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

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.v4 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.

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.v4"
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.
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.
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.
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.

MLflowLogger

Installation

This logger requires mlflow to be installed and configured:

pip install mlflow

Usage

spacy.MLflowLogger.v1 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.

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.v1"
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).
tags	Optional[Dict[str, Any]]	A dictionary of string keys and values to set as tags on the run. If a run is being resumed, these tags are set on the resumed run. If a new run is being created, these tags are set on the new run (default: None).
remove_config_values	List[str]	A list of values to exclude from the config before it is uploaded to MLflow (default: []).

ClearMLLogger

Installation

This logger requires clearml to be installed and configured:

pip install clearml
clearml-init

Usage

spacy.ClearMLLogger.v1 is a logger that tracks the results of each training step using the ClearML tool. To use this logger, ClearML should be installed and you should have initialized (using the command above). The logger will send all the gathered information to your ClearML server, either the hosted free tier or the open source self-hosted server. This logger captures the following information, all of which is visible in the ClearML web UI:

• The full spaCy config file contents.
• Code information such as git repository, commit ID and uncommitted changes.
• Full console output.
• Miscellaneous info such as time, python version and hardware information.
• Output scalars:
  • The final score is logged under the scalar score.
  • Individual component scores are grouped together on one scalar plot (filterable using the web UI).
  • Loss values of different components are logged with the loss_ prefix.

In addition to the above, the following artifacts can also be optionally captured:

• Best model directory (zipped).
• Latest model directory (zipped).
• Dataset used to train.
  • Versioned using ClearML Data and linked to under Configuration -> User Properties on the web UI.

Note that by default, the full (interpolated) training config is sent over to ClearML. 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.ClearMLLogger.v1"
project_name = "Hello ClearML!"
task_name = "My spaCy Task"
model_log_interval = 1000
log_best_dir = training/model-best
log_latest_dir = training/model-last
log_dataset_dir = corpus
remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]

Name	Type	Description
project_name	str	The name of the project in the ClearML interface. The project will be created automatically if it doesn't exist yet.
task_name	str	The name of the ClearML task. A task is an experiment that lives inside a project. Can be non-unique.
remove_config_values	List[str]	A list of values to exclude from the config before it is uploaded to ClearML (default: []).
model_log_interval	Optional[int]	Steps to wait between logging model checkpoints to the ClearML dasboard (default: None). Will have no effect without also setting log_best_dir or log_latest_dir.
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 ClearML artifact (default: None)
log_latest_dir	Optional[str]	Directory containing the latest trained model as saved by spaCy (by default in training/model-last), to be logged and versioned as a ClearML artifact (default: None)
log_dataset_dir	Optional[str]	Directory containing the dataset to be logged and versioned as a ClearML Dataset (default: None).