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Remove orphaned Hyper Dataset SDK reference links
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4
docs/fundamentals/pipelines.md
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@ -25,7 +25,7 @@ create customized, step-specific callbacks.
![Pipeline chart](../img/fundamentals_pipeline.png)
## Simple DAG Pipelines
## Simple DAG pipelines
For a simple, DAG based logic, use the off-the-shelf [`PipelineController`](../references/sdk/automation_controller_pipelinecontroller.md) class to define the DAG (see an example
[here](../guides/pipeline/pipeline_controller)). Once the `PipelineController` object is populated and configured,
@ -56,7 +56,7 @@ It could also be useful to run a pipeline that runs tasks concurrently, training
values simultaneously. See the [Tabular training pipeline](../guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline.md)
example of a pipeline with concurrent steps.
## Custom Pipelines
## Custom pipelines
In cases where a DAG is insufficient (for example, when needing to launch one pipeline, then, if performance is inadequate,
rerun pipeline again), users can apply custom logic, using generic methods to enqueue tasks, implemented in python code.

2
docs/fundamentals/task.md
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@ -268,7 +268,7 @@ cloned = Task.clone(
)
```
A cloned Task starts in [draft](#task-states-and-state-transitions) mode, so its Task configurations can be edited (see
A cloned Task starts in [draft](#task-states) mode, so its Task configurations can be edited (see
[Task.set_parameters](../references/sdk/task.md#set_parameters)).
Once a Task is modified, launch it by pushing it into an execution queue, then a [ClearML Agent](../clearml_agent) will pull
it from the queue and execute the Task.

8
docs/hyperdatasets/annotations.md
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@ -8,7 +8,7 @@ two types of annotations: **Frame objects** and **Frame labels**.
Annotation Tasks can be used to efficiently organize the annotation of frames in Dataset versions (see
[Annotations Task Page](webapp/webapp_annotator.md)).
For information about how to view, create, and manage annotations using the WebApp, see [Annotating Images and Videos](#annotating-images-and-video).
For information about how to view, create, and manage annotations using the WebApp, see [Annotating Images and Videos](webapp/webapp_annotator.md#annotating-images-and-video).
## Frame objects
@ -27,7 +27,7 @@ Frame labels are applied to an entire frame, not a region in a frame.
### Adding a frame object
To add a frame object annotation to a SingleFrame, use the [`SingleFrame.add_annotation`](google.com) method.
To add a frame object annotation to a SingleFrame, use the `SingleFrame.add_annotation` method.
```python
# a bounding box labeled "test" at x=10,y=10 with width of 30px and height of 20px
@ -41,14 +41,14 @@ When adding an annotation there are a few options for entering the annotation's
* `poly2d_xy` - A list of floating points (x,y) to create for single polygon, or a list of Floating points lists for a
complex polygon
* `ellipse2d_xyrrt` - A List consisting of cx, cy, rx, ry, and theta for an ellipse
* And more! See [`SingleFrame.add_annotation`](google.com) for further options.
* And more! See `SingleFrame.add_annotation` for further options.
### Adding a Frame label
Adding a frame label is similar to creating a frame objects, except that coordinates don't need to be specified, since
the whole frame is being referenced.
Use the [`SingleFrame.add_annotation`](google.com) method, but use only the `labels` parameter.
Use the `SingleFrame.add_annotation` method, but use only the `labels` parameter.
```python
# labels for the whole frame

2
docs/hyperdatasets/custom_metadata.md
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@ -30,7 +30,7 @@ frame.metadata['dangerous'] = 'no'
### Adding ROI metadata
Metadata can be added to individual ROIs when adding an annotation to a `frame`, using the [`add_annotation`](google.com)
Metadata can be added to individual ROIs when adding an annotation to a `frame`, using the `add_annotation`
method.
```python

