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InputModel

class InputModel()​

Load an existing model in the system, search by model ID. The Model will be read-only and can be used to pre initialize a network. We can connect the model to a task as input model, then when running remotely override it with the UI.

Load a model from the Model artifactory, based on model_id (uuid) or a model name/projects/tags combination.

  • Parameters

    • model_id (Optional[str]) – The ClearML ID (system UUID) of the input model whose metadata the ClearML Server (backend) stores. If provided all other arguments are ignored

    • name (Optional[str]) – Model name to search and load

    • project (Optional[str]) – Model project name to search model in

    • tags (Optional[Sequence[str]]) – Model tags list to filter by

    • only_published (bool) – If True, filter out non-published (draft) models

comment​

property comment

The comment for the model. Also, use for a model description.

  • Return type

    str

  • Returns

    The model comment / description.

config_dict​

property config_dict

The configuration as a dictionary, parsed from the design text. This usually represents the model configuration. For example, prototxt, an ini file, or Python code to evaluate.

  • Return type

    dict

  • Returns

    The configuration.

config_text​

property config_text

The configuration as a string. For example, prototxt, an ini file, or Python code to evaluate.

  • Return type

    str

  • Returns

    The configuration.

connect​

connect(task, name=None)

Connect the current model to a Task object, if the model is preexisting. Preexisting models include:

  • Imported models (InputModel objects created using the Logger.import_model method).

  • Models whose metadata is already in the ClearML platform, meaning the InputModel object is instantiated from the InputModel class specifying the model’s ClearML ID as an argument.

  • Models whose origin is not ClearML that are used to create an InputModel object. For example, models created using TensorFlow models.

When the experiment is executed remotely in a worker, the input model already specified in the experiment is used.

info

The ClearML Web-App allows you to switch one input model for another and then enqueue the experiment to execute in a worker.

  • Parameters

    • task (object ) – A Task object.

    • name (str ) – The model name to be stored on the Task (default the filename, of the model weights, without the file extension)

  • Return type

    None

InputModel.empty​

classmethod empty(config_text=None, config_dict=None, label_enumeration=None)

Create an empty model object. Later, you can assign a model to the empty model object.

  • Parameters

    • config_text (unconstrained text string ) – The model configuration as a string. This is usually the content of a configuration dictionary file. Specify config_text or config_dict, but not both.

    • config_dict (dict ) – The model configuration as a dictionary. Specify config_text or config_dict, but not both.

    • label_enumeration (dict ) – The label enumeration dictionary of string (label) to integer (value) pairs. (Optional)

      For example:

      {
           "background": 0,
           "person": 1
      }

  • Return type

    InputModel

  • Returns

    An empty model object.

get_all_metadata​

get_all_metadata()

See Model.get_all_metadata_casted if you wish to cast the value to its type (if possible)

  • Return type

    Dict[str, Dict[str, str]]

  • Returns

    Get all metadata as a dictionary of format Dict[key, Dict[value, type]]. The key, value and type entries are all strings. Note that each entry might have an additional ‘key’ entry, repeating the key

get_all_metadata_casted​

get_all_metadata_casted()

  • Return type

    Dict[str, Dict[str, Any]]

  • Returns

    Get all metadata as a dictionary of format Dict[key, Dict[value, type]]. The key and type entries are strings. The value is cast to its type if possible. Note that each entry might have an additional ‘key’ entry, repeating the key

get_local_copy​

get_local_copy(extract_archive=True, raise_on_error=False, force_download=False)

Retrieve a valid link to the model file(s). If the model URL is a file system link, it will be returned directly. If the model URL points to a remote location (http/s3/gs etc.), it will download the file(s) and return the temporary location of the downloaded model.

  • Parameters

    • extract_archive (bool ) – If True, and the model is of type ‘packaged’ (e.g. TensorFlow compressed folder) The returned path will be a temporary folder containing the archive content

    • raise_on_error (bool ) – If True, and the artifact could not be downloaded, raise ValueError, otherwise return None on failure and output log warning.

    • force_download (bool ) – If True, the artifact will be downloaded, even if the model artifact is already cached.

  • Return type

    str

  • Returns

    A local path to the model (or a downloaded copy of it).

get_metadata​

get_metadata(key)

Get one metadata entry value (as a string) based on its key. See Model.get_metadata_casted if you wish to cast the value to its type (if possible)

  • Parameters

    key (str) – Key of the metadata entry you want to get

  • Return type

    Optional[str]

  • Returns

    String representation of the value of the metadata entry or None if the entry was not found

get_metadata_casted​

get_metadata_casted(key)

Get one metadata entry based on its key, casted to its type if possible

  • Parameters

    key (str) – Key of the metadata entry you want to get

  • Return type

    Optional[str]

  • Returns

    The value of the metadata entry, casted to its type (if not possible, the string representation will be returned) or None if the entry was not found

get_weights​

get_weights(raise_on_error=False, force_download=False)

Download the base model and return the locally stored filename.

