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OutputModel

class OutputModel(task=None, config_text=None, config_dict=None, label_enumeration=None, name=None, tags=None, comment=None, framework=None, base_model_id=None)​

Create an output model for a Task (experiment) to store the training results.

The model is read-write and automatically registered as the Task’s output model.

A common use case is to reuse the OutputModel object, and override the weights after storing a model snapshot. Another use case is to create multiple OutputModel objects for a Task, and after a new high score is found, store a model snapshot.

If the model configuration or label enumeration is not provided, values are inherited from the Task’s input model.

info

When executing a Task remotely with a clearml-agent, you can modify the model configuration and/or model’s label enumeration using the ClearML WebApp.

Create a new model and immediately connect it to a task.

We do not allow for Model creation without a task, so we always keep track on how we created the models. In remote execution, Model parameters can be overridden by the Task (such as model configuration & label enumerator).

  • Parameters

    • task (Optional[ForwardRef]) – The Task object with which the OutputModel object is associated.

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

    • label_enumeration (Optional[Mapping[str, int]]) – The label enumeration dictionary of string (label) to integer (value) pairs.

    For example:

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

    • name (Optional[str]) – The name for the newly created model.

    • tags (Optional[List[str]]) – A list of strings which are tags for the model.

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

    • framework (Union[str, Framework, None]) – The framework of the model or a Framework object.

    • base_model_id (Optional[str]) – Model ID to be reused.

archive​

archive()

Archive the model. If the model is already archived, this is a no-op

  • Return type

    None

comment​

property comment: str

A description of the model.

  • Return type

    str

  • Returns

    The model description.

config_dict​

property config_dict: dict

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

  • Return type

    dict

  • Returns

    The configuration.

config_text​

property config_text: str

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

  • Return type

    str

  • Returns

    The configuration.

connect​

connect(task, name=None, **kwargs)

Connect a preexisting model to a Task object. Preexisting models include:

  • Imported models.

  • Models already in the ClearML platform

  • Models from external frameworks (e.g. TensorFlow)

  • Parameters

    • task (Task ) – A Task object.

    • name (Optional[str]) – The model name as it would appear on the Task object. The model’s own name can differ, which is useful when a single Task uses multiple models.

    • kwargs (Any ) –

  • Return type

    None

framework​

property framework: str

The ML framework of the model (for example: PyTorch, TensorFlow, XGBoost, etc.).

  • Return type

    str

  • Returns

    The model’s framework

get_all_metadata​

get_all_metadata()

Returns all metadata as a Dict[key, Dict[value, type]], where key, value, and type are all strings. To get values cast to their original types (if possible), use Model.get_all_metadata_casted.

  • Return type

    Dict[str, Dict[str, str]]

  • Returns

    All metadata in Dict[key, Dict[value, type]] format.

get_all_metadata_casted​

get_all_metadata_casted()

Returns all metadata as a Dict[key, Dict[value, type]], where key and type are strings, and value is cast to its original type where possible. To get all values as strings, use Model.get_all_metadata.

  • Return type

    Dict[str, Dict[str, Any]]

  • Returns

    All metadata in Dict[key, Dict[value, type]] format.

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, extract_archive=False)

Download the base model and return the locally stored filename.

  • Parameters

    • raise_on_error (bool) – If True, raise ValueError if the artifact download fails.

    • force_download (bool) – If True, re-download base model even if a cached copy exists.

    • extract_archive (bool) – If True, extract the downloaded weights file if possible.

  • Return type

    str

  • Returns

    The locally stored file.

get_weights_package​

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

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) – If True, extract weights to a temp directory and return its path. If False (default), return a list of local file paths.

    • raise_on_error (bool) – If True, raise ValueError if the artifact download fails. If False, returns None and logs a warning.

    • force_download (bool) – If True, re-download the base artifact even if a cached copy exists.

    • extract_archive (bool) – If True, extract the downloaded weights file if possible.

  • 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: str

The ID (system UUID) of the model.

  • Return type

    str

  • Returns

    The model ID.

labels​

property labels: Dict[str, int]

Get the label enumeration as a dictionary of string (label) to integer (value) pairs.

For example:

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

  • Return type

    Dict[str, int]

  • Returns

    The label enumeration.

name​

property name: str

The name of the model.

