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2
docs/apps/clearml_task.md
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@ -22,7 +22,7 @@ line arguments, Python module dependencies, and a requirements.txt file!
1. `clearml-task` does its magic! It creates a new task on the [ClearML Server](../deploying_clearml/clearml_server.md),
and, if so directed, enqueues it for execution by a ClearML Agent.
1. While the Task is running on the remote machine, all its console outputs are logged in real-time, alongside your
TensorBoard and matplotlib. You can track your script’s progress and results in the [ClearML Web UI](../webapp/webapp_overview.md)
TensorBoard and matplotlib. You can track your script's progress and results in the [ClearML Web UI](../webapp/webapp_overview.md)
(a link to your task details page in the ClearML Web UI is printed as ClearML Task creates the task).
## Execution Configuration

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docs/faq.md
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@ -1125,7 +1125,7 @@ configuration option `agent.package_manager.system_site_packages` to `true`.
#### How can I use the ClearML API to fetch data? <a className="tr_top_negative" id="api"></a>
You can use the `APIClient` class, which provides a Pythonic interface to access ClearML's backend REST API. Through
an `APIClient` instance, you can access ClearML’s REST API services and endpoints.
an `APIClient` instance, you can access ClearML's REST API services and endpoints.
To use `APIClient`, create an instance of it, then call the method corresponding to the desired REST API endpoint, with
its respective parameters as described in the [REST API reference page](references/api/index.md).

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docs/guides/advanced/execute_remotely.md
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@ -70,7 +70,7 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models
Models created by the experiment appear in the experiment's **ARTIFACTS** tab. ClearML automatically logs and tracks models
and any snapshots created using PyTorch.
![Experiment artifacts](../../img/examples_remote_execution_artifacts.png)

4
docs/guides/clearml_agent/executable_exp_containers.md
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@ -2,7 +2,7 @@
title: Executable Experiment Containers
---
This tutorial demonstrates using [`clearml-agent`](../../clearml_agent.md)’s [`build`](../../clearml_agent/clearml_agent_ref.md#build)
This tutorial demonstrates using [`clearml-agent`](../../clearml_agent.md)'s [`build`](../../clearml_agent/clearml_agent_ref.md#build)
command to package an experiment into an executable container. In this example, you will build a Docker image that, when
run, will automatically execute the [keras_tensorboard.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py)
script.
@ -13,7 +13,7 @@ script.
* [clearml](https://github.com/allegroai/clearml) repo cloned (`git clone https://github.com/allegroai/clearml.git`)
## Creating the ClearML Experiment
1. Set up the experiment’s execution environment:
1. Set up the experiment's execution environment:
```console
cd clearml/examples/frameworks/keras

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docs/guides/clearml_agent/exp_environment_containers.md
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@ -2,7 +2,7 @@
title: Experiment Environment Containers
---
This tutorial demonstrates using [`clearml-agent`](../../clearml_agent.md)’s [`build`](../../clearml_agent/clearml_agent_ref.md#build)
This tutorial demonstrates using [`clearml-agent`](../../clearml_agent.md)'s [`build`](../../clearml_agent/clearml_agent_ref.md#build)
command to build a Docker container replicating the execution environment of an existing task. ClearML Agents can make
use of such containers to execute tasks without having to set up their environment every time.
@ -15,7 +15,7 @@ be used when running optimization tasks.
* [clearml](https://github.com/allegroai/clearml) repo cloned (`git clone https://github.com/allegroai/clearml.git`)
## Creating the ClearML Experiment
1. Set up the experiment’s execution environment:
1. Set up the experiment's execution environment:
```console
cd clearml/examples/frameworks/keras
@ -47,7 +47,7 @@ clearml-agent build --id <TASK_ID> --docker --target new_docker
If the container will not make use of a GPU, add the `--cpu-only` flag
:::
This will create a container with the specified task’s execution environment in the `--target` folder.
This will create a container with the specified task's execution environment in the `--target` folder.
When the Docker build completes, the console output shows:
```console
@ -76,7 +76,7 @@ Make use of the container you've just built by having a ClearML agent make use o
:::
This agent will pull the enqueued task and run it using the `new_docker` image to create the execution environment.
In the task’s **CONSOLE** tab, one of the first logs should be:
In the task's **CONSOLE** tab, one of the first logs should be:
```console
Executing: ['docker', 'run', ..., 'CLEARML_DOCKER_IMAGE=new_docker', ...].

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docs/guides/frameworks/autokeras/autokeras_imdb_example.md
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@ -32,11 +32,11 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
Models created by the experiment appear in the experiment's **ARTIFACTS** tab.
![image](../../../img/examples_keras_18.png)
Clicking on the model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
Clicking on the model's name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view
the model's details and access the model.
![image](../../../img/examples_keras_17.png)

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docs/guides/frameworks/catboost/catboost.md
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@ -29,12 +29,12 @@ Text printed to the console for training progress, as well as all other console
![Experiment console](../../../img/examples_catboost_console.png)
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
Models created by the experiment appear in the experiment's **ARTIFACTS** tab. ClearML automatically logs and tracks
models created using CatBoost.
![Experiment artifacts](../../../img/examples_catboost_artifacts.png)
Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
Clicking on the model name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view
the model's details and access the model.
![Model page](../../../img/examples_catboost_model.png)

2
docs/guides/frameworks/lightgbm/lightgbm_example.md
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@ -25,7 +25,7 @@ ClearML automatically logs the configurations applied to LightGBM. They appear i
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
Models created by the experiment appear in the experiment's **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using LightGBM.
![LightGBM model](../../../img/examples_lightgbm_model.png)

2
docs/guides/frameworks/megengine/megengine_mnist.md
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@ -49,6 +49,6 @@ The model info panel contains the model details, including:
## Console
All console output during the script’s execution appears in the experiment’s **CONSOLE** page.
All console output during the script's execution appears in the experiment's **CONSOLE** page.
![Console tab](../../../img/examples_megengine_console.png)

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docs/guides/frameworks/pytorch/model_updating.md
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@ -33,7 +33,7 @@ output_model = OutputModel(task=task)
## Label Enumeration
The label enumeration dictionary is logged using the [`Task.connect_label_enumeration`](../../../references/sdk/task.md#connect_label_enumeration)
method which will update the task’s resulting model information. The current running task is accessed using the
method which will update the task's resulting model information. The current running task is accessed using the
[`Task.current_task`](../../../references/sdk/task.md#taskcurrent_task) class method.
```python
@ -44,7 +44,7 @@ Task.current_task().connect_label_enumeration(enumeration)
```
:::note Directly Setting Model Enumeration
You can set a model’s label enumeration directly using the [`OutputModel.update_labels`](../../../references/sdk/model_outputmodel.md#update_labels)
You can set a model's label enumeration directly using the [`OutputModel.update_labels`](../../../references/sdk/model_outputmodel.md#update_labels)
method
:::
@ -81,20 +81,20 @@ if CONDITION:
```
## WebApp
The model appears in the task’s **ARTIFACTS** tab.
The model appears in the task's **ARTIFACTS** tab.
![Task artifacts](../../../img/examples_model_update_artifacts.png)
Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view the
model’s details and access the model.
Clicking on the model name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view the
model's details and access the model.
![Model page](../../../img/examples_model_update_model.png)
The model’s **NETWORK** tab displays its configuration.
The model's **NETWORK** tab displays its configuration.
![Model network tab](../../../img/examples_model_update_network.png)
The model’s **LABELS** tab displays its label enumeration.
The model's **LABELS** tab displays its label enumeration.
![Model labels](../../../img/examples_model_update_labels.png)

2
docs/guides/frameworks/pytorch/notebooks/audio/audio_preprocessing_example.md
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@ -17,7 +17,7 @@ ClearML automatically logs the audio samples which the example reports by callin
### Audio Samples
You can play the audio samples by double-clicking the audio thumbnail.
You can play the audio samples by clicking the audio thumbnail.
![image](../../../../../img/examples_audio_preprocessing_example_03.png)

6
docs/guides/frameworks/pytorch/pytorch_mnist.md
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@ -52,12 +52,12 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models
Models created by the experiment appear in the experiment's **ARTIFACTS** tab. ClearML automatically logs and tracks models
and any snapshots created using PyTorch.
![image](../../../img/examples_pytorch_mnist_02.png)
Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
Clicking on the model name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view
the model's details and access the model.
![image](../../../img/examples_pytorch_mnist_03.png)

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docs/guides/frameworks/pytorch/pytorch_tensorboard.md
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@ -40,12 +40,12 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
Models created by the experiment appear in the experiment's **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using PyTorch.
![image](../../../img/examples_pytorch_tensorboard_02.png)
Clicking on a model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
Clicking on a model's name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view
the model's details and access the model.
![image](../../../img/examples_pytorch_tensorboard_03.png)

6
docs/guides/frameworks/pytorch/pytorch_tensorboardx.md
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@ -35,12 +35,12 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
Models created by the experiment appear in the experiment's **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using PyTorch.
![image](../../../img/examples_pytorch_tensorboardx_04.png)
Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
Clicking on the model name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view
the model's details and access the model.
![image](../../../img/examples_pytorch_tensorboardx_model.png)

8
docs/guides/frameworks/pytorch_lightning/pytorch_lightning_example.md
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@ -17,7 +17,7 @@ The test loss and validation loss plots appear in the experiment's page in the C
Resource utilization plots, which are titled **:monitor: machine**, also appear in the **SCALARS** tab. All of these
plots are automatically captured by ClearML.
![PyTorch Lightning console](../../../img/examples_pytorch_lightning_scalars.png)
![PyTorch Lightning scalars](../../../img/examples_pytorch_lightning_scalars.png)
## Hyperparameters
@ -29,12 +29,12 @@ ClearML automatically logs command line options defined with argparse and Tensor
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
Models created by the experiment appear in the experiment's **ARTIFACTS** tab.
![PyTorch Lightning model](../../../img/examples_pytorch_lightning_model.png)
Clicking on a model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view
the model’s details and access the model.
Clicking on a model name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can view
the model's details and access the model.
## Console

6
docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md
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@ -16,12 +16,12 @@ in the ClearML web UI, under **PLOTS**.
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab.
Models created by the experiment appear in the experiment's **ARTIFACTS** tab.
![image](../../../img/examples_sklearn_joblib_example_01.png)
Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can
view the model’s details and access the model.
Clicking on the model name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can
view the model's details and access the model.
![image](../../../img/examples_sklearn_joblib_example_02.png)

6
docs/guides/frameworks/tensorboardx/tensorboardx.md
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@ -33,13 +33,13 @@ Text printed to the console for training progress, as well as all other console
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
Models created by the experiment appear in the experiment's **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using PyTorch.
![image](../../../img/examples_pytorch_tensorboardx_04.png)
Clicking on the model’s name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can
view the model’s details and access the model.
Clicking on the model's name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can
view the model's details and access the model.
![image](../../../img/examples_pytorch_tensorboardx_model.png)

6
docs/guides/frameworks/tensorflow/tensorflow_mnist.md
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@ -30,13 +30,13 @@ All console output appears in **CONSOLE**.
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
Models created by the experiment appear in the experiment's **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using TensorFlow.
![image](../../../img/examples_tensorflow_mnist_03.png)
Clicking on a model’s name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can
view the model’s details and access the model.
Clicking on a model's name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can
view the model's details and access the model.
![image](../../../img/examples_tensorflow_mnist_10.png)

2
docs/guides/frameworks/xgboost/xgboost_metrics.md
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@ -29,6 +29,6 @@ To view the model details, click the model name in the **ARTIFACTS** page, which
## Console
All console output during the script’s execution appears in the experiment’s **CONSOLE** page.
All console output during the script's execution appears in the experiment's **CONSOLE** page.
![Console output](../../../img/examples_xgboost_metric_console.png)

8
docs/guides/frameworks/xgboost/xgboost_sample.md
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@ -15,7 +15,7 @@ classification dataset using XGBoost
## Plots
The feature importance plot and tree plot appear in the project's page in the **ClearML web UI**, under
The feature importance plot and tree plot appear in the experiment's page in the **ClearML web UI**, under
**PLOTS**.
![Feature importance plot](../../../img/examples_xgboost_sample_06.png)
@ -31,12 +31,12 @@ All other console output appear in **CONSOLE**.
## Artifacts
Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks
Models created by the experiment appear in the experiment's **ARTIFACTS** tab. ClearML automatically logs and tracks
models and any snapshots created using XGBoost.
![image](../../../img/examples_xgboost_sample_10.png)
Clicking on the model's name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can
view the model’s details and access the model.
Clicking on the model's name takes you to the [model's page](../../../webapp/webapp_model_viewing.md), where you can
view the model's details and access the model.
![image](../../../img/examples_xgboost_sample_03.png)

20
docs/guides/pipeline/pipeline_controller.md
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@ -50,7 +50,7 @@ The sections below describe in more detail what happens in the controller task a
1. Build the pipeline (see [PipelineController.add_step](../../references/sdk/automation_controller_pipelinecontroller.md#add_step)
method for complete reference):
The pipeline’s [first step](#step-1---downloading-the-datae) uses the pre-existing task
The pipeline's [first step](#step-1---downloading-the-datae) uses the pre-existing task
`pipeline step 1 dataset artifact` in the `examples` project. The step uploads local data and stores it as an artifact.
```python
@ -62,11 +62,11 @@ The sections below describe in more detail what happens in the controller task a
```
The [second step](#step-2---processing-the-data) uses the pre-existing task `pipeline step 2 process dataset` in
the `examples` project. The second step’s dependency upon the first step’s completion is designated by setting it as
the `examples` project. The second step's dependency upon the first step's completion is designated by setting it as
its parent.
Custom configuration values specific to this step execution are defined through the `parameter_override` parameter,
where the first step’s artifact is fed into the second step.
where the first step's artifact is fed into the second step.
Special pre-execution and post-execution logic is added for this step through the use of `pre_execute_callback`
and `post_execute_callback` respectively.
@ -87,7 +87,7 @@ The sections below describe in more detail what happens in the controller task a
```
The [third step](#step-3---training-the-network) uses the pre-existing task `pipeline step 3 train model` in the
`examples` projects. The step uses Step 2’s artifacts.
`examples` projects. The step uses Step 2's artifacts.
1. Run the pipeline.
@ -99,7 +99,7 @@ The sections below describe in more detail what happens in the controller task a
## Step 1 - Downloading the Data
The pipeline’s first step ([step1_dataset_artifact.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/step1_dataset_artifact.py))
The pipeline's first step ([step1_dataset_artifact.py](https://github.com/allegroai/clearml/blob/master/examples/pipeline/step1_dataset_artifact.py))
does the following:
1. Download data using [`StorageManager.get_local_copy`](../../references/sdk/storage.md#storagemanagerget_local_copy)
@ -209,7 +209,7 @@ does the following:
## WebApp
When the experiment is executed, the terminal returns the task ID, and links to the pipeline controller task page and
When the experiment is executed, the console output displays the task ID, and links to the pipeline controller task page and
pipeline page.
```
@ -218,13 +218,13 @@ ClearML results page: https://app.clear.ml/projects/462f48dba7b441ffb34bddb78371
ClearML pipeline page: https://app.clear.ml/pipelines/462f48dba7b441ffb34bddb783711da7/experiments/bc93610688f242ecbbe70f413ff2cf5f
```
The pipeline run’s page contains the pipeline’s structure, the execution status of every step, as well as the run’s
The pipeline run's page contains the pipeline's structure, the execution status of every step, as well as the run's
configuration parameters and output.
![Pipeline DAG](../../img/examples_pipeline_from_tasks_DAG.png)
To view a run’s complete information, click **Full details** on the bottom of the **Run Info** panel, which will open
the pipeline’s [controller task page](../../webapp/webapp_exp_track_visual.md).
To view a run's complete information, click **Full details** on the bottom of the **Run Info** panel, which will open
the pipeline's [controller task page](../../webapp/webapp_exp_track_visual.md).
Click a step to see its summary information.
@ -232,7 +232,7 @@ Click a step to see its summary information.
### Console
Click **DETAILS** to view a log of the pipeline controller’s console output.
Click **DETAILS** to view a log of the pipeline controller's console output.
![Pipeline console](../../img/examples_pipeline_from_tasks_console.png)

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docs/guides/pipeline/pipeline_decorator.md
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@ -77,7 +77,7 @@ To run the pipeline, call the pipeline controller function.
## WebApp
When the experiment is executed, the terminal returns the task ID, and links to the pipeline controller task page and pipeline page.
When the experiment is executed, the console output displays the task ID, and links to the pipeline controller task page and pipeline page.
```
ClearML Task: created new task id=bc93610688f242ecbbe70f413ff2cf5f
@ -85,13 +85,13 @@ ClearML results page: https://app.clear.ml/projects/462f48dba7b441ffb34bddb78371
ClearML pipeline page: https://app.clear.ml/pipelines/462f48dba7b441ffb34bddb783711da7/experiments/bc93610688f242ecbbe70f413ff2cf5f
```
The pipeline run’s page contains the pipeline’s structure, the execution status of every step, as well as the run’s
The pipeline run's page contains the pipeline's structure, the execution status of every step, as well as the run's
configuration parameters and output.
![Pipeline DAG](../../img/examples_pipeline_from_decorator_DAG.png)
To view a run’s complete information, click **Full details** on the bottom of the **Run Info** panel, which will open the
pipeline’s [controller task page](../../webapp/webapp_exp_track_visual.md).
To view a run's complete information, click **Full details** on the bottom of the **Run Info** panel, which will open the
pipeline's [controller task page](../../webapp/webapp_exp_track_visual.md).
Click a step to see an overview of its details.
@ -99,11 +99,11 @@ Click a step to see an overview of its details.
## Console and Code
Click **DETAILS** to view a log of the pipeline controller’s console output.
Click **DETAILS** to view a log of the pipeline controller's console output.
![Pipeline console](../../img/examples_pipeline_from_decorator_console.png)
Click on a step to view its console output. You can also view the selected step’s code by clicking **CODE**
Click on a step to view its console output. You can also view the selected step's code by clicking **CODE**
on top of the console log.
![Pipeline step code](../../img/examples_pipeline_from_decorator_code.png)

16
docs/guides/pipeline/pipeline_functions.md
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@ -66,7 +66,7 @@ logged as required packages for the pipeline execution step.
)
```
The second step in the pipeline uses the `step_two` function and uses as its input the first step’s output.This reference
The second step in the pipeline uses the `step_two` function and uses as its input the first step's output.This reference
implicitly defines the pipeline structure, making `step_one` the parent step of `step_two`.
Its return object will be stored as an artifact under the name `processed_data`.
@ -82,7 +82,7 @@ logged as required packages for the pipeline execution step.
)
```
The third step in the pipeline uses the `step_three` function and uses as its input the second step’s output. This
The third step in the pipeline uses the `step_three` function and uses as its input the second step's output. This
reference implicitly defines the pipeline structure, making `step_two` the parent step of `step_three`.
Its return object will be stored as an artifact under the name `model`:
@ -106,7 +106,7 @@ logged as required packages for the pipeline execution step.
The pipeline will be launched remotely, through the `services` queue, unless otherwise specified.
## WebApp
When the experiment is executed, the terminal returns the task ID, and links to the pipeline controller task page and pipeline page.
When the experiment is executed, the console output displays the task ID, and links to the pipeline controller task page and pipeline page.
```
ClearML Task: created new task id=bc93610688f242ecbbe70f413ff2cf5f
@ -114,13 +114,13 @@ ClearML results page: https://app.clear.ml/projects/462f48dba7b441ffb34bddb78371
ClearML pipeline page: https://app.clear.ml/pipelines/462f48dba7b441ffb34bddb783711da7/experiments/bc93610688f242ecbbe70f413ff2cf5f
```
The pipeline run’s page contains the pipeline’s structure, the execution status of every step, as well as the run’s
The pipeline run's page contains the pipeline's structure, the execution status of every step, as well as the run's
configuration parameters and output.
![Pipeline DAG](../../img/pipeline_from_functions_DAG.png)
To view a run’s complete information, click **Full details** on the bottom of the **Run Info** panel, which will open the
pipeline’s [controller task page](../../webapp/webapp_exp_track_visual.md).
To view a run's complete information, click **Full details** on the bottom of the **Run Info** panel, which will open the
pipeline's [controller task page](../../webapp/webapp_exp_track_visual.md).
Click a step to see an overview of its details.
@ -128,11 +128,11 @@ Click a step to see an overview of its details.
## Console and Code
Click **DETAILS** to view a log of the pipeline controller’s console output.
Click **DETAILS** to view a log of the pipeline controller's console output.
![Pipeline console](../../img/pipeline_from_functions_console.png)
Click on a step to view its console output. You can also view the selected step’s code by clicking **CODE**
Click on a step to view its console output. You can also view the selected step's code by clicking **CODE**
on top of the console log.
![Pipeline step code](../../img/pipeline_from_functions_code.png)

