`, and the task using the dataset will store the dataset's ID in the
`dataset_alias_string` parameter under the `Datasets` hyperparameters section. This way you can easily track which
-dataset the task is using. If you use `alias` with `overridable=True`, you can override the dataset ID from the UI’s
+dataset the task is using. If you use `alias` with `overridable=True`, you can override the dataset ID from the UI's
**CONFIGURATION > HYPERPARAMETERS >** `Datasets` section, allowing you to change the dataset used when running a task
remotely.
@@ -135,8 +135,8 @@ of an entire dataset. This method downloads the dataset to a specific folder (no
the specified folder already has contents, specify whether to overwrite its contents with the dataset contents, using the `overwrite` parameter.
ClearML supports parallel downloading of datasets. Use the `max_workers` parameter of the `Dataset.get_local_copy` or
-`Dataset.get_mutable_copy` methods to specify the number of threads to use when downloading the dataset. By default, it’s
-the number of your machine’s logical cores.
+`Dataset.get_mutable_copy` methods to specify the number of threads to use when downloading the dataset. By default, it's
+the number of your machine's logical cores.
## Modifying Datasets
@@ -225,7 +225,7 @@ By default, the dataset uploads to ClearML's file server. This target storage ov
[`Dataset.create`](#creating-datasets) method.
ClearML supports parallel uploading of datasets. Use the `max_workers` parameter to specify the number of threads to use
-when uploading the dataset. By default, it’s the number of your machine’s logical cores.
+when uploading the dataset. By default, it's the number of your machine's logical cores.
Dataset files must be uploaded before a dataset is [finalized](#finalizing-a-dataset).
@@ -317,9 +317,9 @@ You can enable offline mode in one of the following ways:
* Before creating a dataset, set `CLEARML_OFFLINE_MODE=1`
-All the dataset’s information is zipped and is saved locally.
+All the dataset's information is zipped and is saved locally.
-The dataset task's console output displays the task’s ID and a path to the local dataset folder:
+The dataset task's console output displays the task's ID and a path to the local dataset folder:
```
ClearML Task: created new task id=offline-372657bb04444c25a31bc6af86552cc9
diff --git a/docs/clearml_data/data_management_examples/data_man_python.md b/docs/clearml_data/data_management_examples/data_man_python.md
index 3e0f8cf4..f9b20460 100644
--- a/docs/clearml_data/data_management_examples/data_man_python.md
+++ b/docs/clearml_data/data_management_examples/data_man_python.md
@@ -84,7 +84,7 @@ Now that a new dataset is registered, you can consume it!
The [data_ingestion.py](https://github.com/allegroai/clearml/blob/master/examples/datasets/data_ingestion.py) script
demonstrates data ingestion using the dataset created in the first script.
-The following script gets the dataset and uses [`Dataset.get_local_copy`](../../references/sdk/dataset.md#get_local_copy)
+The following script gets the dataset and uses [`Dataset.get_local_copy()`](../../references/sdk/dataset.md#get_local_copy)
to return a path to the cached, read-only local dataset.
```python
diff --git a/docs/clearml_sdk/task_sdk.md b/docs/clearml_sdk/task_sdk.md
index 2e0959db..096ee30a 100644
--- a/docs/clearml_sdk/task_sdk.md
+++ b/docs/clearml_sdk/task_sdk.md
@@ -175,7 +175,7 @@ See full `Task.init` reference [here](../references/sdk/task.md#taskinit).
You can continue the execution of a previously run task using the `continue_last_task` parameter of the `Task.init`
method. This will retain all of its previous artifacts / models / logs.
-The task will continue reporting its outputs based on the iteration in which it had left off. For example: a task’s last
+The task will continue reporting its outputs based on the iteration in which it had left off. For example: a task's last
train/loss scalar reported was for iteration 100, when continued, the next report will be as iteration 101.
:::note Reproducibility
@@ -216,8 +216,8 @@ task = Task.create(
See full `Task.create` reference [here](../references/sdk/task.md#taskcreate).
## Tracking Task Progress
-Track a task’s progress by setting the task progress property using the [`Task.set_progress`](../references/sdk/task.md#set_progress) method.
-Set a task’s progress to a numeric value between 0 - 100. Access the task’s current progress, using the
+Track a task's progress by setting the task progress property using the [`Task.set_progress`](../references/sdk/task.md#set_progress) method.
+Set a task's progress to a numeric value between 0 - 100. Access the task's current progress, using the
[`Task.get_progress`](../references/sdk/task.md#get_progress) method.
```python
@@ -230,8 +230,8 @@ print(task.get_progress())
task.set_progress(100)
```
-While the task is running, the WebApp will show the task’s progress indication in the experiment table, next to the
-task’s status. If a task failed or was aborted, you can view how much progress it had made.
+While the task is running, the WebApp will show the task's progress indication in the experiment table, next to the
+task's status. If a task failed or was aborted, you can view how much progress it had made.
@@ -239,7 +239,7 @@ task’s status. If a task failed or was aborted, you can view how much progress
-Additionally, you can view a task’s progress in its [INFO](../webapp/webapp_exp_track_visual.md#general-information) tab
+Additionally, you can view a task's progress in its [INFO](../webapp/webapp_exp_track_visual.md#general-information) tab
in the WebApp.
@@ -478,7 +478,7 @@ Function tasks must be created from within a regular task, created by calling `T
ClearML supports distributed remote execution through multiple worker nodes using [`Task.launch_multi_node()`](../references/sdk/task.md#launch_multi_node).
This method creates multiple copies of a task and enqueues them for execution.
-Each copy of the task is called a node. The original task that initiates the nodes’ execution is called the master node.
+Each copy of the task is called a node. The original task that initiates the nodes' execution is called the master node.
```python
Task = task.init(task_name ="my_task", project_name="my_project")
@@ -611,7 +611,7 @@ Upload the execution data that the Task captured offline to the ClearML Server u
```
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
@@ -660,7 +660,7 @@ For example:
task.upload_artifact(name='link', artifact_object='azure://bucket/folder')
```
-* Serialize and upload a Python object. ClearML automatically chooses the file format based on the object’s type, or you
+* Serialize and upload a Python object. ClearML automatically chooses the file format based on the object's type, or you
can explicitly specify the format as follows:
* dict - `.json` (default), `.yaml`
* pandas.DataFrame - `.csv.gz` (default), `.parquet`, `.feather`, `.pickle`
@@ -685,7 +685,7 @@ For example:
See more details in the [Artifacts Reporting example](../guides/reporting/artifacts.md) and in the [SDK reference](../references/sdk/task.md#upload_artifact).
