This guide describes how to install the KNIME Python Integration to be used with KNIME Analytics Platform.
|This guide refers to the KNIME Python Integration that is available since the v3.4 release of KNIME Analytics Platform (not to be confused with the KNIME Python Scripting Extension). The integration is the recommended and most recent way to use arbitrary Python™ scripts in KNIME Analytics Platform and supports both Python 2 as well as Python 3.
The KNIME Python Integration makes use of an existing Python, which is installed alongside KNIME Analytics Platform. As the KNIME Python Integration depends on certain Python packages, the Python installation needs to have these packages installed. Our recommended way to set up such a Python environment is to use the Anaconda Python distribution from Continuum Analytics. In this guide we describe how to install Python and the necessary packages using Anaconda, as well as how to configure the KNIME Python Integration.
This quickstart guide shows you the basic steps required to install the KNIME Python Integration and its prerequisites with Python. We do not provide any further details. If you’d like a more thorough explanation, please refer to the more detailed Anaconda Setup Section.
First, install the KNIME Python Integration. In KNIME Analytics Platform, go to File → Install KNIME Extensions. The KNIME Python Integration can be found under KNIME & Extensions or by entering Python Integration into the search box.
Next, install Anaconda. It is used to manage Python environments. Anaconda can be downloaded here (choose Anaconda with Python 3).
Finally, configure the KNIME Python Integration. Go to the Python Preference page located at File → Preferences. Select KNIME → Python from the list on the left. In the page that opens, select Conda under Python environment configuration. Next, provide the path to your
Anacondainstallation folder (the default installation path is documented here). Once a valid path has been entered, the
condaversion number is shown. Below the
condaversion number you can choose which
condaenvironment to be used for Python 3 and Python 2 by selecting it from a combo box. If you have already set up a Python environment, containing all the necessary dependencies for the KNIME Python Integration, just select it from the list and you are ready to go. If you do not have a suitable environment available, click the New environment… button. This opens the following dialog:
Provide a name for the new environment and click the Create new environment button. This creates a new
condaenvironment containing all the required dependencies for the KNIME Python Integration.
Depending on your internet connection, the environment creation may take a while as all packages need to be downloaded and extracted.
Once the environment is successfully created, the dialog closes and the new environment is selected automatically.
This section describes how to install and configure Anaconda to be used with the KNIME Python Integration. Anaconda allows you to manage several so called
conda environments, which can contain different Python versions and different sets of packages, also using different versions. A
conda environment is essentially a folder that contains a specific Python version and the installed packages. This means you can have several different Python versions installed on your system at the same time in a clean and easy to maintain manner. For KNIME, this is especially useful as it allows you to use Python 3 and Python 2 at the same time without running into version issues; Anaconda keeps each environment nicely encapsulated and independent of all others. Furthermore, Anaconda is able to create predefined environments with a single command and makes it easy to add Python packages to existing ones.
Next, you will learn how to set up an environment that contains the dependencies needed for the KNIME Python Integration.
First, you need to install the latest Anaconda version (
Anaconda ≥ 2019.03, conda ≥ 4.6.2). On the Anaconda download page you can choose between Anaconda with Python 3.x or Python 2.x, however this only affects the root
conda environment, which we will not use (as we are creating our own). Therefore, you can choose either one (if you’re not sure, we suggest selecting Python 3).
Creating a Conda environment
After Anaconda is installed, you need to create a new conda environment. As of KNIME Analytics Platform 3.8.0, there are two options to do this:
Option 1: Automatic (recommended)
A Python environment containing all required dependencies can be automatically created in the KNIME Python Integration Preference page. If you do not explicitly want to create an environment manually, please continue with Setting up the KNIME Python Integration.
Option 2: Manual
If you do not want to create a
conda environment automatically, you can create one manually after Anaconda is installed. Do this with a YAML configuration file, which lists all of the packages to be installed in the newly created environment. We have provided two such configuration files below (one configuration file to create a new Python 3 environment and one file for Python 2). They list all of the dependencies needed for the KNIME Python Integration:
name: py36_knime # Name of the created environment channels: # Repositories to search for packages - defaults - anaconda - conda-forge dependencies: # List of packages that should be installed - python=3.6 # Python - pandas=0.23 # Table data structures - jedi=0.13 # Python script autocompletion - python-dateutil=2.7 # Date and Time utilities - numpy=1.16.1 # N-dimensional arrays - cairo=1.14 # SVG support - pillow=5.3 # Image inputs/outputs - matplotlib=3.0 # Plotting - pyarrow=0.11 # Arrow serialization - IPython=7.1 # Notebook support - nbformat=4.4 # Notebook support - scipy=1.1 # Notebook support - jpype1=0.6.3 # Databases - libiconv=1.15 # MDF Reader node - asammdf=5.13.13 # MDF Reader node
name: py27_knime # Name of the created environment channels: # Repositories to search for packages - defaults - anaconda - conda-forge dependencies: # List of packages that should be installed - python=2.7 # Python - pandas=0.23 # Table data structures - jedi=0.13 # Python script autocompletion - python-dateutil=2.7 # Date and Time utilities - numpy=1.15 # N-dimensional arrays - cairo=1.14 # SVG support - pillow=5.3 # Image inputs/outputs - matplotlib=2.2 # Plotting - IPython=5.8 # Notebook support - nbformat=4.4 # Notebook support - scipy=1.1 # Notebook support - jpype1=0.6.3 # Databases - protobuf=3.5 # Serialization for deprecated Python nodes
|The above configuration files only contain the Python packages that the KNIME Python Integration depends on. If you want to use more Python packages in KNIME you can either add the name of the package at the end of the configuration file or add them after the environment has been created.
