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ultralytics.hub.session.HUBTrainingSession

Session de formation HUB pour les modèles Ultralytics HUB YOLO . Gère l'initialisation du modèle, les battements de cœur et les points de contrôle.

Attributs :

Nom Type Description
agent_id str

Identifiant de l'instance qui communique avec le serveur.

model_id str

Identifiant du modèle YOLO en cours de formation.

model_url str

URL du modèle dans Ultralytics HUB.

api_url str

URL de l'API pour le modèle dans Ultralytics HUB.

auth_header dict

En-tĂŞte d'authentification pour les requĂŞtes de l'API Ultralytics HUB.

rate_limits dict

Limites de débit pour les différents appels API (en secondes).

timers dict

Minuteries pour la limitation du débit.

metrics_queue dict

File d'attente pour les métriques du modèle.

model dict

Les données du modèle sont extraites de Ultralytics HUB.

alive bool

Indique si la boucle de battement de cœur est active.

Code source dans ultralytics/hub/session.py
class HUBTrainingSession:
    """
    HUB training session for Ultralytics HUB YOLO models. Handles model initialization, heartbeats, and checkpointing.

    Attributes:
        agent_id (str): Identifier for the instance communicating with the server.
        model_id (str): Identifier for the YOLO model being trained.
        model_url (str): URL for the model in Ultralytics HUB.
        api_url (str): API URL for the model in Ultralytics HUB.
        auth_header (dict): Authentication header for the Ultralytics HUB API requests.
        rate_limits (dict): Rate limits for different API calls (in seconds).
        timers (dict): Timers for rate limiting.
        metrics_queue (dict): Queue for the model's metrics.
        model (dict): Model data fetched from Ultralytics HUB.
        alive (bool): Indicates if the heartbeat loop is active.
    """

    def __init__(self, identifier):
        """
        Initialize the HUBTrainingSession with the provided model identifier.

        Args:
            identifier (str): Model identifier used to initialize the HUB training session.
                It can be a URL string or a model key with specific format.

        Raises:
            ValueError: If the provided model identifier is invalid.
            ConnectionError: If connecting with global API key is not supported.
            ModuleNotFoundError: If hub-sdk package is not installed.
        """
        from hub_sdk import HUBClient

        self.rate_limits = {
            "metrics": 3.0,
            "ckpt": 900.0,
            "heartbeat": 300.0,
        }  # rate limits (seconds)
        self.metrics_queue = {}  # holds metrics for each epoch until upload
        self.metrics_upload_failed_queue = {}  # holds metrics for each epoch if upload failed
        self.timers = {}  # holds timers in ultralytics/utils/callbacks/hub.py

        # Parse input
        api_key, model_id, self.filename = self._parse_identifier(identifier)

        # Get credentials
        active_key = api_key or SETTINGS.get("api_key")
        credentials = {"api_key": active_key} if active_key else None  # set credentials

        # Initialize client
        self.client = HUBClient(credentials)

        if model_id:
            self.load_model(model_id)  # load existing model
        else:
            self.model = self.client.model()  # load empty model

    def load_model(self, model_id):
        """Loads an existing model from Ultralytics HUB using the provided model identifier."""
        self.model = self.client.model(model_id)
        if not self.model.data:  # then model does not exist
            raise ValueError(emojis("❌ The specified HUB model does not exist"))  # TODO: improve error handling

        self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"

        self._set_train_args()

        # Start heartbeats for HUB to monitor agent
        self.model.start_heartbeat(self.rate_limits["heartbeat"])
        LOGGER.info(f"{PREFIX}View model at {self.model_url} 🚀")

    def create_model(self, model_args):
        """Initializes a HUB training session with the specified model identifier."""
        payload = {
            "config": {
                "batchSize": model_args.get("batch", -1),
                "epochs": model_args.get("epochs", 300),
                "imageSize": model_args.get("imgsz", 640),
                "patience": model_args.get("patience", 100),
                "device": model_args.get("device", ""),
                "cache": model_args.get("cache", "ram"),
            },
            "dataset": {"name": model_args.get("data")},
            "lineage": {
                "architecture": {
                    "name": self.filename.replace(".pt", "").replace(".yaml", ""),
                },
                "parent": {},
            },
            "meta": {"name": self.filename},
        }

        if self.filename.endswith(".pt"):
            payload["lineage"]["parent"]["name"] = self.filename

        self.model.create_model(payload)

        # Model could not be created
        # TODO: improve error handling
        if not self.model.id:
            return

        self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"

        # Start heartbeats for HUB to monitor agent
        self.model.start_heartbeat(self.rate_limits["heartbeat"])

        LOGGER.info(f"{PREFIX}View model at {self.model_url} 🚀")

    def _parse_identifier(self, identifier):
        """
        Parses the given identifier to determine the type of identifier and extract relevant components.

