Edit a report

Edit a report interactively with the App UI or programmatically with the W&B SDK.

Reports consist of blocks. Blocks make up the body of a report. Within these blocks you can add text, images, embedded visualizations, plots from experiments and run, and panels grids.

Panel grids are a specific type of block that hold panels and run sets. Run sets are a collection of runs logged to a project in W&B. Panels are visualizations of run set data.

pip install wandb wandb-workspaces

Add plots

Each panel grid has a set of run sets and a set of panels. The run sets at the bottom of the section control what data shows up on the panels in the grid. Create a new panel grid if you want to add charts that pull data from a different set of runs.

• W&B App
• Report and Workspace API

Enter a forward slash (/) in the report to display a dropdown menu. Select Add panel to add a panel. You can add any panel that is supported by W&B, including a line plot, scatter plot or parallel coordinates chart.

Add plots to a report programmatically with the SDK. Pass a list of one or more plot or chart objects to the panels parameter in the PanelGrid Public API Class. Create a plot or chart object with its associated Python Class.

The proceeding examples demonstrates how to create a line plot and scatter plot.

import wandb
import wandb_workspaces.reports.v2 as wr

report = wr.Report(
    project="report-editing",
    title="An amazing title",
    description="A descriptive description.",
)

blocks = [
    wr.PanelGrid(
        panels=[
            wr.LinePlot(x="time", y="velocity"),
            wr.ScatterPlot(x="time", y="acceleration"),
        ]
    )
]

report.blocks = blocks
report.save()

For more information about available plots and charts you can add to a report programmatically, see wr.panels.

Add run sets

Add run sets from projects interactively with the App UI or the W&B SDK.

• W&B App
• Report and Workspace API

import wandb
import wandb_workspaces.reports.v2 as wr

report = wr.Report(
    project="report-editing",
    title="An amazing title",
    description="A descriptive description.",
)

panel_grids = wr.PanelGrid(
    runsets=[wr.RunSet(project="<project-name>", entity="<entity-name>")]
)

report.blocks = [panel_grids]
report.save()

import wandb

report = wr.Report(
    project="report-editing",
    title="An amazing title",
    description="A descriptive description.",
)

panel_grids = wr.PanelGrid(
    panels=[
        wr.LinePlot(
            title="line title",
            x="x",
            y=["y"],
            range_x=[0, 100],
            range_y=[0, 100],
            log_x=True,
            log_y=True,
            title_x="x axis title",
            title_y="y axis title",
            ignore_outliers=True,
            groupby="hyperparam1",
            groupby_aggfunc="mean",
            groupby_rangefunc="minmax",
            smoothing_factor=0.5,
            smoothing_type="gaussian",
            smoothing_show_original=True,
            max_runs_to_show=10,
            plot_type="stacked-area",
            font_size="large",
            legend_position="west",
        ),
        wr.ScatterPlot(
            title="scatter title",
            x="y",
            y="y",
            # z='x',
            range_x=[0, 0.0005],
            range_y=[0, 0.0005],
            # range_z=[0,1],
            log_x=False,
            log_y=False,
            # log_z=True,
            running_ymin=True,
            running_ymean=True,
            running_ymax=True,
            font_size="small",
            regression=True,
        ),
    ],
    runsets=[wr.RunSet(project="<project-name>", entity="<entity-name>")],
)


report.blocks = [panel_grids]
report.save()

Freeze a run set

A report automatically updates run sets to show the latest data from the project. You can preserve the run set in a report by freezing that run set. When you freeze a run set, you preserve the state of the run set in a report at a point in time.

To freeze a run set when viewing a report, click the snowflake icon in its panel grid near the Filter button.

Group a run set programmatically

Group runs in a run set programmatically with the Workspace and Reports API.

You can group runs in a run set by config values, run metadata or summary metrics. The following table lists the available grouping methods along with the available keys for that grouping method:

Group runs by config values

Group runs by config values to compare runs with similar configurations. Config values are parameters you specify in your run configuration (wandb.init(config=)). To group runs by config values, use the config.<key> syntax, where <key> is the name of the config value you want to group by.

For example, the following code snippet first initializes a run with a config value for group, then groups runs in a report based on the group config value. Replace values for <entity> and <project> with your W&B entity and project names.

import wandb
import wandb_workspaces.reports.v2 as wr

entity = "<entity>"
project = "<project>"

for group in ["control", "experiment_a", "experiment_b"]:
    for i in range(3):
        with wandb.init(entity=entity, project=project, group=group, config={"group": group, "run": i}, name=f"{group}_run_{i}") as run:
            # Simulate some training
            for step in range(100):
                run.log({
                    "acc": 0.5 + (step / 100) * 0.3 + (i * 0.05),
                    "loss": 1.0 - (step / 100) * 0.5
                })

Within your Python script or notebook, you can then group runs by the config.group value:

runset = wr.Runset(
  project=project,
  entity=entity,
  groupby=["config.group"]  # Group by the "group" config value
)

Continuing from the previous example, you can create a report with the grouped run set:

report = wr.Report(
  entity=entity,
  project=project,
  title="Grouped Runs Example",
)

report.blocks = [
  wr.PanelGrid(
      runsets=[runset],
          )
      ]

report.save()

Group runs by run metadata

Group runs by a run’s name (Name), state (State), or job type (JobType).

