trains.task.Task(private=None, **kwargs)¶The Task class is a code template for a Task object which, together with its connected experiment components,
represents the current running experiment. These connected components include hyperparameters, loggers,
configuration, label enumeration, models, and other artifacts.
The term “main execution Task” refers to the Task context for current running experiment. Python experiment scripts can create one, and only one, main execution Task. It is a traceable, and after a script runs and Trains stores the Task in the Trains Server (backend), it is modifiable, reproducible, executable by a worker, and you can duplicate it for further experimentation.
The Task class and its methods allow you to create and manage experiments, as well as perform
advanced experimentation functions, such as autoML.
Warning
Do not construct Task objects directly. Use one of the methods listed below to create experiments or reference existing experiments.
For detailed information about creating Task objects, see the following methods:
Task.init() - Create a new reproducible Task, or reuse one.
Task.create() - Create a new non-reproducible Task.
Task.current_task() - Get the current running Task.
Task.get_task() - Get another Task (whose metadata the Trains Server maintains).
Note
The Trains documentation often refers to a Task as, “Task (experiment)”.
“Task” refers to the class in the Trains Python Client Package, the object in your Python experiment script, and the entity with which Trains Server and Trains Agent work.
“Experiment” refers to your deep learning solution, including its connected components, inputs, and outputs, and is the experiment you can view, analyze, compare, modify, duplicate, and manage using the Trains Web-App (UI).
Therefore, a “Task” is effectively an “experiment”, and “Task (experiment)” encompasses its usage throughout the Trains.
The exception to this Task behavior is sub-tasks (non-reproducible Tasks), which do not use the main execution Task. Creating a sub-task always creates a new Task with a new Task ID.
Warning
Do not construct Task manually!
Please use Task.init() or Task.get_task()
current_task()¶Get the current running Task (experiment). This is the main execution Task (task context) returned as a Task object.
The current running Task (experiment).
init(project_name=None, task_name=None, task_type=<TaskTypes.training: 'training'>, reuse_last_task_id=True, output_uri=None, auto_connect_arg_parser=True, auto_connect_frameworks=True, auto_resource_monitoring=True)¶Creates a new Task (experiment), or returns the existing Task, depending upon the following:
If any of the following are true, Trains creates a new Task and a new Task ID:
a Task in the same project with same name does not exist, or
a Task in the same project with same name does exist and its status is Published, or
the reuse_last_task_id parameter is assigned False.
If all of the following are true, Trains returns the existing Task with the existing Task ID:
a Task in the same project with the same name does exist, and
the Task’s status is Draft, Completed, Failed, or Aborted, and
the reuse_last_task_id parameter is the default value of True.
Warning
When a Python experiment script runs using an existing Task, it overwrites previous experiment output.
project_name (str) – The name of the project in which the experiment will be created. If the project does
not exist, it is created. If project_name is None, the repository name is used. (Optional)
task_name (str) – The name of Task (experiment). If task_name is None, the Python experiment
script’s file name is used. (Optional)
task_type (TaskTypes) –
The task type.
Valid task types:
TaskTypes.training (default)
TaskTypes.testing
TaskTypes.inference
TaskTypes.data_processing
TaskTypes.application
TaskTypes.monitor
TaskTypes.controller
TaskTypes.optimizer
TaskTypes.service
TaskTypes.qc
TaskTypes.custom
reuse_last_task_id (bool) –
Force a new Task (experiment) with a new Task ID, but the same project and Task names.
Note
Trains creates the new Task ID using the previous Id, which is stored in the data cache folder.
The values are:
True - Reuse the last Task ID. (default)
False - Force a new Task (experiment).
