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License: BSD,LGPL,GPL Write Your Build Script
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In this case, we will make a package named `mecab`. $ conda upgrade conda-build Write Your RecipeĪfter creating the working directory, write `meta.yaml` to set package name, version, source repositories, and dependencies. Debian/Ubuntu command): $ sudo apt-get install g++ autoconf makeīefore you start the development, you should install the conda build tool. For this recipe, install the required packages as follows (e.g. If you don’t have any development tools, you should ensure install dependent tools. Prepare Conda EnvironmentĪccording to the official document, it is required to install GCC/G++. Note: Because of a glibc incompatibility, using a docker image doesn’t work to build a package for older Linux distributions such as CentOS 6. $ docker run -i -v $(pwd):/root/mecab -t continuumio/anaconda /bin/bash The following command are executed on your physical machine. Let’s prepare the docker image from anaconda. macOS users, in particular, need to build inside a Docker container because the C libraries that come with a macOS are not compatible with what’s needed here. Using Docker container makes sure the environment isolated, so it prevents troubles from mixed environment both system installed and anaconda installed ones. The use of a Docker container helps ensure library compatibility. We developed this recipe in an Anaconda image for Docker.
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Especially for C extension, this tutorial is a useful resource to read. See more detail of the conda package in the official document.
#HOW TO INSTALL PYSPARK ANACONDA HOW TO#
This post shows how to solve this problem creating a conda recipe with C extension. In Python world, data scientists often want to use Python libraries, such as XGBoost, which includes C/C++ extension. In the previous article, we introduced how to use your favorite Python libraries on an Apache Spark cluster with PySpark. Cloudera Data Science Workbench provides data scientists with secure access to enterprise data with Python, R, and Scala.