Houdini 17.5 Dependency networks

External configuration and data

How to read external configuration and source data in TOPs and use it to drive work.

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It is often useful to drive a TOP network using a configuration file, or input data, or both. For example, you might have a settings file that defines a bunch of variables to use when running the network, and you might run the network once for each part listed in a manifest, or for each asset listed in an asset management system.

TOPs lets you read external data and generate work items/attributes based on the data.

Loading data

  • SQL Database:

    SQL Input runs a query on a database server and generates a work item for each returned row, with attributes taken from the columns.

    SQL Output takes incoming work items and writes an INSERT query to insert a row for each work item, with the columns taken from attributes you specify.

  • CSV (comma separated values, typically exported by a spreadsheet):

    CSV Input reads a CSV file and generates a work item for each row, with attributes taken from the columns.

    CSV Output takes incoming work items and writes out a new CSV file with a row for each work item, with the columns taken from the attributes.

    CSV Concat joins together multiple CSV files. CSV Modify performs various manipulations of CSV files.

  • JSON files:

    JSON Input generates work items with attributes based on JSON files. Because JSON is so free-form, the node has parameters to try to extract items and attributes from various data "shapes".

    JSON Output takes incoming work items and writes out a JSON file with a list of objects representing work items, containing key/value pairs taken from the attributes.

  • The Environment Edit node lets you add extra environment variables to the environment in which work is run.

Manipulating data

  • Using Python:

    If you don’t mind programming, the simplest and most flexible way to pre-process data, or manipulate existing attributes, is with a Python snippet.

    If you want to edit one or more attributes on every incoming work item:

    1. Add a Python Script node. The Python Script node lets you edit incoming work items one at a time.

    2. In the parameters, turn on In process.

    3. Write a script to manipulate attributes. For example:

      # Assume we've ingested data that sets the "detail" attribute to a string
      # such as "low", "medium", or "high", and we want to translate that into
      # a numeric value, eg. -1, 0, or 1.
      # Define a dictionary mapping string values to numeric values
      lookup = {"low": -1, "medium": 0, "high": 1}
      # Get the value of the "detail" string attribute
      detail = work_item.data.intData("detail", 0)
      # Translate the string into the numeric equivalent
      level = lookup.get(detail, 0)
      # Create a new "level" attribute with the numeric equivalent
      work_item.data.setInt("level", level)

    If you want to compute aggregate statistics (for example, the average of an attribute):

    1. Add a Wait for All. This will pause processing until all work items are available, so the average will include all values.

    2. After the Wait for All, add a Python Script node.

    3. In the parameters, set Work item generation to "Dynamic" and turn on In process.

    4. In the script, you can use parent_item to refer to the single item in the Wait for All nodes. That item has a partitionItems list attribute you can use to access the items in the partition:

      # Get the work items in the input Wait for All's partition
      items = part.partitionItems
      # Calculate the average of the "foo" attribute
      total = sum(it.data.floatData("foo", 0) for it in items)
      average = total / float(len(items))
      # Set the average as an attribute on the outgoing item
      work_item.data.setFloat("average", average, 0)
  • Alternatively, you can use TOP nodes that manipulate attributes:

    Attribute Create. You can add or redefine attributes using this node, and you can use expressions to compute the value of the new attribute based on existing attributes.

    Attribute Copy duplicates attributes from work items in one branch onto the work items in another branch, matching the work items up by index or by an attribute value.

    Attribute Delete removes attributes from work items. This can be useful, for example, before outputting to CSV to prevent "scratch" attributes from being written to disk.

Dependency networks


Next steps

  • Running external programs

    How to wrap external functionality in a TOP node.

  • File tags

    Work items track the "results" created by their work. Each result is tagged with a type.

  • Feedback loops

    You can use for-each blocks to process looping, sequential chains of operations on work items.

  • Command servers

    Command blocks let you start up remote processes (such as Houdini or Maya instances), send the server commands, and shut down the server.

  • Integrating PDG and render farm software

    How to use different schedulers to schedule and execute work.

  • Visualizing work item performance

    How to visualize the relative cook times (or file output sizes) of work items in the network.

  • Tips and tricks

    Useful general information and best practices for working with TOPs.


  • All TOPs nodes

    TOP nodes live inside TOP objects and do work.

  • Python API

    The classes and functions in the Python pdg package for working with dependency graphs.