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Referenz fĂźr hub_sdk/base/server_clients.py

Hinweis

Diese Datei ist verfügbar unter https://github.com/ultralytics/hub-sdk/blob/main/hub_sdk/base/server_clients .py. Wenn du ein Problem entdeckst, hilf bitte, es zu beheben, indem du einen Pull Request 🛠️ einreichst. Vielen Dank 🙏!



hub_sdk.base.server_clients.ModelUpload

Basen: APIClient

Quellcode in hub_sdk/base/server_clients.py
class ModelUpload(APIClient):
    def __init__(self, headers):
        """Initialize ModelUpload with API client configuration."""
        super().__init__(f"{HUB_API_ROOT}/v1/models", headers)
        self.name = "model"
        self.alive = True
        self.agent_id = None
        self.rate_limits = {"metrics": 3.0, "ckpt": 900.0, "heartbeat": 300.0}

    def upload_model(self, id, epoch, weights, is_best=False, map=0.0, final=False):
        """
        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.
        """
        try:
            # Determine the correct file path
            weights_path = weights if os.path.isabs(weights) else os.path.join(os.getcwd(), weights)

            # Check if the file exists
            if not Path(weights_path).is_file():
                raise FileNotFoundError(f"File not found: {weights_path}")

            with open(weights_path, "rb") as f:
                file = f.read()

            # Prepare the endpoint and data
            endpoint = f"/{id}/upload"
            data = {"epoch": epoch, "type": "final" if final else "epoch"}
            files = {"best.pt": file} if final else {"last.pt": file}
            if final:
                data["map"] = map
            else:
                data["isBest"] = bool(is_best)

            # Perform the POST request
            response = self.post(endpoint, data=data, files=files, stream=True)

            # Log the appropriate message
            msg = "Model optimized weights uploaded." if final else "Model checkpoint weights uploaded."
            self.logger.debug(msg)
            return response
        except Exception as e:
            self.logger.error(f"Failed to upload file for {self.name}: {e}")

    def upload_metrics(self, id: str, data: dict) -> Optional[Response]:
        """
        Upload a file for a specific entity.

        Args:
            id (str): The unique identifier of the entity to which the file is being uploaded.
            data (dict): The metrics data to upload.

        Returns:
            (Optional[Response]): Response object from the upload_metrics request, or None if it fails.
        """
        try:
            payload = {"metrics": data, "type": "metrics"}
            endpoint = f"{HUB_API_ROOT}/v1/models/{id}"
            r = self.post(endpoint, json=payload)
            self.logger.debug("Model metrics uploaded.")
            return r
        except Exception as e:
            self.logger.error(f"Failed to upload metrics for Model({id}): {e}")

    def export(self, id: str, format: str) -> Optional[Response]:
        """
        Export a file for a specific entity.

        Args:
            id (str): The unique identifier of the entity to which the file is being exported.
            format (str): Path to the file to be Exported.

        Returns:
            (Optional[Response]): Response object from the export request, or None if it fails.
        """
        try:
            payload = {"format": format}
            endpoint = f"/{id}/export"
            return self.post(endpoint, json=payload)
        except Exception as e:
            self.logger.error(f"Failed to export file for Model({id}): {e}")

    @threaded
    def _start_heartbeats(self, model_id: str, interval: int) -> None:
        """
        Begin a threaded heartbeat loop to report the agent's status to Ultralytics HUB.

        This method initiates a threaded loop that periodically sends heartbeats to the Ultralytics HUB
        to report the status of the agent. Heartbeats are sent at regular intervals as defined in the
        'rate_limits' dictionary.

        Args:
            model_id (str): The unique identifier of the model associated with the agent.
            interval (int): The time interval, in seconds, between consecutive heartbeats.

