THIS VERSION OF CREDO IS A BETA. WE ARE REWRITING CREDO at the following link https://github.com/CREDOProject/
This tool is designed to to extend the level of reproducibility, modulability and portability of conventional dockerFiles. This tool provides the possibility to build complex dockerFiles using a graphical interface. Furthermore, the tool stores locally R and Python libraries. The docker folders are compatible to be uploaded on github, since they are splitted to keep each file below 24 Mb. Here is the video tutorial for CREDOgui [https://youtu.be/92RvJe6qqHQ]
For windows install:
- https://docs.microsoft.com/en-gb/windows/wsl/install-manual#step-4---download-the-linux-kernel-update-package
- https://hub.docker.com/editions/community/docker-ce-desktop-windows
For linux install:
For OsX install:
Download and unzip this github or clone it Execute, from a terminal, dockerFileGenerator.sh, for Linux and MAC OSX, and dockerFileGenerator.cmd, if you are running Windows. Ones the script seems to hang, open a web browser and browse http://localhost:3000/ At this point the system is ready to build a personalised dockerFile.
####The O_ layer in our system allows users to build a Docker image that embeds a specific version of Python or R. Users need to provide a folder name where all the required libraries will be stored. By clicking on the Config button, users can edit the configuration file used to build the Dockerfile. This configuration file should contain the necessary commands for loading the user-selected libraries related to Python or R.
download libraryName : this is the classic pip installation. Example is download numpy
downloadgit : download and install library using git. Example is downloadgit https://github.com/httpie/httpie
downloadConda (only for python >= 3.0.0 ): download conda environment and install it. Example is downloadConda biopython. Conda Environment will be stored in /snowflakes/condaName folder of the docker container and to activate it is enough to run the following code
source /snowflakes/condapackageName/bin/activate
downloadbioconda (only for python >= 3.0.0 ): download conda environment and install it. Example is downloadbioconda mageck. Bioconda Environment will be stored in /snowflakes/biocondaName folder of the docker container and to activate it is enough to run the following coder
source /snowflakes/biocondapackageName/bin/activate
bioconductor : install libraries that require bioconductor. Example is bioconductor("GenomicRanges")
cran : install classic libraries from cran repositories. Example is cran("Rtsne")
github : install libraries from github. Example is github("kendomaniac/rCASC")
After this file is completed and saved the runMe.sh file can be run.
In each layer there is an example.sh file that shows how to run it.
These are the parameters :
- Temporary docker name. This name will be used for the dummy docker container. Be sure this name is not already taken from an important container or it will overwrite the existing one.
- Result folder name.
- Absolute path of the folder in which all the results will be stored.
- Absolute path to the configurationFile.txt. ConfigurationFile.txt must contain the absolute path to the host folder (third parameter). This parameter is optional and needed only if you are running dockerFileGenerator in a docker container. Do not pass a fourth input argument if you are running dockerFileGenerator on a local machine.
If users wish to create a Docker image that embeds both Python and R, they need to select the 1_ layer and provide a folder name for the merged Docker image.
To include a GUI accessible by web browser, like Jupyter Notebook or Jupyter Lab in the new Docker image, the 2_ layer is required. In this layer, users must specify the name of the Docker image to be built (rStudio and visual studio are also provided).
To initiate the Docker building process, users can press the Start Docker Generation button. This action will create the Dockerfile, and in the Docker folder, all the required libraries and dependencies necessary to build the Docker image will be stored. This system aims to provide users with a convenient way to generate Docker images tailored to their specific needs, ensuring the inclusion of the desired libraries and tools while maintaining reproducibility and ease of use.
The Layer 4 allows you to install additional programs using the apt package manager during the Docker file generation process. This feature enables you to incorporate specific software into your Docker environment.
To use layer 4, you need to specify the desired apt package names in the configuration file and ensure that the apt package manager is properly configured in the Dockerfile. During the Docker file generation, the specified apt packages will be downloaded and installed within the Docker image. Utilizing layer 4 enables further customization of your Docker environment by including specific programs you require for your analysis or project. Make sure to carefully follow the instructions and accurately specify the names of the necessary apt packages in the configuration file. This way, during the Docker file generation, the desired programs will be correctly installed in the Docker image.
The output console provides the state of the dockerFile creation. The output console will not show all the standard output but just a summa. For the details a log file is generated in the main folder of CREDOgui.
If Jupter, RStudio or visualStudio are present the docker folder will have the extension referring to the installed tool, e.g. _jupyter_lab. In the above mentioned folder, there is the script.sh (Linux/MAC OSX) or script.cmd (Windows), execute it and browse http://localhost:8888/ Please note that the /sharedFolder is the mount in the docker containier of the folder where the scripts.sh is located. If Jupter, RStudio or visualStudio are not present still a script.sh/cmd will be provided, and it will run the docker in a terminal window. The user can directly use the docker through that terminal window.
In the event of a failure during the Docker file generation process, it is essential to perform the following checks:
1)Ensure that the names of the libraries provided in the 0_(python/R) layers are correct and that the appropriate command has been used. For example, certain libraries like GenomicRanges cannot be downloaded from CRAN but must be obtained from Bioconductor. It is crucial to use the correct package manager or repository to download the required libraries.
2)Verify that you have a stable internet connection. Despite implementing numerous checks, the Docker file generation process is susceptible to connection drops. Occasionally, files may be downloaded incompletely or become corrupted, leading to subsequent errors during the Docker build. A reliable and uninterrupted internet connection is necessary to ensure successful Docker file generation.
3)Ensure that you have sufficient disk space available. It is generally recommended to have at least twenty gigabytes of free disk space for Docker image creation. The process involves downloading and storing various dependencies, libraries, and software components, which can occupy a significant amount of disk space. Having an adequate amount of free space on your disk will prevent any issues related to insufficient storage during the Docker file generation.
If you encounter a bug or experience difficulties with the Docker file generation, please provide the following information when seeking assistance:
1)Operating system: Specify the operating system you are using (e.g., Windows, macOS, Linux) and the version.
2)Detailed selection and configuration of each layer: Provide the exact selection and settings for each layer, including the libraries being installed and any additional configurations applied.
Attach the complete log file: Include the log file generated during the Docker file generation process. The log file contains important information about the steps performed and any error messages encountered. Attaching the complete log file will help the support team in diagnosing and resolving the issue effectively.
By providing these details, you enable the support team to understand and reproduce the issue, leading to a more efficient resolution of any problems encountered.
These guidelines aim to assist users in troubleshooting Docker file generation failures and ensure a smooth experience with the tool. If you need further assistance, please don't hesitate to reach out.