Overkill for experimental machine learning, or smaller-scale experiments and workflows. Ideal for running large-scale intensive machine learning workflows on the scalable Apache Spark platform. Additional cost incurred for Linux VM (VM can be stopped when not in use to avoid charges). Lack of control over your development environment and dependencies. The SDK is already installed in your workspace VM, and notebook tutorials are pre-cloned and ready to run. Easy to scale and combine with other custom tools and workflows.Ī slower getting started experience compared to the cloud-based compute instance.Įasiest way to get started. Similar to the cloud-based compute instance (Python is pre-installed), but with additional popular data science and machine learning tools pre-installed. Necessary SDK packages must be installed, and an environment must also be installed if you don't already have one. Run with any build tool, environment, or IDE of your choice. Environmentįull control of your development environment and dependencies. The following table shows each development environment covered in this article, along with pros and cons. Once you can open a terminal and type in conda -version you have successfully installed Conda.Learn how to configure a Python development environment for Azure Machine Learning. There are many variations of installation not just for Windows, Mac and Linux, but each version my had different steps required. To get started, it is best to go directly to the Conda web site and follow the installation instructions for you specific version of your operating system. This means your installation scripts are more secure and less likely to introduce security problems and accidentally remove libraries that other projects depend on. One other important fact to remember is that if you use a conda virtual environment you should never need to use sudo (root) to install Python libraries. This may not be important on your 2nd or 3rd Python project, but as you do more Python projects you will benefit from isolated environments that each have their own versions of each Python libraries that will not conflict with each other. Using Conda allows you to keep each of your Python projects cleanly separated. These environments include all the Python libraries that you need to be a productive MicroPython developer. Raspberry Pi Pico Forum on MicroPython SiteĬreating a Conda Environment for MicroPythonĬonda is a powerful tool for building consistent and stable Python environments.
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