DMF Overview#

The Data Management Framework (DMF) is used to manage all the data needed by the IDAES framework, including flowsheets, models, and results. It stores metadata and data in persistent storage. It does not require that the user run a server or connect to a remote service. The DMF can be accessed through its API or command-line interfaces.


The DMF is designed to allow multiple separate threads of work. These are organized in workspaces. Inside a given workspace, all the information is represented by containers called resources. A resource describes some data in the system in a standard way, so it can be searched and manipulated by the rest of the IDAES framework. Resources can be connected to each other with relations such as “derived”, “contains”, “uses”, and “version”.

Below is an illustration of these components.



The DMF is configured with an optional global configuration file and a required per-workspace configuration file. By default the global file is looked for as .dmf in the user’s home directory. Its main function at the moment is to set the default workspace directory with the workspace keyword. For example:

# global DMF configuration
workspace: ~/data/workspaces/workspace1

The per-workspace configuration has more options. See the documentation in the Workspace class for details. The configuration file is in YAML (or JSON) format. Here is an example file, with some description in comments:

settings:                               # Global settings
  workspace: /home/myuser/ws            # Path to current workspace
workspace:                              # Per-workspace settings
  location: /home/myuser/ws             # Path to this workspace
  name: myws                            # Name of this workspace
  description: my workspace             # Description (if any) of this workspace
  created: 2019-04-09 12:55:05          # Date workspace was created
  modified: 2019-04-09 12:55:05         # Date workspace was modified
  files:                                # Basic information about data files
    count: 3                            # How many files
    total_size: 1.3 MB                  # Total size of the files
  html_documentation_paths:             # List of paths for HTML documentation
    -: /home/myuser/idaes/docs/build
  logging:                              # Logging configuration
    idaes.core.dmf:                          # Name of the logger
        level: DEBUG                    # Log level (Python logging constant)
        output: /tmp/debug.log          # File path or "_stdout_" or "_stderr_"

This configuration file is used whether you use the DMF from the command-line, Jupyter notebook, or in a Python program. For details see the DMF package documentation.

Jupyter notebook usage#

In the Jupyter Notebook, there are some “magics” defined that make initializing the DMF pretty easy. For example:

from idaes.core.dmf import magics
%dmf init path/to/workspace

The code above loads the “%dmf” line magic in the first line, then uses it to initialize the DMF with the workspace at “path/to/workspace”.

From there, other “line magics” will operate in the context of that DMF workspace.

  • %dmf help - Provide help on IDAES objects and classes. See dmf-help.

  • %dmf info - Provide information about DMF current state for whatever ‘topics’ are provided

  • %dmf list - List resources in the current workspace

  • %dmf workspaces - List DMF workspaces; you can do this before %dmf init

DMF help#

The IDAES Python interfaces are documented with Sphinx. This includes automatic translation of the comments and structure of the code into formatted and hyperlinked HTML pages. The %dmf help command lets you easily pull up this documentation for an IDAES module, class, or object. Below are a couple of examples:

# Initialize the DMF first
from idaes.core.dmf import magics
%dmf init path/to/workspace create

# Get help on a module (imported)
from idaes.core import control_volume1d
%dmf help control_volume1d

# Get help on a module (by name, no import)
%dmf help idaes.core.control_volume0d

# Get help on a class
from idaes.core.control_volume1d import ControlVolume1DBlock
%dmf help ControlVolume1DBlock

# Get help on a class (by name, no import)
%dmf help idaes.core.control_volume1d.ControlVolume1DBlock

# Get help on an object (will show help for the object's class)
# This will end up showing the same help as the previous two examples
obj = control_volume1d.ControlVolume1DBlock()
%dmf help obj

The help pages will open in a new window. The location of the built documentation that they use is configured in the per-workspace DMF configuration under the htmldocs keyword (a default value is filled in when the DMF is first initialized).


The contents of a DMF workspace can be shared quite simply because the data is all contained within a directory in the local file system. So, some ways to share (with one or many people) include:

  • Put the workspace directory in a cloud/shared drive like Dropbox , Box , Google Drive , or OneDrive .

  • Put the workspace directory under version control like Git and share that versioned data using Git commands and a service like Github , BitBucket or Gitlab.

  • Package up the directory with a standard archiving utility like “zip” or “tar” and share it like any other file (e.g. attach it to an email).


These modes of sharing allow users to see the same data, but are not designed for real-time collaboration (reading and writing) of the same data. That mode of operation requires a proper database server to mediate operations on the same data. This is in the roadmap for the DMF, but not currently implemented.


See the idaes.core.dmf package documentation that is generated automatically from the source code.