        The method supports different identifier formats:
            - A HUB URL, which starts with HUB_WEB_ROOT followed by '/models/'
            - An identifier containing an API key and a model ID separated by an underscore
            - An identifier that is solely a model ID of a fixed length
            - A local filename that ends with '.pt' or '.yaml'

        Args:
            identifier (str): The identifier string to be parsed.

        Returns:
            (tuple): A tuple containing the API key, model ID, and filename as applicable.

        Raises:
            HUBModelError: If the identifier format is not recognized.
        """

        # Initialize variables
        api_key, model_id, filename = None, None, None

        # Check if identifier is a HUB URL
        if identifier.startswith(f"{HUB_WEB_ROOT}/models/"):
            # Extract the model_id after the HUB_WEB_ROOT URL
            model_id = identifier.split(f"{HUB_WEB_ROOT}/models/")[-1]
        else:
            # Split the identifier based on underscores only if it's not a HUB URL
            parts = identifier.split("_")

            # Check if identifier is in the format of API key and model ID
            if len(parts) == 2 and len(parts[0]) == 42 and len(parts[1]) == 20:
                api_key, model_id = parts
            # Check if identifier is a single model ID
            elif len(parts) == 1 and len(parts[0]) == 20:
                model_id = parts[0]
            # Check if identifier is a local filename
            elif identifier.endswith(".pt") or identifier.endswith(".yaml"):
                filename = identifier
            else:
                raise HUBModelError(
                    f"model='{identifier}' could not be parsed. Check format is correct. "
                    f"Supported formats are Ultralytics HUB URL, apiKey_modelId, modelId, local pt or yaml file."
                )

        return api_key, model_id, filename

    def _set_train_args(self):
        """
        Initializes training arguments and creates a model entry on the Ultralytics HUB.

        This method sets up training arguments based on the model's state and updates them with any additional
        arguments provided. It handles different states of the model, such as whether it's resumable, pretrained,
        or requires specific file setup.

        Raises:
            ValueError: If the model is already trained, if required dataset information is missing, or if there are
                issues with the provided training arguments.
        """
        if self.model.is_trained():
            raise ValueError(emojis(f"Model is already trained and uploaded to {self.model_url} 🚀"))

        if self.model.is_resumable():
            # Model has saved weights
            self.train_args = {"data": self.model.get_dataset_url(), "resume": True}
            self.model_file = self.model.get_weights_url("last")
        else:
            # Model has no saved weights
            self.train_args = self.model.data.get("train_args")  # new response

            # Set the model file as either a *.pt or *.yaml file
            self.model_file = (
                self.model.get_weights_url("parent") if self.model.is_pretrained() else self.model.get_architecture()
            )

        if "data" not in self.train_args:
            # RF bug - datasets are sometimes not exported
            raise ValueError("Dataset may still be processing. Please wait a minute and try again.")

        self.model_file = checks.check_yolov5u_filename(self.model_file, verbose=False)  # YOLOv5->YOLOv5u
        self.model_id = self.model.id

    def request_queue(
        self,
        request_func,
        retry=3,
        timeout=30,
        thread=True,
        verbose=True,
        progress_total=None,
        stream_reponse=None,
        *args,
        **kwargs,
    ):
        def retry_request():
            """Attempts to call `request_func` with retries, timeout, and optional threading."""
            t0 = time.time()  # Record the start time for the timeout
            for i in range(retry + 1):
                if (time.time() - t0) > timeout:
                    LOGGER.warning(f"{PREFIX}Timeout for request reached. {HELP_MSG}")
                    break  # Timeout reached, exit loop

                response = request_func(*args, **kwargs)
                if response is None:
                    LOGGER.warning(f"{PREFIX}Received no response from the request. {HELP_MSG}")
                    time.sleep(2**i)  # Exponential backoff before retrying
                    continue  # Skip further processing and retry

                if progress_total:
                    self._show_upload_progress(progress_total, response)
                elif stream_reponse:
                    self._iterate_content(response)

                if HTTPStatus.OK <= response.status_code < HTTPStatus.MULTIPLE_CHOICES:
                    # if request related to metrics upload
                    if kwargs.get("metrics"):
                        self.metrics_upload_failed_queue = {}
                    return response  # Success, no need to retry

                if i == 0:
                    # Initial attempt, check status code and provide messages
                    message = self._get_failure_message(response, retry, timeout)

                    if verbose:
                        LOGGER.warning(f"{PREFIX}{message} {HELP_MSG} ({response.status_code})")

                if not self._should_retry(response.status_code):
                    LOGGER.warning(f"{PREFIX}Request failed. {HELP_MSG} ({response.status_code}")
                    break  # Not an error that should be retried, exit loop

