Databricks Integration

1. Log into your Package Security Manager (On-prem) instance.
2. Select Policies in the left-hand navigation.
3. Click Create Policy.
4. Name your policy databricks, provide a brief description, and then click Next.
5. Set your policy to allow packages with noarch or linux-64 architectures only, then click Next.
6. Skip setting CVE rules and exclusions by clicking Next to progress the policy configuration.
7. Click Create Policy to save your policy.

1. Select My Channels in the left-hand navigation.
2. Click Add Channel.
3. Name your channel databricks-anaconda.
4. Set your channel to Private.
5. Open the Assign Policy dropdown and select your databricks policy.
6. Click Submit.

1. Open the Manage dropdown menu and select Freeze, then click Freeze.

2. Open the Manage dropdown menu again and select Create Mirror.

3. Complete the Create mirror form with the following configurations:

   • Name: databricks-anaconda-packages
   • External Source URL: https://repo.anaconda.com/pkgs/main
   • Mirror Type: conda

4. Verify that the Run now checkbox is selected.

5. Verify that the Assign Mirror Policy toggle reads APPLY CHANNEL POLICY.

6. Allow the mirror time to complete.

   How do I know when my mirror is complete?

7. Open the Manage dropdown and select Unfreeze, then click Unfreeze.

1. Select My Channels in the left-hand navigation.
2. Click Add Channel.
3. Name your channel databricks-conda-forge.
4. Set your channel to Private.
5. Open the Assign Policy dropdown and select your databricks policy.
6. Click Submit.

1. Open the Manage dropdown menu and select Freeze, then click Freeze.
2. Open the Manage dropdown menu again and select Create Mirror.
3. Complete the Create mirror form with the following configurations:
   • Name: databricks-conda-forge-packages
   • External Source URL: https://conda.anaconda.org/conda-forge
   • Mirror Type: conda
4. Verify that the Run now checkbox is selected.
5. Verify that the Assign Mirror Policy toggle reads APPLY CHANNEL POLICY.
6. Allow the mirror time to complete.
7. Open the Manage dropdown and select Unfreeze, then click Unfreeze.

1. Select Tokens from the left-hand navigation.
2. Click Generate Token.
3. Name your token databricks-token.
4. Set an expiration date for your token that makes sense for your use case.
5. Open the Type dropdown menu and select Resources.
6. Open the Channel dropdown menu and select your databricks-anaconda channel.
7. Open the Permission dropdown menu and select Read.
8. Click Add Resource.
9. Open the Channel dropdown menu and select your databricks-conda-forge channel.
10. Open the Permission dropdown menu and select Read.
11. Click Create.
12. Your token displays in the upper-right corner of the page. Copy the token and save it in a secure location.

To create a secure Python environment in Databricks, you’ll need to build a custom Docker image using Databricks Container Service. This image will contain your conda-based environment and can be used when launching your Databricks cluster.

For more information, see Customize containers with Databricks Container Service and GitHub - databricks/containers.

1. Create a directory on your local machine called dcs-conda by running the following command:

   mkdir dcs-conda

2. Enter your new dcs-conda directory and create a Dockerfile file inside the dcs-conda directory:

   cd dcs-conda
   vi Dockerfile

3. Add the following content to the Dockerfile file:

   # Replace `<FQDN>` with the fully qualified domain name of your Package Security Manager (On-prem) instance
   # Replace `<TOKEN>` with the resource token you just generated
   