  • Return type

    str

  • Returns

    The model name.

original_task​

property original_task: str

Return the ID of the Task that created this model.

  • Return type

    str

  • Returns

    The Task ID

project​

property project: str

Project ID of the model.

  • Return type

    str

  • Returns

    Project ID

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

    None

published​

property published: bool

Get the published state of this model.

  • Return type

    bool

  • Returns

    True if the model is published, False otherwise.

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)

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) – Plot title (metric).

    • series (str) – Series name (variant).

    • matrix (ndarray) – A heat-map matrix (example: confusion matrix).

    • iteration (Optional[int]) – The reported iteration / step.

    • xaxis (Optional[str]) – The x-axis title.

    • yaxis (Optional[str]) – The y-axis title.

    • xlabels (Optional[List[str]]) – Labels for each column of the matrix.

    • ylabels (Optional[List[str]]) – Labels for each row of the matrix.

    • yaxis_reversed (bool) – If set to False, the (0, 0) coordinate is at the bottom left corner. If set to True, the (0, 0) coordinate is at the top left corner.

    • comment (Optional[str]) – A comment displayed with the plot, underneath the title.

    • extra_layout (Optional[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']}}

  • Return type

    None

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)

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 (str) – Plot title (metric).

    • series (str) – Series name (variant).

    • values (Sequence[Union[int, float]]) – The series values. A list of floats, or an N-dimensional Numpy array containing data for each histogram bar.

    • iteration (Optional[int]) – The reported iteration / step. Each iteration creates another plot.

    • labels (Optional[List[str]]) – Labels for each bar group, creating a plot legend labeling each series.

    • xlabels (Optional[List[str]]) – Labels per entry in each bucket in the histogram (vector), creating a set of labels for each histogram bar on the x-axis.

    • xaxis (Optional[str]) – The x-axis title.

    • yaxis (Optional[str]) – The y-axis title.

    • mode (Optional[str]) – Display mode for multiple histograms. The options are:

      • group (default)

      • stack

      • relative

    • data_args (Optional[dict]) – 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[dict]) – 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']}}

  • Return type

    None

report_line_plot​

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

Plot one or more series as lines.

  • Parameters

    • title (str) – Plot title (metric).

    • series (Sequence[SeriesInfo]) – All the series data, one list element for each line in the plot.

    • iteration (Optional[int]) – The reported iteration / step.

    • xaxis (str) – The x-axis title.

    • yaxis (str) – The y-axis title.

    • mode (str) – The type of line plot. The options are: lines (default), markers, lines+markers.

    • reverse_xaxis (bool) – If True, reverse the x-axis (high to low). Defaults to False.

    • comment (Optional[str]) – A comment displayed underneath the plot title.

    • extra_layout (Optional[dict]) – 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']}}

  • Return type

    None

report_matrix​

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

Plot a confusion matrix.

info

This method is the same as Model.report_confusion_matrix.

  • Parameters

    • title (str) – Plot title (metric).

    • series (str) – Series name (variant).

    • matrix (ndarray) – A heat-map matrix (example: confusion matrix).

    • iteration (Optional[int]) – The reported iteration / step.

    • xaxis (Optional[str]) – The x-axis title.

    • yaxis (Optional[str]) – The y-axis title.

    • xlabels (Optional[List[str]]) – Labels for each column of the matrix.

    • ylabels (Optional[List[str]]) – Labels for each row of the matrix.

    • yaxis_reversed (bool) – If set to False, the (0, 0) coordinate is at the bottom left corner. If set to True, the (0, 0) coordinate is at the top left corner.

    • extra_layout (Optional[dict]) – 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']}}

  • Return type

    None

report_scalar​

report_scalar(title, series, value, iteration)

Plot a scalar series.

  • Parameters

    • title (str) – Plot title (metric). Plot more than one scalar series on the same plot by using the same title for each call to this method.

    • series (str) – Series name (variant).

    • 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)

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) – Plot title (metric).

    • series (str) – Series name (variant) of the reported scatter plot.

    • scatter (Union[Sequence[Tuple[float, float]], ndarray]) – The scatter data. numpy.ndarray or list of (pairs of x,y) scatter.

    • iteration (Optional[int]) – The reported iteration / step.

    • xaxis (Optional[str]) – The x-axis title.

