TensorFlow Network Executor

This node executes a TensorFlow deep learning network on a compatible external back end that can be selected by the user.

Options

Back end: The deep learning back end which is used to execute the input network for the given input data.
Input batch size: The number of rows that are processed at a time.

Conversion: The converter that is used to transform the selected input columns into a format that is accepted by the respective network input specification.
Input columns: The table columns that are part of the respective network input. The availability of a column depends on the currently selected input converter.

Conversion: The converter that is used to transform the network output into table columns.
Output columns prefix: The prefix that is used to distinguish between the columns of the different outputs.

Visible devices list: A comma-separated list of GPU ids that determines the 'visible' to 'virtual' mapping of GPU devices. For example, if TensorFlow can see 8 GPU devices in the process, and one wanted to map visible GPU devices 5 and 3 as "/device:GPU:0", and "/device:GPU:1", then one would specify this field as "5,3". This field is similar in spirit to the CUDA_VISIBLE_DEVICES environment variable, except it applies to the visible GPU devices in the process and must list only valid GPU indices.

NOTE:
The GPU driver provides the process with the visible GPUs in an order which is not guaranteed to have any correlation to the *physical* GPU id in the machine. This field is used for remapping "visible" to "virtual", which means this operates only after the process starts. You should set the environment variable CUDA_VISIBLE_DEVICES prior to starting KNIME to ensure the device visibility and order (See CUDA Environment Variables ).
Per process GPU memory fraction: Fraction of the available GPU memory to allocate for each process. 1 means to allocate all of the GPU memory, 0.5 means the process allocates up to ~50% of the available GPU memory.

This node has no views

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

To use this node in KNIME, install the extension KNIME Deep Learning - TensorFlow Integration from the below update site following our NodePit Product and Node Installation Guide:

v5.3

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.3.0.v202312081535

On NodePit since: 2024-07-09

Last update: 2024-11-15

Tags: Streamable

KNIME versions: Since v4.0

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.