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Middleware
Published in Chandrasekar Vuppalapati, Building Enterprise IoT Applications, 2019
setx SPARK_HOME C:\opt\spark\spark-2.1.0-bin-hadoop2.7 setx HADOOP_HOME C:\opt\spark\spark-2.1.0-bin-hadoop2.7setx PYSPARK_DRIVER_PYTHON ipythonsetx PYSPARK_DRIVER_PYTHON_OPTS notebook6) Activate your Anaconda virtual environment with the version of Python that you’d like to use. You could make a new environment (recommended!) to try things out, or YOLO just > conda install pyspark into your main Python environment.7) You need to enable access to the default scratch directory for Hive. First, make sure the directory C:\ tmp\hive is created; if it doesn’t exist, create it.
A Smart Microfactory Design: An Integrated Approach
Published in Wasim Ahmed Khan, Ghulam Abbas, Khalid Rahman, Ghulam Hussain, Cedric Aimal Edwin, Functional Reverse Engineering of Machine Tools, 2019
Syed Osama bin Islam, Liaquat Ali Khan, Azfar Khalid, Waqas Akbar Lughmani
The Anaconda is another open source which makes it even easier to cater for machine learning and Python data science. There are more than 250 famous data science packages, virtual environment manager for Windows, conda packages, MacOS, and Linux packages. TensorFlow, Scikit-learn, and SciPy are easy to install in Anaconda; it is even easy to upgrade environments and complex data packages. Anaconda 3 includes all the libraries required for object-detection API. The TensorFlow Object-detection API uses Protobufs to configure model and training parameters. Before the framework can be used, the Protobuf libraries must be compiled. Protobuf 3.4 is required for compilation; others don’t work. Either add Protobuf in system path or give full path to the protos folder. This should be done by running the following command from the “tensorflow/models/research/” directory: # From tensorflow/models/research/ protoc object_detection/protos/*.proto --python_out=.
Image Processing and Acquisition using Python
Published in Ravishankar Chityala, Sridevi Pudipeddi, Image Processing and Acquisition using Python, 2020
Ravishankar Chityala, Sridevi Pudipeddi
The Anaconda Python Distribution (Ana20a) provides programmers with close to 100 of the most popular scientific Python modules like scientific computation, linear algebra, symbolic computing, image processing, signal processing, visualization, integration of C/C++ programs to Python etc. It is distributed and maintained by Continuum Analytics. It is available for free for academics and is available for a price to all others. In addition to the various modules built into Anaconda, programmers can install other modules using the conda (Ana20b) package manager, without affecting the main distribution.
Tutorial: The LuxPy Python Toolbox for Lighting and Color Science
Published in LEUKOS, 2020
Spyder is an Interactive Development Environment (IDE) for scientific programming in the Python language. It supports IPython and popular Python libraries such as NumPy, SciPy, or matplotlib. It should come installed with the Anaconda distribution in the conda virtual environment. If not, it can be installed by typing: