into one unit, i.e. a container. This way we can ensure that the software or code functions in exactly the same way regardless of where it’s executed. Containers are in many ways similar to virtual machines but more lightweight. Rather than starting up a whole new operating system, containers can use the same kernel (usually Linux) as the system that they’re running on. This makes them much faster and smaller compared to virtual machines. While this might sound a bit technical, actually using containers is quite smooth and very powerful.

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Containers have also proven to be a very good solution for packaging, running and distributing scientific data analyses. Some applications of containers relevant for reproducible research are:

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  • When publishing, package your analyses in a container image and let it accompany the article. This way interested readers can reproduce your analysis at the push of a button.
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  • Packaging your analysis in a container enables you to develop on e.g. your laptop and seamlessly move to cluster or cloud to run the actual analysis.
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  • Say that you are collaborating on a project and you are using Mac while your collaborator is using Windows. You can then set up a container image specific for your project to ensure that you are working in an identical environment.
  • +
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One of the largest and most widely used container-based technologies is Docker. Just as with Git, Docker was designed for software development but is rapidly becoming widely used in scientific research. Another container-based technology is Apptainer (and the related Singularity), which was developed to work well in computer cluster environments such as Uppmax. We will cover both Docker and Apptainer in this course, but the focus will be be on the former (since that is the most widely used and runs on all three operating systems).

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This tutorial depends on files from the course GitHub repo. Take a look at the setup for instructions on how to install Docker if you haven’t done so already, then open up a terminal and go to workshop-reproducible-research/tutorials/containers.

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+Dockage and storage +
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Docker images tend to take up quite a lot of space. In order to do all the exercises in this tutorial you need to have ~10 GB available.

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2 The basics

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We’re almost ready to start, just one last note on nomenclature. You might have noticed that we sometimes refer to “Docker images” and sometimes to “Docker containers”. We use images to start containers, so containers are simply an instances of an image. You can have an image containing, say, a certain Linux distribution, and then start multiple containers running that same OS.

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If you don’t have root privileges you have to prepend all Docker commands with sudo.

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2.1 Downloading images

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Docker containers typically run Linux, so let’s start by downloading an image containing Ubuntu (a popular Linux distribution that is based on only open-source tools) through the command line.

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docker pull ubuntu:latest
+

You will notice that it downloads different layers with weird hashes as names. This represents a very fundamental property of Docker images that we’ll get back to in just a little while. The process should end with something along the lines of:

+
Status: Downloaded newer image for ubuntu:latest
+docker.io/library/ubuntu:latest
+

Let’s take a look at our new and growing collection of Docker images:

+
docker image ls
+

The Ubuntu image should show up in this list, with something looking like this:

+
REPOSITORY       TAG              IMAGE ID            CREATED             SIZE
+ubuntu           latest           d70eaf7277ea        3 weeks ago         72.9MB
+
+
+

2.2 Running containers

+

We can now start a container from the image we just downloaded. We can refer to the image either by “REPOSITORY:TAG” (“latest” is the default so we can omit it) or “IMAGE ID”. The syntax for docker run is docker run [OPTIONS] IMAGE [COMMAND] [ARG...]. To see the available options run docker run --help. The COMMAND part is any command that you want to run inside the container, it can be a script that you have written yourself, a command line tool or a complete workflow. The ARG part is where you put optional arguments that the command will use.

+

Let’s run uname -a to get some info about the operating system. In this case, uname is the COMMAND and -a the ARG. This command will display some general info about your system, and the -a argument tells uname to display all possible information.

+

First run it on your own system (use systeminfo if you are on Windows):

+
uname -a
+

This should print something like this to your command line:

+
Darwin liv433l.lan 15.6.0 Darwin Kernel Version 15.6.0: Mon Oct  2 22:20:08 PDT 2017; root:xnu-3248.71.4~1/RELEASE_X86_64 x86_64
+

Seems like I’m running the Darwin version of MacOS. Then run it in the Ubuntu Docker container:

+
docker run ubuntu uname -a
+

Here I get the following result:

+
Linux 24d063b5d877 5.4.39-linuxkit #1 SMP Fri May 8 23:03:06 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux
+

And now I’m running on Linux! What happens is that we use the downloaded ubuntu image to run a container that has Ubuntu as the operating system, and we instruct Docker to execute uname -a to print the system info within that container. The output from the command is printed to the terminal.

+

Try the same thing with whoami instead of uname -a.

+
+
+

2.3 Running interactively

+

So, seems we can execute arbitrary commands on Linux. This looks useful, but maybe a bit limited. We can also get an interactive terminal with the flags -it.

+
docker run -it ubuntu
+

Your prompt should now look similar to:

+
root@1f339e929fa9:/#
+

You are now using a terminal inside a container running Ubuntu. Here you can do whatever; install, run, remove stuff. Anything you do will be isolated within the container and never affect your host system.