   FROM ubuntu:22.04 AS builder
   RUN apt-get update && apt-get install --yes \
       wget \
       libdigest-sha-perl \
       bzip2 \
       gcc \
       python3-dev \
       libpq-dev \
       libcairo2-dev \
       libdbus-1-dev \
       libgirepository1.0-dev \
       libsnappy-dev \
       git \
       maven && \
       apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
   RUN wget -q https://repo.anaconda.com/miniconda/Miniconda3-py311_25.1.1-2-Linux-x86_64.sh -O miniconda.sh && \
       /bin/bash miniconda.sh -b -p /databricks/conda && \
       rm miniconda.sh
   FROM databricksruntime/minimal:15.4-LTS
   RUN apt-get update && apt-get install --yes git && \
       apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
   COPY --from=builder /databricks/conda /databricks/conda
   COPY env.yml /databricks/.conda-env-def/env.yml
   RUN /databricks/conda/bin/conda config --system --prepend channels https://<FQDN>/api/repo/t/<TOKEN>/databricks-anaconda && \
       /databricks/conda/bin/conda config --system --append channels https://<FQDN>/api/repo/t/<TOKEN>/databricks-conda-forge && \
       /databricks/conda/bin/conda config --system --remove channels https://repo.anaconda.com/pkgs/main && \
       /databricks/conda/bin/conda config --system --remove channels https://repo.anaconda.com/pkgs/r
   RUN /databricks/conda/bin/conda env create --file /databricks/.conda-env-def/env.yml && \
       ln -s /databricks/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh
   RUN /databricks/conda/bin/conda config --system --set channel_priority strict && \
       /databricks/conda/bin/conda config --system --set always_yes True
   RUN rm -f /root/.condarc
   ENV GIT_PYTHON_GIT_EXECUTABLE=/usr/bin/git
   ENV DEFAULT_DATABRICKS_ROOT_CONDA_ENV="dcs-conda"
   ENV DATABRICKS_ROOT_CONDA_ENV="dcs-conda"

4. Create an env.yml file inside the dcs-conda directory:

   vi env.yml

5. Add the following content to the env.yml file:

   # Replace `<TOKEN>` with the resource token you generated earlier
   
   name: dcs-conda
   channels:
     - https://<FQDN>/api/repo/t/<TOKEN>/databricks-anaconda
     - https://<FQDN>/api/repo/t/<TOKEN>/databricks-conda-forge
   dependencies:
     - python=3.11
     - databricks-sdk
     - grpcio
     - grpcio-status
     - ipykernel
     - ipython
     - jedi
     - jinja2
     - matplotlib
     - nomkl
     - numpy
     - pandas
     - pip
     - pyarrow
     - pyccolo
     - setuptools
     - six
     - traitlets
     - wheel

   Please check the recommended package versions in the System environment section of the Databricks Runtime release notes and compatibility documentation.

6. Build the Docker image:

   docker build -t dcs-conda:15.4-psm .

7. Tag and push your custom image to a Docker registry by running the following commands:

   docker tag dcs-conda:15.4-psm anaconda/dcs-conda:15.4-psm
   docker push anaconda/dcs-conda:15.4-psm

1. Open your Databricks workspace.

2. Select Compute from the left-hand navigation, then click Create compute.

3. On the New compute page, specify the Cluster Name.

4. Under Performance, set the Databricks Runtime Version to a version that supports Databricks Container Service. For example - Runtime: 15.4-LTS.

   This version is under long-term support (LTS). For more information, see Databricks support lifecycles.

   ---

   Databricks Runtime for Machine Learning does not support Databricks Container Service.

5. Open the Advanced options dropdown and select the Spark tab.

6. Add the following Spark configurations:

   spark.databricks.isv.product anaconda-psm # Must always be added, regardless of other settings
   spark.databricks.driverNfs.enabled false

   To access volumes on Databricks Container Service, add the following configuration to the compute’s Spark config field as well: spark.databricks.unityCatalog.volumes.enabled true.

7. Select the Docker tab.

8. Select the Use your own Docker container checkbox.

9. Enter your custom Docker image in the Docker Image URL field.

   Docker Image URL examples

10. Open the Authentication dropdown and select an authentication method.

11. Click Create compute.

1. Click New in the top-left corner, then click Notebook.

2. Specify a name for the notebook.

3. Click Connect, then select your cluster from the resource list.

1. In your notebook, run one of the following commands to check that conda is installed:

   !conda --help

   %sh conda --help

   Both commands run shell code from the notebook. !conda --help runs the command in the current shell. %sh conda --help starts a subshell, which is useful for multi-line scripts, but might not have the same environment or path.

2. In your notebook, run the following command to check your source channels:

   !conda config --show channels

MLflow is available through your Anaconda organization channel for use in your Databricks environment.

1. In your notebook, install MLflow from your Anaconda organization channel:

   !conda install mlflow

   This command installs MLflow and all of its dependencies from your Package Security Manager channel.

2. In your notebook, verify the installation:

   import mlflow
   print("MLflow: " + mlflow.__version__)