Continuing from the previous example, you can group your runs by their name with the following code snippet:

runset = wr.Runset(
  project=project,
  entity=entity,
  groupby=["Name"]  # Group by run names
)

Group runs by summary metrics

The following examples demonstrate how to group runs by summary metrics. Summary metrics are the values you log to a run with wandb.Run.log(). After you log a run, you can find the names of your summary metrics in the W&B App under the Summary section of a run’s Overview page.

The syntax for grouping runs by summary metrics is summary.<key>, where <key> is the name of the summary metric you want to group by.

For example, suppose you log a summary metric called acc:

import wandb
import wandb_workspaces.reports.v2 as wr

entity = "<entity>"
project = "<project>"

for group in ["control", "experiment_a", "experiment_b"]:
    for i in range(3):
        with wandb.init(entity=entity, project=project, group=group, config={"group": group, "run": i}, name=f"{group}_run_{i}") as run:
            # Simulate some training
            for step in range(100):
                run.log({
                    "acc": 0.5 + (step / 100) * 0.3 + (i * 0.05),
                    "loss": 1.0 - (step / 100) * 0.5
                })

You can then group runs by the summary.acc summary metric:

runset = wr.Runset(
  project=project,
  entity=entity,
  groupby=["summary.acc"]  # Group by summary values 
)

Filter a run set programmatically

Programmatically filter run sets and add them to a report with the Workspace and Reports API.

The general syntax for a filter expression is:

Filter('key') operation <value>

Where key is the name of the filter, operation is a comparison operator (e.g., >, <, ==, in, not in, or, and and), and <value> is the value to compare against. Filter is a placeholder for the type of filter you want to apply. The following table lists the available filters and their descriptions:

Once you have defined your filters, you can create a report and pass the filtered run sets to wr.PanelGrid(runsets=). See the Report and Workspace API tabs throughout this page for more information on how to add various elements to a report programmatically.

The following examples demonstrate how to filter run sets in a report. Replace values enclosed in <> with your own values.

Config filters

Filter a runset by one or more config values. Config values are parameters you specify in your run configuration (wandb.init(config=)).

For example, the following code snippet first initializes a run with a config value for learning_rate and batch_size, then filters runs in a report based on the learning_rate config value.

import wandb

config = {
    "learning_rate": 0.01,
    "batch_size": 32,
}

with wandb.init(project="<project>", entity="<entity>", config=config) as run:
    # Your training code here
    pass

Within your Python script or notebook, you can then programmatically filter runs that have a learning rate greater than 0.01.

import wandb_workspaces.reports.v2 as wr

runset = wr.Runset(
  entity="<entity>",
  project="<project>",
  filters="Config('learning_rate') > 0.01"
)

You can also filter by multiple config values with the and operator:

runset = wr.Runset(
  entity="<entity>",
  project="<project>",
  filters="Config('learning_rate') > 0.01 and Config('batch_size') == 32"
)

Continuing from the previous example, you can create a report with the filtered runset as follows:

report = wr.Report(
  entity="<entity>",
  project="<project>",
  title="My Report"
)

report.blocks = [
  wr.PanelGrid(
      runsets=[runset],
      panels=[
          wr.LinePlot(
              x="Step",
              y=["accuracy"],
          )
      ]
  )
]

report.save()

Metric filters

Filter run sets based on a run’s: tag (tags), run state (state), run name (displayName), or job type (jobType).

Metric('key') operation [<value>]

For example, consider the following Python snippet that creates three runs and assigns each of them a name:

import wandb

with wandb.init(project="<project>", entity="<entity>") as run:
    for i in range(3):
        run.name = f"run{i+1}"
        # Your training code here
        pass

When you create your report, you can filter runs by their display name. For example, to filter runs with names run1, run2, and run3, you can use the following code:

runset = wr.Runset(
  entity="<entity>",
  project="<project>",
  filters="Metric('displayName') in ['run1', 'run2', 'run3']"
)

The following examples demonstrate how to filter a runset by the run’s state (finished, crashed, or running):

runset = wr.Runset(
  entity="<entity>",
  project="<project>",
  filters="Metric('state') in ['finished']"
)

runset = wr.Runset(
  entity="<entity>",
  project="<project>",
  filters="Metric('state') not in ['crashed']"
)

SummaryMetric filters

The following examples demonstrate how to filter a run set by summary metrics. Summary metrics are the values you log to a run with wandb.Run.log(). After you log a run, you can find the names of your summary metrics in the W&B App under the Summary section of a run’s Overview page.

runset = wr.Runset(
  entity="<entity>",
  project="<project>",
  filters="SummaryMetric('accuracy') > 0.9"
)

runset = wr.Runset(
  entity="<entity>",
  project="<project>",
  filters="Metric('state') in ['finished'] and SummaryMetric('train/train_loss') < 0.5"
)

Tags filters

The following code snippet shows how to filter a runs set by its tags. Tags are values you add to a run (programmatically or with the W&B App).

runset = wr.Runset(
  entity="<entity>",
  project="<project>",
  filters="Tags('training') == 'training'"
)

Add code blocks

Add code blocks to your report interactively with the App UI or with the W&B SDK.

• App UI
• Report and Workspace API

import wandb
import wandb_workspaces.reports.v2 as wr

report = wr.Report(project="report-editing")

report.blocks = [
    wr.CodeBlock(
        code=["this:", "- is", "- a", "cool:", "- yaml", "- file"], language="yaml"
    )
]

report.save()

this:
- is
- a
cool:
- yaml
- file

report = wr.Report(project="report-editing")


report.blocks = [wr.CodeBlock(code=["Hello, World!"], language="python")]

report.save()

Hello, World!

Add markdown

Add markdown to your report interactively with the App UI or with the W&B SDK.

• App UI
• Report and Workspace API

Enter a forward slash (/) in the report to display a dropdown menu. From the dropdown choose Markdown.

Use the wandb.apis.reports.MarkdownBlock Class to create a markdown block programmatically. Pass a string to the text parameter:

import wandb
import wandb_workspaces.reports.v2 as wr

report = wr.Report(project="report-editing")

report.blocks = [
    wr.MarkdownBlock(text="Markdown cell with *italics* and **bold** and $e=mc^2$")
]

This will render a markdown block similar to:

Add HTML elements

Add HTML elements to your report interactively with the App UI or with the W&B SDK.

• App UI
• Report and Workspace API

Enter a forward slash (/) in the report to display a dropdown menu. From the dropdown select a type of text block. For example, to create an H2 heading block, select the Heading 2 option.

Pass a list of one or more HTML elements to wandb.apis.reports.blocks attribute. The proceeding example demonstrates how to create an H1, H2, and an unordered list:

import wandb
import wandb_workspaces.reports.v2 as wr

report = wr.Report(project="report-editing")

report.blocks = [
    wr.H1(text="How Programmatic Reports work"),
    wr.H2(text="Heading 2"),
    wr.UnorderedList(items=["Bullet 1", "Bullet 2"]),
]

report.save()

This will render a HTML elements to the following:

Embed rich media links

Embed rich media within the report with the App UI or with the W&B SDK.

• App UI
• Report and Workspace API

Copy and past URLs into reports to embed rich media within the report. The following animations demonstrate how to copy and paste URLs from Twitter, YouTube, and SoundCloud.

Twitter

Copy and paste a Tweet link URL into a report to view the Tweet within the report.

Youtube

Copy and paste a YouTube video URL link to embed a video in the report.

SoundCloud

Copy and paste a SoundCloud link to embed an audio file into a report.

Pass a list of one or more embedded media objects to the wandb.apis.reports.blocks attribute. The proceeding example demonstrates how to embed video and Twitter media into a report:

import wandb
import wandb_workspaces.reports.v2 as wr

report = wr.Report(project="report-editing")

report.blocks = [
    wr.Video(url="https://www.youtube.com/embed/6riDJMI-Y8U"),
    wr.Twitter(
        embed_html='<blockquote class="twitter-tweet"><p lang="en" dir="ltr">The voice of an angel, truly. <a href="https://twitter.com/hashtag/MassEffect?src=hash&amp;ref_src=twsrc%5Etfw">#MassEffect</a> <a href="https://t.co/nMev97Uw7F">pic.twitter.com/nMev97Uw7F</a></p>&mdash; Mass Effect (@masseffect) <a href="https://twitter.com/masseffect/status/1428748886655569924?ref_src=twsrc%5Etfw">August 20, 2021</a></blockquote>\n'
    ),
]
report.save()

Duplicate and delete panel grids

If you have a layout that you would like to reuse, you can select a panel grid and copy-paste it to duplicate it in the same report or even paste it into a different report.

Highlight a whole panel grid section by selecting the drag handle in the upper right corner. Click and drag to highlight and select a region in a report such as panel grids, text, and headings.

Select a panel grid and press delete on your keyboard to delete a panel grid.

Collapse headers to organize Reports

Collapse headers in a Report to hide content within a text block. When the report is loaded, only headers that are expanded will show content. Collapsing headers in reports can help organize your content and prevent excessive data loading. The proceeding gif demonstrates the process.

Visualize relationships across multiple dimensions

To effectively visualize relationships across multiple dimensions, use a color gradient to represent one of the variables. This enhances clarity and makes patterns easier to interpret.

1. Choose a variable to represent with a color gradient (e.g., penalty scores, learning rates, etc.). This allows for a clearer understanding of how penalty (color) interacts with reward/side effects (y-axis) over training time (x-axis).
2. Highlight key trends. Hovering over a specific group of runs highlights them in the visualization.