+

Now exit the container with exit.

+
+
+

2.4 Containers inside scripts

+

Okay, so Docker lets us work in any OS in a quite convenient way. That would probably be useful on its own, but Docker is much more powerful than that. For example, let’s look at the shell part of the index_genome rule in the Snakemake workflow for the MRSA case study:

+
shell:
+    """
+    bowtie2-build tempfile results/bowtie2/{wildcards.genome_id} > {log}
+    """
+

You may have seen that one can use containers through both Snakemake and Nextflow if you’ve gone through their tutorial’s extra material, but we can also use containers directly inside scripts in a very simple way. Let’s imagine we want to run the above command using containers instead. How would that look? It’s quite simple, really: first we find a container image that has bowtie2 installed, and then prepend the command with docker run <image>.

+

First of all we need to download the genome to index though, so run:

+
curl -o NCTC8325.fa.gz ftp://ftp.ensemblgenomes.org/pub/bacteria/release-37/fasta/bacteria_18_collection/staphylococcus_aureus_subsp_aureus_nctc_8325/dna//Staphylococcus_aureus_subsp_aureus_nctc_8325.ASM1342v1.dna_rm.toplevel.fa.gz
+gunzip -c NCTC8325.fa.gz > tempfile
+

To download and prepare the input for Bowtie2.

+

Now try running the following Bash code:

+
docker run -v $(pwd):/analysis quay.io/biocontainers/bowtie2:2.5.1--py39h3321a2d_0 bowtie2-build /analysis/tempfile /analysis/NCTC8325
+

Docker will automatically download the container image for Bowtie2 version 2.5.1 from the remote repository https://quay.io/repository/biocontainers/bowtie2 and subsequently run the command! This is the docker run [OPTIONS] IMAGE [COMMAND] [ARG...] syntax just like before. In this case quay.io/biocontainers/bowtie2:2.5.1--py39h3321a2d_0 is the IMAGE but instead of first downloading and then running it we point to its remote location directly, which will cause Docker to download it on the fly. The bowtie2-build part is the COMMAND followed by the ARG (the input tempfile and the output index)

+

The -v $(pwd):/analysis part is the OPTIONS which we use to mount the current directory inside the container in order to make the tempfile input available to Bowtie2. More on these so-called “Bind mounts” in Section 4 of this tutorial.

+
+
+
+ +
+
+Quick recap +
+
+
+

In this section we’ve learned:

+
    +
  • How to use docker pull for downloading remotely stored images
  • +
  • How to use docker image ls for getting information about the images we have on our system.
  • +
  • How to use docker run for starting a container from an image.
  • +
  • How to use the -it flag for running in interactive mode.
  • +
  • How to use Docker inside scripts.
  • +
+
+
+
+
+
+

3 Building images

+

In the previous section we downloaded a Docker image of Ubuntu and noticed that it was based on layers, each with a unique hash as id. An image in Docker is based on a number of read-only layers, where each layer contains the differences to the previous layers. If you’ve done the Git tutorial this might remind you of how a Git commit contains the difference to the previous commit. The great thing about this is that we can start from one base layer, say containing an operating system and some utility programs, and then generate many new images based on this, say 10 different project-specific images. This dramatically reduces the storage space requirements. For example, Bioconda (see the Conda tutorial) has one base image and then one individual layer for each of the more than 3000 packages available in Bioconda.

+

Docker provides a convenient way to describe how to go from a base image to the image we want by using a “Dockerfile”. This is a simple text file containing the instructions for how to generate each layer. Docker images are typically quite large, often several GBs, while Dockerfiles are small and serve as blueprints for the images. It is therefore good practice to have your Dockerfile in your project Git repository, since it allows other users to exactly replicate your project environment.

+

We will be looking at a Dockerfile called Dockerfile_slim that is located in your containers directory (where you should hopefully be standing already). We will now go through that file and discuss the different steps and what they do. After that we’ll build the image and test it out. Lastly, we’ll start from that image and make a new one to reproduce the results from the Conda tutorial.

+
+

3.1 Understanding Dockerfiles

+

Here are the first few lines of Dockerfile_slim. Each line in the Dockerfile will typically result in one layer in the resulting image. The format for Dockerfiles is INSTRUCTION arguments. A full specification of the format, together with best practices, can be found here.

+
FROM condaforge/miniforge3
+
+LABEL description = "Minimal image for the NBIS reproducible research course."
+MAINTAINER "John Sundh" john.sundh@scilifelab.se
+

Here we use the instructions FROM, LABEL and MAINTAINER. While LABEL and MAINTAINER is just meta-data that can be used for organizing your various Docker components the important one is FROM, which specifies the base image we want to start from. Because we want to use conda to install packages we will start from an image from the conda-forge community that has conda pre-installed. This image was in turn built using a Dockerfile as a blueprint and then uploaded to Dockerhub. The conda-forge community keeps the Dockerfile in a git repository and you can view the file here. You will see that it starts from an official Ubuntu image (check the first line with the FROM instruction), followed by code to install various packages including conda.

