spaCy

Integrate W&B with spaCy v3 to track training metrics and version models and datasets through the WandbLogger config.

spaCy is an NLP library that provides fast, accurate models. As of spaCy v3, you can use W&B with spacy train to track your spaCy model’s training metrics and to save and version your models and datasets. All it takes is a few added lines in your configuration.

This page is for spaCy users who want to use W&B to monitor training runs, compare experiments, and version the models and datasets produced by spacy train.

Sign up and create an API key

An API key authenticates your machine to W&B. You can generate an API key from your user profile.

For a more streamlined approach, create an API key by going directly to User Settings. Copy the newly created API key immediately and save it in a secure location such as a password manager.

1. Click your user profile icon in the upper right corner.
2. Select User Settings, then scroll to the API Keys section.

Install the wandb library and log in

To install the wandb library locally and log in:

Command Line

1. Set the WANDB_API_KEY environment variable to your API key.

   export WANDB_API_KEY=[YOUR-API-KEY]
   

2. Install the wandb library and log in.

   pip install wandb
   
   wandb login
   

Python

pip install wandb

import wandb
wandb.login()

Python notebook

!pip install wandb

import wandb
wandb.login()

Add the WandbLogger to your spaCy config file

spaCy config files specify all aspects of training, not only logging (GPU allocation, optimizer choice, dataset paths, and more). Minimally, under [training.logger] you need to provide the key @loggers with the value "spacy.WandbLogger.v3", plus a project_name.

For more on how spaCy training config files work and on other options you can pass in to customize training, see spaCy’s documentation.

[training.logger]
@loggers = "spacy.WandbLogger.v3"
project_name = "my_spacy_project"
remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
log_dataset_dir = "./corpus"
model_log_interval = 1000

The following table describes the WandbLogger configuration options:

Name	Description
project_name	str. The name of the W&B project. W&B creates the project automatically if it doesn’t exist yet.
remove_config_values	List[str] . A list of values to exclude from the config before W&B uploads it. [] by default.
model_log_interval	Optional int. None by default. If set, enables model versioning with artifacts. Pass in the number of steps to wait between logging model checkpoints.
log_dataset_dir	Optional str. If you pass a path, W&B uploads the dataset as an artifact at the beginning of training. None by default.
entity	Optional str . If passed, W&B creates the run in the specified entity.
run_name	Optional str . If specified, W&B creates the run with the specified name.

Start training

With the WandbLogger added to your spaCy training config, you can run spacy train as usual and W&B captures the run automatically.

Command Line

python -m spacy train \
    config.cfg \
    --output ./output \
    --paths.train ./train \
    --paths.dev ./dev

Python

python -m spacy train \
    config.cfg \
    --output ./output \
    --paths.train ./train \
    --paths.dev ./dev

Python notebook

!python -m spacy train \
    config.cfg \
    --output ./output \
    --paths.train ./train \
    --paths.dev ./dev

When training begins, spaCy outputs a link to your training run’s W&B page, which takes you to this run’s experiment tracking dashboard in the W&B web UI.