### Using Artifacts
-A task's artifacts are accessed through the task’s *artifact* property which lists the artifacts’ locations.
+A task's artifacts are accessed through the task's *artifact* property which lists the artifacts' locations.
The artifacts can subsequently be retrieved from their respective locations by using:
* `get_local_copy()` - Downloads the artifact and caches it for later use, returning the path to the cached copy.
@@ -742,8 +742,8 @@ Models can also be manually updated independently, without any task. See [Output
### Using Models
-Accessing a task’s previously trained model is quite similar to accessing task artifacts. A task's models are accessed
-through the task’s models property which lists the input models and output model snapshots’ locations.
+Accessing a task's previously trained model is quite similar to accessing task artifacts. A task's models are accessed
+through the task's models property which lists the input models and output model snapshots' locations.
The models can subsequently be retrieved from their respective locations by using `get_local_copy()` which downloads the
model and caches it for later use, returning the path to the cached copy (if using TensorFlow, the snapshots are stored
@@ -790,7 +790,7 @@ using [clearml-agent](../clearml_agent.md) to execute code.
To define parameters manually use the [`Task.set_parameters`](../references/sdk/task.md#set_parameters) method to specify
name-value pairs in a parameter dictionary.
-Parameters can be designated into sections: specify a parameter’s section by prefixing its name, delimited with a slash
+Parameters can be designated into sections: specify a parameter's section by prefixing its name, delimited with a slash
(i.e. `section_name/parameter_name:value`). `General` is the default section.
Call the [`set_parameter`](../references/sdk/task.md#set_parameter) method to set a single parameter.
@@ -847,7 +847,7 @@ The parameters and their section names are case-sensitive
### Tracking Python Objects
ClearML can track Python objects (such as dictionaries and custom classes) as they evolve in your code, and log them to
-your task’s configuration using the [`Task.connect`](../references/sdk/task.md#connect) method. Once objects are connected
+your task's configuration using the [`Task.connect`](../references/sdk/task.md#connect) method. Once objects are connected
to a task, ClearML automatically logs all object elements (e.g. class members, dictionary key-values pairs).
```python
@@ -892,7 +892,7 @@ config_file_yaml = task.connect_configuration(
### User Properties
-A task’s user properties do not impact task execution, so you can add / modify the properties at any stage. Add user
+A task's user properties do not impact task execution, so you can add / modify the properties at any stage. Add user
properties to a task with the [`Task.set_user_properties`](../references/sdk/task.md#set_user_properties) method.
```python
diff --git a/docs/community.md b/docs/community.md
index 1971746e..7c07c839 100644
--- a/docs/community.md
+++ b/docs/community.md
@@ -24,9 +24,9 @@ Follow **ClearML** on [LinkedIn](https://www.linkedin.com/company/clearml).
## Guidelines for Contributing
-Firstly, we thank you for taking the time to contribute!
+Firstly, thank you for taking the time to contribute!
-Contribution comes in many forms:
+Contributions come in many forms:
* Reporting [issues](https://github.com/allegroai/clearml/issues) you've come upon
* Participating in issue discussions in the [issue tracker](https://github.com/allegroai/clearml/issues) and the
diff --git a/docs/faq.md b/docs/faq.md
index 3d0207a2..f586676b 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -286,7 +286,7 @@ To fix this, the registered URL of each model needs to be replaced with its curr
This message is only a warning. ClearML not only detects your current repository and git commit, but also warns you
if you are using uncommitted code. ClearML does this because uncommitted code means this experiment will be difficult
-to reproduce. You can see uncommitted changes in the ClearML Web UI, in the EXECUTION tab of the experiment info panel.
+to reproduce. You can see uncommitted changes in the ClearML Web UI, in the **EXECUTION** tab of the experiment info panel.
#### I do not use argparse for hyperparameters. Do you have a solution?
diff --git a/docs/fundamentals/artifacts.md b/docs/fundamentals/artifacts.md
index 52241166..da0e7e49 100644
--- a/docs/fundamentals/artifacts.md
+++ b/docs/fundamentals/artifacts.md
@@ -30,7 +30,7 @@ pointing directly to the original training task's model.
### Output Models
ClearML stores training results as output models. The `OutputModel` object is instantiated with a task object as an
-argument (see [`task`](../references/sdk/model_outputmodel.md) parameter), so it's automatically registered as the Task’s
+argument (see [`task`](../references/sdk/model_outputmodel.md) parameter), so it's automatically registered as the Task's
output model. Since OutputModel objects are connected to tasks, the models are traceable in experiments.
Output models are read-write so weights can be updated throughout training. Additionally, users can specify a model's
diff --git a/docs/fundamentals/hpo.md b/docs/fundamentals/hpo.md
index fef9337d..ebcf8970 100644
--- a/docs/fundamentals/hpo.md
+++ b/docs/fundamentals/hpo.md
@@ -43,7 +43,7 @@ The preceding diagram demonstrates the typical flow of hyperparameter optimizati
### Supported Optimizers
-The `HyperParameterOptimizer` class contains ClearML’s hyperparameter optimization modules. Its modular design enables
+The `HyperParameterOptimizer` class contains ClearML's hyperparameter optimization modules. Its modular design enables
using different optimizers, including existing software frameworks, enabling simple, accurate, and fast hyperparameter
optimization.
diff --git a/docs/fundamentals/hyperparameters.md b/docs/fundamentals/hyperparameters.md
index d70b52b8..ab062acd 100644
--- a/docs/fundamentals/hyperparameters.md
+++ b/docs/fundamentals/hyperparameters.md
@@ -9,7 +9,7 @@ ClearML supports tracking and managing hyperparameters in each experiment and pr
optimization module](hpo.md). With ClearML's logging and tracking capabilities, experiments can be reproduced, and their
hyperparameters and results can be saved and compared, which is key to understanding model behavior.
-ClearML lets you easily try out different hyperparameter values without changing your original code. ClearML’s [execution
+ClearML lets you easily try out different hyperparameter values without changing your original code. ClearML's [execution
agent](../clearml_agent.md) will override the original values with any new ones you specify through the web UI (see
[Configuration](../webapp/webapp_exp_tuning.md#configuration) in the Tuning Experiments page). It's also possible to
programmatically set experiment parameters.
@@ -93,7 +93,7 @@ making it easier to search / filter experiments. Add user properties to an exper
### Accessing Parameters
-ClearML provides methods to directly access a task’s logged parameters.
+ClearML provides methods to directly access a task's logged parameters.
