From 4660fb8ea0673028085059afcc4a1f34d061f262 Mon Sep 17 00:00:00 2001 From: pollfly <75068813+pollfly@users.noreply.github.com> Date: Sun, 1 May 2022 10:06:09 +0300 Subject: [PATCH] Small edits (#241) --- docs/clearml_data/clearml_data_cli.md | 2 +- docs/clearml_serving/clearml_serving_cli.md | 2 +- docs/guides/pipeline/pipeline_decorator.md | 2 +- .../pipelines/pipelines_sdk_function_decorators.md | 14 +++++++------- docs/pipelines/pipelines_sdk_tasks.md | 6 +++--- 5 files changed, 13 insertions(+), 13 deletions(-) diff --git a/docs/clearml_data/clearml_data_cli.md b/docs/clearml_data/clearml_data_cli.md index 63a07baf..a7c63d0f 100644 --- a/docs/clearml_data/clearml_data_cli.md +++ b/docs/clearml_data/clearml_data_cli.md @@ -34,7 +34,7 @@ clearml-data create --project --name --parents | -`` + ### upload diff --git a/docs/guides/pipeline/pipeline_decorator.md b/docs/guides/pipeline/pipeline_decorator.md index 76e16f26..d5d40164 100644 --- a/docs/guides/pipeline/pipeline_decorator.md +++ b/docs/guides/pipeline/pipeline_decorator.md @@ -62,7 +62,7 @@ decorator overrides the default queue value for the specific step for which it w :::note Execution Modes ClearML provides different pipeline execution modes to accommodate development and production use cases. For additional -details, see [Execution Modes](../../pipelines/pipelines.md#pipeline-controller-execution-options). +details, see [Execution Modes](../../pipelines/pipelines.md#running-your-pipelines). ::: To run the pipeline, call the pipeline controller function. diff --git a/docs/pipelines/pipelines_sdk_function_decorators.md b/docs/pipelines/pipelines_sdk_function_decorators.md index 516d0e5e..38497de2 100644 --- a/docs/pipelines/pipelines_sdk_function_decorators.md +++ b/docs/pipelines/pipelines_sdk_function_decorators.md @@ -86,7 +86,7 @@ def step_one(pickle_data_url: str, extra: int = 43): * `packages` - A list of required packages or a local requirements.txt file. Example: `["tqdm>=2.1", "scikit-learn"]` or `"./requirements.txt"`. If not provided, packages are automatically added based on the imports used inside the function. * `execution_queue` (Optional) - Queue in which to enqueue the specific step. This overrides the queue set with the - [PipelineDecorator.set_default_execution_queue method](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue) + [`PipelineDecorator.set_default_execution_queue method`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue) method. * `continue_on_fail` - If `True`, a failed step does not cause the pipeline to stop (or marked as failed). Notice, that steps that are connected (or indirectly connected) to the failed step are skipped (default `False`) @@ -118,7 +118,7 @@ following arguments: artifact). * Alternatively, provide a list of pairs (source_artifact_name, target_artifact_name), where the first string is the artifact name as it appears on the component Task, and the second is the target artifact name to put on the Pipeline - Task. Example: [('processed_data', 'final_processed_data'), ] + Task. Example: `[('processed_data', 'final_processed_data'), ]` * `monitor_models` (Optional) - Automatically log the step's output models on the pipeline Task. * Provided a list of model names created by the step's Task, they will also appear on the Pipeline itself. Example: `['model_weights', ]` * To select the latest (lexicographic) model use `model_*`, or the last created model with just `*`. Example: `['model_weights_*', ]` @@ -127,14 +127,14 @@ following arguments: Example: `[('model_weights', 'final_model_weights'), ]` You can also directly upload a model or an artifact from the step to the pipeline controller, using the -[PipelineDecorator.upload_model](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorupload_model) -and [PipelineDecorator.upload_artifact](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorupload_artifact) +[`PipelineDecorator.upload_model`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorupload_model) +and [`PipelineDecorator.upload_artifact`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorupload_artifact) methods respectively. ## Controlling Pipeline Execution ### Default Execution Queue -The [PipelineDecorator.set_default_execution_queue](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue) +The [`PipelineDecorator.set_default_execution_queue`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorset_default_execution_queue) method lets you set a default queue through which all pipeline steps will be executed. Once set, step-specific overrides can be specified through the `@PipelineDecorator.component` decorator. @@ -167,7 +167,7 @@ It is possible to run the pipeline logic itself locally, while keeping the pipel #### Debugging Mode In debugging mode, the pipeline controller and all components are treated as regular python functions, with components called synchronously. This mode is great to debug the components and design the pipeline as the entire pipeline is -executed on the developer machine with full ability to debug each function call. Call [PipelineDecorator.debug_pipeline](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratordebug_pipeline) +executed on the developer machine with full ability to debug each function call. Call [`PipelineDecorator.debug_pipeline`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratordebug_pipeline) before the main pipeline logic function call. Example: @@ -183,7 +183,7 @@ In local mode, the pipeline controller creates Tasks for each component, and com into sub-processes running on the same machine. Notice that the data is passed between the components and the logic with the exact same mechanism as in the remote mode (i.e. hyperparameters / artifacts), with the exception that the execution itself is local. Notice that each subprocess is using the exact same python environment as the main pipeline logic. Call -[PipelineDecorator.run_locally](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorrun_locally) +[`PipelineDecorator.run_locally`](../references/sdk/automation_controller_pipelinecontroller.md#pipelinedecoratorrun_locally) before the main pipeline logic function. Example: diff --git a/docs/pipelines/pipelines_sdk_tasks.md b/docs/pipelines/pipelines_sdk_tasks.md index 6daf30aa..bb57b837 100644 --- a/docs/pipelines/pipelines_sdk_tasks.md +++ b/docs/pipelines/pipelines_sdk_tasks.md @@ -54,7 +54,7 @@ Creating a pipeline step from an existing ClearML task means that when the step new task will be launched through the configured execution queue (the original task is unmodified). The new task’s parameters can be [specified](#parameter_override). -Task steps are added using the [PipelineController.add_step](../references/sdk/automation_controller_pipelinecontroller.md#add_step) +Task steps are added using the [`PipelineController.add_step`](../references/sdk/automation_controller_pipelinecontroller.md#add_step) method: ```python @@ -213,8 +213,8 @@ methods respectively. The [`PipelineController.set_default_execution_queue`](../references/sdk/automation_controller_pipelinecontroller.md#set_default_execution_queue) method lets you set a default queue through which all pipeline steps will be executed. Once set, step-specific overrides -can be specified through `execution_queue` of the [PipelineController.add_step](../references/sdk/automation_controller_pipelinecontroller.md#add_step) -or [PipelineController.add_function_step](../references/sdk/automation_controller_pipelinecontroller.md#add_function_step) +can be specified through `execution_queue` of the [`PipelineController.add_step`](../references/sdk/automation_controller_pipelinecontroller.md#add_step) +or [`PipelineController.add_function_step`](../references/sdk/automation_controller_pipelinecontroller.md#add_function_step) methods. ### Running the Pipeline