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@ -52,6 +52,6 @@ ClearML reports these images as debug samples in the **ClearML Web UI**, under t
![image](../../img/examples_reporting_07.png)
Double-click a thumbnail, and the image viewer opens.
Click a thumbnail, and the image viewer opens.
![image](../../img/examples_reporting_07a.png)

4
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@ -8,7 +8,7 @@ example demonstrates using ClearML to log plots and images generated by Matplotl
## Plots
The Matplotlib and Seaborn plots that are reported using the [Logger.report_matplotlib_figure](../../references/sdk/logger.md#report_matplotlib_figure)
method appear in the experiment’s **PLOTS**.
method appear in the experiment's **PLOTS**.
![Experiment Matplotlib plots](../../img/manual_matplotlib_reporting_01.png)
@ -17,6 +17,6 @@ method appear in the experiment’s **PLOTS**.
## Debug Samples
Matplotlib figures can be logged as images by using the [Logger.report_matplotlib_figure](../../references/sdk/logger.md#report_matplotlib_figure)
method, and passing `report_image=True`. The images are stored in the experiment’s **DEBUG SAMPLES**.
method, and passing `report_image=True`. The images are stored in the experiment's **DEBUG SAMPLES**.
![Experiment debug sample](../../img/manual_matplotlib_reporting_03.png)

4
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@ -38,7 +38,7 @@ Logger.current_logger().report_media(
)
```
The reported audio can be viewed in the **DEBUG SAMPLES** tab. Double-click a thumbnail, and the audio player opens.
The reported audio can be viewed in the **DEBUG SAMPLES** tab. Click a thumbnail, and the audio player opens.
![image](../../img/examples_reporting_08.png)
@ -55,6 +55,6 @@ Logger.current_logger().report_media(
)
```
The reported video can be viewed in the **DEBUG SAMPLES** tab. Double-click a thumbnail, and the video player opens.
The reported video can be viewed in the **DEBUG SAMPLES** tab. Click a thumbnail, and the video player opens.
![image](../../img/examples_reporting_09.png)

10
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@ -25,7 +25,7 @@ output_model = OutputModel(task=task)
## Label Enumeration
Set the model’s label enumeration using the [`OutputModel.update_labels`](../../references/sdk/model_outputmodel.md#update_labels)
Set the model's label enumeration using the [`OutputModel.update_labels`](../../references/sdk/model_outputmodel.md#update_labels)
method.
```python
@ -43,14 +43,14 @@ output_model.update_weights(register_uri=model_url)
```
## WebApp
The model appears in the task’s **ARTIFACTS** tab.
The model appears in the task's **ARTIFACTS** tab.
![Task artifacts](../../img/examples_model_logging_artifacts.png)
Clicking on the model name takes you to the [model’s page](../../webapp/webapp_model_viewing.md), where you can view the
model’s details and access the model.
Clicking on the model name takes you to the [model's page](../../webapp/webapp_model_viewing.md), where you can view the
model's details and access the model.
The model’s **LABELS** tab displays its label enumeration.
The model's **LABELS** tab displays its label enumeration.
![Model Labels tab](../../img/examples_model_logging_labels.png)