To get all of a task's parameters and properties (hyperparameters, configuration objects, and user properties), use the
[`Task.get_parameters`](../references/sdk/task.md#get_parameters) method, which will return a dictionary with the parameters,
diff --git a/docs/fundamentals/task.md b/docs/fundamentals/task.md
index 20036e50..af7c6887 100644
--- a/docs/fundamentals/task.md
+++ b/docs/fundamentals/task.md
@@ -63,7 +63,7 @@ The captured [execution output](../webapp/webapp_exp_track_visual.md#experiment-
To view a more in depth description of each task section, see [Tracking Experiments and Visualizing Results](../webapp/webapp_exp_track_visual.md).
### Execution Configuration
-ClearML logs a task’s hyperparameters specified as command line arguments, environment or code level variables. This
+ClearML logs a task's hyperparameters specified as command line arguments, environment or code level variables. This
allows experiments to be reproduced, and their hyperparameters and results can be saved and compared, which is key to
understanding model behavior.
@@ -82,7 +82,7 @@ See [Hyperparameters](hyperparameters.md) for more information.
ClearML allows easy storage of experiments' output products as artifacts that can later be accessed easily and used,
through the [web UI](../webapp/webapp_overview.md) or programmatically.
-ClearML provides methods to easily track files generated throughout your experiments’ execution such as:
+ClearML provides methods to easily track files generated throughout your experiments' execution such as:
- Numpy objects
- Pandas DataFrames
@@ -91,7 +91,7 @@ ClearML provides methods to easily track files generated throughout your experim
- Python objects
- and more!
-Most importantly, ClearML also logs experiments’ input and output models as well as interim model snapshots (see
+Most importantly, ClearML also logs experiments' input and output models as well as interim model snapshots (see
[Models](artifacts.md)).
#### Logging Artifacts
@@ -121,7 +121,7 @@ Available task types are:
* *training* (default) - Training a model
* *testing* - Testing a component, for example model performance
* *inference* - Model inference job (e.g. offline / batch model execution)
-* *controller* - A task that lays out the logic for other tasks’ interactions, manual or automatic (e.g. a pipeline
+* *controller* - A task that lays out the logic for other tasks' interactions, manual or automatic (e.g. a pipeline
controller)
* *optimizer* - A specific type of controller for optimization tasks (e.g. [hyperparameter optimization](hpo.md))
* *service* - Long lasting or recurring service (e.g. server cleanup, auto ingress, sync services etc.)
diff --git a/docs/guides/clearml_agent/reproduce_exp.md b/docs/guides/clearml_agent/reproduce_exp.md
index 2aa1a5b1..8975c082 100644
--- a/docs/guides/clearml_agent/reproduce_exp.md
+++ b/docs/guides/clearml_agent/reproduce_exp.md
@@ -20,7 +20,7 @@ code.
ClearML logs everything needed to reproduce your experiment and its environment (uncommitted changes, used packages, and
more), making it easy to reproduce your experiment's execution environment using ClearML.
-You can reproduce the execution environment of any experiment you’ve run with ClearML on any workload:
+You can reproduce the execution environment of any experiment you've run with ClearML on any workload:
1. Go to the experiment page of the task you want to reproduce in the [ClearML WebApp](../../webapp/webapp_overview.md),
:::tip
diff --git a/docs/guides/frameworks/huggingface/transformers.md b/docs/guides/frameworks/huggingface/transformers.md
index b5753afd..23aa58cd 100644
--- a/docs/guides/frameworks/huggingface/transformers.md
+++ b/docs/guides/frameworks/huggingface/transformers.md
@@ -8,7 +8,7 @@ code. When ClearML is installed in an environment, the Trainer by default uses t
so ClearML automatically logs Transformers models, parameters, scalars, and more.
When the example runs, it creates a ClearML task called `Trainer` in the `HuggingFace Transformers` projects. To change
-the task’s name or project, use the `CLEARML_PROJECT` and `CLEARML_TASK` environment variables respectively.
+the task's name or project, use the `CLEARML_PROJECT` and `CLEARML_TASK` environment variables respectively.
For more information about integrating ClearML into your Transformers code, see [HuggingFace Transformers](../../../integrations/transformers.md).
diff --git a/docs/hyperdatasets/dataset.md b/docs/hyperdatasets/dataset.md
index 4d8c05a3..a45b4808 100644
--- a/docs/hyperdatasets/dataset.md
+++ b/docs/hyperdatasets/dataset.md
@@ -123,7 +123,7 @@ Dataset.delete(
```
This supports deleting sources located in AWS S3, GCP, and Azure Storage (not local storage). The `delete_sources`
-parameter is ignored if `delete_all_versions` is `False`. You can view the deletion process’ progress by passing
+parameter is ignored if `delete_all_versions` is `False`. You can view the deletion process' progress by passing
`show_progress=True` (`tqdm` required).
### Tagging Datasets
diff --git a/docs/hyperdatasets/masks.md b/docs/hyperdatasets/masks.md
index ba80430b..69e7fd98 100644
--- a/docs/hyperdatasets/masks.md
+++ b/docs/hyperdatasets/masks.md
@@ -26,7 +26,7 @@ In the UI, you can view the mapping in a dataset version's [Metadata](webapp/web
-When viewing a frame with a mask corresponding with the version’s mask-label mapping, the UI arbitrarily assigns a color
+When viewing a frame with a mask corresponding with the version's mask-label mapping, the UI arbitrarily assigns a color
to each label. The color assignment can be [customized](webapp/webapp_datasets_frames.md#labels).
For example:
@@ -34,7 +34,7 @@ For example:
-* Frame image with the semantic segmentation mask enabled. Labels are applied according to the dataset version’s
+* Frame image with the semantic segmentation mask enabled. Labels are applied according to the dataset version's
mask-label mapping:
@@ -66,7 +66,7 @@ The frame's sources array contains a masks list of dictionaries that looks somet
}
```
-The masks dictionary includes the frame's masks’ URIs and IDs.
+The masks dictionary includes the frame's masks' URIs and IDs.
## Alpha Channel Masks
For alpha channel, mask RGB pixel values are interpreted as opacity values so that when the mask is applied, only the
@@ -133,10 +133,10 @@ version.set_masks_labels(
)
```
-The relevant label is applied to all masks in the version according to the version’s mask-label mapping dictionary.
+The relevant label is applied to all masks in the version according to the version's mask-label mapping dictionary.
### Registering Frames with Multiple Masks
-Frames can contain multiple masks. To add multiple masks, use the SingleFrame’s `masks_source` property. Input one of
+Frames can contain multiple masks. To add multiple masks, use the SingleFrame's `masks_source` property. Input one of
the following:
* A dictionary with mask string ID keys and mask URI values
* A list of mask URIs. Number IDs are automatically assigned to the masks ("00", "01", etc.)
diff --git a/docs/hyperdatasets/single_frames.md b/docs/hyperdatasets/single_frames.md
index 7d740667..a0bbe458 100644
--- a/docs/hyperdatasets/single_frames.md
+++ b/docs/hyperdatasets/single_frames.md
@@ -103,7 +103,7 @@ The panel below describes the details contained within a `frame`:
:::info
The `mask` dictionary is deprecated. Mask labels and their associated pixel values are now stored in the dataset
- version’s metadata. See [Masks](masks.md).
