在教程期间使用 dagster CLI 出现 DagsterUnmetExecutorRequirementsError
DagsterUnmetExecutorRequirementsError with dagster CLI during tutorial
我刚开始关注 dagster tutorial。我设法获得了 hello_cereal 工作 运行 dagit 和 Python API，但由于某些原因在尝试使用 dagster CLI 时
dagster job execute -f hello_cereal.py
我得到一个 DagsterUnmetExecutorRequirementsError:
2021-10-22 15:33:22 - dagster - ERROR - hello_cereal_job - 272b37fb-9f39-44dc-b63a-dcd3dfbb7956 - 880 - RUN_FAILURE - Execution of run for "hello_cereal_job" failed. Pipeline failure during initialization for pipeline "hello_cereal_job". This may be due to a failure in initializing the executor or one of the loggers.
dagster.core.errors.DagsterUnmetExecutorRequirementsError: You have attempted to use an executor that uses multiple processes with an ephemeral DagsterInstance. A non-ephemeral instance is needed to coordinate execution between multiple processes. You can configure your default instance via $DAGSTER_HOME or ensure a valid one is passed when invoking the python APIs. You can learn more about setting up a persistent DagsterInstance from the DagsterInstance docs here: https://docs.dagster.io/deployment/dagster-instance#default-local-behavior
确实，我没有设置 $DAGSTER_HOME，但由于它正在使用网络 UI 和 Python API 版本，我想知道是否我在其他地方弄错了吗？
我在 macOS BigSur (11.6)、全新的 miniconda 安装 (v4.10.3)、Python 3.9.5 和 dagster 0.13.0.
我遇到了同样的问题。我设置 DAGSTER_HOME 并解决了问题。来自 Dagster 文档 (https://docs.dagster.io/deployment/dagster-instance)
If DAGSTER_HOME is not set, the Dagster tools will use an ephemeral
instance for execution. In this case, the run and event log storages will
be in-memory rather than persisted to disk, and filesystem storage will
use a temporary directory that is cleaned up when the process exits. This
is useful for tests and is the default when invoking Python APIs such as
JobDefinition.execute_in_process directly.
感谢您的报告 - 此问题已在今天 10 月 25 日发布的 0.13.1 版本中得到修复。该命令应该可以工作而无需担心设置 DAGSTER_HOME.
我刚开始关注 dagster tutorial。我设法获得了 hello_cereal 工作 运行 dagit 和 Python API，但由于某些原因在尝试使用 dagster CLI 时
dagster job execute -f hello_cereal.py
我得到一个 DagsterUnmetExecutorRequirementsError:
2021-10-22 15:33:22 - dagster - ERROR - hello_cereal_job - 272b37fb-9f39-44dc-b63a-dcd3dfbb7956 - 880 - RUN_FAILURE - Execution of run for "hello_cereal_job" failed. Pipeline failure during initialization for pipeline "hello_cereal_job". This may be due to a failure in initializing the executor or one of the loggers.
dagster.core.errors.DagsterUnmetExecutorRequirementsError: You have attempted to use an executor that uses multiple processes with an ephemeral DagsterInstance. A non-ephemeral instance is needed to coordinate execution between multiple processes. You can configure your default instance via $DAGSTER_HOME or ensure a valid one is passed when invoking the python APIs. You can learn more about setting up a persistent DagsterInstance from the DagsterInstance docs here: https://docs.dagster.io/deployment/dagster-instance#default-local-behavior
确实，我没有设置 $DAGSTER_HOME，但由于它正在使用网络 UI 和 Python API 版本，我想知道是否我在其他地方弄错了吗？
我在 macOS BigSur (11.6)、全新的 miniconda 安装 (v4.10.3)、Python 3.9.5 和 dagster 0.13.0.
我遇到了同样的问题。我设置 DAGSTER_HOME 并解决了问题。来自 Dagster 文档 (https://docs.dagster.io/deployment/dagster-instance)
If DAGSTER_HOME is not set, the Dagster tools will use an ephemeral instance for execution. In this case, the run and event log storages will be in-memory rather than persisted to disk, and filesystem storage will use a temporary directory that is cleaned up when the process exits. This is useful for tests and is the default when invoking Python APIs such as JobDefinition.execute_in_process directly.
感谢您的报告 - 此问题已在今天 10 月 25 日发布的 0.13.1 版本中得到修复。该命令应该可以工作而无需担心设置 DAGSTER_HOME.