6
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@ -6,13 +6,13 @@ The [using_artifacts_example](https://github.com/allegroai/clearml/blob/master/e
script demonstrates uploading a data file to a task as an artifact and then accessing and utilizing the artifact in a different task.
When the script runs it creates two tasks, `create artifact` and `use artifact from other task`, both of which are associated
with the `examples` project. The first task creates and uploads the artifact, and the second task accesses the first task’s
with the `examples` project. The first task creates and uploads the artifact, and the second task accesses the first task's
artifact and utilizes it.
## Task 1: Uploading an Artifact
The first task uploads a data file as an artifact using the [`Task.upload_artifact`](../../references/sdk/task.md#upload_artifact)
method, inputting the artifact’s name and the location of the file.
method, inputting the artifact's name and the location of the file.
```python
task1.upload_artifact(name='data file', artifact_object='data_samples/sample.json')
@ -21,7 +21,7 @@ task1.upload_artifact(name='data file', artifact_object='data_samples/sample.jso
The task is then closed, using the [`Task.close`](../../references/sdk/task.md#close) method, so another task can be
initialized in the same script.
Artifact details (location and size) can be viewed in ClearML’s **web UI > experiment details > ARTIFACTS tab > OTHER section**.
Artifact details (location and size) can be viewed in ClearML's **web UI > experiment details > ARTIFACTS tab > OTHER section**.
![Artifacts in WebApp](../../img/examples_using_artifacts_1.png)

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@ -151,9 +151,9 @@ Make sure a `clearml-agent` is assigned to that queue.
## WebApp
### Configuration
The values configured through the wizard are stored in the task’s hyperparameters and configuration objects by using the
The values configured through the wizard are stored in the task's hyperparameters and configuration objects by using the
[`Task.connect`](../../references/sdk/task.md#connect) and [`Task.set_configuration_object`](../../references/sdk/task.md#set_configuration_object)
methods respectively. They can be viewed in the WebApp, in the task’s **CONFIGURATION** page under **HYPERPARAMETERS** and **CONFIGURATION OBJECTS > General**.
methods respectively. They can be viewed in the WebApp, in the task's **CONFIGURATION** page under **HYPERPARAMETERS** and **CONFIGURATION OBJECTS > General**.
ClearML automatically logs command line arguments defined with argparse. View them in the experiments **CONFIGURATION**
page under **HYPERPARAMETERS > General**.
@ -161,11 +161,11 @@ page under **HYPERPARAMETERS > General**.
![Autoscaler configuration](../../img/examples_aws_autoscaler_config.png)
The task can be reused to launch another autoscaler instance: clone the task, then edit its parameters for the instance
types and budget configuration, and enqueue the task for execution (you’ll typically want to use a ClearML Agent running
types and budget configuration, and enqueue the task for execution (you'll typically want to use a ClearML Agent running
in [services mode](../../clearml_agent.md#services-mode) for such service tasks).
### Console
All other console output appears in the experiment’s **CONSOLE**.
All other console output appears in the experiment's **CONSOLE**.
![Autoscaler console](../../img/examples_aws_autoscaler_console.png)

8
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@ -6,7 +6,7 @@ The [cleanup service](https://github.com/allegroai/clearml/blob/master/examples/
demonstrates how to use the `clearml.backend_api.session.client.APIClient` class to implement a service that deletes old
archived tasks and their associated files: model checkpoints, other artifacts, and debug samples.
Modify the cleanup service’s parameters to specify which archived experiments to delete and when to delete them.
Modify the cleanup service's parameters to specify which archived experiments to delete and when to delete them.
### Running the Cleanup Service
@ -52,14 +52,14 @@ an `APIClient` object that establishes a session with the ClearML Server, and ac
* [`Task.delete`](../../references/sdk/task.md#delete) - Delete a Task.
## Configuration
The experiment’s hyperparameters are explicitly logged to ClearML using the [`Task.connect`](../../references/sdk/task.md#connect)
method. View them in the WebApp, in the experiment’s **CONFIGURATION** page under **HYPERPARAMETERS > General**.
The experiment's hyperparameters are explicitly logged to ClearML using the [`Task.connect`](../../references/sdk/task.md#connect)
method. View them in the WebApp, in the experiment's **CONFIGURATION** page under **HYPERPARAMETERS > General**.
The task can be reused. Clone the task, edit its parameters, and enqueue the task to run in ClearML Agent [services mode](../../clearml_agent.md#services-mode).
![Cleanup service configuration](../../img/example_cleanup_configuration.png)
## Console
All console output appears in the experiment’s **CONSOLE**.
All console output appears in the experiment's **CONSOLE**.
![Cleanup service console](../../img/examples_cleanup_console.png)

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@ -79,17 +79,17 @@ The script supports the following additional command line options:
## Configuration
ClearML automatically logs command line options defined with argparse. They appear in the experiment’s **CONFIGURATION**
ClearML automatically logs command line options defined with argparse. They appear in the experiment's **CONFIGURATION**
page under **HYPERPARAMETERS > Args**.
![Monitoring configuration](../../img/examples_slack_config.png)
The task can be reused to launch another monitor instance: clone the task, edit its parameters, and enqueue the task for
execution (you’ll typically want to use a ClearML Agent running in [services mode](../../clearml_agent.md#services-mode)
execution (you'll typically want to use a ClearML Agent running in [services mode](../../clearml_agent.md#services-mode)
for such service tasks).
## Console
All console output appears in the experiment’s **CONSOLE** page.
All console output appears in the experiment's **CONSOLE** page.
## Additional Information about slack_alerts.py

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@ -78,7 +78,7 @@ Upload the session's execution data that the Task captured offline to the ClearM
```
You can also use the offline task to update the execution of an existing previously executed task by providing the
previously executed task’s ID. To avoid overwriting metrics, you can specify the initial iteration offset with
previously executed task's ID. To avoid overwriting metrics, you can specify the initial iteration offset with
`iteration_offset`.
```python