        Returns:
            (None): The method does not return a value.
        """
        endpoint = f"{HUB_API_ROOT}/v1/agent/heartbeat/models/{model_id}"
        try:
            self.logger.debug(f"Heartbeats started at {interval}s interval.")
            while self.alive:
                payload = {
                    "agent": AGENT_NAME,
                    "agentId": self.agent_id,
                }
                res = self.post(endpoint, json=payload).json()
                new_agent_id = res.get("data", {}).get("agentId")

                self.logger.debug("Heartbeat sent.")

                # Update the agent id as requested by the server
                if new_agent_id != self.agent_id:
                    self.logger.debug("Agent Id updated.")
                    self.agent_id = new_agent_id
                sleep(interval)
        except Exception as e:
            self.logger.error(f"Failed to start heartbeats: {e}")
            raise e

    def _stop_heartbeats(self) -> None:
        """
        Stop the threaded heartbeat loop.

        This method stops the threaded loop responsible for sending heartbeats to the Ultralytics HUB.
        It sets the 'alive' flag to False, which will cause the loop in '_start_heartbeats' to exit.

        Returns:
            (None): The method does not return a value.
        """
        self.alive = False
        self.logger.debug("Heartbeats stopped.")

    def _register_signal_handlers(self) -> None:
        """
        Register signal handlers for SIGTERM and SIGINT signals to gracefully handle termination.

        Returns:
            (None): The method does not return a value.
        """
        signal.signal(signal.SIGTERM, self._handle_signal)  # Polite request to terminate
        signal.signal(signal.SIGINT, self._handle_signal)  # CTRL + C

    def _handle_signal(self, signum: int, frame: Any) -> None:
        """
        Handle kill signals and prevent heartbeats from being sent on Colab after termination.

        This method does not use frame, it is included as it is passed by signal.

        Args:
            signum (int): Signal number.
            frame: The current stack frame (not used in this method).

        Returns:
            (None): The method does not return a value.
        """
        self.logger.debug("Kill signal received!")
        self._stop_heartbeats()
        sys.exit(signum)

    def predict(self, id: str, image: str, config: Dict[str, Any]) -> Optional[Response]:
        """
        Perform a prediction using the specified image and configuration.

        Args:
            id (str): Unique identifier for the prediction request.
            image (str): Image path for prediction.
            config (dict): Configuration parameters for the prediction.

        Returns:
            (Optional[Response]): Response object from the predict request, or None if upload fails.
        """
        try:
            base_path = os.getcwd()
            image_path = os.path.join(base_path, image)

            if not os.path.isfile(image_path):
                raise FileNotFoundError(f"Image file not found: {image_path}")

            with open(image_path, "rb") as f:
                image_file = f.read()

            files = {"image": image_file}
            endpoint = f"{HUB_API_ROOT}/v1/predict/{id}"
            return self.post(endpoint, files=files, data=config)

        except Exception as e:
            self.logger.error(f"Failed to predict for Model({id}): {e}")

__init__(headers)

Initialisiere ModelUpload mit der API-Client-Konfiguration.

Quellcode in hub_sdk/base/server_clients.py
def __init__(self, headers):
    """Initialize ModelUpload with API client configuration."""
    super().__init__(f"{HUB_API_ROOT}/v1/models", headers)
    self.name = "model"
    self.alive = True
    self.agent_id = None
    self.rate_limits = {"metrics": 3.0, "ckpt": 900.0, "heartbeat": 300.0}

export(id, format)

Exportiere eine Datei fßr eine bestimmte Entität.

Parameter:

Name Typ Beschreibung Standard
id str

Der eindeutige Bezeichner der Einheit, in die die Datei exportiert wird.

erforderlich
format str

Pfad zu der Datei, die exportiert werden soll.

erforderlich

Retouren:

Typ Beschreibung
Optional[Response]

Antwortobjekt der Exportanfrage oder Keine, wenn sie fehlschlägt.

Quellcode in hub_sdk/base/server_clients.py
def export(self, id: str, format: str) -> Optional[Response]:
    """
    Export a file for a specific entity.

