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adding notebooks
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‎.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/

‎LICENSE‎

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‎README.md‎

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# Materials for 2023-CSDMS Clinic 1.2:
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# Materials for Tutorial at NCSTATE 2023-10-30:
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"Emerging open-source Python-based infrastructure for scalable, data-proximate analysis and visualization of model output (a.k.a. the Pangeo Framework)"
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## Start
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1. login to https://nebari-workshop.esipfed.org:
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1. login to https://nebari.openscicomp.io
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```
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username: <your email address>
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password: 123 (you will be prompted to change it)
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python start.py
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```
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1. In the file browser, browse to the 'shared'->'users' and then your username folder.
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1. Browse to "training-2023-CSDMS"
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1. Browse to "tutorial-ncstate"
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1. Click the "clinic_overview.ipynb" notebook to launch it.
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## Deployment notes

‎Start_Dask_Cluster_Nebari.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# nebari.esipfed.org Dask Cluster"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import logging\n",
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"\n",
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"try:\n",
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" from dask_gateway import Gateway\n",
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"except ImportError:\n",
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" logging.error(\"Unable to import Dask Gateway. Are you running in a cloud compute environment?\\n\")\n",
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" raise\n",
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"os.environ['DASK_DISTRIBUTED__SCHEDULER__WORKER_SATURATION'] = \"1.0\"\n",
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"\n",
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"gateway = Gateway()\n",
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"_options = gateway.cluster_options()\n",
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"_options.conda_environment='nebari-git/nebari-git-pangeo' ##<< this is the conda environment we use on nebari.\n",
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"_options.profile = 'Small Worker'\n",
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"_env_to_add={}\n",
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"aws_env_vars=['AWS_ACCESS_KEY_ID',\n",
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" 'AWS_SECRET_ACCESS_KEY',\n",
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" 'AWS_SESSION_TOKEN',\n",
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" 'AWS_DEFAULT_REGION',\n",
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" 'AWS_S3_ENDPOINT']\n",
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"for _e in aws_env_vars:\n",
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" if _e in os.environ:\n",
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" _env_to_add[_e] = os.environ[_e]\n",
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"_options.environment_vars = _env_to_add \n",
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"cluster = gateway.new_cluster(_options) ##<< create cluster via the dask gateway\n",
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"cluster.adapt(minimum=2, maximum=30) ##<< Sets scaling parameters. \n",
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"\n",
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"client = cluster.get_client()\n",
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"\n",
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"print(\"The 'cluster' object can be used to adjust cluster behavior. i.e. 'cluster.adapt(minimum=10)'\")\n",
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"print(\"The 'client' object can be used to directly interact with the cluster. i.e. 'client.submit(func)' \")\n",
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"print(f\"The link to view the client dashboard is:\\n> {client.dashboard_link}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "nebari-git-nebari-git-pangeo",
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"language": "python",
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"name": "conda-env-nebari-git-nebari-git-pangeo-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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},
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"vscode": {
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"interpreter": {
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"hash": "d7ebce313f85fb1ac8949e834c83f371584cb2422d845bf1570c1220fdedc716"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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