ClearML/clearml-docs
1
0
Fork 0
mirror of https://github.com/clearml/clearml-docs synced 2025-04-14 20:53:13 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Small edits (#1031)

Browse Source
This commit is contained in:
pollfly 2025-02-09 19:35:03 +02:00 committed by GitHub
parent 0b45e486f2
commit 9f2935fbbd
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
17 changed files with 88 additions and 88 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
docs/faq.md
View File

@ -161,7 +161,7 @@ clearml-session==0.3.2
#### How can I sort models by a certain metric? <a id="custom-columns"></a>
To sort models by a metric, in the ClearML Web UI,
add a [custom column](webapp/webapp_model_table.md#customizing-the-models-table) in the models table and sort by
add a [custom column](webapp/webapp_model_table.md#customizing-the-models-table) to the model table and sort by
that metric column. Available custom column options depend upon the models in the table and the metrics that have been
attached to them (see [Logging Metrics and Plots](clearml_sdk/model_sdk.md#logging-metrics-and-plots)).

2
docs/fundamentals/projects.md
View File

@ -21,7 +21,7 @@ the project are executed, the model checkpoints (snapshots) and artifacts are st
Users can create and modify projects, and see project details in the [WebApp](../webapp/webapp_home.md).
A project's description can be edited in its [overview](../webapp/webapp_project_overview.md) page. Each project's tasks,
models, and dataviews, can be viewed in the project's [task table](../webapp/webapp_exp_table.md),
[models table](../webapp/webapp_model_table.md), and [dataviews table](../hyperdatasets/webapp/webapp_dataviews.md).
[model table](../webapp/webapp_model_table.md), and [dataview table](../hyperdatasets/webapp/webapp_dataviews.md).
## Usage

18
docs/getting_started/ds/best_practices.md
View File

@ -27,7 +27,7 @@ The goal of this phase is to get a code, dataset, and environment set up, so you
- [ClearML SDK](../../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out [Getting Started](ds_first_steps.md)).
This helps visualizing the results and tracking progress.
- [ClearML Agent](../../clearml_agent.md) helps moving your work to other machines without the hassle of rebuilding the environment every time,
while also creating an easy queue interface that easily lets you drop your experiments to be executed one by one
while also creating an easy queue interface that easily lets you drop your tasks to be executed one by one
(great for ensuring that the GPUs are churning during the weekend).
- [ClearML Session](../../apps/clearml_session.md) helps with developing on remote machines, in the same way that you'd develop on your local laptop!
@ -38,7 +38,7 @@ yields the best performing model for your task!
- The real training (usually) should **not** be executed on your development machine.
- Training sessions should be launched and monitored from a web UI.
- You should continue coding while experiments are being executed without interrupting them.
- You should continue coding while tasks are being executed without interrupting them.
- Stop optimizing your code because your machine struggles, and run it on a beefier machine (cloud / on-prem).
Visualization and comparison dashboards keep your sanity at bay! At this stage you usually have a docker container with all the binaries
@ -58,23 +58,23 @@ that you need.
Track everything--from obscure parameters to weird metrics, it's impossible to know what will end up
improving your results later on!
- Make sure experiments are reproducible! ClearML logs code, parameters, and environment in a single, easily searchable place.
- Make sure tasks are reproducible! ClearML logs code, parameters, and environment in a single, easily searchable place.
- Development is not linear. Configuration / Parameters should not be stored in your git, as
they are temporary and constantly changing. They still need to be logged because who knows, one day...
- Uncommitted changes to your code should be stored for later forensics in case that magic number actually saved the day. Not every line change should be committed.
- Mark potentially good experiments, make them the new baseline for comparison.
- Mark potentially good tasks, make them the new baseline for comparison.
## Visibility Matters
While you can track experiments with one tool, and pipeline them with another, having
While you can track tasks with one tool, and pipeline them with another, having
everything under the same roof has its benefits!
Being able to track experiment progress and compare experiments, and, based on that, send experiments to execution on remote
Being able to track task progress and compare tasks, and, based on that, send tasks to execution on remote
machines (that also build the environment themselves) has tremendous benefits in terms of visibility and ease of integration.
Being able to have visibility in your pipeline, while using experiments already defined in the platform,
Being able to have visibility in your pipeline, while using tasks already defined in the platform,
enables users to have a clearer picture of the pipeline's status
and makes it easier to start using pipelines earlier in the process by simplifying chaining tasks.
Managing datasets with the same tools and APIs that manage the experiments also lowers the barrier of entry into
experiment and data provenance.
Managing datasets with the same tools and APIs that manage the tasks also lowers the barrier of entry into
task and data provenance.

