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2
docs/faq.md
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@ -362,7 +362,7 @@ Your firewall may be preventing the connection. Try one of the following solutio
* Disable certificate verification * Disable certificate verification
:::warning :::warning
For security reasons, it is not recommended to disable certificate verification For security reasons, it is not recommended to disable certificate verification.
::: :::
1. Upgrade ClearML to the current version: 1. Upgrade ClearML to the current version:
``` ```

2
docs/fundamentals/task.md
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@ -103,7 +103,7 @@ ClearML can be configured to upload artifacts to any of the supported types of s
folders, AWS S3 buckets, Google Cloud Storage, and Azure Storage. For more information, see [Storage](../integrations/storage.md). folders, AWS S3 buckets, Google Cloud Storage, and Azure Storage. For more information, see [Storage](../integrations/storage.md).
:::note Debug Sample Storage :::note Debug Sample Storage
Debug samples are handled differently, see [`Logger.set_default_upload_destination`](../references/sdk/logger.md#set_default_upload_destination) Debug samples are handled differently, see [`Logger.set_default_upload_destination`](../references/sdk/logger.md#set_default_upload_destination).
::: :::
#### Accessing Artifacts #### Accessing Artifacts

2
docs/guides/advanced/execute_remotely.md
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@ -6,7 +6,7 @@ The [execute_remotely_example](https://github.com/allegroai/clearml/blob/master/
script demonstrates the use of the [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely) method. script demonstrates the use of the [`Task.execute_remotely`](../../references/sdk/task.md#execute_remotely) method.
:::note :::note
Make sure to have at least one [ClearML Agent](../../clearml_agent.md) running and assigned to listen to the `default` queue Make sure to have at least one [ClearML Agent](../../clearml_agent.md) running and assigned to listen to the `default` queue:
``` ```
clearml-agent daemon --queue default clearml-agent daemon --queue default
``` ```

2
docs/guides/clearml_agent/executable_exp_containers.md
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@ -41,7 +41,7 @@ script.
``` ```
:::tip :::tip
If the container will not make use of a GPU, add the `--cpu-only` flag If the container will not make use of a GPU, add the `--cpu-only` flag.
::: :::
This command will create a Docker container, set up with the execution environment for this experiment in the This command will create a Docker container, set up with the execution environment for this experiment in the

4
docs/guides/clearml_agent/exp_environment_containers.md
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@ -44,7 +44,7 @@ clearml-agent build --id <TASK_ID> --docker --target new_docker
``` ```
:::tip :::tip
If the container will not make use of a GPU, add the `--cpu-only` flag If the container will not make use of a GPU, add the `--cpu-only` flag.
::: :::
This will create a container with the specified task's execution environment in the `--target` folder. This will create a container with the specified task's execution environment in the `--target` folder.
@ -72,7 +72,7 @@ Make use of the container you've just built by having a ClearML agent make use o
``` ```
:::tip :::tip
If the agent will not make use of a GPU, add the `--cpu-only` flag If the agent will not make use of a GPU, add the `--cpu-only` flag.
::: :::
This agent will pull the enqueued task and run it using the `new_docker` image to create the execution environment. This agent will pull the enqueued task and run it using the `new_docker` image to create the execution environment.

2
docs/guides/frameworks/pytorch/model_updating.md
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@ -45,7 +45,7 @@ Task.current_task().connect_label_enumeration(enumeration)
:::note Directly Setting Model Enumeration :::note Directly Setting Model Enumeration
You can set a model's label enumeration directly using the [`OutputModel.update_labels`](../../../references/sdk/model_outputmodel.md#update_labels) You can set a model's label enumeration directly using the [`OutputModel.update_labels`](../../../references/sdk/model_outputmodel.md#update_labels)
method method.
::: :::
## Model Configuration ## Model Configuration

2
docs/guides/ide/remote_jupyter_tutorial.md
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@ -35,7 +35,7 @@ This sets the following arguments:
:::note :::note
Enter a project name using `--project <name>`. If no project is input, the default project Enter a project name using `--project <name>`. If no project is input, the default project
name is "DevOps" name is `DevOps`.
