{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Environments\n", "\n", "This document guides MAAP users in the process of selecting, extending existing environments (the set of libraries availables for analysis) or creating custom environments.\n", "\n", "## Workspaces\n", "\n", "The MAAP ADE offers various workspace options, each workspace coming with its own environment that has pre-installed essential libraries for computing and geospatial analysis. At the time of writing this guide, here are the options : \n", "\n", "![Workspace image options](../_static/workspace_options.png)\n", "\n", "For example, the `MAAP RGEDI Stable` and `MAAP R Stable` workspace options come with various pre-installed R packages. \n", "\n", "**For more information:** Each of these options rely on Docker images that were build off from Dockerfiles that are publicly available in the [MAAP workspace repository](https://github.com/MAAP-Project/maap-workspaces/tree/develop). If you want to learn more about what libraries each image contains, check out this repository." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Extending environments\n", "\n", "Users may need libraries for their specific analysis purposes that are not present in the environments of the different workspace options offered. In this case, ideally, the steps should be the following : \n", "\n", "1. The user explores her/his environment need by extending the environment of an existing workspace or creating her/his custom environment in an existing workspace (see next sections).\n", "2. Once that is done, the user submits a ticket/coordinates with the platform team to create a new workspace option with the requested, finalized environment.\n", "\n", "The above approach is ideal because modifications to the pre-defined workspace environment do not survive a workspace restart (see next sections), and because sharing new experimented environments is valuable.\n", "\n", "The next sections explain how to extend environments or create custom environments, and for this, introduces information regarding which environment management solution we are using. \n", "\n", "### Package manager\n", "\n", "We use `conda` with the libmamba solver as a package manager to install, update or remove packages (libraries). `conda` works with 'environments' that are directories in your local file system containing a set of packages. When you work 'in a given environment', it means that your programs will look for dependencies in that environment's `conda` directory. All workspaces launch with a default environment that has all the pre-installed libraries for that workspace. The actual name of the default environment depends on the workspace chosen by the user. For example, for the pangeo workspace, the default environment is called `pangeo`. If you open a terminal launcher after creating a `Pangeo` workspace : \n", "\n", "\n", "![Pangeo conda environment location](../_static/pangeo_environment_location.png)\n", "\n", "You can notice that a `pangeo` `conda` environment is activated by default, and its libraries are located in `/opt/conda/envs/pangeo`. \n", "\n", "### Extending the default environment in a given workspace session\n", "\n", "*Throughout this document, \"the default workspace environment\" refers to the conda environment activated by default in a given workspace environment. The name of that conda environment differs across workspaces. Any modification to the default workspace environment, or to the `base` environment, does not survive a workspace restart.*\n", "\n", "Extending an existing `conda` environment means adding packages on top of what it contains, which works provided there are no dependency conflicts. You can install libraries using the `conda install` command to install additional packages in your current environment (run `conda --help` to learn more about how to use `conda` commands). All `conda` install commands should use `-c conda-forge` otherwise it's unlikely to work, since many/most of the packages installed already are from conda-forge. For example :\n", "\n", "```\n", "conda install -c conda-forge xarray\n", "```\n", "\n", "libmamba is the default solver, but users are welcome to set the solver to \"classic\" with: \n", "\n", "```\n", "conda install --solver=classic -c conda-forge xarray\n", "```\n", "\n", "However, it is recommended to use configuration files for reproducibility and shareability. With this approach, assuming your configuration file is named `config.yml`, the command to use is : \n", "\n", "```\n", "conda env update -f config.yml\n", "```\n", "\n", "For more details on configuration files, see the [Custom environments section](#Custom-environments) and for an example of this command, refer to the [subsection about updating an environment with a configuration file](#Updating-an-existing-environment-with-a-configuration-file).\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Custom environments\n", "\n", "*For the rest of this README, in each section we provide a link to download an example YAML configuration file.