A string - In addition to a boolean, you can use a string to set a specific value for Task ID (instead of the system generated UUID).
output_uri (str) –
The default location for output models and other artifacts. In the default location, Trains creates a subfolder for the output. The subfolder structure is the following:
<output destination name> / <project name> / <task name>.< Task ID>
The following are examples of output_uri values for the supported locations:
A shared folder: /mnt/share/folder
S3: s3://bucket/folder
Google Cloud Storage: gs://bucket-name/folder
Azure Storage: azure://company.blob.core.windows.net/folder/
Important
For cloud storage, you must install the Trains package for your cloud storage type, and then configure your storage credentials. For detailed information, see Trains Python Client Extras in the “Trains Python Client Reference” section.
auto_connect_arg_parser –
Automatically connect an argparse object to the Task?
The values are:
True - Automatically connect. (default)
False - Do not automatically connect.
A dictionary - In addition to a boolean, you can use a dictionary for fined grained control of connected
arguments. The dictionary keys are argparse variable names and the values are booleans.
The False value excludes the specified argument from the Task’s parameter section.
Keys missing from the dictionary default to True, and an empty dictionary defaults to False.
For example:
auto_connect_arg_parser={'do_not_include_me': False, }
Note
To manually connect an argparse, use Task.connect().
auto_connect_frameworks –
Automatically connect frameworks? This includes patching MatplotLib, XGBoost, scikit-learn, Keras callbacks, and TensorBoard/X to serialize plots, graphs, and the model location to the Trains Server (backend), in addition to original output destination.
The values are:
True - Automatically connect (default)
False - Do not automatically connect
A dictionary - In addition to a boolean, you can use a dictionary for fined grained control of connected
frameworks. The dictionary keys are frameworks and the values are booleans.
Keys missing from the dictionary default to True, and an empty dictionary defaults to False.
For example:
auto_connect_frameworks={'matplotlib': True, 'tensorflow': True, 'pytorch': True,
'xgboost': True, 'scikit': True}
auto_resource_monitoring (bool) –
Automatically create machine resource monitoring plots? These plots appear in in the Trains Web-App (UI), RESULTS tab, SCALARS sub-tab, with a title of :resource monitor:.
The values are:
True - Automatically create resource monitoring plots. (default)
False - Do not automatically create.
The main execution Task (Task context).
create(project_name=None, task_name=None, task_type=<TaskTypes.training: 'training'>)¶Create a new, non-reproducible Task (experiment). This is called a sub-task.
Note
This method always creates a new Task.
To create reproducible Tasks, use the Task.init() method.
project_name (str) – The name of the project in which the experiment will be created.
If project_name is None, and the main execution Task is initialized (see Task.init()),
then the main execution Task’s project is used. Otherwise, if the project does
not exist, it is created. (Optional)
task_name (str) – The name of Task (experiment).
task_type (TaskTypes) –
The task type.
Valid task types:
TaskTypes.training (default)
TaskTypes.testing
TaskTypes.inference
TaskTypes.data_processing
TaskTypes.application
TaskTypes.monitor
TaskTypes.controller
TaskTypes.optimizer
TaskTypes.service
TaskTypes.qc
TaskTypes.custom
A new experiment.
get_task(task_id=None, project_name=None, task_name=None)¶Get a Task by Id, or project name / task name combination.
task_id (str) – The Id (system UUID) of the experiment to get.
If specified, project_name and task_name are ignored.
project_name (str) – The project name of the Task to get.
task_name (str) – The name of the Task within project_name to get.
The Task specified by ID, or project name / experiment name combination.
get_tasks(task_ids=None, project_name=None, task_name=None, task_filter=None)¶Get a list of Tasks by one of the following:
A list of specific Task IDs.
All Tasks in a project matching a full or partial Task name.
All Tasks in any project matching a full or partial Task name.
task_ids (list(str)) – The Ids (system UUID) of experiments to get.
If task_ids specified, then project_name and task_name are ignored.
project_name (str) – The project name of the Tasks to get. To get the experiment
in all projects, use the default value of None. (Optional)
task_name (str) – The full name or partial name of the Tasks to match within the specified
project_name (or all projects if project_name is None).
