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Removing outdated references to Elasticsearch #26

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Removing outdated references to Elasticsearch #26

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6 changes: 3 additions & 3 deletions docs/source/conf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,11 +13,11 @@
import sys
import importlib.util
from pathlib import Path
from importlib.metadata import version as importlib_version
from importlib.metadata import metadata

import sphinx_rtd_theme # noqa: F401

import firewheel
from firewheel.control.repository_db import RepositoryDb
from firewheel.control.model_component_manager import ModelComponentManager
from firewheel.control.model_component_iterator import ModelComponentIterator
Expand All @@ -33,10 +33,10 @@
author = "Sandia National Laboratories"

# The short X.Y version
version = firewheel.__version__
version = importlib_version("firewheel")

# The full version, including alpha/beta/rc tags
release = firewheel.__version__
release = importlib_version("firewheel")


# -- General configuration ---------------------------------------------------
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8 changes: 3 additions & 5 deletions docs/source/system/architecture.rst
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Expand Up @@ -98,8 +98,6 @@ Once the *VM Resource Manager* has successfully finished monitoring and managing
Analytics
---------

Once the experiment is launched, it is important to gather and analyze experimental data.
To assist with this task, FIREWHEEL installs Elasticsearch, Logstash, and Kibana, more commonly known as an ELK stack.
Data from VM Resources can be ingested into ELK by printing to Standard Output (`stdout`).
To make greater use of the ELK stack, VM Resources can also print to `stdout` in JSON format which will be added to Elasticsearch.
Once the data has been ingested, users can use Kibana to create dashboards which visually display experimental data.
Once the experiment is launched, gathering and analyzing experimental data becomes crucial.
To facilitate this process, FIREWHEEL provides seamless logging of VM resource output and generates JSON-formatted logs that can be easily ingested into data analysis tools such as Elasticsearch or Jupyter Notebooks.
After the data has been ingested into your preferred tool, users can visually display the experimental data or conduct analyses as needed.
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