+
+
+
+ +
+
+There are many roads to Rome +
+
+
+

When it comes to choosing the best image to start from there are multiple routes you could take. Say you want to run RStudio in a Conda environment through a Jupyter notebook. You could then start from one of the rocker images for R, a Condaforge image, or a Jupyter image. Or you just start from one of the low-level official images and set up everything from scratch.

+
+
+

Let’s take a look at the next section of Dockerfile_slim.

+
# Use bash as shell
+SHELL ["/bin/bash", "--login", "-c"]
+
+# Set workdir
+WORKDIR /course
+
+# Set time zone
+ENV TZ="Europe/Stockholm"
+ENV DEBIAN_FRONTEND=noninteractive
+

SHELL simply sets which shell to use and WORKDIR determines the directory the container should start in. The ENV instruction is used to set environmental variables and here we use it to set the time zone by declaring a TZ variable. The DEBIAN_FRONTEND=noninteractive line means that we force the subsequent installation to not prompt us to set the time zone manually.

+

The next few lines introduce the important RUN instruction, which is used for executing shell commands:

+
# Install package for setting time zone
+RUN apt-get update && apt-get install -y tzdata && apt-get clean
+
+# Configure Conda
+RUN conda init bash && conda config --set channel_priority strict && \
+    conda config --append channels bioconda && \
+    conda config --append channels r && \
+    conda config --set subdir linux-64
+

The first RUN command installs the tzdata package for managing local time settings in the container. This may not always be required for your Dockerfile but it’s added here because some R packages used in the course require it.

+
+
+
+ +
+
+Note +
+
+
+

While installing things with apt-get inside Dockerfiles is relatively common practice, it’s important to note that this may affect reproducibility, since it’s not common to specify an exact version. The packages installed in this manner are, however, usually not important for the actual analyses performed, but rather help in the building of the container image itself. While not critical, it’s important to note this from a reproducibility perspective.

+
+
+

Next, we run conda init bash to initialize the bash shell inside the image, meaning we can use conda activate in containers that run from the image. In the same RUN statement we also configure the strict channel priority and add appropriate channels with conda config. You’ll probably recognize this from the pre-course-setup. The last part sets the somewhat obscure subdir config parameter pointing to the linux-64 architecture of conda channels.

+

As a general rule, you want each layer in an image to be a “logical unit”. For example, if you want to install a program the RUN command should both retrieve the program, install it and perform any necessary clean up. This is due to how layers work and how Docker decides what needs to be rerun between builds. More on this later.

+

Next up is:

+
# Open port for running Jupyter Notebook
+EXPOSE 8888
+
+# Start Bash shell by default
+CMD /bin/bash
+

EXPOSE opens up the port 8888, so that we can later run a Jupyter Notebook server on that port. CMD is an interesting instruction. It sets what a container should run when nothing else is specified, i.e. if you run docker run [OPTIONS] [IMAGE] without the additional [COMMAND] [ARG]. It can be used for example for printing some information on how to use the image or, as here, start a Bash shell for the user. If the purpose of your image is to accompany a publication then CMD could be to run the workflow that generates the paper figures from raw data, e.g. CMD snakemake -s Snakefile -c 1 generate_figures.

+
+
+

3.2 Building from Dockerfiles

+

Now we understand how a Dockerfile works. Constructing the image itself from the Dockerfile can be done as follows - try it out:

+
+
+
+ +
+
+Image platforms on newer Macs +
+
+
+

If your computer is a MAC with the M1 chip, you may have to add --platform linux/x86_64 to the docker build command.

+
+
+
docker build -f Dockerfile_slim -t my_docker_image .
+

This should result in something similar to this:

+
 [+] Building 2.2s (7/7) FINISHED
+ => [internal] load build definition from Dockerfile_slim                                                                                                                                             0.0s
+ => => transferring dockerfile: 667B                                                                                                                                                                  0.0s
+ => [internal] load .dockerignore                                                                                                                                                                     0.0s
+ => => transferring context: 2B                                                                                                                                                                       0.0s
+ => [internal] load metadata for docker.io/condaforge/miniforge3:latest                                                                                                                               0.0s
+ => [1/3] FROM docker.io/condaforge/miniforge3                                                                                                                                                        0.0s
+ => CACHED [2/3] WORKDIR /course                                                                                                                                                                      0.0s
+ => [3/3] RUN conda init bash && conda config --set channel_priority strict &&     conda config --append channels bioconda &&     conda config --append channels r &&     conda config --set subdir   2.1s
+ => exporting to image                                                                                                                                                                                0.0s
+ => => exporting layers                                                                                                                                                                               0.0s
+ => => writing image sha256:53e6efeaa063eadf44c509c770d887af5e222151f08312e741aecc687e6e8981                                                                                                          0.0s
+ => => naming to docker.io/library/my_docker_image
+

Exactly how the output looks depends on which version of Docker you are using. The -f flag sets which Dockerfile to use and -t tags the image with a name. This name is how you will refer to the image later. Lastly, the . is the path to where the image should be build (. means the current directory). This had no real impact in this case, but matters if you want to import files. Validate with docker image ls that you can see your new image.