+ version's metadata. See [Masks](masks.md).
:::
* `poly` (*[int]*) - Bounding area vertices.
diff --git a/docs/hyperdatasets/webapp/webapp_annotator.md b/docs/hyperdatasets/webapp/webapp_annotator.md
index 0356f4af..d383995c 100644
--- a/docs/hyperdatasets/webapp/webapp_annotator.md
+++ b/docs/hyperdatasets/webapp/webapp_annotator.md
@@ -9,7 +9,7 @@ Use annotation tasks to efficiently organize the annotation of frames in Dataset
-Click on an annotation task card to open the frame viewer, where you can view the task’s frames and annotate them.
+Click on an annotation task card to open the frame viewer, where you can view the task's frames and annotate them.
## Annotation Task Actions
Click 
on the top right
@@ -18,7 +18,7 @@ of an annotation task card to open its context menu and access annotation task a
* **Annotate** - Go to annotation task frame viewer
-* **Info** - View annotation task’s definitions: dataset versions, filters, and frame iteration specification
+* **Info** - View annotation task's definitions: dataset versions, filters, and frame iteration specification
* **Complete** - Mark annotation task as Completed
* **Delete** - Delete annotation task
@@ -112,11 +112,11 @@ or the arrow keys on the keyboard). Closing the frame editor will prompt you to
| Icon (when applicable) | Action | Description |
|---|---|---|
|| Move annotation | Click on a bounded area and drag it. |
-|| Resize annotation| Select an annotation, then click on a bounded area’s vertex and drag it. |
+|| Resize annotation| Select an annotation, then click on a bounded area's vertex and drag it. |
|
|Edit metadata|Hover over an annotation in the list and click the icon to open the edit window. Input the metadata dictionary in JSON format. This metadata is specific to the selected annotation, not the entire frame.|
|
|Lock / Unlock annotation |Click the button on a specific annotation to make it uneditable. You can also click the button on top of the annotations list to lock all annotations in the frame.|
|
|Delete annotation|Click the annotation or bounded area in the frame and then click the button to delete the annotation.|
-|
|Show/hide all annotations |Click the button to view the frame without annotations. When annotations are hidden, they can’t be modified. |
+||Show/hide all annotations |Click the button to view the frame without annotations. When annotations are hidden, they can't be modified. |
||Delete label |In the relevant annotation, click **x** on the label you want to remove.|
### Frame Labels
diff --git a/docs/hyperdatasets/webapp/webapp_datasets.md b/docs/hyperdatasets/webapp/webapp_datasets.md
index 6a21fce7..d9293748 100644
--- a/docs/hyperdatasets/webapp/webapp_datasets.md
+++ b/docs/hyperdatasets/webapp/webapp_datasets.md
@@ -9,10 +9,10 @@ or in List view 
:
+Filter the hyper-datasets to find the one you're looking for more easily. These filters can be applied by clicking 
:
* My Work - Show only hyper-datasets that you created
* Tags - Choose which tags to filter by from a list of tags used in the hyper-datasets.
* Filter by multiple tag values using the **ANY** or **ALL** options, which correspond to the logical "AND" and "OR"
@@ -24,7 +24,7 @@ Filter the hyper-datasets to find the one you’re looking for more easily. Thes
## Project Cards
-In Project view, project cards display a project’s summarized hyper-dataset information:
+In Project view, project cards display a project's summarized hyper-dataset information:
@@ -76,7 +76,7 @@ of a dataset card to open its context menu and access dataset actions:
-* **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 datasets.
* **Edit Metadata** - Modify dataset-level metadata. This will open the metadata edit window, where you can edit the section
diff --git a/docs/hyperdatasets/webapp/webapp_datasets_frames.md b/docs/hyperdatasets/webapp/webapp_datasets_frames.md
index 084c7413..aacf3828 100644
--- a/docs/hyperdatasets/webapp/webapp_datasets_frames.md
+++ b/docs/hyperdatasets/webapp/webapp_datasets_frames.md
@@ -177,11 +177,11 @@ or the arrow keys on the keyboard). Closing the frame editor will prompt you to
| Icon (when applicable) | Action | Description |
|---|---|---|
|| Move annotation | Click on a bounded area and drag it. |
-|| Resize annotation| Select an annotation, then click on a bounded area’s vertex and drag it. |
+|| Resize annotation| Select an annotation, then click on a bounded area's vertex and drag it. |
||Edit metadata|Hover over an annotation in the list and click the icon to open the edit window. Input the metadata dictionary in JSON format. This metadata is specific to the selected annotation, not the entire frame.|
||Lock / Unlock annotation |Click the button on a specific annotation to make it uneditable. You can also click the button on top of the annotations list to lock all annotations in the frame.|
||Delete annotation|Click the annotation or bounded area in the frame and then click the button to delete the annotation.|
-||Show/hide all annotations |Click the button to view the frame without annotations. When annotations are hidden, they can’t be modified. |
+||Show/hide all annotations |Click the button to view the frame without annotations. When annotations are hidden, they can't be modified. |
||Delete label |In the relevant annotation, click **x** on the label you want to remove.|
### Frame Labels
diff --git a/docs/hyperdatasets/webapp/webapp_datasets_versioning.md b/docs/hyperdatasets/webapp/webapp_datasets_versioning.md
index 461c3809..c9745a67 100644
--- a/docs/hyperdatasets/webapp/webapp_datasets_versioning.md
+++ b/docs/hyperdatasets/webapp/webapp_datasets_versioning.md
@@ -30,7 +30,7 @@ In tree view, parent versions that do not match the query where a child version
Access dataset version actions, by right-clicking a version, or through the menu button 
(available on hover).
-* **Rename** - Change the version’s name
+* **Rename** - Change the version's name
* **Create New Version** - Creates a child version of a *Published* dataset version. The new version is created in a *draft*
state, and inherits all the parent version's frames
* **Delete** - Delete the version. Only *Draft* versions can be deleted.
@@ -66,7 +66,7 @@ the previews.
-Use the table view to list the version’s frames in a customizable table. Click 
+Use the table view to list the version's frames in a customizable table. Click
for column customization options.
@@ -122,7 +122,7 @@ Multiple frame filters are applied with a logical OR operator.
