The repo, including this guide, is tested on Linux. Where applicable, we document differences in Windows and MacOS although such documentation may not always be up to date.
In addition to the pip installable package, several extras are provided, including:
[gpu]: Needed for running GPU models.[spark]: Needed for running Spark models.[dev]: Needed for development.[all]:[gpu]|[spark]|[dev][experimental]: Models that are not thoroughly tested and/or may require additional steps in installation).
Follow the Getting Started section in the README to install the package and run the examples.
# 1. Make sure CUDA is installed.
# 2. Follow Steps 1-5 in the Getting Started section in README.md to install the package and Jupyter kernel, adding the gpu extra to the pip install command:
pip install recommenders[gpu]
# 3. Within VSCode:
# a. Open a notebook with a GPU model, e.g., examples/00_quick_start/wide_deep_movielens.ipynb;
# b. Select Jupyter kernel <kernel_name>;
# c. Run the notebook.# 1. Make sure JDK is installed. For example, OpenJDK 11 can be installed using the command
# sudo apt-get install openjdk-11-jdk
# 2. Follow Steps 1-5 in the Getting Started section in README.md to install the package and Jupyter kernel, adding the spark extra to the pip install command:
pip install recommenders[spark]
# 3. Within VSCode:
# a. Open a notebook with a Spark model, e.g., examples/00_quick_start/als_movielens.ipynb;
# b. Select Jupyter kernel <kernel_name>;
# c. Run the notebook.The following instructions were tested on Databricks Runtime 15.4 LTS (Apache Spark version 3.5.0), 14.3 LTS (Apache Spark version 3.5.0), 13.3 LTS (Apache Spark version 3.4.1), and 12.2 LTS (Apache Spark version 3.3.2). We have tested the runtime on python 3.9,3.10 and 3.11.
After an Databricks cluster is provisioned:
# 1. Go to the "Compute" tab on the left of the page, click on the provisioned cluster and then click on "Libraries".
# 2. Click the "Install new" button.
# 3. In the popup window, select "PyPI" as the library source. Enter "recommenders[examples]" as the package name. Click "Install" to install the package.
# 4. Now, repeat the step 3 for below packages:
# a. numpy<2.0.0
# b. pandera<=0.18.3
# c. scipy<=1.13.1This repository includes an end-to-end example notebook that uses Azure Databricks to estimate a recommendation model using matrix factorization with Alternating Least Squares, writes pre-computed recommendations to Azure Cosmos DB, and then creates a real-time scoring service that retrieves the recommendations from Cosmos DB. In order to execute that notebook, you must install the Recommenders repository as a library (as described above), AND you must also install some additional dependencies. With the Quick install method, you just need to pass an additional option to the installation script.
Quick install
This option utilizes the installation script to do the setup. Just run the installation script
with an additional option. If you have already run the script once to upload and install the Recommenders.egg library, you can also add an --overwrite option:
python tools/databricks_install.py --overwrite --prepare-o16n <CLUSTER_ID>
This script does all of the steps described in the Manual setup section below.
Manual setup
You must install three packages as libraries from PyPI:
azure-cli==2.0.56azureml-sdk[databricks]==1.0.8pydocumentdb==2.3.3
You can follow instructions here for details on how to install packages from PyPI.
Additionally, you must install the spark-cosmosdb connector on the cluster. The easiest way to manually do that is to:
- Download the appropriate jar from MAVEN. NOTE This is the appropriate jar for spark versions
3.1.X, and is the appropriate version for the recommended Azure Databricks run-time detailed above. See the Databricks installation script for other Databricks runtimes. - Upload and install the jar by:
- Log into your
Azure Databricksworkspace - Select the
Clustersbutton on the left. - Select the cluster on which you want to import the library.
- Select the
UploadandJaroptions, and click in the box that has the textDrop JAR herein it. - Navigate to the downloaded
.jarfile, select it, and clickOpen. - Click on
Install. - Restart the cluster.
- Log into your
The xlearn package has dependency on cmake. If one uses the xlearn related notebooks or scripts, make sure cmake is installed in the system. The easiest way to install on Linux is with apt-get: sudo apt-get install -y build-essential cmake. Detailed instructions for installing cmake from source can be found here.
For Spark features to work, make sure Java and Spark are installed and respective environment varialbes such as JAVA_HOME, SPARK_HOME and HADOOP_HOME are set properly. Also make sure environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are set to the the same python executable.