16
docs/hyperdatasets/dataset.md
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@ -37,7 +37,7 @@ with an "Example" banner in the WebApp (UI).
### Creating Datasets
Use the [Dataset.create](google.com) method to create a Dataset. It will contain an empty version named `Current`.
Use the `Dataset.create` method to create a Dataset. It will contain an empty version named `Current`.
```python
from allegroai import Dataset
@ -45,7 +45,7 @@ from allegroai import Dataset
myDataset = Dataset.create(dataset_name='myDataset')
```
Or, use the [DatasetVersion.create_new_dataset](google.com) method.
Or, use the `DatasetVersion.create_new_dataset` method.
```python
from allegroai import DatasetVersion
@ -132,7 +132,7 @@ a Dataset version that yields a parent with two children, or when publishing the
## Versioning Usage
Manage Dataset versioning using the [DatasetVersion](google.com) class in the ClearML Enterprise SDK.
Manage Dataset versioning using the DatasetVersion class in the ClearML Enterprise SDK.
### Creating snapshots
@ -140,7 +140,7 @@ If the Dataset contains only one version whose status is *Draft*, snapshots of t
When creating a snapshot, the current version becomes the snapshot (it keeps the same version ID),
and the newly created version (with its new version ID) becomes the current version.
To create a snapshot, use the [DatasetVersion.create_snapshot](google.com) method.
To create a snapshot, use the `DatasetVersion.create_snapshot` method.
#### Snapshot naming
@ -205,7 +205,7 @@ myDataset = DatasetVersion.create_snapshot(dataset_name='MyDataset',
Create a new version from any version whose status is *Published*.
To create a new version, call the [DatasetVersion.create_version](google.com) method, and
To create a new version, call the `DatasetVersion.create_version` method, and
provide:
* Either the Dataset name or ID
* The parent version name or ID from which the child inherits frames
@ -241,7 +241,7 @@ myDataset = DatasetVersion.create_version(dataset_name='MyDataset',
### Getting versions
To get a version or versions, use the [DatasetVersion.get_version](google.com) and [DatasetVersion.get_versions](google.com)
To get a version or versions, use the `DatasetVersion.get_version` and `DatasetVersion.get_versions`
methods, respectively.
**Getting a list of all versions**
@ -281,7 +281,7 @@ myDatasetversion = DatasetVersion.get_version(dataset_name='MyDataset',
### Deleting versions
Delete versions which are status *Draft* using the [Dataset.delete_version](google.com) method.
Delete versions which are status *Draft* using the `Dataset.delete_version` method.
```python
from allegroai import Dataset
@ -293,7 +293,7 @@ myDataset.delete_version(version_name='VersionToDelete')
### Publishing versions
Publish (make read-only) versions which are status *Draft* using the [Dataset.publish_version](google.com) method. This includes the current version, if the Dataset is in
Publish (make read-only) versions which are status *Draft* using the `Dataset.publish_version` method. This includes the current version, if the Dataset is in
the simple version structure.
```python

12
docs/hyperdatasets/dataviews.md
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@ -132,7 +132,7 @@ may repeat. The settings include the following:
### Creating Dataviews
Use the [`allegroai.DataView`](google.com) class to create a DataView object. Instantiate DataView objects, specifying
Use the `allegroai.DataView` class to create a DataView object. Instantiate DataView objects, specifying
iteration settings and additional iteration parameters that control query iterations.
```python
@ -143,7 +143,7 @@ myDataView = DataView(iteration_order=IterationOrder.random, iteration_infinite=
### Adding queries
To add a query to a DataView, use the [`DataView.add_query`](google.com) method and specify Dataset versions,
To add a query to a DataView, use the `DataView.add_query` method and specify Dataset versions,
ROI and / or frame queries, and other criteria.
The `dataset_name` and `version_name` arguments specify the Dataset Version. The `roi_query` and `frame_query` arguments
@ -254,7 +254,7 @@ myDataView.add_query(dataset_name='myDataset',
### Controlling query iteration
Use [`DataView.set_iteration_parameters`](google.com) to manage the order, number, timing, and reproducibility of frames
Use `DataView.set_iteration_parameters` to manage the order, number, timing, and reproducibility of frames
for training.
@ -303,8 +303,8 @@ myDataView.set_iteration_parameters(
### Debiasing input data
Debias input data using the [DataView.add_query](google.com) method's [weight](google.com) argument to add weights. This
is the same [DataView.add_query](google.com) that can be used to specify Dataset versions, and ROI queries and frame queries.
Debias input data using the `DataView.add_query` method's `weight` argument to add weights. This
is the same `DataView.add_query` that can be used to specify Dataset versions, and ROI queries and frame queries.
This example adjusts an imbalance in the input data to improve training for `Car` ROIs that are also `largely occluded`
(obstructed). For every frame containing at least one ROI labeled `Car`, approximately five frames containing at least
@ -357,7 +357,7 @@ Set label enumeration values to maintain data conformity across multiple codebas
It is important to set enumeration values for all labels of importance.
The default value for labels that are not assigned values is `-1`.
To assign enumeration values for labels use the [`DataView.set_labels`](google.com) method, set a mapping of a label
To assign enumeration values for labels use the `DataView.set_labels` method, set a mapping of a label
(string) to an integer for ROI labels in a Dataview object.
If certain ROI labels are [mapped](#mapping-roi-labels) from certain labels **to** other labels,

