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NotebookUtils session management for Fabric

Use notebookutils.session to manage the lifecycle of notebook sessions in Microsoft Fabric. You can stop an interactive session or restart the Python interpreter for cleanup, resource management, and error recovery.

The following table lists the available session management methods:

Method Signature Description
stop stop(): void Stops the current interactive session asynchronously and releases resources. In PySpark, Scala, and R notebooks, accepts an optional detach parameter.
restartPython restartPython(): void Restarts the Python interpreter while keeping the Spark context intact. Available in Python and PySpark notebooks only.

Note

The stop() method is available in Python, PySpark, Scala, and R notebooks. In PySpark, Scala, and R notebooks, stop() accepts an optional detach parameter: stop(detach=True). When detach is True (the default), the session detaches from a high-concurrency session instead of stopping it entirely.

Note

The restartPython() method is only available in Python and PySpark notebooks. It isn't available in Scala or R notebooks.

Important

In pipeline execution mode, sessions automatically stop after code completes. The session.stop() API is primarily intended for interactive sessions where you want to stop the session programmatically instead of clicking the stop button.

Note

The stop() method operates asynchronously in the background and releases Spark session resources so they become available to other sessions in the same pool.

Stop an interactive session

Instead of manually selecting the stop button, you can stop an interactive session by calling an API in your code.

notebookutils.session.stop()

The notebookutils.session.stop() API stops the current interactive session asynchronously in the background. It also stops the Spark session and releases resources occupied by the session, so they're available to other sessions in the same pool.

Note

Code after session.stop() doesn't execute. All in-memory data and variables are lost after the session stops. Save important data before you call session.stop().

Return behavior

The stop() method doesn't return a value. It initiates an asynchronous shutdown of the session.

Restart the Python interpreter

Use notebookutils.session.restartPython() to restart the Python interpreter.

Note

In PySpark (Spark) notebooks, restartPython() restarts only the Python interpreter while keeping the Spark context intact. In Python notebooks, which don't have a Spark context, restartPython() restarts the entire Python process.

notebookutils.session.restartPython()

Return behavior

The restartPython() method doesn't return a value. After the restart completes, code execution continues in the next cell.

Keep these considerations in mind:

  • In the notebook reference run case, restartPython() only restarts the Python interpreter of the current notebook that's being referenced. It doesn't affect the parent notebook.
  • In rare cases, the command might fail due to the Spark reflection mechanism. Adding a retry can mitigate the problem.
  • After calling restartPython(), code execution continues in the next cell. Import newly installed packages in a subsequent cell.

Usage patterns

Graceful cleanup before stopping

Use a try-finally block to ensure cleanup runs before the session stops:

try:
    print("Starting data processing...")
    # ... processing logic here ...

except Exception as e:
    print(f"Processing failed: {str(e)}")
    raise

finally:
    print("Performing cleanup...")
    try:
        notebookutils.fs.unmount("/mnt/data")
    except:
        pass

    notebookutils.session.stop()

Install packages and restart the interpreter

After installing new packages with pip, restart the Python interpreter so the packages are available:

import subprocess
import sys

packages = ["pandas==2.0.0", "numpy==1.24.0"]

print("Installing packages...")
for package in packages:
    subprocess.check_call([sys.executable, "-m", "pip", "install", package])

print("Restarting Python interpreter...")
notebookutils.session.restartPython()

Note

After calling restartPython(), code execution continues in the next cell. Import the newly installed packages in a subsequent cell.

Error recovery with interpreter restart

If the Python interpreter reaches a corrupted state, you can attempt recovery by restarting it:

def recover_from_error():
    """Attempt to recover from errors by restarting Python."""

    try:
        test_value = 1 + 1
    except Exception as e:
        print(f"Python interpreter error: {str(e)}")
        print("Restarting Python interpreter...")
        notebookutils.session.restartPython()
        return False

    return True

if not recover_from_error():
    print("Recovery attempted - check next cell")

Resource cleanup before stopping

Clean up mounted paths, temporary files, and caches before terminating the session:

try:
    df = spark.range(0, 1000000)
    df.cache()
    result = df.count()
    print(f"Processing completed: {result}")

except Exception as e:
    print(f"Operation failed: {str(e)}")
    raise

finally:
    spark.catalog.clearCache()
    print("Stopping session to free resources...")
    notebookutils.session.stop()

Conditional stop for interactive mode only

Check the execution context before stopping to avoid unnecessary calls in pipeline mode:

context = notebookutils.runtime.context

if not context['isForPipeline']:
    print("Interactive mode: stopping session...")
    notebookutils.session.stop()
else:
    print("Pipeline mode: session stops automatically after execution")

Tip

Always save important results—such as writing DataFrames to storage or logging output—before calling session.stop() or session.restartPython(). Both operations discard all in-memory state.

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