We recommend using Homebrew to install the dependencies on macOS, including conda (please remember to add conda's path to $PATH). One may also need to install lightgbm using Homebrew before pip install the package.
If zsh is used, one will need to use pip install 'recommenders[<extras>]' to install <extras>.
For Spark features to work, make sure Java and Spark are installed first. Also make sure environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are set to the the same python executable.
If you want to contribute to Recommenders, please first read the Contributing Guide. You will notice that our development branch is staging.
To start developing, you need to install the latest staging branch in local, the dev package, and any other package you want. For example, for starting developing with GPU models, you can use the following command:
git checkout staging
pip install -e .[dev,gpu]You can decide which packages you want to install, if you want to install all of them, you can use the following command:
git checkout staging
pip install -e .[all]We also provide a devcontainer.json and Dockerfile for developers to facilitate the development on Dev Containers with VS Code and GitHub Codespaces.
VS Code Dev Containers
The typical scenario using Docker containers for development is as follows. Say, we want to develop applications for a specific environment, so
- we create a contaienr with the dependencies required,
- and mount the folder containing the code to the container,
- then code parsing, debugging and testing are all performed against the container. This workflow seperates the development environment from your local environment, so that your local environment won't be affected. The container used here for this end is called Dev Container in the VS Code Dev Containers extension. And the extension eases this development workflow with Docker containers automatically without pain.
To use VS Code Dev Containers, your local machine must have the following applicatioins installed:
Then
- When you open your local Recommenders folder in VS Code, it will
detect devcontainer.json, and
prompt you to Reopen in Container. If you'd like to reopen,
it will create a container with the required environment described
in
devcontainer.json, install a VS Code server in the container, and mount the folder into the container.- If you don't see the prompt, you can use the command Dev Containers: Reopen in Container
- If you don't have a local clone of Recommenders, you can also use
the command Dev Containers: Clone Repository in Container Volume,
and type in a branch/PR URL of Recommenders you'd like to develop
on, such as https://github.com/recommenders-team/recommenders,
https://github.com/recommenders-team/recommenders/tree/staging, or
#2098. VS
Code will create a container with the environment described in
devcontainer.json, and clone the specified branch of Recommenders into the container.
Once everything is set up, VS Code will act as a client to the server in the container, and all subsequent operations on VS Code will be performed against the container.
GitHub Codespaces
GitHub Codespaces also uses devcontainer.json and Dockerfile in the
repo to create the environment on a VM for you to develop on the Web
VS Code. To use the GitHub Codespaces on Recommenders, you can go to
Recommenders
devcontainer.json & Dockerfile
devcontainer.json describes:
- the Dockerfile to use with configurable build arguments, such as
COMPUTEandPYTHON_VERSION. - settings on VS Code server, such as Python interpreter path in the container, Python formatter.
- extensions on VS Code server, such as black-formatter, pylint.
- how to create the Conda environment for Recommenders in
postCreateCommand
Dockerfile is used in 3 places:
- Dev containers on VS Code and GitHub Codespaces
- Testing workflows on AzureML
- Jupyter notebook examples on Docker
Depending on the type of recommender system and the notebook that needs to be run, there are different computational requirements.
Currently, tests are done on Python CPU (the base environment), Python GPU (corresponding to [gpu] extra above) and PySpark (corresponding to [spark] extra above).
Another way is to build a docker image and use the functions inside a docker container.
The process of making a new release and publishing it to PyPI is as follows:
First make sure that the tag that you want to add, e.g. 0.6.0, is added in recommenders.py/__init__.py. Follow the contribution guideline to add the change.
- Make sure that the code in main passes all the tests (unit and nightly tests).
- Create a tag with the version number: e.g.
git tag -a 0.6.0 -m "Recommenders 0.6.0". - Push the tag to the remote server:
git push origin 0.6.0. - When the new tag is pushed, a release pipeline is executed. This pipeline runs all the tests again (PR gate and nightly builds), generates a wheel and a tar.gz which are uploaded to a GitHub draft release.
- Fill up the draft release with all the recent changes in the code.
- Download the wheel and tar.gz locally, these files shouldn't have any bug, since they passed all the tests.
- Install twine:
pip install twine - Publish the wheel and tar.gz to PyPI:
twine upload recommenders*