  • Parameters

    • raise_on_error (bool ) – If True, and the artifact could not be downloaded, raise ValueError, otherwise return None on failure and output log warning.

    • force_download (bool ) – If True, the base model will be downloaded, even if the base model is already cached.

  • Return type

    str

  • Returns

    The locally stored file.

get_weights_package​

get_weights_package(return_path=False, raise_on_error=False, force_download=False)

Download the base model package into a temporary directory (extract the files), or return a list of the locally stored filenames.

  • Parameters

    • return_path (bool ) – Return the model weights or a list of filenames (Optional)

      • True - Download the model weights into a temporary directory, and return the temporary directory path.

      • False - Return a list of the locally stored filenames. (Default)

    • raise_on_error (bool ) – If True, and the artifact could not be downloaded, raise ValueError, otherwise return None on failure and output log warning.

    • force_download (bool ) – If True, the base artifact will be downloaded, even if the artifact is already cached.

  • Return type

    Union[str, List[Path], None]

  • Returns

    The model weights, or a list of the locally stored filenames. if raise_on_error=False, returns None on error.

id​

property id

The ID (system UUID) of the model.

  • Return type

    str

  • Returns

    The model ID.

InputModel.import_model​

classmethod import_model(weights_url, config_text=None, config_dict=None, label_enumeration=None, name=None, project=None, tags=None, comment=None, is_package=False, create_as_published=False, framework=None)

Create an InputModel object from a pre-trained model by specifying the URL of an initial weight file. Optionally, input a configuration, label enumeration, name for the model, tags describing the model, comment as a description of the model, indicate whether the model is a package, specify the model’s framework, and indicate whether to immediately set the model’s status to Published. The model is read-only.

The ClearML Server (backend) may already store the model’s URL. If the input model’s URL is not stored, meaning the model is new, then it is imported and ClearML stores its metadata. If the URL is already stored, the import process stops, ClearML issues a warning message, and ClearML reuses the model.

In your Python experiment script, after importing the model, you can connect it to the main execution Task as an input model using InputModel.connect or Task.connect. That initializes the network.

info

Using the ClearML Web-App (user interface), you can reuse imported models and switch models in experiments.

  • Parameters

    • weights_url (str ) – A valid URL for the initial weights file. If the ClearML Web-App (backend) already stores the metadata of a model with the same URL, that existing model is returned and ClearML ignores all other parameters.

      For example:

      * `https://domain.com/file.bin`

      * `s3://bucket/file.bin`

      * `file:///home/user/file.bin`

    • config_text (unconstrained text string ) – The configuration as a string. This is usually the content of a configuration dictionary file. Specify config_text or config_dict, but not both.

    • config_dict (dict ) – The configuration as a dictionary. Specify config_text or config_dict, but not both.

    • label_enumeration (dict ) – Optional label enumeration dictionary of string (label) to integer (value) pairs.

      For example:

      {
           "background": 0,
           "person": 1
      }

    • name (str ) – The name of the newly imported model. (Optional)

    • project (str ) – The project name to add the model into. (Optional)

    • tags (list ( str ) ) – The list of tags which describe the model. (Optional)

    • comment (str ) – A comment / description for the model. (Optional)

    • is_package (bool ) – Is the imported weights file is a package (Optional)

      • True - Is a package. Add a package tag to the model.

      • False - Is not a package. Do not add a package tag. (Default)

    • create_as_published (bool ) – Set the model’s status to Published (Optional)

      • True - Set the status to Published.

      • False - Do not set the status to Published. The status will be Draft. (Default)

    • framework (str or Framework object ) – The framework of the model. (Optional)

  • Return type

    InputModel

  • Returns

    The imported model or existing model (see above).

labels​

property labels

The label enumeration of string (label) to integer (value) pairs.

  • Return type

    Dict[str, int]

  • Returns

    A dictionary containing labels enumeration, where the keys are labels and the values as integers.

InputModel.load_model​

classmethod load_model(weights_url, load_archived=False)

Load an already registered model based on a pre-existing model file (link must be valid). If the url to the weights file already exists, the returned object is a Model representing the loaded Model. If no registered model with the specified url is found, None is returned.

  • Parameters

    • weights_url (str) – The valid url for the weights file (string).

      Examples:

      "https://domain.com/file.bin" or "s3://bucket/file.bin" or "file:///home/user/file.bin".
      info

      If a model with the exact same URL exists, it will be used, and all other arguments will be ignored.