+
+
+

3.3 Creating your own Dockerfile

+

Now it’s time to make your own Dockerfile to reproduce the results from the Conda tutorial. If you haven’t done the tutorial, it boils down to creating a Conda environment file, setting up that environment, downloading three RNA-seq data files, and running FastQC on those files. We will later package and run the whole RNA-seq workflow in a Docker container, but for now we keep it simple to reduce the size and time required.

+

The Conda tutorial uses a shell script, run_qc.sh, for downloading and running the analysis. A copy of this file should also be available in your current directory. If we want to use the same script we need to include it in the image. A basic outline of what we need to do is:

+
    +
  1. Create a file called Dockerfile_conda
  2. +
  3. Start the image from the my_docker_image we just built
  4. +
  5. Install the package fastqc which is required for the analysis.
  6. +
  7. Add the run_qc.sh script to the image
  8. +
  9. Set the default command of the image to run the run_qc.sh script.
  10. +
+

We’ll now go through these steps in more detail. Try to add the corresponding code to Dockerfile_conda on your own, and if you get stuck you can click to reveal the solution below under “Click to show solution”.

+

Set image starting point

+

To set the starting point of the new image, use the FROM instruction and point to my_docker_image that we built in the previous Building from Dockerfiles step.

+

Install packages

+

Use the RUN instruction to install the package fastqc=0.11.9 with conda. Here there are several options available. For instance we could add an environment file e.g. environment.yml from the Conda tutorial and use conda env create to create an environment from that file. Or we could create an environment directly with conda create. We’ll try this later option here, so add a line that will create an environment named project_mrsa containing the fastqc package, and also clean up packages and cache after installation. Use the -y flag to conda create to avoid the prompt that expects an interaction from the user.

+

In order to have the project_mrsa environment activated upon start-up we need to add two more lines to the Dockerfile. First we need to use a RUN instruction to run echo "source activate project_mrsa" >> ~/.bashrc, and then we need to use the ENV instruction to set the $PATH variable inside the image to /opt/conda/envs/project_mrsa/bin:$PATH.

+

Add the analysis script

+

Use the COPY instruction to Add run_qc.sh to the image. The syntax is COPY SOURCE TARGET. In this case SOURCE is the run_qc.sh script and TARGET is a path inside the image, for simplicity it can be specified with ./.

+

Set default command

+

Use the CMD instruction to set the default command for the image to bash run_qc.sh.

+
+ +
+
+
FROM my_docker_image
+
+RUN conda create -y -n project_mrsa -c bioconda fastqc=0.11.9 && conda clean -a
+
+RUN echo "source activate project_mrsa" >> ~/.bashrc
+
+ENV PATH=/opt/conda/envs/project_mrsa/bin:$PATH
+
+COPY run_qc.sh .
+
+CMD bash run_qc.sh
+
+
+
+

Build the image and tag it my_docker_conda (remember to add --platform linux/x86_64 to the build command if you are using a Mac with the Apple chip).

+
docker build -t my_docker_conda -f Dockerfile_conda .
+

Verify that the image was built using docker image ls.

+
+
+
+ +
+
+Quick recap +
+
+
+

In this section we’ve learned:

+
    +
  • How the keywords FROM, LABEL, MAINTAINER, RUN, ENV, SHELL, WORKDIR, and CMD can be used when writing a Dockerfile.
  • +
  • How to use docker build to construct and tag an image from a Dockerfile.
  • +
  • How to create your own Dockerfile.
  • +
+
+
+
+
+
+

4 Managing containers

+

When you start a container with docker run it is given an unique id that you can use for interacting with the container. Let’s try to run a container from the image we just created:

+
docker run my_docker_conda
+

If everything worked run_qc.sh is executed and will first download and then analyse the three samples. Once it’s finished you can list all containers, including those that have exited.

+
docker container ls --all
+

This should show information about the container that we just ran. Similar to:

+
CONTAINER ID   IMAGE            COMMAND                  CREATED         STATUS          PORTS      NAMES
+b6f7790462c4   my_docker_conda   "tini -- /bin/bash -…"  3 minutes ago   Up 24 seconds   8888/tcp   sad_maxwell
+

If we run docker run without any flags, your local terminal is attached to the container. This enables you to see the output of run_qc.sh, but also disables you from doing anything else in the meantime. We can start a container in detached mode with the -d flag. Try this out and run docker container ls to validate that the container is running.