This method supports regular expressions for name matching. (Optional)
task_ids – list of unique task id string (if exists other parameters are ignored)
project_name – project name (str) the task belongs to (use None for all projects)
task_name – task name (str) in within the selected project Return any partial match of task_name, regular expressions matching is also supported If None is passed, returns all tasks within the project
task_filter (dict) – filter and order Tasks. See service.tasks.GetAllRequest for details
The Tasks specified by the parameter combinations (see the parameters).
artifacts¶A read-only dictionary of Task artifacts (name, artifact).
The artifacts.
models¶Read-only dictionary of the Task’s loaded/stored models
A dictionary of models loaded/stored {‘input’: list(Model), ‘output’: list(Model)}.
clone(source_task=None, name=None, comment=None, parent=None, project=None)¶Create a duplicate (a clone) of a Task (experiment). The status of the cloned Task is Draft
and modifiable.
Use this method to manage experiments and for autoML.
source_task (str) – The Task to clone. Specify a Task object or a Task ID. (Optional)
name (str) – The name of the new cloned Task. (Optional)
comment (str) – A comment / description for the new cloned Task. (Optional)
parent (str) –
The Id of the parent Task of the new Task.
If parent is not specified, then parent is set to source_task.parent.
If parent is not specified and source_task.parent is not available, then
parent set to to source_task.
project (str) – The Id of the project in which to create the new Task.
If None, the new task inherits the original Task’s project. (Optional)
The new cloned Task (experiment).
enqueue(task, queue_name=None, queue_id=None)¶Enqueue a Task for execution, by adding it to an execution queue.
Note
A worker daemon must be listening at the queue for the worker to fetch the Task and execute it, see Use Case Examples on the “Trains Agent Reference page.
task (Task/str) – The Task to enqueue. Specify a Task object or Task ID.
queue_name (str) – The name of the queue. If not specified, then queue_id must be specified.
queue_id (str) – The Id of the queue. If not specified, then queue_name must be specified.
An enqueue JSON response.
{
"queued": 1,
"updated": 1,
"fields": {
"status": "queued",
"status_reason": "",
"status_message": "",
"status_changed": "2020-02-24T15:05:35.426770+00:00",
"last_update": "2020-02-24T15:05:35.426770+00:00",
"execution.queue": "2bd96ab2d9e54b578cc2fb195e52c7cf"
}
}
queued - The number of Tasks enqueued (an integer or null).
updated - The number of Tasks updated (an integer or null).
fields
status - The status of the experiment.
status_reason - The reason for the last status change.
status_message - Information about the status.
status_changed - The last status change date and time (ISO 8601 format).
last_update - The last Task update time, including Task creation, update, change, or events for
this task (ISO 8601 format).
execution.queue - The Id of the queue where the Task is enqueued. null indicates not enqueued.
dequeue(task)¶Dequeue (remove) a Task from an execution queue.
task (Task/str) – The Task to dequeue. Specify a Task object or Task ID.
A dequeue JSON response.
{
"dequeued": 1,
"updated": 1,
"fields": {
"status": "created",
"status_reason": "",
"status_message": "",
"status_changed": "2020-02-24T16:43:43.057320+00:00",
"last_update": "2020-02-24T16:43:43.057320+00:00",
"execution.queue": null
}
}
dequeued - The number of Tasks enqueued (an integer or null).
fields
status - The status of the experiment.
status_reason - The reason for the last status change.
status_message - Information about the status.
status_changed - The last status change date and time in ISO 8601 format.
last_update - The last time the Task was created, updated,changed or events for this task were reported.
execution.queue - The Id of the queue where the Task is enqueued. null indicates not enqueued.
updated - The number of Tasks updated (an integer or null).
Add Tags to this task. Old tags are not deleted. When executing a Task (experiment) remotely, this method has no effect).
tags – A list of tags which describe the Task to add.
connect(mutable)¶Connect an object to a Task object. This connects an experiment component (part of an experiment) to the experiment. For example, connect hyperparameters or models.
mutable (object) –
The experiment component to connect. The object can be any object Task supports integrating, including:
argparse - An argparse object for parameters.
dict - A dictionary for parameters.