4
docs/hyperdatasets/frame_groups.md
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@ -69,7 +69,7 @@ myVersion.add_frames(frames)
### Accessing a FrameGroup
To access a FrameGroup, use the [DatasetVersion.get_single_frame](google.com) method, just like when
To access a FrameGroup, use the `DatasetVersion.get_single_frame` method, just like when
[accessing a SingleFrame](single_frames.md#accessing-singleframes).
```python
@ -102,7 +102,7 @@ myVersion.update_frames(frames)
### Deleting frames
To delete a FrameGroup, use the [DatasetVersion.delete_frames](google.com) method, just like when deleting a
To delete a FrameGroup, use the `DatasetVersion.delete_frames` method, just like when deleting a
SingleFrame, except that a FrameGroup is being referenced.
```python

12
docs/hyperdatasets/single_frames.md
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@ -218,11 +218,11 @@ There are also options to populate the instance with:
* A dictionary of annotation objects - `annotations`
* A URI link to a mask file for the frame - `mask_source`
For more information, see [SingleFrame](google.com).
For more information, see the `SingleFrame` class description.
### Adding SingleFrames to a Dataset Version
Use the [`DatasetVersion.add_frames`](google.com) method to add SingleFrames to a [Dataset version](dataset.md#dataset-versioning)
Use the `DatasetVersion.add_frames` method to add SingleFrames to a [Dataset version](dataset.md#dataset-versioning)
(see [Creating snapshots](dataset.md#creating-snapshots) or [Creating child versions](dataset.md#creating-child-versions)).
```python
@ -248,7 +248,7 @@ myDatasetversion.add_frames(frames)
### Accessing SingleFrames
To access a SingleFrame, use the [DatasetVersion.get_single_frame](google.com) method.
To access a SingleFrame, use the `DatasetVersion.get_single_frame` method.
```python
from allegroai import DatasetVersion
@ -266,9 +266,9 @@ To access a SingleFrame, the following must be specified:
### Updating SingleFrames
To update a SingleFrame:
* Access the SingleFrame by calling the [DatasetVersion.get_single_frame](google.com) method,
* Access the SingleFrame by calling the `DatasetVersion.get_single_frame` method,
* Make changes to the frame
* Update the frame in a DatasetVersion using the [DatasetVersion.update_frames](google.com) method.
* Update the frame in a DatasetVersion using the `DatasetVersion.update_frames` method.
```python
frames = []
@ -296,7 +296,7 @@ myDatasetVersion.update_frames(frames)
### Deleting frames
To delete a SingleFrame, use the [DatasetVersion.delete_frames](google.com) method.
To delete a SingleFrame, use the `DatasetVersion.delete_frames` method.
```python
frames = []

2
docs/hyperdatasets/task.md
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@ -21,7 +21,7 @@ task.connect(dataview)
### Accessing a Task's Dataviews
Use the [Task.get_dataviews](google.com) method to access the Dataviews that are connected to a Task.
Use the `Task.get_dataviews` method to access the Dataviews that are connected to a Task.
```python
task.get_dataviews():