For example, the dataset version in the image below has two frame filters. "Frame Filter 1" has the same two rules
described in the example above. "Frame Filter 2" specifies an ROI rule for the frame to contain an ROI with the label
-`dog`. So the frames returned are those that match ALL of Frame Filter 1’s rules OR ALL of Frame Filter 2’s rules.
+`dog`. So the frames returned are those that match ALL of Frame Filter 1's rules OR ALL of Frame Filter 2's rules.
@@ -193,7 +193,7 @@ Use the **Grouping** menu to select one of the following options:
* Group by URL - Show a single preview for all FrameGroups with the same context
#### Preview Source
-When using multi-source FrameGroups, users can choose which of the FrameGroups’ sources will be displayed as the preview.
+When using multi-source FrameGroups, users can choose which of the FrameGroups' sources will be displayed as the preview.
Select a source from the **PREVIEW SOURCE** menu.
Choose the `Default preview source` option to present the first available source.
diff --git a/docs/hyperdatasets/webapp/webapp_dataviews.md b/docs/hyperdatasets/webapp/webapp_dataviews.md
index ba250ca3..92a84518 100644
--- a/docs/hyperdatasets/webapp/webapp_dataviews.md
+++ b/docs/hyperdatasets/webapp/webapp_dataviews.md
@@ -11,7 +11,7 @@ to the specific task's **DATAVIEWS** tab (see [Experiment Dataviews](webapp_exp_
View the Dataviews table in table view 
or in details view 
,
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.
+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
diff --git a/docs/hyperdatasets/webapp/webapp_exp_comparing.md b/docs/hyperdatasets/webapp/webapp_exp_comparing.md
index cf4bef36..7a69aad0 100644
--- a/docs/hyperdatasets/webapp/webapp_exp_comparing.md
+++ b/docs/hyperdatasets/webapp/webapp_exp_comparing.md
@@ -16,7 +16,7 @@ The comparison page opens in the **DETAILS** tab, showing a column for each expe
## Dataviews
-In the **Details** tab, you can view differences in the experiments’ nominal values. Each experiment’s information is
+In the **Details** tab, you can view differences in the experiments' nominal values. Each experiment's information is
displayed in a column, so each field is lined up side-by-side. Expand the **DATAVIEWS**
section to view all the Dataview fields side-by-side (filters, iterations, label enumeration, etc.). The differences between the
experiments are highlighted. Obscure identical fields by switching on the `Hide Identical Fields` toggle.
diff --git a/docs/integrations/autokeras.md b/docs/integrations/autokeras.md
index 68764e64..170c0928 100644
--- a/docs/integrations/autokeras.md
+++ b/docs/integrations/autokeras.md
@@ -16,7 +16,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* AutoKeras model files
diff --git a/docs/integrations/catboost.md b/docs/integrations/catboost.md
index eafa475c..f64e900c 100644
--- a/docs/integrations/catboost.md
+++ b/docs/integrations/catboost.md
@@ -16,7 +16,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* CatBoost model files
@@ -115,6 +115,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
-Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
+Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.
diff --git a/docs/integrations/fastai.md b/docs/integrations/fastai.md
index add2c6e8..1fd3af67 100644
--- a/docs/integrations/fastai.md
+++ b/docs/integrations/fastai.md
@@ -16,7 +16,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* `fastai` model files
diff --git a/docs/integrations/hydra.md b/docs/integrations/hydra.md
index 5eaa41e0..1df3e3a6 100644
--- a/docs/integrations/hydra.md
+++ b/docs/integrations/hydra.md
@@ -41,10 +41,10 @@ The agent executes the code with the modifications you made in the UI, even over
Clone your experiment, then modify your Hydra parameters via the UI in one of the following ways:
* Modify the OmegaConf directly:
- 1. In the experiment’s **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, set `_allow_omegaconf_edit_` to `True`
- 1. In the experiment’s **CONFIGURATION > CONFIGURATION OBJECTS > OmegaConf** section, modify the OmegaConf values
+ 1. In the experiment's **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, set `_allow_omegaconf_edit_` to `True`
+ 1. In the experiment's **CONFIGURATION > CONFIGURATION OBJECTS > OmegaConf** section, modify the OmegaConf values
* Add an experiment hyperparameter:
- 1. In the experiment’s **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, make sure `_allow_omegaconf_edit_` is set
+ 1. In the experiment's **CONFIGURATION > HYPERPARAMETERS > HYDRA** section, make sure `_allow_omegaconf_edit_` is set
to `False`
1. In the same section, click `Edit`, which gives you the option to add parameters. Input parameters from the OmegaConf
that you want to modify using dot notation. For example, if your OmegaConf looks like this:
diff --git a/docs/integrations/ignite.md b/docs/integrations/ignite.md
index e5dbc4ce..cff03ad7 100644
--- a/docs/integrations/ignite.md
+++ b/docs/integrations/ignite.md
@@ -8,7 +8,7 @@ instructions.
:::
[PyTorch Ignite](https://pytorch.org/ignite/index.html) is a library for training and evaluating neural networks in
-PyTorch. You can integrate ClearML into your code using Ignite’s built-in loggers: [TensorboardLogger](#tensorboardlogger)
+PyTorch. You can integrate ClearML into your code using Ignite's built-in loggers: [TensorboardLogger](#tensorboardlogger)
and [ClearMLLogger](#clearmllogger).
## TensorboardLogger
@@ -92,7 +92,7 @@ Integrate ClearML with the following steps:
# Attach the logger to the trainer to log model's weights as a histogram
clearml_logger.attach(trainer, log_handler=WeightsHistHandler(model), event_name=Events.EPOCH_COMPLETED(every=100))
- # Attach the logger to the trainer to log model’s gradients as scalars
+ # Attach the logger to the trainer to log model's gradients as scalars
clearml_logger.attach(
trainer, log_handler=GradsScalarHandler(model), event_name=Events.ITERATION_COMPLETED(every=100)
)
diff --git a/docs/integrations/keras.md b/docs/integrations/keras.md
index c6236975..698c9465 100644
--- a/docs/integrations/keras.md
+++ b/docs/integrations/keras.md
@@ -17,7 +17,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* Keras models
@@ -77,7 +77,7 @@ See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).
