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spacy-loggers: Logging utilities for spaCy

Starting with spaCy v3.2, alternate loggers are moved into a separate package so that they can be added and updated independently from the core spaCy library.

spacy-loggers currently provides loggers for:

• Weights & Biases
• MLflow
• ClearML
• PyTorch
• CuPy

spacy-loggers also provides additional utility loggers to facilitate interoperation between individual loggers.

If you'd like to add a new logger or logging option, please submit a PR to this repo!

Setup and installation

spacy-loggers should be installed automatically with spaCy v3.2+, so you usually don't need to install it separately. You can install it with pip or from the conda channel conda-forge:

pip install spacy-loggers

conda install -c conda-forge spacy-loggers

Loggers

WandbLogger

Installation

This logger requires wandb to be installed and configured:

pip install wandb
wandb login

Usage

spacy.WandbLogger.v5 is a logger that sends the results of each training step to the dashboard of the Weights & Biases tool. To use this logger, Weights & Biases should be installed, and you should be logged in. The logger will send the full config file to W&B, as well as various system information such as memory utilization, network traffic, disk IO, GPU statistics, etc. This will also include information such as your hostname and operating system, as well as the location of your Python executable.

spacy.WandbLogger.v4 and below automatically call the default console logger. However, starting with spacy.WandbLogger.v5, console logging must be activated through the use of the ChainLogger. This allows the user to configure the console logger's parameters according to their preferences.

Note that by default, the full (interpolated) training config is sent over to the W&B dashboard. If you prefer to exclude certain information such as path names, you can list those fields in "dot notation" in the remove_config_values parameter. These fields will then be removed from the config before uploading, but will otherwise remain in the config file stored on your local system.

Example config

[training.logger]
@loggers = "spacy.WandbLogger.v5"
project_name = "monitor_spacy_training"
remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
log_dataset_dir = "corpus"
model_log_interval = 1000

Name	Type	Description
project_name	str	The name of the project in the Weights & Biases interface. The project will be created automatically if it doesn't exist yet.
remove_config_values	List[str]	A list of values to exclude from the config before it is uploaded to W&B (default: []).
model_log_interval	Optional[int]	Steps to wait between logging model checkpoints to the W&B dasboard (default: None). Added in spacy.WandbLogger.v2.
log_dataset_dir	Optional[str]	Directory containing the dataset to be logged and versioned as a W&B artifact (default: None). Added in spacy.WandbLogger.v2.
entity	Optional[str]	An entity is a username or team name where you're sending runs. If you don't specify an entity, the run will be sent to your default entity, which is usually your username (default: None). Added in spacy.WandbLogger.v3.
run_name	Optional[str]	The name of the run. If you don't specify a run name, the name will be created by the wandb library (default: None). Added in spacy.WandbLogger.v3.
log_best_dir	Optional[str]	Directory containing the best trained model as saved by spaCy (by default in training/model-best), to be logged and versioned as a W&B artifact (default: None). Added in spacy.WandbLogger.v4.
log_latest_dir	Optional[str]	Directory containing the latest trained model as saved by spaCy (by default in training/model-latest), to be logged and versioned as a W&B artifact (default: None). Added in spacy.WandbLogger.v4.
log_custom_stats	Optional[List[str]]	A list of regular expressions that will be applied to the info dictionary passed to the logger (default: None). Statistics and metrics that match these regexps will be automatically logged. Added in spacy.WandbLogger.v5.

MLflowLogger

Installation

This logger requires mlflow to be installed and configured:

pip install mlflow

Usage

spacy.MLflowLogger.v2 is a logger that tracks the results of each training step using the MLflow tool. To use this logger, MLflow should be installed. At the beginning of each model training operation, the logger will initialize a new MLflow run and set it as the active run under which metrics and parameters wil be logged. The logger will then log the entire config file as parameters of the active run. After each training step, the following actions are performed:

• The final score is logged under the metric score.
• Individual component scores are logged under their default names.
• Loss values of different components are logged with the loss_ prefix.
• If the final score is higher than the previous best score (for the current run), the model artifact is additionally uploaded to MLflow. This action is only performed if the output_path argument is provided during the training pipeline initialization phase.

By default, the tracking API writes data into files in a local ./mlruns directory.

spacy.MLflowLogger.v1 and below automatically call the default console logger. However, starting with spacy.MLflowLogger.v2, console logging must be activated through the use of the ChainLogger. This allows the user to configure the console logger's parameters according to their preferences.

Note that by default, the full (interpolated) training config is sent over to MLflow. If you prefer to exclude certain information such as path names, you can list those fields in "dot notation" in the remove_config_values parameter. These fields will then be removed from the config before uploading, but will otherwise remain in the config file stored on your local system.

Example config

[training.logger]
@loggers = "spacy.MLflowLogger.v2"
experiment_id = "1"
run_name = "with_fast_alignments"
nested = False
remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]

Name	Type	Description
run_id	Optional[str]	Unique ID of an existing MLflow run to which parameters and metrics are logged. Can be omitted if experiment_id and run_id are provided (default: None).
experiment_id	Optional[str]	ID of an existing experiment under which to create the current run. Only applicable when run_id is None (default: None).
run_name	Optional[str]	Name of new run. Only applicable when run_id is None (default: None).
nested	bool	Controls whether run is nested in parent run. True creates a nested run (default: False).