4
docs/getting_started/ds/ds_first_steps.md
View File

@ -99,7 +99,7 @@ Now you can use ClearML in your notebook!
In ClearML, experiments are organized as [Tasks](../../fundamentals/task.md).
ClearML automatically logs your experiment and code, including outputs and parameters from popular ML frameworks,
ClearML automatically logs your task and code, including outputs and parameters from popular ML frameworks,
once you integrate the ClearML [SDK](../../clearml_sdk/clearml_sdk.md) with your code. To control what ClearML automatically logs, see this [FAQ](../../faq.md#controlling_logging).
At the beginning of your code, import the `clearml` package:
@ -115,7 +115,7 @@ To ensure full automatic logging, it is recommended to import the `clearml` pack
Then initialize the Task object in your `main()` function, or the beginning of the script.
```python
task = Task.init(project_name='great project', task_name='best experiment')
task = Task.init(project_name='great project', task_name='best task')
```
If the project does not already exist, a new one is created automatically.

8
docs/getting_started/ds/ds_second_steps.md
View File

@ -151,14 +151,14 @@ Once everything is neatly logged and displayed, use the [comparison tool](../../
## Track Experiments
The experiments table is a powerful tool for creating dashboards and views of your own projects, your team's projects, or the entire development.
The task table is a powerful tool for creating dashboards and views of your own projects, your team's projects, or the entire development.
![Experiments table](../../img/webapp_experiment_table.png#light-mode-only)
![Experiments table](../../img/webapp_experiment_table_dark.png#dark-mode-only)
![Task table](../../img/webapp_experiment_table.png#light-mode-only)
![Task table](../../img/webapp_experiment_table_dark.png#dark-mode-only)
### Creating Leaderboards
Customize the [experiments table](../../webapp/webapp_exp_table.md) to fit your own needs, adding desired views of parameters, metrics, and tags.
Customize the [task table](../../webapp/webapp_exp_table.md) to fit your own needs, adding desired views of parameters, metrics, and tags.
You can filter and sort based on parameters and metrics, so creating custom views is simple and flexible.
Create a dashboard for a project, presenting the latest Models and their accuracy scores, for immediate insights.

6
docs/getting_started/mlops/mlops_best_practices.md
View File

@ -12,7 +12,7 @@ If you are afraid of clutter, use the archive option, and set up your own [clean
- Track the code base. There is no reason not to add metrics to any process in your workflow, even if it is not directly ML. Visibility is key to iterative improvement of your code / workflow.
- Create per-project [leaderboards](../../guides/ui/building_leader_board.md) based on custom columns
(hyperparameters and performance accuracy), and bookmark them (full URL will always reproduce the same view and table).
- Share experiments with your colleagues and team-leaders.
- Share tasks with your colleagues and team-leaders.
Invite more people to see how your project is progressing, and suggest they add metric reporting for their own.
These metrics can later be part of your own in-house monitoring solution, don't let good data go to waste :)
@ -26,10 +26,10 @@ Once you have a Task in ClearML, you can clone and edit its definitions in the U
## Advanced Automation
- Create daily / weekly cron jobs for retraining best performing models on.
- Create data monitoring & scheduling and launch inference jobs to test performance on any new coming dataset.
- Once there are two or more experiments that run after another, group them together into a [pipeline](../../pipelines/pipelines.md).
- Once there are two or more tasks that run after another, group them together into a [pipeline](../../pipelines/pipelines.md).
## Manage Your Data
Use [ClearML Data](../../clearml_data/clearml_data.md) to version your data, then link it to running experiments for easy reproduction.
Use [ClearML Data](../../clearml_data/clearml_data.md) to version your data, then link it to running tasks for easy reproduction.
Make datasets machine agnostic (i.e. store original dataset in a shared storage location, e.g. shared-folder / S3 / Gs / Azure).
ClearML Data supports efficient Dataset storage and caching, differentiable and compressed.