## Examples
-Take a look at ClearML’s Keras examples. The examples use Keras and ClearML in different configurations with
+Take a look at ClearML's Keras examples. The examples use Keras and ClearML in different configurations with
additional tools like TensorBoard and Matplotlib:
* [Keras with Tensorboard](../guides/frameworks/keras/keras_tensorboard.md) - Demonstrates ClearML logging a Keras model,
and plots and scalars logged to TensorBoard
@@ -127,6 +127,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
-Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
+Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.
diff --git a/docs/integrations/keras_tuner.md b/docs/integrations/keras_tuner.md
index 5267f791..1273974d 100644
--- a/docs/integrations/keras_tuner.md
+++ b/docs/integrations/keras_tuner.md
@@ -36,7 +36,7 @@ Integrate ClearML into your Keras Tuner optimization script by doing the followi
)
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Output Keras model
* Optimization trial scalars - scalar plot showing metrics for all runs
* Hyperparameter optimization summary plot - Tabular summary of hyperparameters tested and their metrics by trial ID
diff --git a/docs/integrations/lightgbm.md b/docs/integrations/lightgbm.md
index 963e7f1f..b1ff4c80 100644
--- a/docs/integrations/lightgbm.md
+++ b/docs/integrations/lightgbm.md
@@ -17,7 +17,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* LightGBM model files
@@ -116,6 +116,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
-Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
+Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.
diff --git a/docs/integrations/megengine.md b/docs/integrations/megengine.md
index fa45d80b..06a210f9 100644
--- a/docs/integrations/megengine.md
+++ b/docs/integrations/megengine.md
@@ -16,7 +16,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* MegEngine model files
@@ -112,6 +112,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
-Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
+Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.
diff --git a/docs/integrations/monai.md b/docs/integrations/monai.md
index 337ff66d..d215f23c 100644
--- a/docs/integrations/monai.md
+++ b/docs/integrations/monai.md
@@ -66,7 +66,7 @@ change the task's name or project, use the `task_name` and `project_name` parame
The task captures the images logged by the image handler, metrics logged with the stats handler, as well as source code,
uncommitted changes, installed packages, console output, and more.
-You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
+You can see all the captured data in the task's page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
View the logged images in the WebApp, in the experiment's **Debug Samples** tab.
diff --git a/docs/integrations/optuna.md b/docs/integrations/optuna.md
index cfed5462..1666b21c 100644
--- a/docs/integrations/optuna.md
+++ b/docs/integrations/optuna.md
@@ -6,7 +6,7 @@ title: Optuna
which makes use of different samplers such as grid search, random, bayesian, and evolutionary algorithms. You can integrate
Optuna into ClearML's automated hyperparameter optimization.
-The [HyperParameterOptimizer](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class contains ClearML’s
+The [HyperParameterOptimizer](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class contains ClearML's
hyperparameter optimization modules. Its modular design enables using different optimizers, including existing software
frameworks, like Optuna, enabling simple,
accurate, and fast hyperparameter optimization. The Optuna ([`automation.optuna.OptimizerOptuna`](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md)),
diff --git a/docs/integrations/pytorch.md b/docs/integrations/pytorch.md
index a7e2305a..8d931af1 100644
--- a/docs/integrations/pytorch.md
+++ b/docs/integrations/pytorch.md
@@ -16,7 +16,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* PyTorch models
@@ -86,7 +86,7 @@ Take a look at ClearML's PyTorch examples. The examples use PyTorch and ClearML
additional tools, like argparse, TensorBoard, and matplotlib:
* [PyTorch MNIST](../guides/frameworks/pytorch/pytorch_mnist.md) - Demonstrates ClearML automatically logging models created with PyTorch, and `argparse` command line parameters
-* [PyTorch with Matplotlib](../guides/frameworks/pytorch/pytorch_matplotlib.md) - Demonstrates ClearML’s automatic logging PyTorch models and matplotlib images. The images are stored in the resulting ClearML experiment's **Debug Samples**
+* [PyTorch with Matplotlib](../guides/frameworks/pytorch/pytorch_matplotlib.md) - Demonstrates ClearML's automatic logging PyTorch models and matplotlib images. The images are stored in the resulting ClearML experiment's **Debug Samples**
* [PyTorch with TensorBoard](../guides/frameworks/pytorch/pytorch_tensorboard.md) - Demonstrates ClearML automatically logging PyTorch models, and scalars, debug samples, and text logged using TensorBoard's `SummaryWriter`
* [PyTorch TensorBoard Toy](../guides/frameworks/pytorch/tensorboard_toy_pytorch.md) - Demonstrates ClearML automatically logging debug samples logged using TensorBoard's `SummaryWriter`
* [PyTorch TensorBoardX](../guides/frameworks/pytorch/pytorch_tensorboardx.md) - Demonstrates ClearML automatically logging PyTorch models, and scalars, debug samples, and text logged using TensorBoardX's `SummaryWriter`
diff --git a/docs/integrations/pytorch_lightning.md b/docs/integrations/pytorch_lightning.md
index 9fecd65e..dc62a0b1 100644
--- a/docs/integrations/pytorch_lightning.md
+++ b/docs/integrations/pytorch_lightning.md
@@ -18,7 +18,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* PyTorch Models
@@ -43,8 +43,7 @@ To control a task's framework logging, use the `auto_connect_frameworks` paramet
Completely disable all automatic logging by setting the parameter to `False`. For finer grained control of logged
frameworks, input a dictionary, with framework-boolean pairs.
-For example, the following code will log PyTorch models, but will not log any information reported to TensorBoard.
-:
+For example, the following code will log PyTorch models, but will not log any information reported to TensorBoard:
```python
auto_connect_frameworks={
@@ -143,7 +142,7 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
-Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
+Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.
diff --git a/docs/integrations/scikit_learn.md b/docs/integrations/scikit_learn.md
index b35856ff..77b7d370 100644
--- a/docs/integrations/scikit_learn.md
+++ b/docs/integrations/scikit_learn.md
@@ -17,7 +17,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* Joblib model files
diff --git a/docs/integrations/tensorboard.md b/docs/integrations/tensorboard.md
index b8bb3250..d0bbbcdb 100644
--- a/docs/integrations/tensorboard.md
+++ b/docs/integrations/tensorboard.md
@@ -56,7 +56,7 @@ See more information about explicitly logging information to a ClearML Task:
* [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
### Examples
-Take a look at ClearML’s TensorBoard examples:
+Take a look at ClearML's TensorBoard examples:
* [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md) - Demonstrates logging TensorBoard outputs and TensorFlow flags
* [TensorBoard Toy](../guides/frameworks/tensorflow/tensorboard_toy.md) - Demonstrates logging TensorBoard histograms, scalars, images, text, and TensorFlow flags
* [Tensorboard with PyTorch](../guides/frameworks/pytorch/pytorch_tensorboard.md) - Demonstrates logging TensorBoard scalars, debug samples, and text integrated in code that uses PyTorch
\ No newline at end of file
diff --git a/docs/integrations/tensorboardx.md b/docs/integrations/tensorboardx.md
index f00fbb9e..be8732d9 100644
--- a/docs/integrations/tensorboardx.md
+++ b/docs/integrations/tensorboardx.md
@@ -56,7 +56,7 @@ See more information about explicitly logging information to a ClearML Task:
### Examples
-Take a look at ClearML’s TensorboardX examples:
+Take a look at ClearML's TensorboardX examples:
* [TensorboardX with PyTorch](../guides/frameworks/tensorboardx/tensorboardx.md) - Demonstrates ClearML logging TensorboardX scalars, debug
samples, and text in code using PyTorch
diff --git a/docs/integrations/tensorflow.md b/docs/integrations/tensorflow.md
index b46b6567..7bac4d24 100644
--- a/docs/integrations/tensorflow.md
+++ b/docs/integrations/tensorflow.md
@@ -17,7 +17,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* TensorFlow definitions
@@ -75,17 +75,17 @@ See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).