*\n", "\n", "You can use the `conda` CLI to create a new, custom environment. The parameters (the list of libraries, the location where to search for them, etc...) can be passed either from a configuration YAML file or directly on the console. We recommend using the first option (a YAML file is easier to share and modify). \n", "\n", "### Basic custom environment\n", "\n", ".. note::\n", "Example config file for a basic custom environment [here](./example_conda_configuration_files/env-example.yml).\n", "\n", "This configuration installs specific versions `python`, `pandas` and `geopandas` from `conda-forge`. If versions aren't specified, the latest is installed. We recommend to always specify the version for reproducibility. The basic command to create this environment would be :\n", "\n", "```\n", "conda env create -f env-example.yml\n", "```\n", "\n", "However, this stores this environment files in `/opt/conda`, which is a directory that is recreated when the workspace restarts, and so packages stored in that directory by the user are lost if a workspace restarts. Therefore, you want to specify another storage location in your user directory with the `--prefix` parameter\n", "\n", "```\n", "conda env create -f env-example.yml --prefix /projects/env/env-example\n", "```\n", "\n", "and to activate it : \n", "\n", "```\n", "conda activate /projects/env/env-example\n", "```\n", "\n", "### Updating an existing environment with a configuration file\n", "\n", ".. note::\n", "Example config file for updating the `pangeo` environment [here](./example_conda_configuration_files/env-extend.yml).\n", "\n", "You can *update* an existing environment with a configuration file as well. For example, let's assume you have a `conda` environment with a set of packages already installed in it (for example the `pangeo` environment, or another default workspace environment), but it doesn't have `xarray` and `geopandas`. Using the linked example config : \n", "\n", "```\n", "conda env update -f env-extend.yml\n", "```\n", "\n", "This command will update the active environment by adding `xarray` and `geopandas`, provided it does not cause conflicts with the existing libraries. \n", "\n", "\n", "### Using `pip` for python packages\n", "\n", ".. note::\n", "Example config file for using pip install [here](./example_conda_configuration_files/env-with-pip.yml).\n", "\n", "Some python packages might not be availabe in the channel you are using, or in any `conda` channel. If that package however is in `PyPI` (the official python package repository), one can use `pip` within a `conda` environment to download packages. The recommended way is to specify this in the configuration file. In the linked example, we add `stackstac` as a dependency to install from `PyPI` because it is not available in the `conda-forge` channel. \n", "\n", "### Using custom environments in jupyter notebooks\n", "\n", ".. note::\n", "Example config file for this section [here](./example_conda_configuration_files/env-with-ipykernel.yml).\n", "\n", "The following instruction steps are for python kernels.\n", "\n", "- Make sure ipykernel is listed as a dependency in your configuration file.\n", "- Create your environment using the linked configuration file.\n", "- Install the environment as a kernel by running the following command (parameter values follow the example mentioned):\n", " ```\n", " python -m ipykernel install --user --name env-with-ipykernel --display-name \"Python env-with-ipykernel\"\n", " ```\n", " The above command installs the environment as a kernel in Jupyter, making it accessible in the notebook with a display name of \"Python env-with-ipykernel\".\n", "- Wait around 30 seconds and launch a new notebook. Among the kernel options, you should see \"Python env-with-ipykernel\" listed. Below you can see a screenshot that shows what this step looks like:\n", "![Register a kernel with a conda environment and launch a notebook with it](../_static/launch_custom_kernel_conda.png)\n", "- Remove by listing kernelspecs `jupyter kernelspec list` to find name, then `jupyter kernelspec remove `\n", "\n", "### Suggested packages for custom environment\n", "\n", ".. note::\n", "Example config file for installing maap-py via pip [here](./example_conda_configuration_files/env-with-maap-py.yml)\n", "\n", "MAAP users typically use the python `maap-py`. It's pre-installed in all workspaces, in the default workspace environment. Any custom environment should specify it, otherwise it is not going to be accessible from that environment. However, `maap-py` is not packaged in a public package repository, like `PyPI` or `conda-forge`. It is possible to install it directly from its Github repository with `pip` though. See the configuration example linked. You can note that in the example, `maap-py` is 'versioned' using one of the `maap-py` git version tags. You can find the most recent `maap-py` tags on the [github repository in the \"releases\" page](https://github.com/MAAP-Project/maap-py/releases) :\n", "\n", "![git version tags](../_static/git_tags_maap_py.png)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.11.1 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.1" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "vscode": { "interpreter": { "hash": "5c7b89af1651d0b8571dde13640ecdccf7d5a6204171d6ab33e7c296e100e08a" } } }, "nbformat": 4, "nbformat_minor": 4 }