+

By default, Docker keeps containers after they have exited. This can be convenient for debugging or if you want to look at logs, but it also consumes huge amounts of disk space. It’s therefore a good idea to always run with --rm, which will remove the container once it has exited.

+

If we want to enter a running container, there are two related commands we can use, docker attach and docker exec. docker attach will attach local standard input, output, and error streams to a running container. This can be useful if your terminal closed down for some reason or if you started a terminal in detached mode and changed your mind. docker exec can be used to execute any command in a running container. It’s typically used to peak in at what is happening by opening up a new shell. Here we start the container in detached mode and then start a new interactive shell so that we can see what happens. If you use ls inside the container you can see how the script generates file in the data and results directories. Note that you will be thrown out when the container exits, so you have to be quick.

+
docker run -d --rm --name my_container my_docker_conda
+docker exec -it my_container /bin/bash
+
+

4.1 Bind mounts

+

There are obviously some advantages to isolating and running your data analysis in containers, but at some point you need to be able to interact with the rest of the host system (e.g. your laptop) to actually deliver the results. This is done via bind mounts. When you use a bind mount, a file or directory on the host machine is mounted into a container. That way, when the container generates a file in such a directory it will appear in the mounted directory on your host system.

+
+
+
+ +
+
+Tip +
+
+
+

Docker also has a more advanced way of data storage called volumes. Volumes provide added flexibility and are independent of the host machine’s file system having a specific directory structure available. They are particularly useful when you want to share data between containers.

+
+
+

Say that we are interested in getting the resulting html reports from FastQC in our container. We can do this by mounting a directory called, say, fastqc_results in your current directory to the /course/results/fastqc directory in the container. Try this out by running:

+
docker run --rm -v $(pwd)/fastqc_results:/course/results/fastqc my_docker_conda
+

Here the -v flag to docker run specifies the bind mount in the form of directory/on/your/computer:/directory/inside/container. $(pwd) simply evaluates to the working directory on your computer.

+

Once the container finishes validate that it worked by opening one of the html reports under fastqc_results/.

+

We can also use bind mounts for getting files into the container rather than out. We’ve mainly been discussing Docker in the context of packaging an analysis pipeline to allow someone else to reproduce its outcome. Another application is as a kind of very powerful environment manager, similarly to how we’ve used Conda before. If you’ve organized your work into projects, then you can mount the whole project directory in a container and use the container as the terminal for running stuff while still using your normal OS for editing files and so on. Let’s try this out by mounting our current directory and start an interactive terminal. Note that this will override the CMD command, so we won’t start the analysis automatically when we start the container.

+
docker run -it --rm -v $(pwd):/course/ my_docker_conda /bin/bash
+

If you run ls you will see that all the files in the container/ directory are there.

+
+
+
+ +
+
+Quick recap +
+
+
+

In this section we’ve learned:

+
    +
  • How to use docker run for starting a container and how the flags -d and --rm work.
  • +
  • How to use docker container ls for displaying information about the containers.
  • +
  • How to use docker attach and docker exec to interact with running containers.
  • +
  • How to use bind mounts to share data between the container and the host system.
  • +
+
+
+
+
+
+

5 Sharing images

+

There would be little point in going through all the trouble of making your analyses reproducible if you can’t distribute them to others. Luckily, sharing Docker containers is extremely easy, and can be done in several ways. One of the more common ways to share Docker images is through container registries and repositories.

+

For example, a Docker registry is a service that stores Docker images, which could be hosted by a third party, publicly or privately. One of the most common registries is Docker Hub, which is a registry hosted by Docker itself. A repository, on the other hand, is a collection of container images with the same name but different tags (i.e. versions), for example ubuntu:latest or ubuntu:20.04. Repositories are stored in registries.

+
+
+
+ +
+
+Note +
+
+
+

Remember that we now have some clashing nomenclature between Git repositories (which we covered in the Git tutorial) and container repositories, so be aware of which one you’re talking about!

+
+
+

There are many registries out there, but here are some that might be of interest to you who are taking this course:

+ +

The most common registry is probably Docker Hub, which lets you host unlimited public images and one private image for free (after which they charge a small fee). The GitHub Container Registry is also quite handy if you’re already using GitHub. Let’s see how it’s done using Docker Hub!

+
    +
  1. Register for an account on Docker Hub.

  2. +
  3. Use docker login -u your_dockerhub_id to login to the Docker Hub registry. Or use the Sign in button in Docker Desktop.

  4. +
  5. When you build an image, tag it with -t your_dockerhub_id/image_name, rather than just image_name.

  6. +
  7. Once the image has been built, upload it to Docker Hub with docker push your_dockerhub_id/image_name.

  8. +
  9. If another user runs docker run your_dockerhub_id/image_name the image will automatically be retrieved from Docker Hub. You can use docker pull for downloading without running.

