dx ls. The file listing is refreshed every 10s and it is possible to enforce a refresh by clicking on the whirl arrow icon in the top right corner of the file browser.[DX] prepended to the notebook name in the tab of all opened notebooks.[DX] that is prepended to its name in the tab of all opened notebooks.dx downloadand access the downloaded local file from Jupyter notebook./mnt/project folder. Reading the content of the files in /mnt/project dynamically fetches the content from the DNAnexus platform, so this method uses minimal disk space in the JupyterLab execution environment, but uses more api calls to fetch the content.dx upload in bash console.dx download the DNAnexus notebook from the current DNAnexus project to the JupyterLab environment and export the downloaded notebook. For exporting local notebook to certain formats, the following commands might be needed beforehand: apt-get update && apt-get install texlive-xetex texlive-fonts-recommended texlive-plain-generic.cmd input and additional input files using the in input file array to the DxJupyterLab app. The provided command will run in the /opt/notebooks/directory and any output files generated in this directory will be uploaded to the project and returned in the out output field of the job that ran DxJupyterLab app.papermillcommand that is pre-installed in the DxJupyterLab environment to execute notebooks non-interactively. For example, to execute all the cells in a notebook and produce an output notebook:notebook.ipynb is the input notebook to thepapermillcommand, which is passed to the dxjupyterlab app using thein input, and output_notebook.ipynb is the name of the output notebook, which will contain the result of executing the input notebook and will be uploaded to the project at the end of app's execution. See the DxJupyterLab app page for details.Duplicate; after a few seconds, a notebook with the same name and a "copy" suffix should appear in the project..Notebook_archive folder with a timestamp suffix added to its name and its ID is saved in the properties of the new file. Saving notebooks directly in the project ensures that your analyses won't be lost when the DXJupyterLab session ends.Update duration button). Even if the DxJupyterLab webpage is closed, the termination will be executed at the set timestamp. Job lengths have an upper limit of 30 days, which cannot be extended.DNAnexus > End Session).DNAnexus > Create Snapshot).docker commit and docker save commands). Any installed packages and files created locally are saved to a snapshot file, with the exception of directories /home/dnanexus and /mnt/, which are not included. This file is then uploaded to the project to .Notebook_snapshots and can be passed as input the next time the app is started.NOTE: If many large files are created locally, the resulting snapshots will take very long to save and load. In general, it is recommended not to snapshot more than 1 GB of locally saved data/packages and rely on downloading larger files as needed.
dx run from a notebook or the Terminal. If thebillToof the project that the JupyterLab session is running in is not licensed to start detached executions, the started jobs will be the subjobs of the interactive JupyterLab session. In this case, the --project argument todx runwill be ignored and the JupyterLab job's workspace is used to run the job, not the given project. Also, if the attached subjob fails or is terminated on the DNAnexus platform, the whole job tree will be terminated, including the interactive JupyterLab session.https://job-xxxx.dnanexus.cloud, where job-xxxx is the ID of the job that runs the app. Only the user who launched the JupyterLab job has access to the JupyterLab environment. Other users will see a “403 Permission Forbidden” message under the JupyterLab session's URL.NOTE: The JupyterLab server runs in the Miniconda Python 3.6 environment and Ubuntu:18.04 is the container's operating system.
feature options available are PYTHON_R, ML, IP, and STATA. Selecting thePYTHON_R feature (default option) loads the environment with Python3 and R kernel and interpreter. Selecting the ML feature loads the environment with Python3 and Machine Learning packages such as TensorFlow, PyTorch, CNTK as well as the Image Processing package NiPype, but it does not contain R. Selecting the IMAGE_PROCESSING feature loads the environment with Python3 and Image Processing packages such as NiPype, FreeSurfer and FSL but it does not contain R. The FreeSurfer package requires a license to run. Details about license creation and usage can be found here. The STATA feature requires a license to run.dx-toolkit,the standard Python 3.6.5 libraries, and R 4.1.3 libraries:STATA feature, you need a valid Stata license to run the session. See here for more information about running Stata in JupyterLab. Starting the app with STATA allows the above libraries from PYTHON_R to be included, and additionally includes:ML feature allows the following libraries to be included:IMAGE_PROCESSING feature allows the following libraries to be included: