Logger

class Logger()

The Logger class is the ClearML console log and metric statistics interface, and contains methods for explicit reporting.

Explicit reporting extends ClearML automagical capturing of inputs and output. Explicit reporting methods include scalar plots, line plots, histograms, confusion matrices, 2D and 3D scatter diagrams, text logging, tables, and image uploading and reporting.

In the ClearML Web-App (UI), Logger output appears in the RESULTS tab, CONSOLE, SCALARS, PLOTS, and DEBUG SAMPLES sub-tabs. When you compare experiments, Logger output appears in the comparisons.

danger

Do not construct Logger objects directly.

You must get a Logger object before calling any of the other Logger class methods by calling Task.get_logger or Logger.current_logger.

danger

Do not construct Logger manually! Please use Logger.get_current

Logger.current_logger

classmethod current_logger()

Get the Logger object for the main execution Task, the current running Task, if one exists. If no Logger object exists, this method creates one and returns it. Therefore, you can call this method from anywhere in the code.

logger = Logger.current_logger()

Return type
Logger
Returns
The Logger object (a singleton) for the current running Task.

report_text

report_text(msg, level=20, print_console=True, *args, _)**

For explicit reporting, print text to the log. Optionally, print a log level and print to the console.

For example:

logger.report_text('log some text', level=logging.DEBUG, print_console=False)

You can view the reported text in the ClearML Web-App (UI), RESULTS tab, CONSOLE sub-tab.

Parameters
- msg (str ) – The text to log.
- level (int ) – The log level from the Python logging package. The default value is logging.INFO.
- print_console (bool ) – In addition to the log, print to the console.
  The values are:
  - True - Print to the console. (default)
  - False - Do not print to the console.
- args (Any ) –
- _ (Any ) –
Return type
None

report_scalar

report_scalar(title, series, value, iteration)

For explicit reporting, plot a scalar series.

For example, plot a scalar series:

logger = Logger.current_logger()
scalar_series = [random.randint(0,10) for i in range(10)]
for iteration in range(10):
    logger.report_scalar(
        title='scalar metrics', series='series', value=scalar_series[iteration], iteration=iteration
    )

You can view the scalar plots in the ClearML Web-App (UI), RESULTS tab, SCALARS sub-tab.

Parameters
- title (str ) – The title (metric) of the plot. Plot more than one scalar series on the same plot by using the same title for each call to this method.
- series (str ) – The series name (variant) of the reported scalar.
- value (float ) – The value to plot per iteration.
- iteration (int ) – The reported iteration / step (x-axis of the reported time series)
Return type
None

report_single_value

report_single_value(name, value)

Reports a single value metric (for example, total experiment accuracy or mAP) You can view the metrics in the ClearML Web-App (UI), RESULTS tab, SCALARS sub-tab.

Parameters
- name (str) – Metric’s name
- value (float) – Metric’s value
Return type
None

report_vector

report_vector(title, series, values, iteration=None, labels=None, xlabels=None, xaxis=None, yaxis=None, mode=None, extra_layout=None)

For explicit reporting, plot a vector as (default stacked) histogram.

info

This method is the same as Logger.report_histogram. This method is deprecated, use Logger.report_histogram instead.

For example:

vector_series = np.random.randint(10, size=10).reshape(2,5)
logger.report_vector(title='vector example', series='vector series', values=vector_series, iteration=0,
     labels=['A','B'], xaxis='X axis label', yaxis='Y axis label')

You can view the vectors plot in the ClearML Web-App (UI), RESULTS tab, PLOTS sub-tab.

Parameters
- title – The title (metric) of the plot.
- series – The series name (variant) of the reported histogram.
- values – The series values. A list of floats, or an N-dimensional Numpy array containing data for each histogram bar.
- iteration – The reported iteration / step. Each iteration creates another plot.
- labels – Labels for each bar group, creating a plot legend labeling each series. (Optional)
- xlabels – Labels per entry in each bucket in the histogram (vector), creating a set of labels for each histogram bar on the x-axis. (Optional)
- xaxis – The x-axis title. (Optional)
- yaxis – The y-axis title. (Optional)
- mode – Multiple histograms mode, stack / group / relative. Default is ‘group’.
- extra_layout – optional dictionary for layout configuration, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/layout/ example: extra_layout={‘showlegend’: False, ‘plot_bgcolor’: ‘yellow’}

report_histogram

report_histogram(title, series, values, iteration=None, labels=None, xlabels=None, xaxis=None, yaxis=None, mode=None, data_args=None, extra_layout=None)

For explicit reporting, plot a (default grouped) histogram. Notice this function will not calculate the histogram, it assumes the histogram was already calculated in values

For example:

vector_series = np.random.randint(10, size=10).reshape(2,5)
logger.report_histogram(title='histogram example', series='histogram series',
     values=vector_series, iteration=0, labels=['A','B'], xaxis='X axis label', yaxis='Y axis label')

You can view the reported histograms in the ClearML Web-App (UI), RESULTS tab, PLOTS sub-tab.

Parameters
- title – The title (metric) of the plot.
- series – The series name (variant) of the reported histogram.
- values – The series values. A list of floats, or an N-dimensional Numpy array containing data for each histogram bar.
- iteration – The reported iteration / step. Each iteration creates another plot.
- labels – Labels for each bar group, creating a plot legend labeling each series. (Optional)
- xlabels – Labels per entry in each bucket in the histogram (vector), creating a set of labels for each histogram bar on the x-axis. (Optional)
- xaxis – The x-axis title. (Optional)
- yaxis – The y-axis title. (Optional)
- mode – Multiple histograms mode, stack / group / relative. Default is ‘group’.
- data_args – optional dictionary for data configuration, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/bar/ example: data_args={‘orientation’: ‘h’, ‘marker’: {‘color’: ‘blue’}}
- extra_layout – optional dictionary for layout configuration, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/bar/ example: extra_layout={‘xaxis’: {‘type’: ‘date’, ‘range’: [‘2020-01-01’, ‘2020-01-31’]}}

report_table

report_table(title, series, iteration=None, table_plot=None, csv=None, url=None, extra_layout=None, extra_data=None)

For explicit reporting, report a table plot.

One and only one of the following parameters must be provided.

table_plot - Pandas DataFrame or Table as list of rows (list)
csv - CSV file
url - URL to CSV file

For example:

df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
        'num_wings': [2, 0, 0, 0],
        'num_specimen_seen': [10, 2, 1, 8]},
        index=['falcon', 'dog', 'spider', 'fish'])

logger.report_table(title='table example',series='pandas DataFrame',iteration=0,table_plot=df)

You can view the reported tables in the ClearML Web-App (UI), RESULTS tab, PLOTS sub-tab.

Parameters
- title – The title (metric) of the table.
- series – The series name (variant) of the reported table.
- iteration – The reported iteration / step.
- table_plot – The output table plot object
- csv – path to local csv file
- url – A URL to the location of csv file.
- extra_layout – optional dictionary for layout configuration, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/layout/ For example:
```
logger.report_table(
    title='table example',
    series='pandas DataFrame',
    iteration=0,
    table_plot=df,
    extra_layout={'height': 600}
)
```
- extra_data – optional dictionary for data configuration, like column width, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/table/ For example:
```
logger.report_table(
    title='table example',
    series='pandas DataFrame',
    iteration=0,
    table_plot=df,
    extra_data={'columnwidth': [2., 1., 1., 1.]}
)
```

report_line_plot

report_line_plot(title, series, xaxis, yaxis, mode='lines', iteration=None, reverse_xaxis=False, comment=None, extra_layout=None)

For explicit reporting, plot one or more series as lines.

Parameters
- title (str ) – The title (metric) of the plot.
- series (list ) – All the series data, one list element for each line in the plot.
- iteration (int ) – The reported iteration / step.
- xaxis (str ) – The x-axis title. (Optional)
- yaxis (str ) – The y-axis title. (Optional)
- mode (str ) – The type of line plot.
  The values are:
  - lines (default)
  - markers
  - lines+markers
- reverse_xaxis (bool ) – Reverse the x-axis.
  The values are:
  - True - The x-axis is high to low (reversed).
  - False - The x-axis is low to high (not reversed). (default)
- comment (str ) – A comment displayed with the plot, underneath the title.
- extra_layout (dict ) – optional dictionary for layout configuration, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/scatter/ example: extra_layout={‘xaxis’: {‘type’: ‘date’, ‘range’: [‘2020-01-01’, ‘2020-01-31’]}}

info

This method is the same as Logger.report_scatter2d with :param:mode='lines'. This method is deprecated, use Logger.report_scatter2d instead.

report_scatter2d

report_scatter2d(title, series, scatter, iteration=None, xaxis=None, yaxis=None, labels=None, mode='lines', comment=None, extra_layout=None)

For explicit reporting, report a 2d scatter plot.

For example:

scatter2d = np.hstack((np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1))))
logger.report_scatter2d(title="example_scatter", series="series", iteration=0, scatter=scatter2d,
     xaxis="title x", yaxis="title y")

Plot multiple 2D scatter series on the same plot by passing the same title and iteration values to this method:

scatter2d_1 = np.hstack((np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1))))
logger.report_scatter2d(title="example_scatter", series="series_1", iteration=1, scatter=scatter2d_1,
     xaxis="title x", yaxis="title y")

scatter2d_2 = np.hstack((np.atleast_2d(np.arange(0, 10)).T, np.random.randint(10, size=(10, 1))))
logger.report_scatter2d("example_scatter", "series_2", iteration=1, scatter=scatter2d_2,
     xaxis="title x", yaxis="title y")

Parameters
- title (str ) – The title (metric) of the plot.
- series (str ) – The series name (variant) of the reported scatter plot.
- scatter (list ) – The scatter data. numpy.ndarray or list of (pairs of x,y) scatter:
- iteration (int ) – The reported iteration / step.
- xaxis (str ) – The x-axis title. (Optional)
- yaxis (str ) – The y-axis title. (Optional)
- labels (list ( str ) ) – Labels per point in the data assigned to the scatter parameter. The labels must be in the same order as the data.
- mode (str ) – The type of scatter plot. The values are:
  - lines
  - markers
  - lines+markers
- comment (str ) – A comment displayed with the plot, underneath the title.
- extra_layout (dict ) – optional dictionary for layout configuration, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/scatter/ example: extra_layout={‘xaxis’: {‘type’: ‘date’, ‘range’: [‘2020-01-01’, ‘2020-01-31’]}}

report_scatter3d

report_scatter3d(title, series, scatter, iteration=None, xaxis=None, yaxis=None, zaxis=None, labels=None, mode='markers', fill=False, comment=None, extra_layout=None)

For explicit reporting, plot a 3d scatter graph (with markers).

Parameters
- title (str ) – The title (metric) of the plot.
- series (str ) – The series name (variant) of the reported scatter plot.
- list ] scatter (Union [ numpy.ndarray , ) – The scatter data. list of (pairs of x,y,z), list of series [[(x1,y1,z1)…]], or numpy.ndarray
- iteration (int ) – The reported iteration / step.
- xaxis (str ) – The x-axis title. (Optional)
- yaxis (str ) – The y-axis title. (Optional)
- zaxis (str ) – The z-axis title. (Optional)
- labels (list ( str ) ) – Labels per point in the data assigned to the scatter parameter. The labels must be in the same order as the data.
- mode (str ) – The type of scatter plot. The values are: lines, markers, lines+markers. For example:
```
scatter3d = np.random.randint(10, size=(10, 3))
logger.report_scatter3d(title="example_scatter_3d", series="series_xyz", iteration=1, scatter=scatter3d,
     xaxis="title x", yaxis="title y", zaxis="title z")
```
- fill (bool ) – Fill the area under the curve. The values are:
  - True - Fill
  - False - Do not fill (default)
- comment (str ) – A comment displayed with the plot, underneath the title.
- extra_layout (dict ) – optional dictionary for layout configuration, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/scatter3d/ example: extra_layout={‘xaxis’: {‘type’: ‘date’, ‘range’: [‘2020-01-01’, ‘2020-01-31’]}}

report_confusion_matrix

report_confusion_matrix(title, series, matrix, iteration=None, xaxis=None, yaxis=None, xlabels=None, ylabels=None, yaxis_reversed=False, comment=None, extra_layout=None)

For explicit reporting, plot a heat-map matrix.

For example:

confusion = np.random.randint(10, size=(10, 10))
logger.report_confusion_matrix("example confusion matrix", "ignored", iteration=1, matrix=confusion,
     xaxis="title X", yaxis="title Y")

Parameters
- title (str ) – The title (metric) of the plot.
- series (str ) – The series name (variant) of the reported confusion matrix.
- matrix (numpy.ndarray ) – A heat-map matrix (example: confusion matrix)
- iteration (int ) – The reported iteration / step.
- xaxis (str ) – The x-axis title. (Optional)
- yaxis (str ) – The y-axis title. (Optional)
- xlabels (list ( str ) ) – Labels for each column of the matrix. (Optional)
- ylabels (list ( str ) ) – Labels for each row of the matrix. (Optional)
- yaxis_reversed (bool ) – If False 0,0 is at the bottom left corner. If True, 0,0 is at the top left corner
- comment (str ) – A comment displayed with the plot, underneath the title.
- extra_layout (dict ) – optional dictionary for layout configuration, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/heatmap/ example: extra_layout={‘xaxis’: {‘type’: ‘date’, ‘range’: [‘2020-01-01’, ‘2020-01-31’]}}

report_matrix

report_matrix(title, series, matrix, iteration=None, xaxis=None, yaxis=None, xlabels=None, ylabels=None, yaxis_reversed=False, extra_layout=None)

For explicit reporting, plot a confusion matrix.

info

This method is the same as Logger.report_confusion_matrix. This method is deprecated, use Logger.report_confusion_matrix instead.

Parameters
- title (str ) – The title (metric) of the plot.
- series (str ) – The series name (variant) of the reported confusion matrix.
- matrix (numpy.ndarray ) – A heat-map matrix (example: confusion matrix)
- iteration (int ) – The reported iteration / step.
- xaxis (str ) – The x-axis title. (Optional)
- yaxis (str ) – The y-axis title. (Optional)
- xlabels (list ( str ) ) – Labels for each column of the matrix. (Optional)
- ylabels (list ( str ) ) – Labels for each row of the matrix. (Optional)
- yaxis_reversed (bool ) – If False, 0,0 is in the bottom left corner. If True, 0,0 is in the top left corner
- extra_layout (dict ) – optional dictionary for layout configuration, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/heatmap/ example: extra_layout={‘xaxis’: {‘type’: ‘date’, ‘range’: [‘2020-01-01’, ‘2020-01-31’]}}

report_surface

report_surface(title, series, matrix, iteration=None, xaxis=None, yaxis=None, zaxis=None, xlabels=None, ylabels=None, camera=None, comment=None, extra_layout=None)

For explicit reporting, report a 3d surface plot.

info

This method plots the same data as Logger.report_confusion_matrix, but presents the data as a surface diagram not a confusion matrix.

surface_matrix = np.random.randint(10, size=(10, 10))
logger.report_surface("example surface", "series", iteration=0, matrix=surface_matrix,
     xaxis="title X", yaxis="title Y", zaxis="title Z")

Parameters
- title (str ) – The title (metric) of the plot.
- series (str ) – The series name (variant) of the reported surface.
- matrix (numpy.ndarray ) – A heat-map matrix (example: confusion matrix)
- iteration (int ) – The reported iteration / step.
- xaxis (str ) – The x-axis title. (Optional)
- yaxis (str ) – The y-axis title. (Optional)
- zaxis (str ) – The z-axis title. (Optional)
- xlabels (list ( str ) ) – Labels for each column of the matrix. (Optional)
- ylabels (list ( str ) ) – Labels for each row of the matrix. (Optional)
- camera (list ( float ) ) – X,Y,Z coordinates indicating the camera position. The default value is (1,1,1).
- comment (str ) – A comment displayed with the plot, underneath the title.
- extra_layout (dict ) – optional dictionary for layout configuration, passed directly to plotly See full details on the supported configuration: https://plotly.com/javascript/reference/surface/ example: extra_layout={‘xaxis’: {‘type’: ‘date’, ‘range’: [‘2020-01-01’, ‘2020-01-31’]}}

report_image

report_image(title, series, iteration=None, local_path=None, image=None, matrix=None, max_image_history=None, delete_after_upload=False, url=None)

For explicit reporting, report an image and upload its contents.

This method uploads the image to a preconfigured bucket (see Logger.set_default_upload_destination) describing the task ID, title, series and iteration.

For example:

matrix = np.eye(256, 256, dtype=np.uint8)*255
matrix = np.concatenate((np.atleast_3d(matrix), np.zeros((256, 256, 2), dtype=np.uint8)), axis=2)
logger.report_image("test case", "image color red", iteration=1, image=m)

image_open = Image.open(os.path.join("&lt;image_path&gt;", "&lt;image_filename&gt;"))
logger.report_image("test case", "image PIL", iteration=1, image=image_open)

One and only one of the following parameters must be provided.

local_path
url
image
matrix

Parameters
- title – The title (metric) of the image.
- series – The series name (variant) of the reported image.
- iteration – The reported iteration / step.
- local_path – A path to an image file.
- url – A URL for the location of a pre-uploaded image.
- image – Image data (RGB).
- matrix – Deprecated, Image data (RGB).
  info
  The matrix parameter is deprecated. Use the image parameters.
- max_image_history – The maximum number of images to store per metric/variant combination. For an unlimited number, use a negative value. The default value is set in global configuration (default=``5``).
- delete_after_upload – After the upload, delete the local copy of the image. The values are:
  - True - Delete after upload.
  - False - Do not delete after upload. (default)

report_media

report_media(title, series, iteration=None, local_path=None, stream=None, file_extension=None, max_history=None, delete_after_upload=False, url=None)

Report media upload its contents, including images, audio, and video.

Media is uploaded to a preconfigured bucket (see setup_upload()) with a key (filename) describing the task ID, title, series and iteration.

One and only one of the following parameters must be provided

local_path
stream
url

If you use stream for a BytesIO stream to upload, file_extension must be provided.

Parameters
- title (str ) – The title (metric) of the media.
- series (str ) – The series name (variant) of the reported media.
- iteration (int ) – The reported iteration / step.
- local_path (str ) – A path to a media file.
- stream – BytesIO stream to upload. If provided, file_extension must also be provided.
- url (str ) – A URL to the location of a pre-uploaded media.
- file_extension – A file extension to use when stream is passed.
- max_history (int ) – The maximum number of media files to store per metric/variant combination. Use negative value for unlimited. Default is set in global configuration (default=5)
- delete_after_upload (bool ) – After the file is uploaded, delete the local copy
  - True - Delete
  - False - Do not delete

report_plotly

report_plotly(title, series, figure, iteration=None)

Report a Plotly figure (plot) directly

Plotly figure can be a plotly.graph_objs._figure.Figure or a dictionary as defined by plotly.js

Parameters
- title (str ) – The title (metric) of the plot.
- series (str ) – The series name (variant) of the reported plot.
- iteration (int ) – The reported iteration / step.
- figure (dict ) – A plotly Figure object or a plotly dictionary

report_matplotlib_figure

report_matplotlib_figure(title, series, figure, iteration=None, report_image=False, report_interactive=True)

Report a matplotlib figure / plot directly

matplotlib.figure.Figure / matplotlib.pyplot

Parameters
- title (str ) – The title (metric) of the plot.
- series (str ) – The series name (variant) of the reported plot.
- iteration (int ) – The reported iteration / step.
- figure (MatplotlibFigure ) – A matplotlib Figure object
- report_image – Default False. If True, the plot will be uploaded as a debug sample (png image), and will appear under the debug samples tab (instead of the Plots tab).
- report_interactive – If True (default), it will try to convert the matplotlib into interactive plot in the UI. If False, the matplotlib is saved as is and will be non-interactive (except zooming in/out)

set_default_upload_destination

set_default_upload_destination(uri)

Set the destination storage URI (for example, S3, Google Cloud Storage, a file path) for uploading debug images.

The images are uploaded separately. A link to each image is reported.

info

Credentials for the destination storage are specified in the ClearML configuration file, ~/clearml.conf.

Parameters
uri (str ) – example: ‘s3://bucket/directory/’ or ‘file:///tmp/debug/’
Return type
None
Returns
True, if the destination scheme is supported (for example, s3://, file://, or gs://). False, if not supported.

get_default_upload_destination

get_default_upload_destination()

Get the destination storage URI (for example, S3, Google Cloud Storage, a file path) for uploading debug images (see Logger.set_default_upload_destination).

Return type
str
Returns
The default upload destination URI.
For example: s3://bucket/directory/, or file:///tmp/debug/.

flush

flush(wait=False)

Flush cached reports and console outputs to backend.

Parameters
wait (bool) – Wait for all outstanding uploads and events to be sent (default False)
Return type
bool
Returns
True, if successfully flushed the cache. False, if failed.

get_flush_period

get_flush_period()

Get the Logger flush period.

Return type
Optional[float]
Returns
The logger flush period in seconds.

set_flush_period

set_flush_period(period)

Set the logger flush period.

Deprecated - Use sdk.development.worker.report_period_sec to externally control the flush period.

Parameters
period (float ) – The period to flush the logger in seconds. To set no periodic flush, specify None or 0.
Return type
None

set_default_debug_sample_history

set_default_debug_sample_history(max_history)

Set the default maximum debug sample history when reporting media/debug samples. Overrides the configuration file defaults. When reporting debug samples with the same title/series combination and running iterations, only the last X samples are stored (in other words samples are overwritten). The default history size set with max_history is used when calling report_image, report_media etc. without specifying max_history

Parameters
max_history (int) – Number of samples (files) to store on a unique set of title/series being reported with different iteration counters. This is used to make sure users do not end up exploding storage on server storage side.
For example the following code sample will store the last 5 images even though we are reporting 100 samples.
```
logger.set_default_debug_sample_history(5)
for i in range(100):
    logger.report_image(title='image', series='sample', iteration=i, ...)
```
Return type
None
Returns

get_default_debug_sample_history

get_default_debug_sample_history()

Return the default max debug sample history when reporting media/debug samples. If value was not set specifically, the function returns the configuration file default value.

Return type
int
Returns
default number of samples (files) to store on a unique set of title/series being reported with different iteration counters. This is used to make sure users do not end up exploding storage on server storage side.

report_image_and_upload

report_image_and_upload(title, series, iteration=None, path=None, matrix=None, max_image_history=None, delete_after_upload=False)

Deprecated: Deprecated since version 0.13.0: Use Logger.report_image instead

capture_logging

capture_logging()

Return context capturing all the logs (via logging) reported under the context

Return type
ForwardRef
Returns
a ContextManager

Logger.tensorboard_auto_group_scalars

classmethod tensorboard_auto_group_scalars(group_scalars=False)

Group together TensorBoard scalars that do not have a title, or assign a title/series with the same tag.

Parameters
group_scalars (bool) – Group TensorBoard scalars without a title
The values are:
- True - Scalars without specific titles are grouped together in the “Scalars” plot, preserving backward compatibility with ClearML automagical behavior.
- False - TensorBoard scalars without titles get a title/series with the same tag. (default)
Return type
None

Logger.tensorboard_single_series_per_graph

classmethod tensorboard_single_series_per_graph(single_series=False)

Deprecated, this is now controlled from the UI! Group TensorBoard scalar series together or in separate plots.

Parameters
single_series (bool) – Group TensorBoard scalar series together
The values are:
- True - Generate a separate plot for each TensorBoard scalar series.
- False - Group the TensorBoard scalar series together in the same plot. (default)
Return type
None

Logger.matplotlib_force_report_non_interactive

classmethod matplotlib_force_report_non_interactive(force)

If True, all matplotlib are always converted to non-interactive static plots (images), appearing in under the Plots section. If False (default), matplotlib figures are converted into interactive web UI plotly figures, in case figure conversion fails, it defaults to non-interactive plots.

Parameters
force (bool) – If True, all matplotlib figures are converted automatically to non-interactive plots.
Return type
None

Logger.set_reporting_nan_value

classmethod set_reporting_nan_value(value, warn_period=1000)

When a NaN value is encountered, it is reported as a floating value (by default 0) and the user is warned. This function is used to change the value NaN is converted to and the warning period.

Parameters
- value (float) – The value NaN is converted to
- warn_period (int) – Number of times NaN is encountered and converted until the next warning
Return type
None

Logger.set_reporting_inf_value

classmethod set_reporting_inf_value(value, warn_period=1000)

When an inf value is encountered, it is reported as a floating value (by default 0) and the user is warned. This function is used to change the value inf is converted to and the warning period.

Parameters
- value (float) – The value inf is converted to
- warn_period (int) – Number of times inf is encountered and converted until the next warning
Return type
None

class Logger()​

Logger.current_logger​

report_text​

report_scalar​

report_single_value​

report_vector​

report_histogram​

report_table​

report_line_plot​

report_scatter2d​

report_scatter3d​

report_confusion_matrix​

report_matrix​

report_surface​

report_image​

report_media​

report_plotly​

report_matplotlib_figure​

set_default_upload_destination​

get_default_upload_destination​

flush​

get_flush_period​

set_flush_period​

set_default_debug_sample_history​

get_default_debug_sample_history​

report_image_and_upload​

capture_logging​

Logger.tensorboard_auto_group_scalars​

Logger.tensorboard_single_series_per_graph​

Logger.matplotlib_force_report_non_interactive​

Logger.set_reporting_nan_value​

Logger.set_reporting_inf_value​

class Logger()

Logger.current_logger

report_text

report_scalar

report_single_value

report_vector

report_histogram

report_table

report_line_plot

report_scatter2d

report_scatter3d

report_confusion_matrix

report_matrix

report_surface

report_image

report_media

report_plotly

report_matplotlib_figure

set_default_upload_destination

get_default_upload_destination

flush

get_flush_period

set_flush_period

set_default_debug_sample_history

get_default_debug_sample_history

report_image_and_upload

capture_logging

Logger.tensorboard_auto_group_scalars

Logger.tensorboard_single_series_per_graph

Logger.matplotlib_force_report_non_interactive

Logger.set_reporting_nan_value

Logger.set_reporting_inf_value