    • yaxis (Optional[str]) – The y-axis title.

    • labels (Optional[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 options are: lines (default), markers, lines+markers.

    • comment (Optional[str]) – A comment displayed with the plot, underneath the title.

    • extra_layout (Optional[dict]) – 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']}}

  • Return type

    None

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)

Plot a 3D scatter graph. 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",
)

  • Parameters

    • title (str) – Plot title (metric)

    • series (str) – Series name (variant)

    • scatter (Union[Sequence[Tuple[float, float, float]], ndarray]) – The scatter data as

      • a list of (x,y,z) tuples

      • a nested list [[(x1,y1,z1)...]], or

      • a numpy.ndarray.

    • iteration (Optional[int]) – The reported iteration / step.

    • xaxis (Optional[str]) – The x-axis title.

    • yaxis (Optional[str]) – The y-axis title.

    • zaxis (Optional[str]) – The z-axis title.

    • labels (Optional[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 options are: markers (default), lines, lines+markers.

    • fill (bool) – If True, fill the area under the curve. Defaults to False.

    • comment (Optional[str]) – A comment displayed underneath the plot title.

    • extra_layout (Optional[dict]) – 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']}}

  • Return type

    None

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)

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) – Plot title (metric).

    • series (str) – Series name (variant).

    • matrix (ndarray) – A heat-map matrix (example: confusion matrix).

    • iteration (Optional[int]) – The reported iteration / step.

    • xaxis (Optional[str]) – The x-axis title.

    • yaxis (Optional[str]) – The y-axis title.

    • zaxis (Optional[str]) – The z-axis title.

    • xlabels (Optional[List[str]]) – Labels for each column of the matrix (optional).

    • ylabels (Optional[List[str]]) – Labels for each row of the matrix (optional).

    • camera (Optional[Sequence[float]]) – (X,Y,Z) coordinates indicating the camera position. The default value is (1,1,1).

    • comment (Optional[str]) – A comment displayed underneath the plot title.

    • extra_layout (Optional[dict]) – 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']}}

  • Return type

    None

report_table​

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

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 (str) – Table title (metric).

    • series (str) – Series name (variant).

    • iteration (Optional[int]) – The reported iteration / step.

    • table_plot (Union[DataFrame, Sequence[Sequence], None]) – The output table plot object.

    • csv (Optional[str]) – Path to local CSV file.

    • url (Optional[str]) – A URL to the location of CSV file.

    • extra_layout (Optional[Dict]) – 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}

  • 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)

Plot a vector as a (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 (str) – Plot title (metric).

    • series (str) – Series name (variant).

    • values (Sequence[Union[int, float]]) – Vector data as a list of floats or an N-dimensional Numpy array containing data for each histogram bar.

    • iteration (Optional[int]) – The reported iteration / step. Each iteration creates another plot.

    • labels (Optional[List[str]]) – Labels for each bar group, creating a plot legend labeling each series.

    • xlabels (Optional[List[str]]) – Labels per entry in each bucket in the histogram (vector), creating a set of labels for each histogram bar on the x-axis.

    • xaxis (Optional[str]) – The x-axis title.

    • yaxis (Optional[str]) – The y-axis title.

    • mode (Optional[str]) – Display mode for multiple histograms. The options are:

      • group (default)

      • stack

      • relative

    • extra_layout (Optional[dict]) – 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'}

  • Return type

    None

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

OutputModel.set_default_upload_uri​

classmethod set_default_upload_uri(output_uri)

Set the default upload URI for all OutputModels.

  • Parameters

    output_uri (Optional[str]) – URL for uploading models. Examples:

    • https://demofiles.demo.clear.ml

    • s3://bucket/

    • gs://bucket/

    • azure://bucket/

    • file:///mnt/shared/nfs

  • Return type

    None

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, False otherwise.

set_upload_destination​

set_upload_destination(uri)

Set the URI of the storage destination for uploaded model weight files. Supported storage destinations include S3, Google Cloud Storage, and file locations.

Using this method, file uploads are separate and then a link to each is stored in the model object.

info

For storage requiring credentials, the credentials are stored in the ClearML configuration file, ~/clearml.conf.

  • Parameters

    uri (str) – The URI of the upload storage destination.