    Args:
        id (str): The unique identifier of the entity to which the file is being exported.
        format (str): Path to the file to be Exported.

    Returns:
        (Optional[Response]): Response object from the export request, or None if it fails.
    """
    try:
        payload = {"format": format}
        endpoint = f"/{id}/export"
        return self.post(endpoint, json=payload)
    except Exception as e:
        self.logger.error(f"Failed to export file for Model({id}): {e}")

predict(id, image, config)

FĂźhre eine Vorhersage mit dem angegebenen Bild und der Konfiguration durch.

Parameter:

Name Typ Beschreibung Standard
id str

Eindeutiger Bezeichner fĂźr die Vorhersageanfrage.

erforderlich
image str

Bildpfad fĂźr die Vorhersage.

erforderlich
config dict

Konfigurationsparameter fĂźr die Vorhersage.

erforderlich

Retouren:

Typ Beschreibung
Optional[Response]

Antwortobjekt aus der Prädiktionsanfrage oder Keine, wenn der Upload fehlschlägt.

Quellcode in hub_sdk/base/server_clients.py
def predict(self, id: str, image: str, config: Dict[str, Any]) -> Optional[Response]:
    """
    Perform a prediction using the specified image and configuration.

    Args:
        id (str): Unique identifier for the prediction request.
        image (str): Image path for prediction.
        config (dict): Configuration parameters for the prediction.

    Returns:
        (Optional[Response]): Response object from the predict request, or None if upload fails.
    """
    try:
        base_path = os.getcwd()
        image_path = os.path.join(base_path, image)

        if not os.path.isfile(image_path):
            raise FileNotFoundError(f"Image file not found: {image_path}")

        with open(image_path, "rb") as f:
            image_file = f.read()

        files = {"image": image_file}
        endpoint = f"{HUB_API_ROOT}/v1/predict/{id}"
        return self.post(endpoint, files=files, data=config)

    except Exception as e:
        self.logger.error(f"Failed to predict for Model({id}): {e}")

upload_metrics(id, data)

Lade eine Datei fßr eine bestimmte Entität hoch.

Parameter:

Name Typ Beschreibung Standard
id str

Der eindeutige Bezeichner der Einheit, auf die die Datei hochgeladen wird.

erforderlich
data dict

Die hochzuladenden Metrikdaten.

erforderlich

Retouren:

Typ Beschreibung
Optional[Response]

Antwortobjekt der upload_metrics-Anfrage oder Keine, wenn sie fehlschlägt.

Quellcode in hub_sdk/base/server_clients.py
def upload_metrics(self, id: str, data: dict) -> Optional[Response]:
    """
    Upload a file for a specific entity.

    Args:
        id (str): The unique identifier of the entity to which the file is being uploaded.
        data (dict): The metrics data to upload.

    Returns:
        (Optional[Response]): Response object from the upload_metrics request, or None if it fails.
    """
    try:
        payload = {"metrics": data, "type": "metrics"}
        endpoint = f"{HUB_API_ROOT}/v1/models/{id}"
        r = self.post(endpoint, json=payload)
        self.logger.debug("Model metrics uploaded.")
        return r
    except Exception as e:
        self.logger.error(f"Failed to upload metrics for Model({id}): {e}")

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

Lade einen ModellprĂźfpunkt auf Ultralytics HUB hoch.

Parameter:

Name Typ Beschreibung Standard
epoch int

Die aktuelle Trainingsepoche.

erforderlich
weights str

Pfad zur Datei mit den Modellgewichten.

erforderlich
is_best bool

Zeigt an, ob das aktuelle Modell das bisher beste ist.

False
map float

Mittlere durchschnittliche Genauigkeit des Modells.

0.0
final bool

Gibt an, ob das Modell das endgĂźltige Modell nach dem Training ist.