56
docs/getting_started/mlops/mlops_first_steps.md
View File

@ -17,7 +17,7 @@ are fully utilized at all times become daunting tasks.
This can create overhead that derails you from your core work!
ClearML Agent was designed to deal with such issues and more! It is a tool responsible for executing experiments on remote machines: on-premises or in the cloud! ClearML Agent provides the means to reproduce and track experiments in your
ClearML Agent was designed to deal with such issues and more! It is a tool responsible for executing tasks on remote machines: on-premises or in the cloud! ClearML Agent provides the means to reproduce and track tasks in your
machine of choice through the ClearML WebApp with no need for additional code.
The agent will set up the environment for a specific Task's execution (inside a Docker, or bare-metal), install the
@ -54,40 +54,40 @@ required python packages, and execute and monitor the process.
:::tip Agent Deployment Modes
ClearML Agents can be deployed in:
* [Virtual environment mode](../../clearml_agent/clearml_agent_execution_env.md): Agent creates a new venv to execute an experiment.
* [Docker mode](../../clearml_agent/clearml_agent_execution_env.md#docker-mode): Agent executes an experiment inside a
* [Virtual environment mode](../../clearml_agent/clearml_agent_execution_env.md): Agent creates a new venv to execute a task.
* [Docker mode](../../clearml_agent/clearml_agent_execution_env.md#docker-mode): Agent executes a task inside a
Docker container.
For more information, see [Running Modes](../../fundamentals/agents_and_queues.md#running-modes).
:::
## Clone an Experiment
Experiments can be reproduced (cloned) for validation or as a baseline for further experimentation.
## Clone a Task
Tasks can be reproduced (cloned) for validation or as a baseline for further experimentation.
Cloning a task duplicates the task's configuration, but not its outputs.
**To clone an experiment in the ClearML WebApp:**
1. Click on any project card to open its [experiments table](../../webapp/webapp_exp_table.md).
1. Right-click one of the experiments on the table.
1. Click **Clone** in the context menu, which will open a **CLONE EXPERIMENT** window.
**To clone a task in the ClearML WebApp:**
1. Click on any project card to open its [task table](../../webapp/webapp_exp_table.md).
1. Right-click one of the tasks on the table.
1. Click **Clone** in the context menu, which will open a **CLONE TASK** window.
1. Click **CLONE** in the window.
The newly cloned experiment will appear and its info panel will slide open. The cloned experiment is in draft mode, so
The newly cloned task will appear and its info panel will slide open. The cloned task is in draft mode, so
it can be modified. You can edit the Git / code references, control the python packages to be installed, specify the
Docker container image to be used, or change the hyperparameters and configuration files. See [Modifying Tasks](../../webapp/webapp_exp_tuning.md#modifying-experiments) for more information about editing experiments in the UI.
Docker container image to be used, or change the hyperparameters and configuration files. See [Modifying Tasks](../../webapp/webapp_exp_tuning.md#modifying-tasks) for more information about editing tasks in the UI.
## Enqueue an Experiment
Once you have set up an experiment, it is now time to execute it.
## Enqueue a Task
Once you have set up a task, it is now time to execute it.
**To execute an experiment through the ClearML WebApp:**
1. Right-click your draft experiment (the context menu is also available through the <img src="/docs/latest/icons/ico-bars-menu.svg" alt="Menu" className="icon size-md space-sm" />
button on the top right of the experiment's info panel)
1. Click **ENQUEUE,** which will open the **ENQUEUE EXPERIMENT** window
**To execute a task through the ClearML WebApp:**
1. Right-click your draft task (the context menu is also available through the <img src="/docs/latest/icons/ico-bars-menu.svg" alt="Menu" className="icon size-md space-sm" />
button on the top right of the task's info panel)
1. Click **ENQUEUE,** which will open the **ENQUEUE TASK** window
1. In the window, select `default` in the queue menu
1. Click **ENQUEUE**
This action pushes the experiment into the `default` queue. The experiment's status becomes *Pending* until an agent
assigned to the queue fetches it, at which time the experiment's status becomes *Running*. The agent executes the
experiment, and the experiment can be [tracked and its results visualized](../../webapp/webapp_exp_track_visual.md).
This action pushes the task into the `default` queue. The task's status becomes *Pending* until an agent