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@ -50,7 +50,7 @@ Use [`Logger.report_matplotlib_figure()`](../references/sdk/logger.md#report_mat
a matplotlib figure, and specify its title and series names, and iteration number:
```
```python
logger = task.get_logger()
area = (40 * np.random.rand(N))**2

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@ -6,7 +6,7 @@ title: Project Dashboard
The ClearML Project Dashboard App is available under the ClearML Pro plan
:::
The Project Dashboard Application provides an overview of a project or workspace’s progress. It presents an aggregated
The Project Dashboard Application provides an overview of a project or workspace's progress. It presents an aggregated
view of task status and a chosen metric over time, as well as project GPU and worker usage. It also supports alerts/warnings
on completed/failed Tasks via Slack integration.
@ -15,7 +15,7 @@ on completed/failed Tasks via Slack integration.
values from the file, which can be modified before launching the app instance
* **Dashboard Title** - Name of the project dashboard instance, which will appear in the instance list
* **Monitoring** - Select what the app instance should monitor. The options are:
* Project - Monitor a specific project. You can select an option to also monitor the specified project’s subprojects
* Project - Monitor a specific project. You can select an option to also monitor the specified project's subprojects
* Entire workspace - Monitor all projects in your workspace
:::caution
@ -34,7 +34,7 @@ of the chosen metric over time.
* Alert Iteration Threshold - Minimum number of task iterations to trigger Slack alerts (tasks that fail prior to the threshold will be ignored)
* **Additional options**
* Track manual (non agent-run) experiments as well - Select to include in the dashboard experiments that were not executed by an agent
* Alert on completed experiments - Select to include completed tasks in alerts: in the dashboard’s Task Alerts section and in Slack Alerts.
* Alert on completed experiments - Select to include completed tasks in alerts: in the dashboard's Task Alerts section and in Slack Alerts.
* **Export Configuration** - Export the app instance configuration as a JSON file, which you can later import to create
a new instance with the same configuration.
@ -48,7 +48,7 @@ Once a project dashboard instance is launched, its dashboard displays the follow
* Experiments Summary - Number of tasks by status over time
* Monitoring - GPU utilization and GPU memory usage
* Metric Monitoring - An aggregated view of the values of a metric over time
* Project’s Active Workers - Number of workers currently executing experiments in the monitored project
* Project's Active Workers - Number of workers currently executing experiments in the monitored project
* Workers Table - List of active workers
* Task Alerts
* Failed tasks - Failed experiments and their time of failure summary

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@ -6,7 +6,7 @@ title: Overview
ClearML Applications are available under the ClearML Pro plan
:::
Use ClearML’s GUI Applications to manage ML workloads and automatically run your recurring workflows without any coding.
Use ClearML's GUI Applications to manage ML workloads and automatically run your recurring workflows without any coding.
![Apps page](../../img/apps_overview_page.png)
@ -18,23 +18,23 @@ ClearML provides the following applications:
* [**AWS Autoscaler**](apps_aws_autoscaler.md) - Optimize AWS EC2 instance usage according to a defined instance budget
* [**GCP Autoscaler**](apps_gcp_autoscaler.md) - Optimize GCP instance usage according to a defined instance budget
* [**Hyperparameter Optimization**](apps_hpo.md) - Find the parameter values that yield the best performing models
* **Nvidia Clara** - Train models using Nvidia’s Clara framework
* **Nvidia Clara** - Train models using Nvidia's Clara framework
* [**Project Dashboard**](apps_dashboard.md) - High-level project monitoring with Slack alerts
* [**Task Scheduler**](apps_task_scheduler.md) - Schedule tasks for one-shot and/or periodic execution at specified times (available under ClearML Enterprise Plan)
* [**Trigger Manager**](apps_trigger_manager) - Define tasks to be run when predefined events occur (available under ClearML Enterprise Plan)
## App Pages Layout
Each application’s page is split into two sections:
Each application's page is split into two sections:
* App Instance List - Launch new app instances and view previously launched instances. Click on an instance to view its
dashboard. Hover over it to access the [app instance actions](#app-instance-actions).
* App Instance Dashboard - The main section of the app page: displays the selected app instance’s status and results.
* App Instance Dashboard - The main section of the app page: displays the selected app instance's status and results.
![App format](../../img/apps_format_overview.png)
## Launching an App Instance
1. Choose the desired app
1. Click the `Launch New` button <img src="/docs/latest/icons/ico-add.svg" alt="Add new" className="icon size-md space-sm" /> to open the app’s configuration wizard
1. Click the `Launch New` button <img src="/docs/latest/icons/ico-add.svg" alt="Add new" className="icon size-md space-sm" /> to open the app's configuration wizard
1. Fill in the configuration details
1. **Launch**

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@ -18,7 +18,7 @@ top-level projects are displayed. Click on a project card to view the project's
Click on a dataset card to navigate to its [Version List](webapp_dataset_viewing.md), where you can view the
dataset versions' lineage and contents.
Filter the datasets to find the one you’re looking for more easily. These filters can be applied by clicking <img src="/docs/latest/icons/ico-filter-off.svg" alt="Filter" className="icon size-md" />:
Filter the datasets to find the one you're looking for more easily. These filters can be applied by clicking <img src="/docs/latest/icons/ico-filter-off.svg" alt="Filter" className="icon size-md" />:
* My Work - Show only datasets that you created
* Tags - Choose which tags to filter by from a list of tags used in the datasets.
* Filter by multiple tag values using the **ANY** or **ALL** options, which correspond to the logical "AND" and "OR"
@ -29,7 +29,7 @@ Filter the datasets to find the one you’re looking for more easily. These filt
## Project Cards
In Project view, project cards display a project’s summarized dataset information:
In Project view, project cards display a project's summarized dataset information:
<div class="max-w-50">
@ -74,7 +74,7 @@ of a dataset card to open its context menu and access dataset actions.
</div>
* **Rename** - Change the dataset’s name
* **Rename** - Change the dataset's name
* **Add Tag** - Add label to the dataset to help easily classify groups of dataset.