    • load_archived (bool ) – Load archived models

      • True - Load the registered Model, if it is archived.

      • False - Ignore archive models.

  • Return type

    InputModel

  • Returns

    The InputModel object, or None if no model could be found.

name​

property name

The name of the model.

  • Return type

    str

  • Returns

    The model name.

project​

property project

project ID of the model.

  • Return type

    str

  • Returns

    project ID (str).

publish​

publish()

Set the model to the status published and for public use. If the model’s status is already published, then this method is a no-op.

  • Return type

    ()

InputModel.query_models​

classmethod query_models(project_name=None, model_name=None, tags=None, only_published=False, include_archived=False, max_results=None, metadata=None)

Return Model objects from the project artifactory. Filter based on project-name / model-name / tags. List is always returned sorted by descending last update time (i.e. latest model is the first in the list)

  • Parameters

    • project_name (Optional[str]) – Optional, filter based project name string, if not given query models from all projects

    • model_name (Optional[str]) – Optional Model name as shown in the model artifactory

    • tags (Optional[Sequence[str]]) – Optional filter models based on list of tags, example: [‘production’, ‘verified’, ‘-qa’] Notice use ‘-‘ prefix to filter out tags.

    • only_published (bool) – If True, only return published models.

    • include_archived (bool) – If True, return archived models.

    • max_results (Optional[int]) – Optional return the last X models, sorted by last update time (from the most recent to the least).

    • metadata (Optional[Dict[str, str]]) – Filter based on metadata. This parameter is a dictionary. Notice that the type of the metadata field is not required.

  • Return type

    List[Model]

  • Returns

    ModeList of Models objects

InputModel.remove​

classmethod remove(model, delete_weights_file=True, force=False, raise_on_errors=False)

Remove a model from the model repository. Optional, delete the model weights file from the remote storage.

  • Parameters

    • model (Union[str, Model]) – Model ID or Model object to remove

    • delete_weights_file (bool) – If True (default), delete the weights file from the remote storage

    • force (bool) – If True, remove model even if other Tasks are using this model. default False.

    • raise_on_errors (bool) – If True, throw ValueError if something went wrong, default False.

  • Return type

    bool

  • Returns

    True if Model was removed successfully partial removal returns False, i.e. Model was deleted but weights file deletion failed

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))
model.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_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)
model.report_histogram(title='histogram example', series='histogram series',
     values=vector_series, iteration=0, labels=['A','B'], xaxis='X axis label', yaxis='Y axis label')

  • 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_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’]}}

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 Model.report_confusion_matrix.

  • 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

    • 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_scalar​

report_scalar(title, series, value, iteration)

For explicit reporting, plot a scalar series.

  • 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_scatter2d​

report_scatter2d(title, series, scatter, iteration=None, xaxis=None, yaxis=None, labels=None, mode='line', 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))))
model.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))))
model.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))))
model.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))
      model.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_single_value​

report_single_value(name, value)

Reports a single value metric (for example, total experiment accuracy or mAP)

  • Parameters

    • name (str) – Metric’s name

    • value (float) – Metric’s value

  • Return type

    None

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 Model.report_confusion_matrix, but presents the data as a surface diagram not a confusion matrix.

surface_matrix = np.random.randint(10, size=(10, 10))
model.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_table​

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

For explicit report, 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'])

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

  • 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/ example: extra_layout={‘height’: 600}

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.

For example:

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

  • 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’}

set_all_metadata​

set_all_metadata(metadata, replace=True)

Set metadata based on the given parameters. Allows replacing all entries or updating the current entries.

  • Parameters

    • metadata (Dict[str, Dict[str, str]]) – A dictionary of format Dict[key, Dict[value, type]] representing the metadata you want to set

    • replace (bool) – If True, replace all metadata with the entries in the metadata parameter. If False, keep the old metadata and update it with the entries in the metadata parameter (add or change it)

  • Return type

    bool

  • Returns

    True if the metadata was set and False otherwise

set_metadata​

set_metadata(key, value, v_type=None)

Set one metadata entry. All parameters must be strings or castable to strings

  • Parameters

    • key (str) – Key of the metadata entry

    • value (str) – Value of the metadata entry

    • v_type (Optional[str]) – Type of the metadata entry

  • Return type

    bool

  • Returns

    True if the metadata was set and False otherwise

system_tags​

property system_tags

A list of system tags describing the model.

  • Return type

    List[str]

  • Returns

    The list of tags.

tags​

property tags

A list of tags describing the model.

  • Return type

    List[str]

  • Returns

    The list of tags.

task​

property task

Return the creating task ID

  • Return type

    str

  • Returns

    The Task ID (str)

url​

property url

Return the url of the model file (or archived files)

  • Return type

    str

  • Returns

    The model file URL.