assigned to the queue fetches it, at which time the task's status becomes *Running*. The agent executes the
task, and the task can be [tracked and its results visualized](../../webapp/webapp_exp_track_visual.md).
## Programmatic Interface
@ -95,7 +95,7 @@ experiment, and the experiment can be [tracked and its results visualized](../..
The cloning, modifying, and enqueuing actions described above can also be performed programmatically.
### First Steps
#### Access Previously Executed Experiments
#### Access Previously Executed Tasks
All Tasks in the system can be accessed through their unique Task ID, or based on their properties using the [`Task.get_task`](../../references/sdk/task.md#taskget_task)
method. For example:
```python
@ -106,15 +106,15 @@ executed_task = Task.get_task(task_id='aabbcc')
Once a specific Task object has been obtained, it can be cloned, modified, and more. See [Advanced Usage](#advanced-usage).
#### Clone an Experiment
#### Clone a Task
To duplicate an experiment, use the [`Task.clone`](../../references/sdk/task.md#taskclone) method, and input either a
To duplicate a task, use the [`Task.clone`](../../references/sdk/task.md#taskclone) method, and input either a
Task object or the Task's ID as the `source_task` argument.
```python
cloned_task = Task.clone(source_task=executed_task)
```
#### Enqueue an Experiment
#### Enqueue a Task
To enqueue the task, use the [`Task.enqueue`](../../references/sdk/task.md#taskenqueue) method, and input the Task object
with the `task` argument, and the queue to push the task into with `queue_name`.
@ -129,7 +129,7 @@ Before execution, use a variety of programmatic methods to manipulate a task obj
[Hyperparameters](../../fundamentals/hyperparameters.md) are an integral part of Machine Learning code as they let you
control the code without directly modifying it. Hyperparameters can be added from anywhere in your code, and ClearML supports multiple ways to obtain them!
Users can programmatically change cloned experiments' parameters.
Users can programmatically change cloned tasks' parameters.
For example:
```python
@ -200,7 +200,7 @@ min_max_values = executed_task.get_last_scalar_metrics()
full_scalars = executed_task.get_reported_scalars()
```
#### Query Experiments
#### Query Tasks
You can also search and query Tasks in the system. Use the [`Task.get_tasks`](../../references/sdk/task.md#taskget_tasks)
class method to retrieve Task objects and filter based on the specific values of the Task - status, parameters, metrics and more!
@ -219,7 +219,7 @@ Data is probably one of the biggest factors that determines the success of a pro
the model's configuration, code, and results (such as accuracy) is key to deducing meaningful insights into model behavior.
[ClearML Data](../../clearml_data/clearml_data.md) lets you version your data, so it's never lost, fetch it from every
machine with minimal code changes, and associate data to experiment results.
machine with minimal code changes, and associate data to task results.
Logging data can be done via command line, or programmatically. If any preprocessing code is involved, ClearML logs it
as well! Once data is logged, it can be used by other experiments.
as well! Once data is logged, it can be used by other tasks.

28
docs/hyperdatasets/webapp/webapp_dataviews.md
View File

@ -1,33 +1,33 @@
---
title: The Dataviews Table
title: The Dataview table
---
The **Dataviews table** is a [customizable](#customizing-the-dataviews-table) list of Dataviews associated with a project.
The **Dataview table** is a [customizable](#customizing-the-dataviews-table) list of Dataviews associated with a project.
Use it to view and create Dataviews, and access their info panels.
The table lists independent Dataview objects. To see Dataviews logged by a task, go
to the specific task's **DATAVIEWS** tab (see [Task Dataviews](webapp_exp_track_visual.md)).
View the Dataviews table in table view <img src="/docs/latest/icons/ico-table-view.svg" alt="Table view" className="icon size-md space-sm" />
View the Dataview 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 Dataviews according to
columns of interest. Use the details view to access a selected Dataview's details, while keeping the Dataview list in view.
Details view can also be accessed by double-clicking a specific Dataview in the table view to open its details view.
You can archive Dataviews so the Dataviews table doesn't get too cluttered. Click **OPEN ARCHIVE** on the top of the
You can archive Dataviews so the Dataview table doesn't get too cluttered. Click **OPEN ARCHIVE** on the top of the
table to open the archive and view all archived Dataviews. From the archive, you can restore