::: :::
After launching the command, the `clearml-agent` listening to the `default` queue spins a remote Jupyter environment with After launching the command, the `clearml-agent` listening to the `default` queue spins a remote Jupyter environment with

8
docs/guides/reporting/clearml_logging_example.md
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@ -23,8 +23,8 @@ The scalar plots appear in the **web UI** in **SCALARS**.
```python ```python
# report two scalar series on two different graphs # report two scalar series on two different graphs
for i in range(10): for i in range(10):
logger.report_scalar("graph A", "series A", iteration=i, value=1./(i+1)) logger.report_scalar(title="graph A", series="series A", iteration=i, value=1./(i+1))
logger.report_scalar("graph B", "series B", iteration=i, value=10./(i+1)) logger.report_scalar(title="graph B", series="series B", iteration=i, value=10./(i+1))
``` ```
![Separate scalar plots](../../img/colab_explicit_reporting_01.png) ![Separate scalar plots](../../img/colab_explicit_reporting_01.png)
@ -32,8 +32,8 @@ for i in range(10):
```python ```python
# report two scalar series on the same graph # report two scalar series on the same graph
for i in range(10): for i in range(10):
logger.report_scalar("unified graph", "series A", iteration=i, value=1./(i+1)) logger.report_scalar(title="unified graph", series="series A", iteration=i, value=1./(i+1))
logger.report_scalar("unified graph", "series B", iteration=i, value=10./(i+1)) logger.report_scalar(title="unified graph", series="series B", iteration=i, value=10./(i+1))
``` ```
![Unified scalar plots](../../img/colab_explicit_reporting_02.png) ![Unified scalar plots](../../img/colab_explicit_reporting_02.png)

35
docs/guides/reporting/html_reporting.md
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@ -15,15 +15,15 @@ When the script runs, it creates an experiment named `html samples reporting` in
## Reporting HTML URLs ## Reporting HTML URLs
Report HTML by URL, using the [Logger.report_media](../../references/sdk/logger.md#report_media) method's `url` parameter. Report HTML by URL using [`Logger.report_media()`](../../references/sdk/logger.md#report_media)'s `url` parameter.
See the example script's [report_html_url](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L16) See the example script's [`report_html_url`](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L16)
function, which reports the ClearML documentation's home page. function, which reports the ClearML documentation's home page.
```python ```python
Logger.current_logger().report_media( Logger.current_logger().report_media(
"html", title="html",
"url_html", series="url_html",
iteration=iteration, iteration=iteration,
url="https://clear.ml/docs/latest/docs/index.html" url="https://clear.ml/docs/latest/docs/index.html"
) )
@ -39,20 +39,23 @@ Report the following using the `Logger.report_media` parameter method `local_pat
### Interactive HTML ### Interactive HTML
See the example script's [report_html_periodic_table](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L26) function, which reports a file created from Bokeh sample data. See the example script's [`report_html_periodic_table`](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L26) function, which reports a file created from Bokeh sample data.
```python ```python
Logger.current_logger().report_media( Logger.current_logger().report_media(
"html", "periodic_html", iteration=iteration, local_path="periodic.html" title="html",
series="periodic_html",
iteration=iteration,
local_path="periodic.html"
) )
``` ```
### Bokeh GroupBy HTML ### Bokeh GroupBy HTML
See the example script's [report_html_groupby](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L117) function, which reports a Pandas GroupBy with nested HTML, created from Bokeh sample data. See the example script's [`report_html_groupby`](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L117) function, which reports a Pandas GroupBy with nested HTML, created from Bokeh sample data.
