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You can install python packages in a conda environment while your notebook is running. On the HPC system, use:Īny other Python packages you need to run your notebook should be installed with Conda. For linx, you will find a list of installers. To install Miniconda on Linux, you need to locate and download the installer package for your system. If you're not familiar with Anaconda, check it out here. It is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages.
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Miniconda is a free minimal installer for conda. For best performance, we recommend using Miniconda. Anaconda is a large package and has a lot of overhead. Anaconda is a common package manager used for data science, but it it not recommended for use on HPC systems and running jupyter notebooks remotely. Typically, users install Anaconda on local systems. Running Juypter notebooks relies on you handling your own python jupyter package installation.
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By default, these notebooks are not secure, and potentially expose a users local files to unwanted users. The user obtains this URL and enters it into a local web browser, where the notebook is available as long as the process on the remote machine is up and running.
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In the latter case, the notebooks are launched via a process that creates a unique URL that is composed of the hostname plus an available port (chosen by the jupyter application) plus a one-time token. Notebooks can be launched locally and access local file systems, or they can be launched on a remote machine, which provides access to a user's files on the remote system. Jupyter has emerged as a de facto standard for data scientists and other scientific domains.
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Jupyter Notebooks are interactive web tools known as a computational notebooks, which researchers can use to combine software code, explanatory text and multimedia resources, and computational output, in a single document. Running Jupyter Notebook on SDSC HPC System Jupyter Notebook Overview