Dataviews to remove them from the archive. You can also permanently delete Dataviews.
You can download the Dataviews table as a CSV file by clicking <img src="/docs/latest/icons/ico-download.svg" alt="Download" className="icon size-md space-sm" />
You can download the Dataview table as a CSV file by clicking <img src="/docs/latest/icons/ico-download.svg" alt="Download" className="icon size-md space-sm" />
and choosing one of these options:
* **Download onscreen items** - Download the values for Dataviews currently visible on screen
* **Download all items** - Download the values for all Dataviews in this project that match the current active filters
The downloaded data consists of the currently displayed table columns.
![Dataviews table](../../img/hyperdatasets/webapp_dataviews_table.png)
![Dataview table](../../img/hyperdatasets/webapp_dataviews_table.png)
The Dataviews table includes the following columns:
The Dataview table includes the following columns:
|Column|Description|Type|
|--|--|--|
@ -41,9 +41,9 @@ The Dataviews table includes the following columns:
Dynamically order the columns by dragging a column heading
to a new position.
## Customizing the Dataviews Table
## Customizing the Dataview Table
The Dataviews table can be customized. Changes are persistent (cached in the browser), and represented in the URL.
The Dataview table can be customized. Changes are persistent (cached in the browser), and represented in the URL.
Save customized settings in a browser bookmark, and share the URL with teammates.
Customize the table using any of the following:
@ -70,17 +70,17 @@ all the Dataviews in the project. The customizations of these two views are save
## Dataview Actions
The following table describes the actions that can be performed from the Dataviews table.
The following table describes the actions that can be performed from the Dataview table.
Access these actions with the context menu in any of the following ways:
* In the Dataviews table, right-click a Dataview, or hover over a Dataview and click <img src="/docs/latest/icons/ico-dots-v-menu.svg" alt="Dot menu" className="icon size-md space-sm" />
* In the Dataview table, right-click a Dataview, or hover over a Dataview and click <img src="/docs/latest/icons/ico-dots-v-menu.svg" alt="Dot menu" className="icon size-md space-sm" />
* In a Dataview info panel, click the menu button <img src="/docs/latest/icons/ico-bars-menu.svg" alt="Bar menu" className="icon size-md space-sm" />
| ClearML Action | Description |
|---|---|
| Details | View Dataview details, including input datasets, label mapping, and iteration control. Can also be accessed by double-clicking a Dataview in the Dataviews table. |
| Details | View Dataview details, including input datasets, label mapping, and iteration control. Can also be accessed by double-clicking a Dataview in the Dataview table. |
| Archive | Move Dataview to the Dataview's archive. |
| Restore | Action available in the archive. Restore a Dataview to the active Dataviews table. |
| Restore | Action available in the archive. Restore a Dataview to the active Dataview table. |
| Delete | Action available in the archive. Permanently delete a Dataview. |
| Clone | Make an exact copy of a Dataview that is editable. |
| Move to Project | Move a Dataview to another project. |
@ -97,7 +97,7 @@ Select multiple Dataviews, then use either the context menu, or the batch action
operations on the selected Dataviews. The context menu shows the number of Dataviews that can be affected by each action.
The same information can be found in the batch action bar, in a tooltip that appears when hovering over an action icon.
![Dataviews table batch operations](../../img/hyperdatasets/webapp_dataviews_context_menu.png)
![Dataview table batch operations](../../img/hyperdatasets/webapp_dataviews_context_menu.png)
## Creating a Dataview

2
docs/webapp/datasets/webapp_dataset_viewing.md
View File

@ -107,7 +107,7 @@ Access these actions with the context menu by right-clicking a version on the da
|-----|----|
|Add Tag |User-defined labels added to versions for grouping and organization. |
|Archive| Move dataset versions to the dataset's archive. |
|Restore|Action available in the archive. Restore a version to the active dataset versions table.|
|Restore|Action available in the archive. Restore a version to the active dataset version table.|
|Delete| Delete an archived version and its artifacts. This action is available only from the dataset's archive. |
![Dataset actions](../../img/webapp_dataset_actions.png#light-mode-only)

4
docs/webapp/pipelines/webapp_pipeline_table.md
View File

@ -1,5 +1,5 @@
---
title: The Pipeline Runs Table