```python ```python
Logger.current_logger().report_media( Logger.current_logger().report_media(
"html", title="html",
"pandas_groupby_nested_html", series="pandas_groupby_nested_html",
iteration=iteration, iteration=iteration,
local_path="bar_pandas_groupby_nested.html", local_path="bar_pandas_groupby_nested.html",
) )
@ -60,20 +63,26 @@ Logger.current_logger().report_media(
### Bokeh Graph HTML ### Bokeh Graph HTML
See the example script's [report_html_graph](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L162) function, which reports a Bokeh plot created from Bokeh sample data. See the example script's [`report_html_graph`](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L162) function, which reports a Bokeh plot created from Bokeh sample data.
```python ```python
Logger.current_logger().report_media( Logger.current_logger().report_media(
"html", "Graph_html", iteration=iteration, local_path="graph.html" title="html",
series="Graph_html",
iteration=iteration,
local_path="graph.html"
) )
``` ```
### Bokeh Image HTML ### Bokeh Image HTML
See the example script's [report_html_image](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L195) function, which reports an image created from Bokeh sample data. See the example script's [`report_html_image`](https://github.com/allegroai/clearml/blob/master/examples/reporting/html_reporting.py#L195) function, which reports an image created from Bokeh sample data.
```python ```python
Logger.current_logger().report_media( Logger.current_logger().report_media(
"html", "Spectral_html", iteration=iteration, local_path="image.html" title="html",
series="Spectral_html",
iteration=iteration,
local_path="image.html"
) )
``` ```

12
docs/guides/reporting/image_reporting.md
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@ -22,17 +22,17 @@ Report images using several formats by calling [`Logger.report_image()`](../../r
```python ```python
# report image as float image # report image as float image
m = np.eye(256, 256, dtype=np.float) m = np.eye(256, 256, dtype=np.float)
Logger.current_logger().report_image("image", "image float", iteration=iteration, image=m) Logger.current_logger().report_image(title="image", series="image float", iteration=iteration, image=m)
# report image as uint8 # report image as uint8
m = np.eye(256, 256, dtype=np.uint8) * 255 m = np.eye(256, 256, dtype=np.uint8) * 255
Logger.current_logger().report_image("image", "image uint8", iteration=iteration, image=m) Logger.current_logger().report_image(title="image", series="image uint8", iteration=iteration, image=m)
# report image as uint8 RGB # report image as uint8 RGB
m = np.concatenate((np.atleast_3d(m), np.zeros((256, 256, 2), dtype=np.uint8)), axis=2) m = np.concatenate((np.atleast_3d(m), np.zeros((256, 256, 2), dtype=np.uint8)), axis=2)
Logger.current_logger().report_image( Logger.current_logger().report_image(
"image", title="image",
"image color red", series="image color red",
iteration=iteration, iteration=iteration,
image=m image=m
) )
@ -40,8 +40,8 @@ Logger.current_logger().report_image(
# report PIL Image object # report PIL Image object
image_open = Image.open(os.path.join("data_samples", "picasso.jpg")) image_open = Image.open(os.path.join("data_samples", "picasso.jpg"))
Logger.current_logger().report_image( Logger.current_logger().report_image(
"image", title="image",
"image PIL", series="image PIL",
iteration=iteration, iteration=iteration,
image=image_open image=image_open
) )

8
docs/guides/reporting/pandas_reporting.md
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@ -26,8 +26,8 @@ df = pd.DataFrame(
) )
df.index.name = "id" df.index.name = "id"
Logger.current_logger().report_table( Logger.current_logger().report_table(
"table pd", title="table pd",
"PD with index", series="PD with index",
iteration=iteration, iteration=iteration,
table_plot=df table_plot=df
) )
@ -43,8 +43,8 @@ Report CSV files by providing the URL location of the CSV file in the `url` para
# Report table - CSV from path # Report table - CSV from path
csv_url = "https://raw.githubusercontent.com/plotly/datasets/master/Mining-BTC-180.csv" csv_url = "https://raw.githubusercontent.com/plotly/datasets/master/Mining-BTC-180.csv"
Logger.current_logger().report_table( Logger.current_logger().report_table(
"table csv", title="table csv",
"remote csv", series="remote csv",
iteration=iteration, iteration=iteration,
url=csv_url url=csv_url
) )

8
docs/guides/reporting/scalar_reporting.md
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@ -16,19 +16,19 @@ To report more than one series on the same plot, use the same `title` argument.