For example, for Python 3 you can use the
py36_knime.yml and download it to any folder on your system (e.g. your home folder). In order to create an environment from this file, open a shell (Linux), terminal (Mac), or Anaconda prompt (Windows, can be found by entering
anaconda in Windows Search), change the directory to the folder that contains the configuration file and execute the following command:
conda env create -f py36_knime.yml
This command creates a new environment with the name provided at the top of the configuration file (of course you can change the name). It also downloads and installs all of the listed packages (depending on your internet speed, this may take a while).
If you want to use both Python 3 and Python 2 at the same time, just repeat the above steps using the respective configuration file.
|The list of dependencies for Python 3 and Python 2 is almost the same, however version numbers change.
After Anaconda has successfully created the environment, Python is all set up and you are ready to proceed with Setting up the KNIME Python Integration.
Further information on how to manage Anaconda environments can be found here.
Manually installing additional Python packages
The Anaconda configuration files listed above only contain the packages to be installed so that the KNIME Python Integration works properly. Hence, if you want to use Python packages other than the ones listed in the configuration files, these can be easily added manually after the environment has been created. E.g. if you want to use functionality from
scikit-learn in KNIME Python nodes, you can use the following command:
conda install --name <ENV_NAME> scikit-learn
<ENV_NAME> with the name of the environment in which you want to install the package.
You can easily specify a specific version of the package with e.g.
Further information on how to manage Anaconda packages can be found here.
On Mac, there may be issues with the
matplotlib package. The following error:
libc++abi.dylib: terminating with uncaught exception of type NSException
can be resolved by executing the following commands:
mkdir ~/.matplotlib echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
Setting up the KNIME Python Integration
This section describes how to install and configure the KNIME Python Integration using an existing Anaconda environment. If you haven’t set up Anaconda and/or the recommended Python environment yet, please refer to the Anaconda Setup guide.
From KNIME Analytics Platform, install the KNIME Python Integration by going to File → Install KNIME Extensions. The KNIME Python Integration can be found under KNIME & Extensions or by entering Python Integration into the search box.
Configure the KNIME Python Integration
Now tell KNIME which Python environment should be used. Go to the Preference page of the KNIME Python Integration located at File → Preferences, and then select KNIME → Python from the list on the left. A dialog opens giving you two options for configuring the Python environment:
Option 1: Conda (recommended)
Select Conda under Python environment configuration. The dialog should look like the screenshot shown below.
In this dialog, provide the path to the folder containing your
Anaconda installation (the default installation path is documented here). Once you have entered a valid path, the installed
conda version is displayed and KNIME automatically checks for all available
conda environments. Underneath the
conda version number, you can choose which
conda environment should be used for Python 3 and Python 2 by selecting it from a combo box. If you have already set up a Python environment containing all the necessary dependencies for the KNIME Python Integration, just select it from the list and you are ready to go. If you do not have a suitable environment available, click the New environment… button. This opens the following dialog:
Provide a name for the new environment and click the Create new environment button. This creates a new
conda environment containing all required dependencies for the KNIME Python Integration.
|Depending on your internet connection, the environment creation may take a while as all packages need to be downloaded and extracted.
Once the environment is successfully created, the dialog closes and the new environment is selected automatically. If everything worked out fine, the Python version is now shown below the environment selection and you are ready to go.
Option 2: Manual
If you choose the manual option, you have to point KNIME to a start script which activates the environment you want to use for Python 2 and Python 3 respectively. This option assumes that you have created a suitable Python environment earlier by following the instructions given under Option 2: Manually of the Creating a Conda environment Section.
In order to use the created Anaconda environment for the KNIME Python Integration, you need to create a start script (shell script on Linux and Mac, bat file on Windows).