* **Delete** - Delete the dataset and all of its versions. To delete a dataset, all its versions must first be
archived.

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@ -24,7 +24,7 @@ Each node in the graph represents a dataset version, and shows the following det
* Version size
* Version update time
* Version details button - Hover over the version and click <img src="/docs/latest/icons/ico-console.svg" alt="console" className="icon size-md space-sm" />
to view the version’s [details panel](#version-details-panel)
to view the version's [details panel](#version-details-panel)
:::tip archiving versions
You can archive dataset versions so the versions list doesn't get too cluttered. Click **OPEN ARCHIVE** on the top of
@ -65,7 +65,7 @@ On the right side of the dataset version panel, view the **VERSION INFO** which
</div>
To view a version’s detailed information, click **Full details**, which will open the dataset version’s [task page](../webapp_exp_track_visual.md).
To view a version's detailed information, click **Full details**, which will open the dataset version's [task page](../webapp_exp_track_visual.md).
![Dataset task info](../../img/webapp_dataset_task_page.png)
@ -84,7 +84,7 @@ to view the version's details panel. The panel includes three tabs:
![preview](../../img/webapp_dataset_preview.png)
* **CONSOLE** - The dataset version’s console output
* **CONSOLE** - The dataset version's console output
![console](../../img/webapp_dataset_console.png)

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@ -11,9 +11,9 @@ view, all pipelines are shown side-by-side. In Project view, pipelines are organ
top-level projects are displayed. Click on a project card to view the project's pipelines.
Click on a pipeline card to navigate to its [Pipeline Runs Table](webapp_pipeline_table.md), where you can view the
pipeline structure, configuration, and outputs of all the pipeline’s runs, as well as create new runs.
pipeline structure, configuration, and outputs of all the pipeline's runs, as well as create new runs.
Filter the pipelines to find the one you’re looking for more easily. These filters can be applied by clicking <img src="/docs/latest/icons/ico-filter-off.svg" alt="Filter" className="icon size-md" />:
Filter the pipelines to find the one you're looking for more easily. These filters can be applied by clicking <img src="/docs/latest/icons/ico-filter-off.svg" alt="Filter" className="icon size-md" />:
* My Work - Show only pipelines that you created
* Tags - Choose which tags to filter by from a list of tags used in the pipelines.
* Filter by multiple tag values using the **ANY** or **ALL** options, which correspond to the logical "AND" and "OR"
@ -46,7 +46,7 @@ In List view, the pipeline cards display summarized pipeline information:
</div>
* Pipeline name
* Time since the pipeline’s most recent run
* Time since the pipeline's most recent run
* Run summary - Number of *Running*/*Pending*/*Completed*/*Failed* runs
* Tags
@ -62,7 +62,7 @@ of a pipeline card to open its context menu and access pipeline actions.
</div>
* **Rename** - Change the pipeline’s name
* **Rename** - Change the pipeline's name
* **Add Tag** - Add label to the pipeline to help easily classify groups of pipelines.
* **Delete** - Delete the pipeline: delete all its runs and any models/artifacts produced (a list of remaining artifacts
is returned). To delete a pipeline, all its runs must first be archived.

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@ -2,14 +2,14 @@
title: The Pipeline Runs Table
---
The pipeline runs table is a [customizable](#customizing-the-runs-table) list of the pipeline’s runs. Use it to
view a run’s details, and manage runs (create, continue, or abort). The runs table's auto-refresh allows users
The pipeline runs table is a [customizable](#customizing-the-runs-table) list of the pipeline's runs. Use it to
view a run's details, and manage runs (create, continue, or abort). The runs table's auto-refresh allows users
to continually monitor run progress.
View the runs table in table view <img src="/docs/latest/icons/ico-table-view.svg" alt="Table view" className="icon size-md space-sm" />
or in details view <img src="/docs/latest/icons/ico-split-view.svg" alt="Details view" className="icon size-md space-sm" />,
using the buttons on the top left of the page. Use the table view for a comparative view of your runs according to
columns of interest. Use the details view to access a selected run’s details, while keeping the pipeline runs list in view.
columns of interest. Use the details view to access a selected run's details, while keeping the pipeline runs list in view.
Details view can also be accessed by double-clicking a specific pipeline run in the table view to open its details view.
You can archive pipeline runs so the runs table doesn't get too cluttered. Click **OPEN ARCHIVE** on the top of the
@ -32,7 +32,7 @@ The models table contains the following columns:
| Column | Description | Type |
|---|---|---|
| **RUN** | Pipeline run identifier | String |
| **VERSION** | The pipeline version number. Corresponds to the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md#class-pipelinecontroller)’s and [PipelineDecorator](../../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)’s `version` parameter | Version string |
| **VERSION** | The pipeline version number. Corresponds to the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md#class-pipelinecontroller)'s and [PipelineDecorator](../../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)’s `version` parameter | Version string |
| **TAGS** | Descriptive, user-defined, color-coded tags assigned to run. | Tag |
| **STATUS** | Pipeline run's status. See a list of the [task states and state transitions](../../fundamentals/task.md#task-states). For Running, Failed, and Aborted runs, you will also see a progress indicator next to the status. See [here](../../pipelines/pipelines.md#tracking-pipeline-progress). | String |
| **USER** | User who created the run. | String |

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@ -2,7 +2,7 @@
title: Pipeline Run Details
---
The run details panel shows the pipeline’s structure and the execution status of every step, as well as the run’s
The run details panel shows the pipeline's structure and the execution status of every step, as well as the run's
configuration parameters and output.
![Pipeline structure](../../img/webapp_pipeline_DAG.png)
@ -15,7 +15,7 @@ Each step shows:
* Step status
* Step execution time
* Step log button - Hover over the step and click <img src="/docs/latest/icons/ico-console.svg" alt="console" className="icon size-md space-sm" />
to view the step’s [details panel](#run-and-step-details-panel)
to view the step's [details panel](#run-and-step-details-panel)
While the pipeline is running, the steps’ details and colors are updated.