karltayeb/laplace-bfs

A systematic comparison of Bayes factor approximations

Overview

Topics:

Latest release: None, Last update: 2024-11-21

Linting: linting: failed, Formatting: formatting: failed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.

When using Mamba, run

mamba create -c conda-forge -c bioconda --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/karltayeb/laplace-bfs . --tag None

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

To run the workflow with automatic deployment of all required software via conda/mamba, use

snakemake --cores all --sdm conda

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Describe how to configure the workflow (using config.yaml and maybe additional files). All of them need to be present with example entries inside of the config folder.

Linting and formatting

Linting results

 1Lints for rule simulation_one (line 17, /tmp/tmpte4vsjbs/workflow/rules/simulation.smk):
 2    * No log directive defined:
 3      Without a log directive, all output will be printed to the terminal. In
 4      distributed environments, this means that errors are harder to discover.
 5      In local environments, output of concurrent jobs will be mixed and become
 6      unreadable.
 7      Also see:
 8      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
 9    * Specify a conda environment or container for each rule.:
10      This way, the used software for each specific step is documented, and the
11      workflow can be executed on any machine without prerequisites.
12      Also see:
13      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
14      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
15
16Lints for rule estimate_c_glm (line 27, /tmp/tmpte4vsjbs/workflow/rules/simulation.smk):
17    * No log directive defined:
18      Without a log directive, all output will be printed to the terminal. In
19      distributed environments, this means that errors are harder to discover.
20      In local environments, output of concurrent jobs will be mixed and become
21      unreadable.
22      Also see:
23      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
24    * Specify a conda environment or container for each rule.:
25      This way, the used software for each specific step is documented, and the
26      workflow can be executed on any machine without prerequisites.
27      Also see:
28      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
29      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers
30
31Lints for rule estimate_c_glm_all (line 32, /tmp/tmpte4vsjbs/workflow/rules/simulation.smk):
32    * No log directive defined:
33      Without a log directive, all output will be printed to the terminal. In
34      distributed environments, this means that errors are harder to discover.
35      In local environments, output of concurrent jobs will be mixed and become
36      unreadable.
37      Also see:
38      https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
39    * Specify a conda environment or container for each rule.:
40      This way, the used software for each specific step is documented, and the
41      workflow can be executed on any machine without prerequisites.
42      Also see:
43      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#integrated-package-management
44      https://snakemake.readthedocs.io/en/latest/snakefiles/deployment.html#running-jobs-in-containers

Formatting results

1[DEBUG] 
2[DEBUG] In file "/tmp/tmpte4vsjbs/workflow/rules/simulation.smk":  Formatted content is different from original
3[DEBUG] 
4[DEBUG] In file "/tmp/tmpte4vsjbs/workflow/Snakefile":  Formatted content is different from original
5[INFO] 2 file(s) would be changed 😬
6
7snakefmt version: 0.10.2