# report two scalar series on the same graph # report two scalar series on the same graph
for i in range(100): for i in range(100):
Logger.current_logger().report_scalar( Logger.current_logger().report_scalar(
"unified graph", "series A", iteration=i, value=1./(i+1) title="unified graph", series="series A", iteration=i, value=1./(i+1)
) )
Logger.current_logger().report_scalar( Logger.current_logger().report_scalar(
"unified graph", "series B", iteration=i, value=10./(i+1) title="unified graph", series="series B", iteration=i, value=10./(i+1)
) )
# report two scalar series on two different graphs # report two scalar series on two different graphs
for i in range(100): for i in range(100):
Logger.current_logger().report_scalar( Logger.current_logger().report_scalar(
"graph A", "series A", iteration=i, value=1./(i+1) title="graph A", series="series A", iteration=i, value=1./(i+1)
) )
Logger.current_logger().report_scalar( Logger.current_logger().report_scalar(
"graph B", "series B", iteration=i, value=10./(i+1) title="graph B", series="series B", iteration=i, value=10./(i+1)
) )
``` ```

28
docs/guides/reporting/scatter_hist_confusion_mat_reporting.md
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@ -23,8 +23,8 @@ To report more than one series on the same plot, use same the `title` argument.
# report a single histogram # report a single histogram
histogram = np.random.randint(10, size=10) histogram = np.random.randint(10, size=10)
Logger.current_logger().report_histogram( Logger.current_logger().report_histogram(
"single_histogram", title="single_histogram",
"random histogram", series="random histogram",
iteration=iteration, iteration=iteration,
values=histogram, values=histogram,
xaxis="title x", xaxis="title x",
@ -35,8 +35,8 @@ Logger.current_logger().report_histogram(
histogram1 = np.random.randint(13, size=10) histogram1 = np.random.randint(13, size=10)
histogram2 = histogram * 0.75 histogram2 = histogram * 0.75
Logger.current_logger().report_histogram( Logger.current_logger().report_histogram(
"two_histogram", title="two_histogram",
"series 1", series="series 1",
iteration=iteration, iteration=iteration,
values=histogram1, values=histogram1,
xaxis="title x", xaxis="title x",
@ -65,8 +65,8 @@ Report confusion matrices by calling [`Logger.report_confusion_matrix()`](../../
# report confusion matrix # report confusion matrix
confusion = np.random.randint(10, size=(10, 10)) confusion = np.random.randint(10, size=(10, 10))
Logger.current_logger().report_confusion_matrix( Logger.current_logger().report_confusion_matrix(
"example_confusion", title="example_confusion",
"ignored", series="ignored",
iteration=iteration, iteration=iteration,
matrix=confusion, matrix=confusion,
xaxis="title X", xaxis="title X",
@ -79,8 +79,8 @@ Logger.current_logger().report_confusion_matrix(
```python ```python
# report confusion matrix with 0,0 is at the top left # report confusion matrix with 0,0 is at the top left
Logger.current_logger().report_confusion_matrix( Logger.current_logger().report_confusion_matrix(
"example_confusion_0_0_at_top", title="example_confusion_0_0_at_top",
"ignored", series="ignored",
iteration=iteration, iteration=iteration,
matrix=confusion, matrix=confusion,
xaxis="title X", xaxis="title X",
@ -101,8 +101,8 @@ scatter2d = np.hstack(
# report 2d scatter plot with lines # report 2d scatter plot with lines
Logger.current_logger().report_scatter2d( Logger.current_logger().report_scatter2d(
"example_scatter", title="example_scatter",
"series_xy", series="series_xy",
iteration=iteration, iteration=iteration,
scatter=scatter2d, scatter=scatter2d,
xaxis="title x", xaxis="title x",
@ -111,8 +111,8 @@ Logger.current_logger().report_scatter2d(
# report 2d scatter plot with markers # report 2d scatter plot with markers
Logger.current_logger().report_scatter2d( Logger.current_logger().report_scatter2d(