    For example:

    • s3://bucket/directory/

    • file:///tmp/debug/

  • Return type

    None

system_tags​

property system_tags: List[str]

A list of system tags describing the model.

  • Return type

    List[str]

  • Returns

    The list of tags.

tags​

property tags: List[str]

A list of tags describing the model.

  • Return type

    List[str]

  • Returns

    The list of tags.

task​

property task: str

The ID of the task connected to this model. If no task is connected, returns the ID of the task that originally created it.

  • Return type

    str

  • Returns

    The Task ID

unarchive​

unarchive()

Unarchive the model. If the model is not archived, this is a no-op

  • Return type

    None

update_design​

update_design(config_text=None, config_dict=None)

Update the model configuration. Store a blob of text for custom usage.

info

This method’s behavior is lazy. The design update is only forced when the weights are updated.

  • Parameters

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

  • Return type

    bool

  • Returns

    True if the update was successful, False if it was not.

update_labels​

update_labels(labels)

Update the label enumeration.

  • Parameters

    labels (Mapping[str, int]) – The label enumeration dictionary of string (label) to integer (value) pairs.

    For example:

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

  • Return type

    Any

update_weights​

update_weights(weights_filename=None, upload_uri=None, target_filename=None, auto_delete_file=True, register_uri=None, iteration=None, update_comment=True, is_package=False, async_enable=True)

Update the model weights from a local file.

info

Uploading the model is a background process. This method returns immediately.

  • Parameters

    • weights_filename (Optional[str]) – The name of the locally stored weights file to upload. Specify weights_filename or register_uri, but not both.

    • upload_uri (Optional[str]) – The URI of the storage destination for model weights upload. The default value is the previously used URI.

    • target_filename (Optional[str]) – The newly created filename in the storage destination location. The default value is the weights_filename value.

    • auto_delete_file (bool) – If True (default), delete the temporary file after uploading.

    • register_uri (Optional[str]) – The URI of an already uploaded weights file. The URI must be valid. Specify register_uri or weights_filename, but not both.

    • iteration (Optional[int]) – The iteration number.

    • update_comment (bool) – If True (default), append the local weights filename to the model comment (to maintain provenance).

    • is_package (bool) – If True, mark the weights file as a compressed package, usually a zip file. Defaults to False.

    • async_enable (bool) – If True (default), upload in the background and return immediately. If False, block until the upload completes. Raises an error if the upload fails.

  • Return type

    str

  • Returns

    The uploaded URI.

update_weights_package​

update_weights_package(weights_filenames=None, weights_path=None, upload_uri=None, target_filename=None, auto_delete_file=True, iteration=None, async_enable=True)

Update the model weights from a local file, or from directory containing multiple files.

info

Uploading the model is a background process. This method returns immediately.

  • Parameters

    • weights_filenames (Optional[Sequence[str]]) – The file names of the locally stored model files. Specify weights_filenames, or weights_path, but not both.

    • weights_path (Optional[str]) – The directory path to a package. All the files in the directory will be uploaded. Specify weights_path or weights_filenames, but not both.

    • upload_uri (Optional[str]) – The URI of the storage destination for the model weights upload. The default is the previously used URI.

    • target_filename (Optional[str]) – The newly created filename in the storage destination URI location. The default is the value specified in the weights_filename parameter.

    • auto_delete_file (bool) – If True (default), delete temporary file after uploading.

    • iteration (Optional[int]) – The iteration number.

    • async_enable (bool) – Whether to upload model in background or to block. Will raise an error in the main thread if the weights failed to be uploaded or not.

  • Return type

    str

  • Returns

    The uploaded URI for the weights package.

upload_storage_uri​

property upload_storage_uri: str

The URI of the storage destination for uploaded model weight files.

  • Return type

    str

  • Returns

    The URI string

url​

property url: str

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

  • Return type

    str

  • Returns

    The model file URL.

OutputModel.wait_for_uploads​

classmethod wait_for_uploads(timeout=None, max_num_uploads=None)

Wait for any pending or in-progress model uploads to complete. If no uploads are pending or in-progress, then the wait_for_uploads returns immediately.

  • Parameters

    • timeout (Optional[float]) – The timeout interval to wait for uploads (seconds).

    • max_num_uploads (Optional[int]) – The maximum number of uploads to wait for.

  • Return type

    None