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Blocking and Async Python with Pulumi

    A Python Pulumi program runs an asyncio) event loop to create the resources that the Pulumi program describes—this isn’t directly visible to the Python program.

    A consequence of this is that blocking calls prevent progress from being made by the Pulumi runtime by preventing the event loop from processing asynchronous tasks.

    The pulumi.Output object can be used to pass the output from both synchronous and asynchronous calls without blocking the event loop.

    For synchronous or blocking code, this should be executed on another thread with the future result captured as a pulumi.Output—using asyncio.to_thread and pulumi.Output.from_input respectively.

    For asynchronous code the coroutine or other awaitable result can also be passed to pulumi.Output.from_input to create a pulumi.Output object.


    As covered in Concepts, when a Pulumi program is run it creates resources and the dependencies or connections between them. Consider the following example (taken from the Pulumi blog article Programming the Cloud with Python):

    import mimetypes
    import os
    from pulumi import export, FileAsset
    from pulumi_aws import s3
    web_bucket = s3.Bucket('s3-website-bucket', website={
        "index_document": "index.html"
    content_dir = "www"
    for file in os.listdir(content_dir):
        filepath = os.path.join(content_dir, file)
        mime_type, _ = mimetypes.guess_type(filepath)
        obj = s3.BucketObject(file,

    Here the resources are an S3 Bucket and some number of S3 BucketObjects—one for each file in the www directory. The bucket objects are told which bucket they will be put in using the id of the bucket. This establishes the dependency between the BucketObject and the Bucket.

    The type of the bucket id is Output[str]. The pulumi.Output type holds a piece of data, in this case the id of the bucket, this is not necessarily known during the execution of the program, and is held internally within the output object as a future—the awaitable result of an asynchronous operation.

    In this way the Pulumi program can proceed and build the graph of resources without having to wait for each resource in turn. When Pulumi is previewing a stack the value of an output may never be known. When resources are then created that can be done concurrently according to the dependency graph that the Pulumi program defines.

    Blocking and Asynchronous Code

    The example above, and many or most Python Pulumi programs, contains no explicit asynchronous code. While the resources are connected using futures, the asynchronous evaluation all happens behind the scenes.

    This can cause problems both with blocking code and with explicitly asynchronous code

    • Blocking code will prevent the event loop on the Pulumi program’s thread from pumping while the blocking code is executing
    • Asynchronous code may not be executed unless it is explicitly scheduled. For example, it is not possible to call asyncio.run from within a Pulumi program because there is already an event loop running.

    Blocking Code

    A Pulumi program is a single threaded Python program. This means that the asynchronous operations on resources cannot happen while blocking code is evaluating. For simple Pulumi programs this is not consequential, but for more complex programs it is possible to be waiting on blocking code that causes all progress to be stalled waiting for that blocking code to complete.

    Examples of this might include calls to requests.get, or subprocess.run that are used to collect information needed to configure the Pulumi program or provide input from external sources.

    If the result from the blocking code is used to provide the input for a resource, then the blocking call can be wrapped into a future—in the form of a pulumi.Output object—using pulumi.Output.from_input. This allows the following pattern:

    import asyncio
    import subprocess
    def blocking_operation(foo: str) -> str:
      An example function that calls a subprocess and captures its output
      res = subprocess.run(['my-cli', foo], encoding='utf-8', capture_output=True)
      if res.returncode is not 0:
        raise Error(f'my-cli returned {res.returncode}: {res.stderr}')
      return res.stdout
    # Use asyncio to call the blocking_operation function on another thread
    # This returns a coroutine
    mycli_coro = asyncio.to_thread(blocking_operation, 'bar')
    # The coroutine is awaitable and can be converted into a pulumi.Output
    mycli_output = pulumi.Output.from_input(mycli_coro)

    If an output needs to be transformed, for example if a blocking call returns a complex object from which a value needs to be extracted (itself as a pulumi.Output) then pulumi.Output.apply can be used to transform an output value

    Asynchronous Code

    Explicitly async code can be used in exactly the same way—pulumi.Output.from_input can convert any awaitable value into a pulumi.Output. To use the same example as above with an equivalent asynchronous implementation:

    import asyncio
    import shlex
    async def async_operation(foo: str) -> str:
      An example async function that calls a subprocess and captures its output
      proc = await asyncio.create_subprocess_shell(
        f'my-cli {shlex.quote(foo)}',
      stdout, stderr = await proc.communicate()
      if proc.returncode is not 0:
        raise Error(f'my-cli returned {proc.returncode}: {stderr.decode()}')
      return stdout.decode
    # Calling an async function directly (without the await keyword) returns a
    # coroutine
    mycli_coro = async_operation('bar')
    # The coroutine is awaitable and can be converted into a pulumi.Output
    mycli_output = pulumi.Output.from_input(mycli_coro)


    The examples above both use calling a local process to demonstrate a pattern that is useful in Python Pulumi programs. This applies to other blocking and async requirements too, for example if you need to call a REST API you might use requests.get or an async equivalent such as httpx.AsyncClient.get

    For running other processes, you can stay entirely within the Pulumi programming model by using the Command package. With this package both of the examples above can be simplified to the following:

    import shlex
    from pulumi_command import local
    def call_my_cli(foo: str) -> Output[str]:
        As example function that uses the Command package to run a local process
        my_cli = local.Command('my-cli', create=f'my-cli {shlex.quote(foo)})')
        return my_cli.stdout
    # The return from call_my_cli is already a pulumi.Output
    mycli_output = call_my_cli('bar')

    If the input to the function needs to be a pulumi.Output itself—i.e. if it is the result of creating another resource, or calling another command, then we can use pulumi.Output.concat as follows:

    import shlex
    from pulumi_command import local
    import pulumi_random as random
    def call_my_cli(foo: Output[str]) -> Output[str]:
        As example function that uses the Command package to run a local process
        my_cli = local.Command('my-cli',
            create=pulumi.Output.concat('my-cli ', foo))
        return my_cli.stdout
    # Create a random value
    random_string = random.RandomString("random-value")
    # random_string.result is a pulumi.Output[str]
    # The return from call_my_cli is already a pulumi.Output
    mycli_output = call_my_cli(random_string.result)
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