If you are using Linux or Mac, here’s an example shell script for the Python environment:
#! /bin/bash # Start by making sure that the anaconda folder is on the PATH # so that the source activate command works. # This isn't necessary if you already know that # the anaconda bin dir is on the PATH export PATH="<PATH_WHERE_YOU_INSTALLED_ANACONDA>/bin:$PATH" conda activate <ENVIRONMENT_NAME> python "$@" 1>&1 2>&2
On Windows, the script looks like this:
@REM Adapt the folder in the PATH to your system @SET PATH=<PATH_WHERE_YOU_INSTALLED_ANACONDA>\Scripts;%PATH% @CALL activate <ENVIRONMENT_NAME> || ECHO Activating python environment failed @python %*
These example scripts need to be edited in order to point to the location of your Anaconda installation and to
activate the correct Anaconda environment. I.e. replace the
<PATH_WHERE_YOU_INSTALLED_ANACONDA> with the location of your Anaconda installation (the default installation path is documented here) and
<ENVIRONMENT_NAME> with the name of the
conda environment the script should start and which you created in the Anaconda Setup guide (e.g. in this case
py36_knime for Python 3.6 or
py27_knime for Python 2.7).
For example on Windows, create a new bat script named e.g.
py36.sh on Linux or Mac) and paste the corresponding script to the file.
On Linux/Mac you additionally need to make the file executable (i.e.
chmod gou+x py36.sh).
Once you have created the start script, you’re almost finished setting up Python. The last thing to do is to point KNIME to the start script you just created. Do this in the Preference page of the KNIME Python Integration located at File → Preferences. Select KNIME → Python from the list on the left. The dialog should look like the screenshot shown below.
On this page you need to provide the path to the script/bat file you created to start Python. If you like, you can have configurations for both Python 2 and Python 3 (as is shown above). Just select the one that you would like to have as the default. If everything is set correctly, the Python version is now shown in the dialog window and you are ready to go.
You can choose which serialization library should be used by the KNIME Python Integration to transfer data from KNIME Analytics Platform to Python.
|This option does not usually need to be changed and can be left as the default.
Some of these serialization libraries have additional dependencies stated below, however if you followed the Anaconda Setup, all required dependencies are already included (see YAML configuration files on the Anaconda Setup guide). Currently, there are three options:
Flatbuffers Column Serialization (default & recommended): no additional dependencies
Apache Arrow: depends on
CSV (Experimental): depends on
Configure Python environments per node
Besides setting up Python for your entire KNIME workspace via the Preference page, you can also configure individual nodes to use specific Python environments. This is done via flow variables
python3Command that each Python scripting node offers under the Flow Variables tab in its configuration dialog. Both variables accept the path to a Python start script like in the Manual case above. Which of the two flow variables is respected depends on whether a node is using Python 2 or Python 3. This can either be configured via option Use Python Version under the Options tab in the node’s configuration dialog or via flow variable
pythonVersionOption which accepts either python2 or python3 as value.
Load Jupyter notebooks from KNIME
Existing Jupyter notebooks can be accessed within Python Script nodes using the
knime_jupyter Python module (
knime_jupyter will be imported automatically). Notebooks can be opened via the function
knime_jupyter.load_notebook, which returns a standard Python module. The
load_notebook function needs the path (path to the folder that contains the notebook file) and the name of the notebook (filename) as arguments. After a notebook has been loaded, you can call functions that are defined in the code cells of the notebook like any other function of a Python module. Furthermore, you can print the textual content of each cell of a Jupyter notebook using the function
knime_jupyter.print_notebook. It takes the same arguments as the
An example script for a Python Script node loading a notebook could look like this:
# Path to the folder containing the notebook, e.g. the folder 'data' contained # in my workflow folder notebook_directory = "knime://knime.workflow/data/" # Filename of the notebook notebook_name = "sum_table.ipynb" # Load the notebook as a Python module my_notebook = knime_jupyter.load_notebook(notebook_directory, notebook_name) # Print its textual contents knime_jupyter.print_notebook(notebook_directory, notebook_name) # Call a function 'sum_each_row' defined in the notebook output_table = my_notebook.sum_each_row(input_table)
print_notebook functions have two optional arguments:
notebook_version: The Jupyter notebook format major version. Sometimes the version can’t be read from a notebook file. In these cases, this option allows to specify the expected version in order to avoid compatibility issues. Should be an integer.
only_include_tag: Only load cells that are annotated with the given custom cell tag (since Jupyter 5.0.0). This is useful to mark cells that are intended to be used in a Python module. All other cells are excluded. This is e.g. helpful to exclude cells that do visualization or contain demo code. Should be a string.
The Python nodes support code completion similar to an IDE. Just hit
command-space on Mac) e.g. after
knime_jupyter. in order to show the available methods and documentation (
knime_jupyter refers to the imported
knime_jupyter Python module, e.g. see script example above).
The Jupyter notebook support for the KNIME Python Integration depends on the packages
scipy, which are already included if you used the configuration files from the Anaconda Setup.
You can find example workflows using the
knime_jupyter Python module on our EXAMPLES server.
Similar to the KNIME Deep Learning Integration, the MDF Reader node requires certain Python packages to be installed in the Python 3 environment. Since the v4.1 release of KNIME Analytics Platform, these will be automatically installed if you set up your Python environment via the Python Preference page (see here). The required packages are the following:
numpy=1.16.1 libiconv=1.15 asammdf=5.13.13
The MDF Reader node requires a newer
numpy version (
1.16.1) compared to the numpy version (
1.15) required before.