TaskParameters - A TaskParameters object.
model - A model object for initial model warmup, or for model update/snapshot uploading.
The result returned when connecting the object, if supported.
Raise an exception on unsupported objects.
connect_configuration(configuration)¶Connect a configuration dictionary or configuration file (pathlib.Path / str) to a Task object. This method should be called before reading the configuration file.
Later, when creating an output model, the model will include the contents of the configuration dictionary or file.
For example, a local file:
config_file = task.connect_configuration(config_file)
my_params = json.load(open(config_file,'rt'))
A parameter dictionary:
my_params = task.connect_configuration(my_params)
configuration –
The configuration. This is usually the configuration used in the model training process. Specify one of the following:
A dictionary - A dictionary containing the configuration. Trains stores the configuration in the Trains Server (backend), in a HOCON format (JSON-like format) which is editable.
A pathlib2.Path string - A path to the configuration file. Trains stores the content of the file.
A local path must be relative path. When executing a Task remotely in a worker, the contents brought
from the Trains Server (backend) overwrites the contents of the file.
If a dictionary is specified, then a dictionary is returned. If pathlib2.Path / string is specified, then a path to a local configuration file is returned. Configuration object.
connect_label_enumeration(enumeration)¶Connect a label enumeration dictionary to a Task (experiment) object.
Later, when creating an output model, the model will include the label enumeration dictionary.
enumeration (dict) –
A label enumeration dictionary of string (label) to integer (value) pairs.
For example:
{
'background': 0,
'person': 1
}
The label enumeration dictionary (JSON).
get_logger()¶Get a Logger object for reporting, for this task context. You can view all Logger report output associated with the Task for which this method is called, including metrics, plots, text, tables, and images, in the Trains Web-App (UI).
The Logger for the Task (experiment).
mark_started()¶Manually mark a Task as started (happens automatically)
mark_stopped()¶Manually mark a Task as stopped (also used in _at_exit())
flush(wait_for_uploads=False)¶Flush any outstanding reports or console logs.
wait_for_uploads (bool) –
Wait for all outstanding uploads to complete before existing the flush?
True - Wait
False - Do not wait (default)
reset(set_started_on_success=False, force=False)¶Reset a Task. Trains reloads a Task after a successful reset.
When a worker executes a Task remotely, the Task does not reset unless
the force parameter is set to True (this avoids accidentally clearing logs and metrics).
set_started_on_success (bool) –
If successful, automatically set the Task to started?
True - If successful, set to started.
False - If successful, do not set to started. (default)
force (bool) –
Force a Task reset, even when executing the Task (experiment) remotely in a worker?
True - Force
False - Do not force (default)
close()¶Close the current Task. Enables you to manually shutdown the task.
Warning
Only call Task.close() if you are certain the Task is not needed.
register_artifact(name, artifact, metadata=None, uniqueness_columns=True)¶Register (add) an artifact for the current Task. Registered artifacts are dynamically sychronized with the Trains Server (backend). If a registered artifact is updated, the update is stored in the Trains Server (backend). Registered artifacts are primarily used for Data Audition.
The currently supported registered artifact object type is a pandas.DataFrame.
See also Task.unregister_artifact() and Task.get_registered_artifacts().
Note
Trains also supports uploaded artifacts which are one-time uploads of static artifacts that are not
dynamically sychronized with the Trains Server (backend). These static artifacts include
additional object types. For more information, see Task.upload_artifact().
name (str) –
The name of the artifact.
