cascade.meta#

The dash-based server to view meta-data and compare different snapshots using deep diff.

It can work with Repos or Workspaces

__init__(path: str) → None[source]#

serve(*args: Any, **kwargs: Any)[source]#

The tool which allows user to visualize training history of model versions. Uses shows how metrics of models changed over time and how models with different hyperparameters depend on each other.

__init__(container: Union[Workspace, Repo, ModelLine], last_lines: Optional[int] = None, last_models: Optional[int] = None, update_period_sec: int = 3) → None[source]#

Parameters:

• container (Union[Workspace, Repo, ModelLine]) – Container of models to be viewed

• last_lines (int, optional) – Constraints the number of lines back from the last one to view

• last_models (int, optional) – For each line constraints the number of models back from the last one to view

• update_period_sec (int, default is 3) – Update period in seconds

plot(metric: str, show: bool = False) → Any[source]#

Plots training history of model versions using plotly.

Parameters:

• metric (str) – Metric should be present in meta of at least one model in repo

• show (bool, optional) – Whether to return and show or just return figure

serve(metric: Optional[str] = None, **kwargs: Any) → None[source]#

Runs dash-based server with HistoryViewer, updating plots in real-time.

Parameters:

• metric – One of the metrics in the repo. May be left None and chosen later in the interface

• optional – One of the metrics in the repo. May be left None and chosen later in the interface

• **kwargs – Arguments for app.run_server() for example port or host

Note

This feature needs dash to be installed.

The class to view all metadata in folders and subfolders.

__getitem__(index: int) → List[Dict[Any, Any]][source]#

Returns:

meta – Meta object that was read from file

Return type:

Meta

__init__(root: str, filt: Optional[Dict[Any, Any]] = None) → None[source]#

Parameters:

• root (str) – path to the folder containing metadata files

• filt (Dict, optional) – dictionary that specifies which values that should be present in meta for example to find all models use filt={'type': 'model'}

See also

cascade.base.MetaHandler

Interface for viewing metrics in model meta files uses Repo to extract metrics of all models if any. As metrics it uses data from metrics field in models’ meta and as parameters it uses params field.

__getitem__(key: Union[int, str, slice])[source]#

Sets the scope of the viewer after creation. Basically creates new viewer with another scope.

__init__(repo: Union[Repo, ModelLine], scope: Optional[Union[int, str, slice]] = None) → None[source]#

Parameters:

• repo (Repo) – Repo object to extract metrics from

• scope (Union[int, str, slice]) – Index or a name of line to view. Can be set using __getitem__

get_best_by(metric: str, maximize: bool = True) → Model[source]#

Loads the best model by the given metric

Parameters:

• metric (str) – Name of the metric

• maximize (bool) – The direction of choosing the best model: True if greater is better and False if less is better

Raises:

• TypeError if metric objects cannot be sorted. If only one model in repo, then –

• returns it without error since no sorting involved. –

plot_table(show: bool = False)[source]#

Uses plotly to graphically show table with metrics and parameters.

reload_table() → None[source]#

serve(page_size: int = 50, include: Optional[List[str]] = None, exclude: Optional[List[str]] = None, **kwargs: Any) → None[source]#

Runs dash-based server with interactive table of metrics and parameters

Parameters:

• page_size (int, optional) – Size of the table in rows on one page

• include (List[str], optional:) – List of parameters or metrics to be added. Only they will be present along with some default

• exclude (List[str], optional:) – List of parameters or metrics to be excluded from table

• **kwargs – Arguments of dash app. Can be ip or port for example

serve(**kwargs: Any) → None[source]#