spaCy

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

spaCy is a popular “industrial-strength” NLP library: fast, accurate models with a minimum of fuss. As of spaCy v3, W&B can now be used with spacy train to track your spaCy model’s training metrics as well as to save and version your models and datasets. And all it takes is a few added lines in your configuration.

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 are used to specify all aspects of training, not just 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, check out 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

Name	Description
project_name	str. The name of the W&B Project. 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. [] 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. None by default.
log_dataset_dir	Optional str. If passed a path, the dataset will be uploaded as an Artifact at the beginning of training. None by default.
entity	Optional str . If passed, the run will be created in the specified entity
run_name	Optional str . If specified, the run will be created with the specified name.

Start training

Once you have added the WandbLogger to your spaCy training config you can run spacy train as usual.

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, a link to your training run’s W&B page will be output which will take you to this run’s experiment tracking dashboard in the W&B web UI.