Warning
If an artifact with the same name was previously registered, it is overwritten.
artifact (object) – The artifact object.
metadata (dict) – A dictionary of key-value pairs for any metadata. This dictionary appears with the experiment in the Trains Web-App (UI), ARTIFACTS tab.
uniqueness_columns – A Sequence of columns for artifact uniqueness comparison criteria, or the default
value of True. If True, the artifact uniqueness comparison criteria is all the columns,
which is the same as artifact.columns.
unregister_artifact(name)¶Unregister (remove) a registered artifact. This removes the artifact from the watch list that Trains uses to synchronize artifacts with the Trains Server (backend).
Important
Calling this method does not remove the artifact from a Task. It only stops Trains from monitoring the artifact.
When this method is called, Trains immediately takes the last snapshot of the artifact.
get_registered_artifacts()¶Get a dictionary containing the Task’s registered (dynamically synchronized) artifacts (name, artifact object).
Note
After calling get_registered_artifacts, you can still modify the registered artifacts.
The registered (dynamically synchronized) artifacts.
upload_artifact(name, artifact_object, metadata=None, delete_after_upload=False)¶Upload (add) a static artifact to a Task object. The artifact is uploaded in the background.
The currently supported upload (static) artifact types include:
string / pathlib2.Path - A path to artifact file. If a wildcard or a folder is specified, then Trains creates and uploads a ZIP file.
dict - Trains stores a dictionary as .json file and uploads it.
pandas.DataFrame - Trains stores a pandas.DataFrame as .csv.gz (compressed CSV) file and uploads it.
numpy.ndarray - Trains stores a numpy.ndarray as .npz file and uploads it.
PIL.Image - Trains stores a PIL.Image as .png file and uploads it.
name (str) –
The artifact name.
Warning
If an artifact with the same name was previously uploaded, then it is overwritten.
artifact_object (object) – The artifact object.
metadata (dict) – A dictionary of key-value pairs for any metadata. This dictionary appears with the experiment in the Trains Web-App (UI), ARTIFACTS tab.
delete_after_upload (bool) –
After the upload, delete the local copy of the artifact?
True - Delete the local copy of the artifact.
False - Do not delete. (default)
The status of the upload.
True - Upload succeeded.
False - Upload failed.
If the artifact object type is not supported, raise a ValueError.
get_models()¶Return a dictionary with {‘input’: [], ‘output’: []} loaded/stored models of the current Task Input models are files loaded in the task, either manually or automatically logged Output models are files stored in the task, either manually or automatically logged Automatically logged frameworks are for example: TensorFlow, Keras, PyTorch, ScikitLearn(joblib) etc.
A dictionary with keys input/output, each is list of Model objects.
Example:
{'input': [trains.Model()], 'output': [trains.Model()]}
is_current_task()¶Deprecated since version 0.13.0: This method is deprecated. Use Task.is_main_task() instead.
Is this Task object the main execution Task (initially returned by Task.init())?
Is this Task object the main execution Task?
True - Is the main execution Task.
False - Is not the main execution Task.
is_main_task()¶Is this Task object the main execution Task (initially returned by Task.init())?
Note
If Task.init() was never called, this method will not create
it, making this test more efficient than:
Task.init() == task
Is this Task object the main execution Task?
True - Is the main execution Task.
False - Is not the main execution Task.
set_model_config(config_text=None, config_dict=None)¶Deprecated since version 0.14.1: Use Task.connect_configuration() instead.
get_model_config_text()¶Deprecated since version 0.14.1: Use Task.connect_configuration() instead.
get_model_config_dict()¶Deprecated since version 0.14.1: Use Task.connect_configuration() instead.
set_model_label_enumeration(enumeration=None)¶Set the label enumeration for the Task object before creating an output model. Later, when creating an output model, the model will inherit these properties.
enumeration (dict) –
A label enumeration dictionary of string (label) to integer (value) pairs.
For example:
{
'background': 0,
'person': 1
}
get_last_iteration()¶Get the last reported iteration, which is the last iteration for which the Task reported a metric.
Note
The maximum reported iteration is not in the local cache. This method sends a request to the Trains Server (backend).