False
Quellcode in hub_sdk/base/server_clients.py
def upload_model(self, id, epoch, weights, is_best=False, map=0.0, final=False):
    """
    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.
    """
    try:
        # Determine the correct file path
        weights_path = weights if os.path.isabs(weights) else os.path.join(os.getcwd(), weights)

        # Check if the file exists
        if not Path(weights_path).is_file():
            raise FileNotFoundError(f"File not found: {weights_path}")

        with open(weights_path, "rb") as f:
            file = f.read()

        # Prepare the endpoint and data
        endpoint = f"/{id}/upload"
        data = {"epoch": epoch, "type": "final" if final else "epoch"}
        files = {"best.pt": file} if final else {"last.pt": file}
        if final:
            data["map"] = map
        else:
            data["isBest"] = bool(is_best)

        # Perform the POST request
        response = self.post(endpoint, data=data, files=files, stream=True)

        # Log the appropriate message
        msg = "Model optimized weights uploaded." if final else "Model checkpoint weights uploaded."
        self.logger.debug(msg)
        return response
    except Exception as e:
        self.logger.error(f"Failed to upload file for {self.name}: {e}")



hub_sdk.base.server_clients.ProjectUpload

Basen: APIClient

Quellcode in hub_sdk/base/server_clients.py
class ProjectUpload(APIClient):
    def __init__(self, headers: dict):
        """
        Initialize the class with the specified headers.

        Args:
            headers: The headers to use for API requests.
        """
        super().__init__(f"{HUB_API_ROOT}/v1/projects", headers)
        self.name = "project"

    def upload_image(self, id: str, file: str) -> Optional[Response]:
        """
        Upload a project file to the hub.

        Args:
            id (str): The ID of the dataset to upload.
            file (str): The path to the dataset file to upload.

        Returns:
            (Optional[Response]): Response object from the upload image request, or None if it fails.
        """
        base_path = os.getcwd()
        file_path = os.path.join(base_path, file)
        file_name = os.path.basename(file_path)

        with open(file_path, "rb") as image_file:
            project_image = image_file.read()
        try:
            files = {"file": (file_name, project_image)}
            endpoint = f"/{id}/upload"
            r = self.post(endpoint, files=files)
            self.logger.debug("Project Image uploaded successfully.")
            return r
        except Exception as e:
            self.logger.error(f"Failed to upload image for {self.name}({id}): {str(e)}")

__init__(headers)

Initialisiere die Klasse mit den angegebenen Kopfzeilen.

Parameter:

Name Typ Beschreibung Standard
headers dict

Die Kopfzeilen, die fĂźr API-Anfragen verwendet werden sollen.

erforderlich
Quellcode in hub_sdk/base/server_clients.py
def __init__(self, headers: dict):
    """
    Initialize the class with the specified headers.

    Args:
        headers: The headers to use for API requests.
    """
    super().__init__(f"{HUB_API_ROOT}/v1/projects", headers)
    self.name = "project"

upload_image(id, file)

Lade eine Projektdatei in den Hub hoch.

Parameter:

Name Typ Beschreibung Standard
id str

Die ID des hochzuladenden Datensatzes.

erforderlich
file str

Der Pfad zu der hochzuladenden Datensatzdatei.

erforderlich

Retouren:

Typ Beschreibung
Optional[Response]

Antwortobjekt der Bild-Upload-Anforderung oder Keine, wenn sie fehlschlägt.

Quellcode in hub_sdk/base/server_clients.py
def upload_image(self, id: str, file: str) -> Optional[Response]:
    """
    Upload a project file to the hub.

    Args:
        id (str): The ID of the dataset to upload.
        file (str): The path to the dataset file to upload.