## Examples
-Take a look at ClearML’s TensorFlow examples. The examples use TensorFlow and ClearML in different configurations with
+Take a look at ClearML's TensorFlow examples. The examples use TensorFlow and ClearML in different configurations with
additional tools, like Abseil and TensorBoard:
* [TensorFlow MNIST](../guides/frameworks/tensorflow/tensorflow_mnist.md) - Demonstrates ClearML's automatic logging of
model checkpoints, TensorFlow definitions, and scalars logged using TensorFlow methods
-* [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md) - Demonstrates ClearML’s automatic
+* [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md) - Demonstrates ClearML's automatic
logging of TensorBoard output and TensorFlow definitions.
-* [TensorBoard Toy](../guides/frameworks/tensorflow/tensorboard_toy.md) - Demonstrates ClearML’s automatic logging of
+* [TensorBoard Toy](../guides/frameworks/tensorflow/tensorboard_toy.md) - Demonstrates ClearML's automatic logging of
TensorBoard scalars, histograms, images, and text, as well as all console output and TensorFlow Definitions.
* [Absl flags](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/absl_flags.py) - Demonstrates
-ClearML’s automatic logging of parameters defined using `absl.flags`
+ClearML's automatic logging of parameters defined using `absl.flags`
## Remote Execution
ClearML logs all the information required to reproduce an experiment on a different machine (installed packages,
@@ -129,6 +129,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
-Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
+Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.
diff --git a/docs/integrations/transformers.md b/docs/integrations/transformers.md
index f3a4306d..08574b86 100644
--- a/docs/integrations/transformers.md
+++ b/docs/integrations/transformers.md
@@ -25,7 +25,7 @@ All you have to do is install and set up ClearML:
clearml-init
```
-That’s it! In every training run from now on, the ClearML experiment
+That's it! In every training run from now on, the ClearML experiment
manager will capture:
* Source code and uncommitted changes
* Hyperparameters - PyTorch trainer [parameters](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/trainer#transformers.TrainingArguments)
@@ -38,7 +38,7 @@ and TensorFlow definitions
* And more
All of this is captured into a [ClearML Task](../fundamentals/task.md). By default, a task called `Trainer` is created
-in the `HuggingFace Transformers` project. To change the task’s name or project, use the `CLEARML_PROJECT` and `CLEARML_TASK`
+in the `HuggingFace Transformers` project. To change the task's name or project, use the `CLEARML_PROJECT` and `CLEARML_TASK`
environment variables
:::tip project names
@@ -48,7 +48,7 @@ task within the `example` project.
In order to log the models created during training, set the `CLEARML_LOG_MODEL` environment variable to `True`.
-You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
+You can see all the captured data in the task's page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
@@ -79,7 +79,7 @@ and shuts down instances as needed, according to a resource budget that you set.
-Use ClearML’s web interface to edit task details, like configuration parameters or input models, then execute the task
+Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:
* Clone the experiment
* Edit the hyperparameters and/or other details
@@ -88,6 +88,6 @@ with the new configuration on a remote machine:
The ClearML Agent executing the task will use the new values to [override any hard coded values](../clearml_agent.md).
## Hyperparameter Optimization
-Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
+Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.
diff --git a/docs/integrations/xgboost.md b/docs/integrations/xgboost.md
index 90dcd77f..aa0d9400 100644
--- a/docs/integrations/xgboost.md
+++ b/docs/integrations/xgboost.md
@@ -17,7 +17,7 @@ from clearml import Task
task = Task.init(task_name="", project_name="")
```
-And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
+And that's it! This creates a [ClearML Task](../fundamentals/task.md) which captures:
* Source code and uncommitted changes
* Installed packages
* XGBoost model files
@@ -143,6 +143,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
```
## Hyperparameter Optimization
-Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
+Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md)
for more information.
diff --git a/docs/integrations/yolov5.md b/docs/integrations/yolov5.md
index 83f966d0..150afc26 100644
--- a/docs/integrations/yolov5.md
+++ b/docs/integrations/yolov5.md
@@ -27,7 +27,7 @@ built in logger:
clearml-init
```
-That’s it! Now, whenever you train a model using YOLOv5, the run will be captured and tracked by ClearML – no additional
+That's it! Now, whenever you train a model using YOLOv5, the run will be captured and tracked by ClearML – no additional
code necessary.
## Training YOLOv5 with ClearML
@@ -54,7 +54,7 @@ manager will capture:
* And more
All of this is captured into a [ClearML Task](../fundamentals/task.md). By default, a task called `Training` is created
-in the `YOLOv5` project. To change the task’s name or project, use the `--project` and `--name` arguments when running
+in the `YOLOv5` project. To change the task's name or project, use the `--project` and `--name` arguments when running
the `train.py` script.
```commandline
@@ -66,7 +66,7 @@ ClearML uses `/` as a delimiter for subprojects: using `example/sample` as a nam
task within the `example` project.
:::
-You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
+You can see all the captured data in the task's page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
Additionally, you can view all of your YOLOv5 runs tracked by ClearML in the [Experiments Table](../webapp/webapp_model_table.md).
Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model.
You can also select multiple experiments and directly [compare](../webapp/webapp_exp_comparing.md) them.
@@ -94,7 +94,7 @@ dataset using the link in the yaml file or the scripts provided by YOLOv5, you g
```
You can use any dataset, as long as you maintain this folder structure.
-Copy the dataset’s corresponding yaml file to the root of the dataset folder.
+Copy the dataset's corresponding yaml file to the root of the dataset folder.
```
..
@@ -171,7 +171,7 @@ and shuts down instances as needed, according to a resource budget that you set.
-Use ClearML’s web interface to edit task details, like configuration parameters or input models, then execute the task
+Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task
with the new configuration on a remote machine:
* Clone the experiment
* Edit the hyperparameters and/or other details
@@ -200,7 +200,7 @@ if RANK in {-1, 0}:
```
## Hyperparameter Optimization
-Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
+Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find
the hyperparameter values that yield the best performing models.