The last reported iteration number.
set_last_iteration(last_iteration)¶Forcefully set the last reported iteration, which is the last iteration for which the Task reported a metric.
last_iteration (int) – The last reported iteration number.
set_initial_iteration(offset=0)¶Set initial iteration, instead of zero. Useful when continuing training from previous checkpoints
offset (int) – Initial iteration (at starting point)
Newly set initial offset.
get_initial_iteration()¶Return the initial iteration offset, default is 0 Useful when continuing training from previous checkpoints
Initial iteration offset.
get_last_scalar_metrics()¶Get the last scalar metrics which the Task reported. This is a nested dictionary, ordered by title and series.
For example:
{
'title': {
'series': {
'last': 0.5,
'min': 0.1,
'max': 0.9
}
}
}
The last scalar metrics.
get_parameters_as_dict()¶Get the Task parameters as a raw nested dictionary.
Note
The values are not parsed. They are returned as is.
set_parameters_as_dict(dictionary)¶Set the parameters for the Task object from a dictionary. The dictionary can be nested.
This does not link the dictionary to the Task object. It does a one-time update. This
is the same behavior as the Task.connect() method.
execute_remotely(queue_name=None, clone=False, exit_process=True)¶If task is running locally (i.e., not by trains-agent), then clone the Task and enqueue it for remote
execution; or, stop the execution of the current Task, reset its state, and enqueue it. If exit==True,
exit this process.
Note
If the task is running remotely (i.e., trains-agent is executing it), this call is a no-op
(i.e., does nothing).
queue_name – The queue name used for enqueueing the task. If None, this call exits the process
without enqueuing the task.
clone –
Clone the Task and execute the newly cloned Task?
The values are:
True - A cloned copy of the Task will be created, and enqueued, instead of this Task.
False - The Task will be enqueued.
exit_process –
The function call will leave the calling process at the end?
True - Exit the process (exit(0)).
False - Do not exit the process.
Warning
If clone==False, then exit_process must be True.
wait_for_status(status=(<TaskStatusEnum.completed: 'completed'>, <TaskStatusEnum.stopped: 'stopped'>, <TaskStatusEnum.closed: 'closed'>), raise_on_status=(<TaskStatusEnum.failed: 'failed'>, ), check_interval_sec=60.0)¶Wait for a task to reach a defined status.
status – Status to wait for. Defaults to (‘completed’, ‘stopped’, ‘closed’, )
raise_on_status – Raise RuntimeError if the status of the tasks matches one of these values. Defaults to (‘failed’).
check_interval_sec – Interval in seconds between two checks. Defaults to 60 seconds.
RuntimeError if the status is one of {raise_on_status}.
set_credentials(api_host=None, web_host=None, files_host=None, key=None, secret=None, host=None)¶Set new default Trains Server (backend) host and credentials.
These credentials will be overridden by either OS environment variables, or the Trains configuration
file, trains.conf.
Warning
Credentials must be set before initializing a Task object.
For example, to set credentials for a remote computer:
Task.set_credentials(api_host='http://localhost:8008', web_host='http://localhost:8080',
files_host='http://localhost:8081', key='optional_credentials', secret='optional_credentials')
task = Task.init('project name', 'experiment name')
api_host (str) – The API server url. For example, host='http://localhost:8008'
web_host (str) – The Web server url. For example, host='http://localhost:8080'
files_host (str) – The file server url. For example, host='http://localhost:8081'
key (str) – The user key (in the key/secret pair). For example, key='thisisakey123'
secret (str) – The user secret (in the key/secret pair). For example, secret='thisisseceret123'
host (str) – The host URL (overrides api_host). For example, host='http://localhost:8008'
TaskStatusEnum¶An enumeration.
add_requirements(package_name, package_version=None)¶Force the adding of a package to the requirements list. If package_version is not specified, use the
installed package version, if found.
package_name (str) – The package name to add to the “Installed Packages” section of the task.
