diff --git a/docs/Documentation/Development/Languages/Python/dask.md b/docs/Documentation/Development/Languages/Python/dask.md
index aaceb0cb1..0d7cdac26 100644
--- a/docs/Documentation/Development/Languages/Python/dask.md
+++ b/docs/Documentation/Development/Languages/Python/dask.md
@@ -142,6 +142,98 @@ python dask_slurm_example.py
 
 Note that although 2 jobs are requested, Dask launches the jobs dynamically, so depending on the status of the job queue, your results may indicate that only a single node was used.
 
+### Batch Runners
+
+Alternatively, the [`dask-jobqueue`](https://jobqueue.dask.org/en/latest/index.html#) library provides [`batch runners`](https://jobqueue.dask.org/en/stable/runners-overview.html) that are desgined to make it simple to kick off Python scripts as multi-node HPC jobs. In contrast to the dynamic cluster, the batch runner starts when all requested nodes are available.
+
+The following example was modified from the official [`dask-jobqueue`](https://jobqueue.dask.org/en/latest/index.html#) help to reflect usage on Kestrel.
+
+??? example "`dask_slurm_runner_example.py`"
+
+    ```python
+    # dask_slurm_runner_example.py
+    import os
+    import getpass
+    import random
+    from dask.distributed import Client
+    from dask_jobqueue.slurm import SLURMRunner
+
+    user_name = getpass.getuser()
+    job_id = int(os.environ["SLURM_JOB_ID"])
+    n_tasks = int(os.environ["SLURM_NTASKS"])
+    n_nodes = int(os.environ["SLURM_NNODES"])
+    try:
+        # This is necessary to specify the correct amount of memory for each worker
+        mem_per_node = int(os.environ["SLURM_MEM_PER_NODE"])
+        mem_worker = (1e6 * mem_per_node) / (n_tasks / n_nodes)
+    except:
+        print("Couldn't determine SLURM worker memory.")
+        raise
+
+    # When entering the SlurmRunner context manager processes will decide if they should be
+    # the client, schdeduler or a worker.
+    # Only process ID 1 executes the contents of the context manager.
+    # All other processes start the Dask components and then block here forever.
+    with SLURMRunner(
+        scheduler_file=f"/scratch/{user_name}/scheduler-{job_id}.json",
+        scheduler_options={
+            "dashboard_address": f":{random.randint(30000, 40000)}",
+            "interface": "hsn0",
+        },
+        worker_options={
+            "memory_limit": mem_worker,
+            "local_directory": f"/scratch/{user_name}",
+            "interface": "hsn0",
+        },
+    ) as runner:
+        # The runner object contains the scheduler address info and can be used to construct a client.
+        with Client(runner) as client:
+
+            # Wait for all the workers to be ready before continuing.
+            client.wait_for_workers(runner.n_workers)
+
+            print(f"Dask cluster dashboard at: {client.dashboard_link}")
+            print(f"Dask cluster scheduler address: {client.scheduler.address}")
+
+            # Then we can submit some work to the Dask scheduler.
+            assert client.submit(lambda x: x + 1, 10).result() == 11
+            assert client.submit(lambda x: x + 1, 20, workers=2).result() == 21
+
+            print("Dask SLURMRunner is working!")
+
+    # When process ID 1 exits the SlurmRunner context manager it sends a graceful shutdown to the Dask processes.
+    ```
+
+The python script is submitted to SLURM via a sbatch script. Note that because this example job requests two partial nodes, it is submitted to the [`shared` partition](../../../Systems/Kestrel/Running/index.md#shared-node-partition). Be sure to replace `your-HPC-account` accordingly.
+
+??? example "`dask_slurm_runner_launcher.sh`"
+
+    ```bash
+    #!/bin/bash
+    #SBATCH --nodes=2
+    #SBATCH --ntasks=4
+    #SBATCH --mem=8G
+    #SBATCH --partition=shared
+    #SBATCH --time=10
+    #SBATCH --account=your-HPC-account # replace with your HPC account
+    #SBATCH --output=slurm-%j.log
+
+    export MALLOC_TRIM_THRESHOLD_=65536
+
+    ml conda
+    conda activate /path/to/dask-env
+    srun -n 4 python -u dask_slurm_runner_example.py
+    ```
+
+Note that SlurmRunner does not start a distributed nanny process, which would normally set the limits for resources consumed by Dask workers. Thus, you must manually export the `MALLOC_TRIM_THRESHOLD_` variable, which sets the minimum amount of contiguous free memory required to trigger a release of memory back to the system from each worker. You can find further details on worker memory managament [`here`](https://distributed.dask.org/en/stable/worker-memory.html).
+
+The job is then launched as:
+
+```bash
+sbatch dask_slurm_runner_launcher.sh
+```
+
+
 
 ## Dask MPI