OpenCV is a cross-platform library, which means that it is accessible on several programming languages like Python, C++, etc. It is essentially used to perform operations on pictures or videos. OpenCV (Open Source Computer Vision Library) is a library of programming functions fundamentally focused on real-time computer vision. It contains a lot of plugins and packages like NumPy, Pandas, OpenCV, etc. One such Python IDE is Anaconda, open-source software and one of the most popular IDEs used by programmers worldwide for Python and R Programming. Read more about environments at and conda-pack on the project page.There are many Python IDEs that are available on the internet. Exporting an environment using conda is ideal for recreating environments across different platforms and operating systems. Alternatively, conda-pack archives an entire environment including the package binaries, which is useful in low or no bandwidth situations. Conda will use a spec list to download the exact packages in an environment. Spec list and conda-pack create platform and operating system specific copies of an environment. Creating a clone of an environment can provide a custom base environment or snapshot of the environment. # as long as some version of Python is already installed on the machine.Ĭonda provides multiple ways of reproducing project environments. # Note that this command can also be run without activating the environment # Cleanup prefixes from in the active environment. # libraries will work fine, but things that require prefix cleanups # Use Python without activating or fixing the prefixes. # Unpack environment into directory `my_env` # Pack environment located at an explicit path into my_ Conda-pack is available at conda-forge or PyPI.Ĭonda-forge: conda install -c conda-forge conda-pack To install conda-pack, make sure you are in the root or base environment so that it is available in sub-environments. Keep in mind that conda-pack is both platform and operating system specific and that the target computer must have the same platform and OS as the source computer. All the previous methods download packages from their respective repositories to create an environment. This is useful when you want to reproduce an environment with limited or no internet access. To create an environment: conda env create -f environment.yml Conda PackĬonda-pack is a command line tool that archives a conda environment, which includes all the binaries of the packages installed in the environment. * Note that if you have an existing environment.yml file in the path, conda will overwrite that file. To export an environment.yml file: conda env export environment.yml Another difference is that -export also includes packages installed using pip whereas a spec list does not. Only the package names are listed and conda is left to build the environment based on the package names. The difference between a spec list and an environment.yml file is that the environment.yml file is not operating system specific and is formatted using YAML. Sharing a project environment across platforms and operating systems can also be done using the -export option to generate an environment.yml file. To recreate the environment: conda create -name python-course -file spec-list.txt Environment.yml To write the environment specifications to a file: conda list -explicit spec-list.txt For example, if you’re teaching a course and need to copy the environment for course exercises, the -explicit option will produce a list of packages. If you need to reproduce an environment across computers with the same operating system, you can generate a spec list. The -clone option will create a clone (or snapshot) of the environment, conda create -name snapshot -clone myenv Spec List However, you can also create your own base environment with packages you frequently use. On installation, conda creates a base environment. You might want to take a snapshot of your project environment as a backup.Ĭonda provides a number of ways to preserve and move environments. For example, you would want to move a web application to server with a public address or copy a set of tools you frequently use to a USB drive. Once you have your project environment set up and your code written and tested, you may want to move it to another machine.
Because the Python ecosystem of packages is both wide and deep, part of Conda’s job is to install packages that don’t conflict with each other. If you’re new to Python, environments create an isolated environment to manage dependencies in a project. In addition to managing packages, Conda is also an environment manager. and conda-forge, the open-source community for conda Python packages. Conda is known as a package manager for Python and R packages produced by Anaconda, Inc.
0 kommentar(er)