package_version – The package version requirements. If None, then use the installed version.
cache_dir¶The cache directory which is used to store the Task related files.
completed(ignore_errors=True)¶The signal indicating that this Task completed.
get_all(session=None, log=None, **kwargs)¶List all the Tasks based on specific projection.
session (Session) – The session object used for sending requests to the API.
log (logging.Logger) – The Log object.
kwargs (dict) –
Keyword args passed to the GetAllRequest
(see backend_api.services.v2_5.tasks.GetAllRequest)
For example:
status='completed', 'search_text'='specific_word', 'user'='user_id', 'project'='project_id'
The API response.
get_base_docker()¶Get the base Docker command (image) that is set for this experiment.
get_label_num_description()¶Get a dictionary of label number to a string pairs representing all labels associated with this number on the model labels.
get_labels_enumeration()¶Get the label enumeration dictionary label enumeration dictionary of string (label) to integer (value) pairs.
A dictionary containing the label enumeration.
get_model_design()¶Get the model configuration as blob of text.
The model configuration as blob of text.
get_num_of_classes()¶number of classes based on the task’s labels
get_output_destination(extra_path=None, **kwargs)¶Get the task’s output destination, with an optional suffix
get_output_log_web_page()¶Return the Task results & outputs web page address. For example: https://demoapp.trains.allegro.ai/projects/216431/experiments/60763e04/output/log
http/s URL link.
get_parameter(name, default=None)¶Get a value for a parameter.
name – Parameter name
default – Default value
The Parameter value (or default value if parameter is not defined).
get_reported_console_output(number_of_reports=1)¶Return a list of console outputs reported by the Task. Retrieved outputs are the most updated console outputs.
number_of_reports (int) – The number of reports to return. The default value is 1, indicating the
last (most updated) console output
A list of strings, each entry corresponds to one report.
get_reported_scalars(max_samples=0, x_axis='iter')¶Return a nested dictionary for the scalar graphs, where the first key is the graph title and the second is the series name. Value is a dict with ‘x’: values and ‘y’: values
Note
This call is not cached, any call will retrieve all the scalar reports from the back-end. If the Task has many scalars reported, it might take long for the call to return.
Example:
{'title': {'series': {
'x': [0, 1 ,2],
'y': [10, 11 ,12],
}}}
max_samples (int) – Maximum samples per series to return. Default is 0 returning all scalars. With sample limit, average scalar values inside sampling window.
x_axis (str) – scalar x_axis, possible values: ‘iter’: iteration (default), ‘timestamp’: seconds from start, ‘iso_time’: absolute time
dict: Nested scalar graphs: dict[title(str), dict[series(str), dict[axis(str), list(float)]]]
get_status()¶Return The task status without refreshing the entire Task object object (only the status property)
“queued”, “published”, “publishing”, “unknown”]
str: Task status as string (TaskStatusEnum)
input_model¶A model manager used to handle the input model object
labels_stats¶Get accumulated label stats for the current/last frames iteration
mark_failed(ignore_errors=True, status_reason=None, status_message=None)¶The signal that this Task stopped.
metrics_manager¶A metrics manager used to manage the metrics related to this task
output_model¶A model manager used to manage the output model object
publish(ignore_errors=True)¶The signal that this Task will be published
reporter¶Returns a simple metrics reporter instance.
running_locally()¶Is the task running locally (i.e., trains-agent is not executing it)?
