* Local (Linux, MacOS or Windows) or [DSVM](https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/) (Linux or Windows)
There are different ways one may use the recommenders utilities. The most convenient one is probably by installing the `recommenders` package from [PyPI](https://pypi.org).
Another way is to build a docker image and use the functions inside a [docker container](#setup-guide-for-docker).
Another alternative is to run all the recommender utilities directly from a local copy of the source code. This requires installing all the necessary dependencies from Anaconda and PyPI. For instructions on how to do this, see [this guide](conda.md).
* This is pre-installed on Azure DSVM such that one can run the following steps directly. To setup on your local machine, [Miniconda](https://docs.conda.io/en/latest/miniconda.html) is a quick way to get started.
**NOTE** 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](https://cmake.org/install/).
**NOTE** the models from Cornac require installation of `libpython` i.e. using `sudo apt-get install -y libpython3.6` or `libpython3.7`, depending on the version of Python.
If you use conda, to set these variables every time the environment is activated, you can follow the steps of this [guide](https://conda.io/docs/user-guide/tasks/manage-environments.html#macos-and-linux).
This will export the variables every time we do `conda activate reco_pyspark`. To unset these variables when we deactivate the environment, create the file `$RECO_ENV/etc/conda/deactivate.d/env_vars.sh` and add:
To set these variables every time the environment is activated, we can follow the steps of this [guide](https://conda.io/docs/user-guide/tasks/manage-environments.html#windows).
First, get the path of the environment `reco_pyspark` is installed:
for /f "delims=" %A in ('conda env list ^| grep reco_pyspark ^| awk "{print $NF}"') do set "RECO_ENV=%A"
Then, create the file `%RECO_ENV%\etc\conda\activate.d\env_vars.bat` and add:
It is straightforward to install the recommenders package within a [virtual environment](https://docs.python.org/3/library/venv.html). However, setting up CUDA for use with a GPU can be cumbersome. We thus
recommend setting up [Nvidia docker](https://github.com/NVIDIA/nvidia-docker) and running the virtual environment within a container, as the most convenient way to do this.
If you prefer to use [virtualenv](https://virtualenv.pypa.io/en/latest/index.html#) instead of venv, you may follow the above steps, except you will need to replace the line
If you are using the DSVM, you can [connect to JupyterHub](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro#jupyterhub-and-jupyterlab) by browsing to `https://your-vm-ip:8000`.
* We found that there can be problems if the Spark version of the machine is not the same as the one in the [conda file](conda.md). You can use the option `--pyspark-version` to address this issue.
* When running Spark on a single local node it is possible to run out of disk space as temporary files are written to the user's home directory. To avoid this on a DSVM, we attached an additional disk to the DSVM and made modifications to the Spark configuration. This is done by including the following lines in the file at `/dsvm/tools/spark/current/conf/spark-env.sh`.
* Another source of problems is when the variable `SPARK_HOME` is not set correctly. In the Azure DSVM, `SPARK_HOME` is by default `/dsvm/tools/spark/current`. We need to unset it:
* We found that there might be conflicts between the current MMLSpark jars available in the DSVM and the ones used by the library. In that case, it is better to remove those jars and rely on loading them from Maven or other repositories made available by MMLSpark team.
An example of how to create an Azure Databricks workspace and an Apache Spark cluster within the workspace can be found from [here](https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal). To utilize deep learning models and GPUs, you may setup GPU-enabled cluster. For more details about this topic, please see [Azure Databricks deep learning guide](https://docs.azuredatabricks.net/applications/deep-learning/index.html).
You can setup the repository as a library on Databricks either manually or by running an [installation script](tools/databricks_install.py). Both options assume you have access to a provisioned Databricks workspace and cluster and that you have appropriate permissions to install libraries.
> * Setup CLI authentication for [Azure Databricks CLI (command-line interface)](https://docs.azuredatabricks.net/user-guide/dev-tools/databricks-cli.html#install-the-cli). Please find details about how to create a token and set authentication [here](https://docs.azuredatabricks.net/user-guide/dev-tools/databricks-cli.html#set-up-authentication). Very briefly, you can install and configure your environment with the following commands.
The installation script has a number of options that can also deal with different databricks-cli profiles, install a version of the mmlspark library, overwrite the libraries, or prepare the cluster for operationalization. For all options, please see:
**Note** If you are planning on running through the sample code for operationalization [here](examples/05_operationalize/als_movie_o16n.ipynb), you need to prepare the cluster for operationalization. You can do so by adding an additional option to the script run. <CLUSTER_ID> is the same as that mentioned above, and can be identified by running `databricks clusters list` and selecting the appropriate cluster.
1. Clone the Microsoft Recommenders repository to your local computer.
2. Zip the contents inside the Recommenders folder (Azure Databricks requires compressed folders to have the `.egg` suffix, so we don't use the standard `.zip`):
3. Once your cluster has started, go to the Databricks workspace, and select the `Home` button.
4. Your `Home` directory should appear in a panel. Right click within your directory, and select `Import`.
5. In the pop-up window, there is an option to import a library, where it says: `(To import a library, such as a jar or egg, click here)`. Select `click here`.
6. In the next screen, select the option `Upload Python Egg or PyPI` in the first menu.
7. Next, click on the box that contains the text `Drop library egg here to upload` and use the file selector to choose the `Recommenders.egg` file you just created, and select `Open`.
8. Click on the `Create library`. This will upload the egg and make it available in your workspace.
9. Finally, in the next menu, attach the library to your cluster.
* For the [recommenders](recommenders) import to work on Databricks, it is important to zip the content correctly. The zip has to be performed inside the Recommenders folder, if you zip directly above the Recommenders folder, it won't work.
This 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](examples/05_operationalize/als_movie_o16n.ipynb), 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](tools/databricks_install.py).
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:
You can follow instructions [here](https://docs.azuredatabricks.net/user-guide/libraries.html#install-a-library-on-a-cluster) for details on how to install packages from PyPI.
Additionally, you must install the [spark-cosmosdb connector](https://docs.databricks.com/spark/latest/data-sources/azure/cosmosdb-connector.html) on the cluster. The easiest way to manually do that is to:
1. Download the [appropriate jar](https://search.maven.org/remotecontent?filepath=com/azure/cosmos/spark/azure-cosmos-spark_3-1_2-12/4.3.1/azure-cosmos-spark_3-1_2-12-4.3.1.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.
A [Dockerfile](tools/docker/Dockerfile) is provided to build images of the repository to simplify setup for different environments. You will need [Docker Engine](https://docs.docker.com/install/) installed on your system.
First make sure that the tag that you want to add, e.g. `0.6.0`, is added in [recommenders.py/__init__.py](recommenders.py/__init__.py). Follow the [contribution guideline](CONTRIBUTING.md) to add the change.