title: The Pipeline Run Table
---
The pipeline runs table is a [customizable](#customizing-the-runs-table) list of the pipeline's runs. Use it to
@ -31,7 +31,7 @@ The downloaded data consists of the currently displayed table columns.
## Run Table Columns
The models table contains the following columns:
The pipeline run table contains the following columns:
| Column | Description | Type |
|---|---|---|

2
docs/webapp/webapp_exp_reproducing.md
View File

@ -32,7 +32,7 @@ the original task to become the clone's parent.
## Resetting
To reset a task:
1. In the tasks table, right-click the relevant task and click **Reset**.
1. In the task table, right-click the relevant task and click **Reset**.
1. In the `Reset Task` modal, if you want the task's artifacts and debug samples to be deleted from the
ClearML file server, click the checkbox
1. Click **Reset**

2
docs/webapp/webapp_model_comparing.md
View File

@ -7,7 +7,7 @@ You can view the differences in model details, configuration, scalar values, and
## Selecting Models to Compare
To select models to compare:
1. Go to a models table that includes the models to be compared.
1. Go to a model table that includes the models to be compared.
1. Select the models to compare. Once multiple models are selected, the batch action bar appears.
1. In the batch action bar, click **COMPARE**.

2
docs/webapp/webapp_model_endpoints.md
View File

@ -2,7 +2,7 @@
title: Model Endpoints
---
The Model Endpoints table lists all currently live (active, and being brought up) model endpoints, allowing you to view
The Model Endpoint table lists all currently live (active, and being brought up) model endpoints, allowing you to view
endpoint details and monitor status over time. Whenever you deploy a model through the [ClearML Deploy UI applications](applications/apps_overview.md#deploy),
it will be listed in the table.

32
docs/webapp/webapp_model_table.md
View File

@ -1,33 +1,33 @@
---
title: The Models Table
title: The Model table
---
The models table is a [customizable](#customizing-the-models-table) list of models associated with the tasks in a project. From the models table,
The model table is a [customizable](#customizing-the-models-table) list of models associated with the tasks in a project. From the model table,
view model details, and modify, publish, archive, tag, and move models to other projects.
View the models table in table view <img src="/docs/latest/icons/ico-table-view.svg" alt="Table view" className="icon size-md space-sm" />
View the model 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 models according to
columns of interest. Use the details view to access a selected model's details, while keeping the model list in view.
Details view can also be accessed by double-clicking a specific model in the table view to open its details view.
You can archive models so the models table doesn't get too cluttered. Click **OPEN ARCHIVE** on the top of the
You can archive models so the model table doesn't get too cluttered. Click **OPEN ARCHIVE** on the top of the
table to open the archive and view all archived models. From the archive, you can restore
models to remove them from the archive, and permanently delete models.
You can download the models table as a CSV file by clicking <img src="/docs/latest/icons/ico-download.svg" alt="Download" className="icon size-md space-sm" />
You can download the model table as a CSV file by clicking <img src="/docs/latest/icons/ico-download.svg" alt="Download" className="icon size-md space-sm" />
and choosing one of these options:
* **Download onscreen items** - Download the values for models currently visible on screen
* **Download all items** - Download the values for all models in this project that match the current active filters
The downloaded data consists of the currently displayed table columns.
![Models table](../img/webapp_models_01.png#light-mode-only)
![Models table](../img/webapp_models_01_dark.png#dark-mode-only)
![Model table](../img/webapp_models_01.png#light-mode-only)
![Model table](../img/webapp_models_01_dark.png#dark-mode-only)
## Models Table Columns
## Model Table Columns
The models table contains the following columns:
The model table contains the following columns:
| Column | Description | Type |
|---|---|---|
@ -44,9 +44,9 @@ The models table contains the following columns:
| *Metadata* | User defined metadata key column. Available options depend upon the models in the table. | String |
## Customizing the Models Table
## Customizing the Model table
The models table is customizable. Changes are persistent (cached in the browser) and represented in the URL, so customized settings