    Returns:
        (Optional[Response]): Response object from the upload image request, or None if it fails.
    """
    base_path = os.getcwd()
    file_path = os.path.join(base_path, file)
    file_name = os.path.basename(file_path)

    with open(file_path, "rb") as image_file:
        project_image = image_file.read()
    try:
        files = {"file": (file_name, project_image)}
        endpoint = f"/{id}/upload"
        r = self.post(endpoint, files=files)
        self.logger.debug("Project Image uploaded successfully.")
        return r
    except Exception as e:
        self.logger.error(f"Failed to upload image for {self.name}({id}): {str(e)}")



hub_sdk.base.server_clients.DatasetUpload

Basen: APIClient

Quellcode in hub_sdk/base/server_clients.py
class DatasetUpload(APIClient):
    def __init__(self, headers: dict):
        """
        Initialize the class with the specified headers.

        Args:
            headers: The headers to use for API requests.
        """
        super().__init__(f"{HUB_API_ROOT}/v1/datasets", headers)
        self.name = "dataset"

    def upload_dataset(self, id, file) -> Optional[Response]:
        """
        Upload a dataset file to the hub.

        Args:
            id (str): The ID of the dataset to upload.
            file (str): The path to the dataset file to upload.

        Returns:
            (Optional[Response]): Response object from the upload dataset request, or None if it fails.
        """
        try:
            if Path(f"{file}").is_file():
                with open(file, "rb") as f:
                    dataset_file = f.read()
                endpoint = f"/{id}/upload"
                filename = file.split("/")[-1]
                files = {filename: dataset_file}
                r = self.post(endpoint, files=files, stream=True)
                self.logger.debug("Dataset uploaded successfully.")
                return r
        except Exception as e:
            self.logger.error(f"Failed to upload dataset for {self.name}({id}): {str(e)}")

__init__(headers)

Initialisiere die Klasse mit den angegebenen Kopfzeilen.

Parameter:

Name Typ Beschreibung Standard
headers dict

Die Kopfzeilen, die fĂźr API-Anfragen verwendet werden sollen.

erforderlich
Quellcode in hub_sdk/base/server_clients.py
def __init__(self, headers: dict):
    """
    Initialize the class with the specified headers.

    Args:
        headers: The headers to use for API requests.
    """
    super().__init__(f"{HUB_API_ROOT}/v1/datasets", headers)
    self.name = "dataset"

upload_dataset(id, file)

Lade eine Datensatzdatei in den Hub hoch.

Parameter:

Name Typ Beschreibung Standard
id str

Die ID des hochzuladenden Datensatzes.

erforderlich
file str

Der Pfad zu der hochzuladenden Datensatzdatei.

erforderlich

Retouren:

Typ Beschreibung
Optional[Response]

Antwortobjekt der Anfrage zum Hochladen von Datensätzen oder Keine, wenn sie fehlschlägt.

Quellcode in hub_sdk/base/server_clients.py
def upload_dataset(self, id, file) -> Optional[Response]:
    """
    Upload a dataset file to the hub.

    Args:
        id (str): The ID of the dataset to upload.
        file (str): The path to the dataset file to upload.

    Returns:
        (Optional[Response]): Response object from the upload dataset request, or None if it fails.
    """
    try:
        if Path(f"{file}").is_file():
            with open(file, "rb") as f:
                dataset_file = f.read()
            endpoint = f"/{id}/upload"
            filename = file.split("/")[-1]
            files = {filename: dataset_file}
            r = self.post(endpoint, files=files, stream=True)
            self.logger.debug("Dataset uploaded successfully.")
            return r
    except Exception as e:
        self.logger.error(f"Failed to upload dataset for {self.name}({id}): {str(e)}")



hub_sdk.base.server_clients.is_colab()

PrĂźfe, ob das aktuelle Skript in einem Google Colab-Notizbuch ausgefĂźhrt wird.

Retouren:

Typ Beschreibung
bool

True, wenn es in einem Colab-Notebook läuft, sonst False.

Quellcode in hub_sdk/base/server_clients.py
def is_colab():
    """
    Check if the current script is running inside a Google Colab notebook.

    Returns:
        (bool): True if running inside a Colab notebook, False otherwise.
    """
    return "COLAB_RELEASE_TAG" in os.environ or "COLAB_BACKEND_VERSION" in os.environ