  10. +
+

If you want to refer to a Docker image in for example a publication, it’s very important that it’s the correct version of the image. This is handled via the ‘tags’ (e.g. docker build -t your_dockerhub_id/image_name:tag_name) that we introduced in Containers 2: The basics and used when building images in Containers 3: Building images.

+
+
+
+ +
+
+Tip +
+
+
+

On Docker Hub it is also possible to link to your Bitbucket or GitHub account and select repositories from which you want to automatically build and distribute Docker images. The Docker Hub servers will then build an image from the Dockerfile in your Git repository and make it available for download using docker pull. That way, you don’t have to bother manually building and pushing using docker push. The GitHub repository for this course is linked to Docker Hub and the Docker images are built automatically from Dockerfile and Dockerfile_slim, triggered by changes made to the GitHub repository. You can take a look at the course on Docker Hub here.

+
+
+
+
+
+ +
+
+Quick recap +
+
+
+

In this section we’ve learned:

+
    +
  • How container registries and repositories work
  • +
  • How to use Docker Hub to share Docker images
  • +
+
+
+
+
+

6 Packaging the case study

+

During these tutorials we have been working on a case study about the multi-resistant bacteria MRSA. Here we will build and run a Docker container that contains all the work we’ve done so far.

+
    +
  • We’ve set up a GitHub repository for version control and for hosting our project.
  • +
  • We’ve defined a Conda environment that specifies the packages we’re depending on in the project.
  • +
  • We’ve constructed a Snakemake workflow that performs the data analysis and keeps track of files and parameters.
  • +
  • We’ve written a Quarto document that takes the results from the Snakemake workflow and summarizes them in a report.
  • +
+

The workshop-reproducible-research/tutorials/containers directory contains the final versions of all the files we’ve generated in the other tutorials: environment.yml, Snakefile, config.yml and code/supplementary_material.qmd. The only difference compared to the other tutorials is that we have also included the rendering of the Supplementary Material HTML file into the Snakemake workflow as the rule make_supplementary. Running all of these steps will take some time to execute (around 20 minutes or so), in particular if you’re on a slow internet connection.

+

Now take a look at Dockerfile. Everything should look quite familiar to you, since it’s basically the same steps as in the image we constructed in the Building images section, although with some small modifications. The main difference is that we add the project files needed for executing the workflow (mentioned in the previous paragraph), and install the conda packages using environment.yml. If you look at the CMD command you can see that it will run the whole Snakemake workflow by default.

+

Now run docker build as before, tag the image with my_docker_project (remember the --platform linux/x86_64 flag if you’re on a new Mac with the Apple chip):

+
docker build -t my_docker_project -f Dockerfile .
+

Go get a coffee while the image builds (or you could use docker pull nbisweden/workshop-reproducible-research which will download the same image).

+

Validate with docker image ls. Now all that remains is to run the whole thing with docker run. We just want to get the results, so mount the directory /course/results/ to, say, results/ in your current directory. Click below to see how to write the command.

+
+ +
+
+

If building your own image:

+
docker run -v $(pwd)/results:/course/results my_docker_project
+

If you pulled the image from DockerHub:

+
docker run -v $(pwd)/results:/course/results nbisweden/workshop-reproducible-research
+
+
+
+

Well done! You now have an image that allows anyone to exactly reproduce your analysis workflow (if you first docker push to Dockerhub that is).

+
+
+
+ +
+
+Tip +
+
+
+

If you’ve done the Jupyter tutorial, you know that Jupyter Notebook runs as a web server. This makes it very well suited for running in a Docker container, since we can just expose the port Jupyter Notebook uses and redirect it to one of our own. You can then work with the notebooks in your browser just as you’ve done before, while it’s actually running in the container. This means you could package your data, scripts and environment in a Docker image that also runs a Jupyter Notebook server. If you make this image available, say on Dockerhub, other researchers could then download it and interact with your data/code via the fancy interactive Jupyter notebooks that you have prepared for them. We haven’t made any fancy notebooks for you, but we have set up a Jupyter Notebook server. Try it out if you want to (replace the image name with your version if you’ve built it yourself):

+
docker run -it nbisweden/workshop-reproducible-research jupyter notebook -allow-root --no-browser
+
+
+
+
+

7 Apptainer

+

Apptainer is a container software alternative to Docker. It was originally developed as Singularity by researchers at Lawrence Berkeley National Laboratory (read more about this below) with focus on security, scientific software, and HPC clusters. One of the ways in which Apptainer is more suitable for HPC is that it very actively restricts permissions so that you do not gain access to additional resources while inside the container. Apptainer also, unlike Docker, stores images as single files using the Singularity Image Format (SIF). A SIF file is self-contained and can be moved around and shared like any other file, which also makes it easy to work with on an HPC cluster.