The model table is customizable. Changes are persistent (cached in the browser) and represented in the URL, so customized settings
can be saved in a browser bookmark and shared with other ClearML users to collaborate.
Customize the table using any of the following:
@ -77,18 +77,18 @@ all the models in the project. The customizations of these two views are saved s
## Model Actions
The following table describes the actions that can be done from the models table, including the states that
The following table describes the actions that can be done from the model table, including the states that
allow each feature. Model states are *Draft* (editable) and *Published* (read-only).
Access these actions in any of the following ways:
* In the models table, right-click a model, or hover over a model and click <img src="/docs/latest/icons/ico-dots-v-menu.svg" alt="Dot menu" className="icon size-md space-sm" /> to
* In the model table, right-click a model, or hover over a model and click <img src="/docs/latest/icons/ico-dots-v-menu.svg" alt="Dot menu" className="icon size-md space-sm" /> to
open the context menu
* In a model's info panel, click the menu button <img src="/docs/latest/icons/ico-bars-menu.svg" alt="Bar menu" className="icon size-md space-sm" />
* Through the batch action bar, available at screen bottom when multiple models are selected
| ClearML Action | Description | States Valid for the Action |
|---|---|--|
| Details | View model details, which include general information, the model configuration, and label enumeration. Can also be accessed by double-clicking a model in the models table | Any state |
| Details | View model details, which include general information, the model configuration, and label enumeration. Can also be accessed by double-clicking a model in the model table | Any state |
| Publish | Publish a model to prevent changes to it. *Published* models are read-only. If a model is Published, its task also becomes Published (read-only). | *Draft* |
| Archive | Move model to the project's archive. | Any state |
| Restore | Action available in the archive. Restore a model to the main model table. | Any state |
@ -109,8 +109,8 @@ Select multiple models, then use either the context menu, or the bar that appear
operations on the selected models. The context menu shows the number of models that can be affected by each action.
The same information can be found in the bottom menu, in a tooltip that appears when hovering over an action icon.
![Models table batch operations](../img/webapp_models_table_batch_operations.png#light-mode-only)
![Models table batch operations](../img/webapp_models_table_batch_operations_dark.png#dark-mode-only)
![Model table batch operations](../img/webapp_models_table_batch_operations.png#light-mode-only)
![Model table batch operations](../img/webapp_models_table_batch_operations_dark.png#dark-mode-only)
## Tagging Models

2
docs/webapp/webapp_model_viewing.md
View File

@ -2,7 +2,7 @@
title: Model Details
---
In the models table, double-click on a model to view and/or modify the following:
In the model table, double-click on a model to view and/or modify the following:
* General model information
* Model configuration
* Model label enumeration

2
docs/webapp/webapp_overview.md
View File

@ -64,7 +64,7 @@ Search results are returned for the different ClearML objects:
* Tasks - Searching a task table looks for matches in the tasks' name, ID, description and input and
output models. The enterprise version also includes task Dataviews' hyper-datasets and versions.
* Models - Searching a Model table looks for matches in the models' name, ID and description.
* Dataviews (Enterprise only) - Searching a Dataviews table looks for matches in the Dataviews' name, ID, description,
* Dataviews (Enterprise only) - Searching a Dataview table looks for matches in the Dataviews' name, ID, description,
hyper-datasets, and versions.
* Datasets - Searching the datasets page looks for matches in the datasets' name, ID, and description. In a dataset's
version table, a search looks for matches in the versions' name, ID, and description.

4
docs/webapp/webapp_reports.md
View File

@ -307,8 +307,8 @@ MarkDown code for a table looks like this:
The rendered output should look like this:
![Reports table](../img/reports_table.png#light-mode-only)
![Reports table](../img/reports_table_dark.png#dark-mode-only)
![Report table](../img/reports_table.png#light-mode-only)
![Report table](../img/reports_table_dark.png#dark-mode-only)
Add the table column names in the first row; each name is preceded and followed by a pipe (`|`).
In the second row, add sets of at least three hyphens (`---`) for each column, and add a pipe before and after each set