True, if the task is running locally. False, if the task is not running locally.
save_exec_model_design_file(filename='model_design.txt', use_cache=False)¶Save execution model design to file
set_artifacts(artifacts_list=None)¶List of artifacts (tasks.Artifact) to update the task
artifacts_list (list) – list of artifacts (type tasks.Artifact)
set_base_docker(docker_cmd)¶Set the base docker image for this experiment If provided, this value will be used by trains-agent to execute this experiment inside the provided docker image.
set_comment(comment)¶Set a comment / description for the Task.
comment (str) – The comment / description for the Task.
set_input_model(model_id=None, model_name=None, update_task_design=True, update_task_labels=True)¶Set a new input model for the Task. The model must be “ready” (status is Published) to be used as the
Task’s input model.
model_id – The Id of the model on the Trains Server (backend). If model_name is not specified,
then model_id must be specified.
model_name – The model name. The name is used to locate an existing model in the Trains Server
(backend). If model_id is not specified, then model_name must be specified.
update_task_design –
Update the Task’s design?
True - Trains copies the Task’s model design from the input model.
False - Trains does not copy the Task’s model design from the input model.
update_task_labels –
Update the Task’s label enumeration?
True - Trains copies the Task’s label enumeration from the input model.
False - Trains does not copy the Task’s label enumeration from the input model.
set_name(name)¶Set the Task name.
name (str) – The name of the Task.
set_parameter(name, value, description=None)¶Set a single Task parameter. This overrides any previous value for this parameter.
name – The parameter name.
value – The parameter value.
description –
The parameter description.
Note
The description is not yet in use.
set_parameters(*args, **kwargs)¶Set the parameters for a Task. This method sets a complete group of key-value parameter pairs, but does not support parameter descriptions (the input is a dictionary of key-value pairs).
args – Positional arguments, which are one or more dictionary or (key, value) iterable. They are merged into a single key-value pair dictionary.
kwargs – Key-value pairs, merged into the parameters dictionary created from args.
set_parent(parent)¶Set the parent task for the Task. :param parent: The parent task id (or parent Task object) for the Task. Set None for no parent. :type parent: str or Task
started(ignore_errors=True)¶The signal that this Task started.
status¶The Task’s status. To keep the Task updated. Trains reloads the Task status information only, when this value is accessed.
return str: TaskStatusEnum status
stopped(ignore_errors=True)¶The signal that this Task stopped.
update_model_desc(new_model_desc_file=None)¶Change the Task’s model description.
update_output_model(model_uri, name=None, comment=None, tags=None)¶Update the Task’s output model. Use this method to update the output model when you have a local model URI,
for example, storing the weights file locally, and specifying a file://path/to/file URI)
Important
This method only updates the model’s metadata using the API. It does not upload any data.
model_uri (str) – The URI of the updated model weights file.
name (str) – The updated model name. (Optional)
comment (str) – The updated model description. (Optional)
tags ([str]) – The updated model tags. (Optional)
update_output_model_and_upload(model_file, name=None, comment=None, tags=None, async_enable=False, cb=None, iteration=None)¶Update the Task’s output model weights file. First, Trains uploads the file to the preconfigured output
destination (see the Task’s output.destination property or call the setup_upload method),
then Trains updates the model object associated with the Task an API call. The API call uses with the URI
of the uploaded file, and other values provided by additional arguments.
model_file (str) – The path to the updated model weights file.
name (str) – The updated model name. (Optional)
comment (str) – The updated model description. (Optional)
tags (list) – The updated model tags. (Optional)
async_enable (bool) –
Request asynchronous upload?
True - The API call returns immediately, while the upload and update are scheduled in another thread.
False - The API call blocks until the upload completes, and the API call updating the model returns.
(default)
cb (callable) – Asynchronous callback. A callback. If async_enable is set to True,
this is a callback that is invoked once the asynchronous upload and update complete.
iteration (int) – iteration number for the current stored model (Optional)
The URI of the uploaded weights file. If async_enable is set to True,
this is the expected URI, as the upload is probably still in progress.
update_parameters(*args, **kwargs)¶Update the parameters for a Task. This method updates a complete group of key-value parameter pairs, but does not support parameter descriptions (the input is a dictionary of key-value pairs).
args – Positional arguments, which are one or more dictionary or (key, value) iterable. They are merged into a single key-value pair dictionary.
kwargs – Key-value pairs, merged into the parameters dictionary created from args.