                time.sleep(2**i)  # Exponential backoff for retries

            # if request related to metrics upload and exceed retries
            if response is None and kwargs.get("metrics"):
                self.metrics_upload_failed_queue.update(kwargs.get("metrics", None))

            return response

        if thread:
            # Start a new thread to run the retry_request function
            threading.Thread(target=retry_request, daemon=True).start()
        else:
            # If running in the main thread, call retry_request directly
            return retry_request()

    def _should_retry(self, status_code):
        """Determines if a request should be retried based on the HTTP status code."""
        retry_codes = {
            HTTPStatus.REQUEST_TIMEOUT,
            HTTPStatus.BAD_GATEWAY,
            HTTPStatus.GATEWAY_TIMEOUT,
        }
        return status_code in retry_codes

    def _get_failure_message(self, response: requests.Response, retry: int, timeout: int):
        """
        Generate a retry message based on the response status code.

        Args:
            response: The HTTP response object.
            retry: The number of retry attempts allowed.
            timeout: The maximum timeout duration.

        Returns:
            (str): The retry message.
        """
        if self._should_retry(response.status_code):
            return f"Retrying {retry}x for {timeout}s." if retry else ""
        elif response.status_code == HTTPStatus.TOO_MANY_REQUESTS:  # rate limit
            headers = response.headers
            return (
                f"Rate limit reached ({headers['X-RateLimit-Remaining']}/{headers['X-RateLimit-Limit']}). "
                f"Please retry after {headers['Retry-After']}s."
            )
        else:
            try:
                return response.json().get("message", "No JSON message.")
            except AttributeError:
                return "Unable to read JSON."

    def upload_metrics(self):
        """Upload model metrics to Ultralytics HUB."""
        return self.request_queue(self.model.upload_metrics, metrics=self.metrics_queue.copy(), thread=True)

    def upload_model(
        self,
        epoch: int,
        weights: str,
        is_best: bool = False,
        map: float = 0.0,
        final: bool = False,
    ) -> None:
        """
        Upload a model checkpoint to Ultralytics HUB.

        Args:
            epoch (int): The current training epoch.
            weights (str): Path to the model weights file.
            is_best (bool): Indicates if the current model is the best one so far.
            map (float): Mean average precision of the model.
            final (bool): Indicates if the model is the final model after training.
        """
        if Path(weights).is_file():
            progress_total = Path(weights).stat().st_size if final else None  # Only show progress if final
            self.request_queue(
                self.model.upload_model,
                epoch=epoch,
                weights=weights,
                is_best=is_best,
                map=map,
                final=final,
                retry=10,
                timeout=3600,
                thread=not final,
                progress_total=progress_total,
                stream_reponse=True,
            )
        else:
            LOGGER.warning(f"{PREFIX}WARNING ⚠️ Model upload issue. Missing model {weights}.")

    def _show_upload_progress(self, content_length: int, response: requests.Response) -> None:
        """
        Display a progress bar to track the upload progress of a file download.

        Args:
            content_length (int): The total size of the content to be downloaded in bytes.
            response (requests.Response): The response object from the file download request.

        Returns:
            None
        """
        with TQDM(total=content_length, unit="B", unit_scale=True, unit_divisor=1024) as pbar:
            for data in response.iter_content(chunk_size=1024):
                pbar.update(len(data))

    def _iterate_content(self, response: requests.Response) -> None:
        """
        Process the streamed HTTP response data.

        Args:
            response (requests.Response): The response object from the file download request.

        Returns:
            None
        """
        for _ in response.iter_content(chunk_size=1024):
            pass  # Do nothing with data chunks

__init__(identifier)

Initialise la session de formation HUBTrainingSession avec l'identifiant du modèle fourni.

Paramètres :

Nom Type Description DĂ©faut
identifier str

Identifiant du modèle utilisé pour initialiser la session de formation HUB. Il peut s'agir d'une chaîne URL ou d'une clé de modèle au format spécifique.

requis

Augmente :

Type Description
ValueError

Si l'identifiant du modèle fourni n'est pas valide.

ConnectionError

Si la connexion avec la clé API globale n'est pas prise en charge.

ModuleNotFoundError

Si le paquet hub-sdk n'est pas installé.

Code source dans ultralytics/hub/session.py
def __init__(self, identifier):
    """
    Initialize the HUBTrainingSession with the provided model identifier.

    Args:
        identifier (str): Model identifier used to initialize the HUB training session.
            It can be a URL string or a model key with specific format.