"example_scatter", title="example_scatter",
"series_markers", series="series_markers",
iteration=iteration, iteration=iteration,
scatter=scatter2d, scatter=scatter2d,
xaxis="title x", xaxis="title x",
@ -122,8 +122,8 @@ Logger.current_logger().report_scatter2d(
# report 2d scatter plot with lines and markers # report 2d scatter plot with lines and markers
Logger.current_logger().report_scatter2d( Logger.current_logger().report_scatter2d(
"example_scatter", title="example_scatter",
"series_lines+markers", series="series_lines+markers",
iteration=iteration, iteration=iteration,
scatter=scatter2d, scatter=scatter2d,
xaxis="title x", xaxis="title x",

4
docs/guides/services/slack_alerts.md
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@ -18,7 +18,7 @@ The Slack API token and channel you create are required to configure the Slack a
1. Login to your Slack account. 1. Login to your Slack account.
1. Go to [https://api.slack.com/apps/new](https://api.slack.com/apps/new). 1. Go to [https://api.slack.com/apps/new](https://api.slack.com/apps/new).
1. In **App Name**, enter an app name; for example, "ClearML Bot". 1. In **App Name**, enter an app name. For example, "ClearML Bot".
1. In **Development Slack Workspace**, select a workspace. 1. In **Development Slack Workspace**, select a workspace.
1. Click **Create App**. 1. Click **Create App**.
1. In **Basic Information**, under **Display Information**, complete the following: 1. In **Basic Information**, under **Display Information**, complete the following:
@ -31,7 +31,7 @@ The Slack API token and channel you create are required to configure the Slack a
* **channels:read** * **channels:read**
* **chat:write** * **chat:write**
1. In **OAuth Tokens & Redirect URLs**: 1. In **OAuth Tokens & Redirect URLs**:
1. Click **Install App to Workspace** 1. Click **Install App to Workspace**.
1. In the confirmation dialog, click **Allow**. 1. In the confirmation dialog, click **Allow**.
1. Click **Copy** to copy the **Bot User OAuth Access Token**. 1. Click **Copy** to copy the **Bot User OAuth Access Token**.

2
docs/integrations/yolov8.md
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@ -137,7 +137,7 @@ task = Task.init(
# Load a model # Load a model
model_variant = "yolov8n" model_variant = "yolov8n"
# Log "model_variant" parameter to task # Log "model_variant" parameter to task
task.set_parameter("model_variant", model_variant) task.set_parameter(name="model_variant", value=model_variant)
# Load the YOLOv8 model # Load the YOLOv8 model
model = YOLO(f'{model_variant}.pt') model = YOLO(f'{model_variant}.pt')

2
docs/webapp/applications/apps_hpo.md
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@ -3,7 +3,7 @@ title: Hyperparameter Optimization
--- ---
:::info Pro Plan Offering :::info Pro Plan Offering
The ClearML HPO App is available under the ClearML Pro plan The ClearML HPO App is available under the ClearML Pro plan.
::: :::
The Hyperparameter Optimization Application finds the set of parameter values that optimize a specific metric(s) for your The Hyperparameter Optimization Application finds the set of parameter values that optimize a specific metric(s) for your

2
docs/webapp/applications/apps_vscode.md
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@ -3,7 +3,7 @@ title: VS Code
--- ---
:::important Enterprise Feature :::important Enterprise Feature
The VS Code application is available under the ClearML Enterprise plan The VS Code application is available under the ClearML Enterprise plan.
::: :::
The VS Code UI application allows you to launch a remote VS Code session on a machine that better meets resource needs. The VS Code UI application allows you to launch a remote VS Code session on a machine that better meets resource needs.