Dask distributed features a nice web interface for monitoring the execution of a Dask computation graph.
By default, when no custom Client is specified, Arboretum creates a LocalCluster instance with the diagnostics dashboard disabled:
... local_cluster = LocalCluster(diagnostics_port=None) client = Client(local_cluster) ...
local_cluster = LocalCluster() # diagnostics dashboard is enabled custom_client = Client(local_cluster) ... network = grnboost2(expression_data=ex_matrix, tf_names=tf_names, client=custom_client) # specify the custom client
By default, the dashboard is available on port
For more information, consult:
However, in some fields (like single-cell genomics), the default is inversed: the rows represent genes and the columns represent the observations.
In order to maintain an API that is as lean is possible, Arboretum adopts the scikit-learn convention (rows=observations, columns=features). This means that the user is responsible for providing the data in the right shape.