+
+
+
+ +
+
+Apptainer and Singularity +
+
+
+

The open source Singularity project was renamed to Apptainer in 2021. The company Sylabs still keeps their commercial branch of the project under the Singularity name, and offer a free ‘Community Edition’ version. The name change was done in order to clarify the distinction between the open source project and the various commercial versions. At the moment there is virtually no difference to you as a user whether you use Singularity or Apptainer, but eventually it’s very likely that the two will diverge.

+
+
+

While it is possible to define and build Apptainer images from scratch, in a manner similar to what you’ve already learned for Docker, this is not something we will cover here (but feel free to read more about this in e.g. the Apptainer docs.

+

The reasons for not covering Apptainer more in-depth are varied, but it basically boils down to it being more or less Linux-only, unless you use Virtual Machines (VMs). Even with this you’ll run into issues of incompatibility of various kinds, and these issues are further compounded if you’re on one of the new ARM64-Macs. You also need root (admin) access in order to actually build Apptainer images regardless of platform, meaning that you can’t build them on e.g. Uppmax, even though Apptainer is already installed there. You can, however, use the --remote flag, which runs the build on Apptainer’s own servers. This doesn’t work in practice a lot of the time, though, since most scientist will work in private Git repositories so that their research and code is not available to anybody, and the --remote flag requires that e.g. the environment.yml file is publicly available.

+

There are very good reasons to use Apptainer, however, the major one being that you aren’t allowed to use Docker on most HPC systems! One of the nicer features of Apptainer is that it can convert Docker images directly for use within Apptainer, which is highly useful for the cases when you already built your Docker image or if you’re using a remotely available image stored on e.g. DockerHub. For a lot of scientific work based in R and/or Python, however, it is most often the case that you build your own images, since you have a complex dependency tree of software packages not readily available in existing images. So, we now have another problem for building our own images:

+
    +
  1. Only Apptainer is allowed on HPC systems, but you can’t build images there due to not having root access.
  2. +
  3. You can build Apptainer images locally and transfer them to HPCs, but this is problematic unless you’re running Linux natively.
  4. +
+

Seems like a “catch 22”-problem, right? There are certainly workarounds (some of which we have already mentioned) but most are roundabout or difficult to get working for all use-cases. Funnily enough, there’s a simple solution: run Apptainer locally from inside a Docker container! Conceptually very meta, yes, but works very well in practice. What we are basically advocating for is that you stick with Docker for most of your container-based work, but convert your Docker images using Apptainer-in-Docker whenever you need to work on an HPC. This is of course not applicable to Linux users or those of you who are fine with working through using VMs and managing any issues that arise from doing that.

+
+
+
+ +
+
+Summary +
+
+
+

Apptainer is a great piece of software that is easiest to use if you’re working on a Linux environment. Docker is, however, easier to use from a cross-platform standpoint and covers all use-cases except running on HPCs. Running on HPCs can be done by converting existing Docker images at runtime, while building images for use on HPCs can be done using local Docker images and Apptainer-in-Docker.

+
+
+
+

7.1 Apptainer-in-Docker

+

By creating a bare-bones, Linux-based Docker image with Apptainer you can build Apptainer images locally on non-Linux operating systems. There is already a good image setup for just this, and it is defined in this GitHub repository. Looking at the instructions there we can see that we need to do the following:

+
docker run \
+    --rm \
+    -v /var/run/docker.sock:/var/run/docker.sock \
+    -v $(pwd):/work \
+    kaczmarj/apptainer \
+    build <IMAGE>.sif docker-daemon://<IMAGE>:<TAG>
+

You already know about docker run, the --rm flag and bind mounts using -v. The /var/run/docker.sock part is the Unix socket that the Docker daemon listens to by default, meaning that it is needed for us to be able to specify the location of the Docker container we want to convert to a SIF file. The kaczmarj/apptainer part after the bind mounts is the image location hosted at DockerHub, while the last line is the Apptainer command that actually does the conversion. All we need to do is to replace the <IMAGE> part with the Docker image we want to convert, e.g. my_docker_image.

+
    +
  • Replace <IMAGE> and <TAG> with one of your locally available Docker images and one of its tags and run the command - remember that you can use docker image ls to check what images you have available.
  • +
+

In the end you’ll have a SIF file (e.g. my_docker_image.sif) that you can transfer to an HPC such as Uppmax and run whatever analyses you need. If you want to be able to do this without having to remember all the code you can check out the this script.

+
+
+

7.2 Running Apptainer

+

The following exercises assume that you have a login to the Uppmax HPC cluster in Uppsala, but will also work for any other system that has Apptainer installed - like if you managed to install Apptainer on your local system or have access to some other HPC cluster. Let’s try to convert the Docker image for this course directly from DockerHub:

+
apptainer pull mrsa_proj.sif docker://nbisweden/workshop-reproducible-research
+

This should result in a SIF file called mrsa_proj.sif.

