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Title: A Case for Integrating Experimental Containers with Notebooks
Computational notebooks have gained much pop- ularity as a way of documenting research processes; they allow users to express research narrative by integrating ideas expressed as text, process expressed as code, and results in one executable document. However, the environments in which the code can run are currently limited, often containing only a fraction of the resources of one node, posing a barrier to many computations. In this paper, we make the case that integrating complex experimental environments, such as virtual clusters or complex networking environments that can be provisioned via infrastructure clouds, into computational notebooks will significantly broaden their reach and at the same time help realize the potential of clouds as a platform for repeatable research. To support our argument, we describe the integration of Jupyter notebooks into the Chameleon cloud testbed, which allows the user to define complex experimental environments and then assign processes to elements of this environment similarly to the way a laptop user may switch between different desktops. We evaluate our approach on an actual experiment from both the development and replication perspective.  more » « less
Award ID(s):
1743358
NSF-PAR ID:
10195660
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the 11th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2019)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    CREATE TABLE events( version INTEGER, timestamp TEXT, provider TEXT, spec TEXT, origin TEXT, ref TEXT, guessed_ref TEXT ); CREATE INDEX idx_timestamp ON events(timestamp);
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    • For records where ref is not available, we attempted to clone the named reference given by spec rather than the specific commit (see below). The guessed_ref field records the commit found at the time of cloning. If the branch was updated since the container was launched, this will not be the exact version that was used, and instead will refer to whatever was available at the time (early 2021). Depending on the application, this might still be useful information. Selecting only records with version 4 (or non-null ref) will exclude these guessed commits. May be null.

    The Binder launch dataset identifies the source repos that were used, but doesn't give any indication of their contents. We crawled GitHub to get the actual specification files in the repos which were fed into repo2docker when preparing the notebook environments, as well as filesystem metadata of the repos. Some repos were deleted/made private at some point, and were thus skipped. This is indicated by the absence of any row for the given commit (or absence of both ref and guessed_ref in the events table). The schema is as follows.

    CREATE TABLE spec_files ( ref TEXT NOT NULL PRIMARY KEY, ls TEXT, runtime BLOB, apt BLOB, conda BLOB, pip BLOB, pipfile BLOB, julia BLOB, r BLOB, nix BLOB, docker BLOB, setup BLOB, postbuild BLOB, start BLOB );

    Here ref corresponds to ref and/or guessed_ref from the events table. For each repo, we collected spec files into the following fields (see the repo2docker docs for details on what these are). The records in the database are simply the verbatim file contents, with no parsing or further processing performed.

    • runtime: runtime.txt
    • apt: apt.txt
    • conda: environment.yml
    • pip: requirements.txt
    • pipfile: Pipfile.lock or Pipfile
    • julia: Project.toml or REQUIRE
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    • nix: default.nix
    • docker: Dockerfile
    • setup: setup.py
    • postbuild: postBuild
    • start: start

    The ls field gives a metadata listing of the repo contents (excluding the .git directory). This field is JSON encoded with the following structure based on JSON types:

    • Object: filesystem directory. Keys are file names within it. Values are the contents, which can be regular files, symlinks, or subdirectories.
    • String: symlink. The string value gives the link target.
    • Number: regular file. The number value gives the file size in bytes.
    CREATE TABLE clean_specs ( ref TEXT NOT NULL PRIMARY KEY, conda_channels TEXT, conda_packages TEXT, pip_packages TEXT, apt_packages TEXT );

    The clean_specs table provides parsed and validated specifications for some of the specification files (currently Pip, Conda, and APT packages). Each column gives either a JSON encoded list of package requirements, or null. APT packages have been validated using a regex adapted from the repo2docker source. Pip packages have been parsed and normalized using the Requirement class from the pkg_resources package of setuptools. Conda packages have been parsed and normalized using the conda.models.match_spec.MatchSpec class included with the library form of Conda (distinct from the command line tool). Users might want to use these parsers when working with the package data, as the specifications can become fairly complex.

    The missing table gives the repos that were not accessible, and event_logs records which log files have already been added. These tables are used for updating the dataset and should not be of interest to users.

     
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