Project: Prefect (Orchestration & Management / Scheduling & Orchestration)
File: runtime-context.md

description: The runtime context allows global access to information about the current run. tags: flows subflows tasks deployments search: boost: 2 Prefect tracks information about the current flow or task run with a run context. Think of the run context as a global variable that allows the Prefect engine to determine relationships between your runs such as which flow your task was called from. The run context itself contains many internal objects used by Prefect to manage execution of your run and is only available in specific situations. For this reason, we expose a simple interface that only includes the items you care about and dynamically retrieves additional information when necessary. We call this the "runtime context" as it contains information that can be accessed only during a run. !!! tip "Mock values through environment variable" You may want to mock certain values for testing purposes. For example, by manually setting an ID or a scheduled start time to ensure your code is functioning properly. You can mock values in runtime through an environment variable using the schema `PREFECTRUNTIME{SUBMODULE}{KEY_NAME}=value`: <div class="terminal"> ```bash export PREFECTRUNTIMETASKRUNFAKEKEY='foo' $ python -c 'from prefect.runtime import taskrun; print(taskrun.fake_key)' # "foo" ``` </div> If the environment variable mocks an existing runtime attribute, the value is cast to the same type. This works for runtime attributes of basic types (`bool`, `int`, `float` and `str`) and `pendulum.DateTime`. For complex types like `list` or `dict`, we suggest mocking them using or a similar tool. The `prefect.runtime` module is the home for all runtime context access. Each major runtime concept has its own submodule: `deployment`: Access information about the deployment for the current run `flow_run`: Access information about the current flow run `task_run`: Access information about the current task run For example: ```python hllines="2 6 7 12 13" title="myruntime_info.py" from prefect import flow, task from prefect import runtime @flow(log_prints=True) def my_flow(x): print("My name is", runtime.flow_run.name) print("I belong to deployment", runtime.deployment.name) my_task(2) @task def my_task(y): print("My name is", runtime.task_run.name) print("Flow run parameters:",

my_flow(1) ``` Running this file produces output similar to the following: <div class="terminal"> ```bash 10:08:02.948 | INFO | prefect.engine - Created flow run 'solid-gibbon' for flow 'my-flow' 10:08:03.555 | INFO | Flow run 'solid-gibbon' - My name is solid-gibbon 10:08:03.558 | INFO | Flow run 'solid-gibbon' - I belong to deployment None 10:08:03.703 | INFO | Flow run 'solid-gibbon' - Created task run 'mytask-0' for task 'mytask' 10:08:03.704 | INFO | Flow run 'solid-gibbon' - Executing 'my_task-0' immediately... 10:08:04.006 | INFO | Task run 'mytask-0' - My name is mytask-0 10:08:04.007 | INFO | Task run 'my_task-0' - Flow run parameters: {'x': 1} 10:08:04.105 | INFO | Task run 'my_task-0' - Finished in state Completed() 10:08:04.968 | INFO | Flow run 'solid-gibbon' - Finished in state Completed('All states completed.') ``` </div> Above, we demonstrated access to information about the current flow run, task run, and deployment. When run without a deployment (through `python myruntimeinfo.py`), you should see `"I belong to deployment None"` logged. When information is not available, the runtime will always return an empty value. Because this flow was run outside of a deployment, there is no deployment data. If this flow was run as part of a deployment, we'd see the name of the deployment instead. See the for a full list of available attributes. Access the current run context with `prefect.context.getruncontext()`. This function raises an exception if no run context is available, meaning you are not in a flow or task run. If a task run context is available, it will be returned even if a flow run context is available. Alternatively, you can access the flow run or task run context explicitly. For example, this allows you to access the flow run context from a task run. Note that we do not send the flow run context to distributed task workers because the context is costly to serialize and deserialize. ```python from prefect.context import FlowRunContext, TaskRunContext flowrunctx = FlowRunContext.get() taskrunctx = TaskRunContext.get() ``` Unlike `getruncontext`, these method calls will not raise an error if the context is not available. Instead, they will return `None`.