+

In the Docker image we included the code needed for the workflow in the /course directory of the image. These files are of course also available in the Apptainer image. However, a Apptainer image is read-only. This will be a problem if we try to run the workflow within the /course directory, since the workflow will produce files and Snakemake will create a .snakemake directory. Instead, we need to provide the files externally from our host system and simply use the Apptainer image as the environment to execute the workflow in (i.e. all the software and dependencies).

+

In your current working directory (workshop-reproducible-research/tutorials/containers/) the vital MRSA project files are already available (Snakefile, config.yml and code/supplementary_material.qmd). Since Apptainer bind mounts the current working directory we can simply execute the workflow and generate the output files using:

+
apptainer run mrsa_proj.sif
+

This executes the default run command, which is snakemake -rp -c 1 --configfile config.yml (as defined in the original Dockerfile). Once completed you should see a bunch of directories and files generated in your current working directory, including the results/ directory containing the final HTML report.

+
+
+
+ +
+
+Quick recap +
+
+
+

In this section we’ve learned:

+
    +
  • How to build a Apptainer image using Apptainer inside Docker.
  • +
  • How to convert Docker images to Apptainer images.
  • +
  • How to run Apptainer images.
  • +
+
+
+
+
+
+

8 Extra material

+

Containers can be large and complicated, but once you start using them regularly you’ll find that you start understand these complexities. There are lots of different things you can do with images and containers in general, especially when it comes to optimising build time or final image size. Here is some small tips and tricks that you can be inspired from!

+

If you want to read more about containers in general you can check out these resources:

+
    +
  • A “Get started with Docker” at the Docker website.
  • +
  • An early paper on the subject of using Docker for reproducible research.
  • +
+
+

8.1 Building for multiple platforms

+

With the newer ARM64 architectures introduced by Apple one often runs into the problem of not having an architecture-native image to run with. This is sometimes okay since the Rosetta2 software can emulate the old AMD64 architecture on newer ARM64 computers, but results in a performance hit. One could just build for ARM64 using --platform=linux/arm64 instead, but then somebody who doesn’t have the new architecture can’t run it. There is a way around this, however: multi-platform builds. We can build for multiple platforms at the same time and push those to e.g. DockerHub and anybody using those images will automatically pull the one appropriate for their computer. Here’s how to do it:

+
    +
  • Start by checking the available builders using docker buildx ls.
  • +
+

You should only see the default builder, which does not have access to multi-platform builds. Let’s create a new builder that does have access to it:

+
    +
  • Run the following: docker buildx create --name mybuilder --driver docker-container --bootstrap.

  • +
  • Switch to using the new builder with docker buildx use mybuilder and check that it worked with docker buildx ls.

  • +
+

All that’s needed now is to build and push the images! The following command assumes that you have an account with <username> at DockerHub and you’re pushing the <image> image:

+
docker buildx build --platform linux/amd64,linux/arm64 -t <username>/<image>:latest --push .
+
    +
  • Execute the above command with your username and your image.
  • +
+

That’s it! Now anybody who does e.g. docker pull <username>/<image> will get an image appropriate for their architecture whether they are on AMD64 or ARM64!

+
+
+
+ +
+
+An alias to buildx +
+
+
+

You can type docker buildx install to make the docker build into an alias for docker buildx, allowing you to run multi-platform builds using docker build. Use docker buildx uninstall to remove this alias.

+
+
+ + +
+
+ +
+ +
+ + + + + + + \ No newline at end of file diff --git a/pages/images/dag_mrsa.svg b/pages/images/dag_mrsa.svg new file mode 100644 index 00000000..8bd05ab6 --- /dev/null +++ b/pages/images/dag_mrsa.svg @@ -0,0 +1,229 @@ + + + + + + +snakemake_dag + + +0 + +get_genome_fasta + + +6 + +index_genome + + +0->6 + + + + +1 + +sort_bam + + +4 + +generate_count_table + + +1->4 + + + + +2 + +align_to_genome + + +12 + +sort_bam + + +2->12 + + + + +3 + +fastqc +id: SRR935090 + + +5 + +multiqc + + +3->5 + + + + +11 + +all + + +4->11 + + + + +5->11 + + + + +6->2 + + + + +8 + +align_to_genome + + +6->8 + + + + +9 + +align_to_genome + + +6->9 + + + + +7 + +sort_bam + + +7->4 + + + + +8->7 + + + + +9->1 + + + + +10 + +generate_rulegraph + + +10->11 + + + + +12->4 + + + + +13 + +fastqc +id: SRR935091 + + +13->5 + + + + +14 + +fastqc +id: SRR935092 + + +14->5 + + + + +15 + +get_SRA_by_accession +sra_id: SRR935092 + + +15->2 + + + + +15->14 + + + + +16 + +get_SRA_by_accession +sra_id: SRR935091 + + +16->8 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