    Raises:
        ValueError: If the provided model identifier is invalid.
        ConnectionError: If connecting with global API key is not supported.
        ModuleNotFoundError: If hub-sdk package is not installed.
    """
    from hub_sdk import HUBClient

    self.rate_limits = {
        "metrics": 3.0,
        "ckpt": 900.0,
        "heartbeat": 300.0,
    }  # rate limits (seconds)
    self.metrics_queue = {}  # holds metrics for each epoch until upload
    self.metrics_upload_failed_queue = {}  # holds metrics for each epoch if upload failed
    self.timers = {}  # holds timers in ultralytics/utils/callbacks/hub.py

    # Parse input
    api_key, model_id, self.filename = self._parse_identifier(identifier)

    # Get credentials
    active_key = api_key or SETTINGS.get("api_key")
    credentials = {"api_key": active_key} if active_key else None  # set credentials

    # Initialize client
    self.client = HUBClient(credentials)

    if model_id:
        self.load_model(model_id)  # load existing model
    else:
        self.model = self.client.model()  # load empty model

create_model(model_args)

Initialise une session de formation HUB avec l'identifiant de modèle spécifié.

Code source dans ultralytics/hub/session.py
def create_model(self, model_args):
    """Initializes a HUB training session with the specified model identifier."""
    payload = {
        "config": {
            "batchSize": model_args.get("batch", -1),
            "epochs": model_args.get("epochs", 300),
            "imageSize": model_args.get("imgsz", 640),
            "patience": model_args.get("patience", 100),
            "device": model_args.get("device", ""),
            "cache": model_args.get("cache", "ram"),
        },
        "dataset": {"name": model_args.get("data")},
        "lineage": {
            "architecture": {
                "name": self.filename.replace(".pt", "").replace(".yaml", ""),
            },
            "parent": {},
        },
        "meta": {"name": self.filename},
    }

    if self.filename.endswith(".pt"):
        payload["lineage"]["parent"]["name"] = self.filename

    self.model.create_model(payload)

    # Model could not be created
    # TODO: improve error handling
    if not self.model.id:
        return

    self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"

    # Start heartbeats for HUB to monitor agent
    self.model.start_heartbeat(self.rate_limits["heartbeat"])

    LOGGER.info(f"{PREFIX}View model at {self.model_url} 🚀")

load_model(model_id)

Charge un modèle existant à partir de Ultralytics HUB en utilisant l'identifiant de modèle fourni.

Code source dans ultralytics/hub/session.py
def load_model(self, model_id):
    """Loads an existing model from Ultralytics HUB using the provided model identifier."""
    self.model = self.client.model(model_id)
    if not self.model.data:  # then model does not exist
        raise ValueError(emojis("❌ The specified HUB model does not exist"))  # TODO: improve error handling

    self.model_url = f"{HUB_WEB_ROOT}/models/{self.model.id}"

    self._set_train_args()

    # Start heartbeats for HUB to monitor agent
    self.model.start_heartbeat(self.rate_limits["heartbeat"])
    LOGGER.info(f"{PREFIX}View model at {self.model_url} 🚀")

upload_metrics()

Télécharge les mesures du modèle sur Ultralytics HUB.

Code source dans ultralytics/hub/session.py
def upload_metrics(self):
    """Upload model metrics to Ultralytics HUB."""
    return self.request_queue(self.model.upload_metrics, metrics=self.metrics_queue.copy(), thread=True)

upload_model(epoch, weights, is_best=False, map=0.0, final=False)

Télécharge un modèle de point de contrôle sur Ultralytics HUB.

Paramètres :

Nom Type Description DĂ©faut
epoch int

L'Ă©poque de formation actuelle.

requis
weights str

Chemin d'accès au fichier des poids du modèle.

requis
is_best bool

Indique si le modèle actuel est le meilleur jusqu'à présent.

False
map float

Précision moyenne du modèle.

0.0
final bool

Indique si le modèle est le modèle final après la formation.

False
Code source dans ultralytics/hub/session.py
def upload_model(
    self,
    epoch: int,
    weights: str,
    is_best: bool = False,
    map: float = 0.0,
    final: bool = False,
) -> None:
    """
    Upload a model checkpoint to Ultralytics HUB.

    Args:
        epoch (int): The current training epoch.
        weights (str): Path to the model weights file.
        is_best (bool): Indicates if the current model is the best one so far.
        map (float): Mean average precision of the model.
        final (bool): Indicates if the model is the final model after training.
    """
    if Path(weights).is_file():
        progress_total = Path(weights).stat().st_size if final else None  # Only show progress if final
        self.request_queue(
            self.model.upload_model,
            epoch=epoch,
            weights=weights,
            is_best=is_best,
            map=map,
            final=final,
            retry=10,
            timeout=3600,
            thread=not final,
            progress_total=progress_total,
            stream_reponse=True,
        )
    else:
        LOGGER.warning(f"{PREFIX}WARNING ⚠️ Model upload issue. Missing model {weights}.")





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (5), Burhan-Q (1)