To run hyperparameter optimization locally, you can use the [template script](https://github.com/ultralytics/yolov5/blob/master/utils/loggers/clearml/hpo.py)
diff --git a/docs/integrations/yolov8.md b/docs/integrations/yolov8.md
index 7a3630f3..a11c263f 100644
--- a/docs/integrations/yolov8.md
+++ b/docs/integrations/yolov8.md
@@ -38,7 +38,7 @@ segmentation, and classification. Get the most out of YOLOv8 with ClearML:
clearml-init
```
-That’s it! Now, whenever you train a model using YOLOv8, the run will be captured and tracked by ClearML – no additional
+That's it! Now, whenever you train a model using YOLOv8, the run will be captured and tracked by ClearML – no additional
code necessary.
## Training YOLOv8 with ClearML
@@ -64,7 +64,7 @@ manager will capture:
* And more
All of this is captured into a [ClearML Task](../fundamentals/task.md): a task with your training script's name
-created in a `YOLOv8` ClearML project. To change the task’s name or project, pass the `name` and `project` arguments in one of
+created in a `YOLOv8` ClearML project. To change the task's name or project, pass the `name` and `project` arguments in one of
the following ways:
* Via the SDK:
@@ -89,7 +89,7 @@ ClearML uses `/` as a delimiter for subprojects: using `example/sample` as a nam
task within the `example` project.
:::
-You can see all the captured data in the task’s page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
+You can see all the captured data in the task's page of the ClearML [WebApp](../webapp/webapp_exp_track_visual.md).
Additionally, you can view all of your YOLOv8 runs tracked by ClearML in the [Experiments Table](../webapp/webapp_model_table.md).
Add custom columns to the table, such as mAP values, so you can easily sort and see what is the best performing model.
You can also select multiple experiments and directly [compare](../webapp/webapp_exp_comparing.md) them.
@@ -115,7 +115,7 @@ shuts down instances as needed, according to a resource budget that you set.
### Cloning, Editing, and Enqueuing
ClearML logs all the information required to reproduce an experiment, but you may also want to change a few parameters
-and task details when you re-run an experiment, which you can do through ClearML’s UI.
+and task details when you re-run an experiment, which you can do through ClearML's UI.
In order to be able to override parameters via the UI,
you have to run your code to [create a ClearML Task](../clearml_sdk/task_sdk.md#task-creation), which will log all the
diff --git a/docs/model_registry.md b/docs/model_registry.md
index 8115592c..af9d0b43 100644
--- a/docs/model_registry.md
+++ b/docs/model_registry.md
@@ -40,14 +40,15 @@ need to do is [instantiate a ClearML Task](clearml_sdk/task_sdk.md#task-creation
framework's training results as output models.
Automatic logging is supported for the following frameworks:
-* TensorFlow (see [code example](guides/frameworks/tensorflow/tensorflow_mnist.md))
-* Keras (see [code example](guides/frameworks/keras/keras_tensorboard.md))
-* PyTorch (see [code example](guides/frameworks/pytorch/pytorch_mnist.md))
-* scikit-learn (only using joblib) (see [code example](guides/frameworks/scikit-learn/sklearn_joblib_example.md))
-* XGBoost (only using joblib) (see [code example](guides/frameworks/xgboost/xgboost_sample.md))
-* FastAI (see [code example](guides/frameworks/fastai/fastai_with_tensorboard.md))
-* MegEngine (see [code example](guides/frameworks/megengine/megengine_mnist.md))
-* CatBoost (see [code example](guides/frameworks/catboost/catboost.md))
+* [TensorFlow](integrations/tensorflow.md)
+* [Keras](integrations/keras.md)
+* [PyTorch](integrations/pytorch.md)
+* [scikit-learn](integrations/scikit_learn.md) (only using joblib)
+* [XGBoost](integrations/xgboost.md) (only using joblib)
+* [Fast.ai](integrations/fastai.md)
+* [MegEngine](integrations/megengine.md)
+* [CatBoost](integrations/catboost.md)
+* [MONAI](integrations/monai.md)
You may want more control over which models are logged. Use the `auto_connect_framework` parameter of [`Task.init()`](references/sdk/task.md#taskinit)
to control automatic logging of frameworks.
diff --git a/docs/webapp/webapp_exp_comparing.md b/docs/webapp/webapp_exp_comparing.md
index dbe30a5e..da5daa2f 100644
--- a/docs/webapp/webapp_exp_comparing.md
+++ b/docs/webapp/webapp_exp_comparing.md
@@ -3,7 +3,7 @@ title: Comparing Experiments
---
The ClearML Web UI provides features for comparing experiments, allowing to locate, visualize, and analyze the
-differences in experiments’ results and their causes. You can view the differences in:
+differences in experiments' results and their causes. You can view the differences in:
* [Details](#side-by-side-textual-comparison) - Compare experiment source code, package versions, models, configuration
objects, and other details.
* Hyperparameters
@@ -87,7 +87,7 @@ navigate between search results.
### Tabular Scalar Comparison
-The **Scalars** tab **Values** view lays out the experiments’ metric values in a table: a row per metric/variant and a
+The **Scalars** tab **Values** view lays out the experiments' metric values in a table: a row per metric/variant and a
column for each experiment. Select from the dropdown menu which metric values to display:
* Last Values: The last reported values for each experiment
* Min Values: The minimal value reported throughout the experiment execution
@@ -101,7 +101,7 @@ Switch on the **Show row extremes** toggle to highlight each variant's maximum a
### Parallel Coordinates Mode
-The **Hyperparameters** tab's **Parallel Coordinates** comparison shows experiments’ hyperparameter impact on a specific metric.
+The **Hyperparameters** tab's **Parallel Coordinates** comparison shows experiments' hyperparameter impact on a specific metric.
**To compare by metric:**
1. Under **Performance Metric**, select a metric to compare for
@@ -122,7 +122,7 @@ To focus on a specific experiment, hover over its name in the graph legend.
To hide an experiment, click its name in the graph legend (click again to bring back).
### Plot Comparison
-The **Scalars** (Graph view) and **Plots** tabs compare experiments’ plots.
+The **Scalars** (Graph view) and **Plots** tabs compare experiments' plots.
The **Scalars** tab displays scalar values as time series line charts. The **Plots** tab compares the last reported
iteration sample of each metric/variant combination per compared experiment.
|
-|`--files`| Files / folders to add. Items will be uploaded to the dataset’s designated storage. | 
|
+|`--id`|Dataset's ID|