fastai

If you’re using fastai to train your models, W&B has an easy integration using the WandbCallback. Explore the details in interactive docs with examples →

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, you can generate an API key by going directly to the W&B authorization page. Copy the displayed API key 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.
3. Click Reveal. Copy the displayed API key. To hide the API key, reload the page.

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
   

Add the WandbCallback to the learner or fit method

import wandb
from fastai.callback.wandb import *

# start logging a wandb run
wandb.init(project="my_project")

# To log only during one training phase
learn.fit(..., cbs=WandbCallback())

# To log continuously for all training phases
learn = learner(..., cbs=WandbCallback())

If you use version 1 of Fastai, refer to the Fastai v1 docs.

WandbCallback Arguments

WandbCallback accepts the following arguments:

Args	Description
log	Whether to log the model’s: gradients , parameters, all or None (default). Losses & metrics are always logged.
log_preds	whether we want to log prediction samples (default to True).
log_preds_every_epoch	whether to log predictions every epoch or at the end (default to False)
log_model	whether we want to log our model (default to False). This also requires SaveModelCallback
model_name	The name of the file to save, overrides SaveModelCallback
log_dataset	False (default) True will log folder referenced by learn.dls.path. a path can be defined explicitly to reference which folder to log. Note: subfolder “models” is always ignored.
dataset_name	name of logged dataset (default to folder name).
valid_dl	DataLoaders containing items used for prediction samples (default to random items from learn.dls.valid.
n_preds	number of logged predictions (default to 36).
seed	used for defining random samples.

For custom workflows, you can manually log your datasets and models:

• log_dataset(path, name=None, metadata={})
• log_model(path, name=None, metadata={})

Note: any subfolder “models” will be ignored.

Distributed Training

fastai supports distributed training by using the context manager distrib_ctx. W&B supports this automatically and enables you to track your Multi-GPU experiments out of the box.

Review this minimal example:

Script

import wandb
from fastai.vision.all import *
from fastai.distributed import *
from fastai.callback.wandb import WandbCallback

wandb.require(experiment="service")
path = rank0_first(lambda: untar_data(URLs.PETS) / "images")

def train():
    dls = ImageDataLoaders.from_name_func(
        path,
        get_image_files(path),
        valid_pct=0.2,
        label_func=lambda x: x[0].isupper(),
        item_tfms=Resize(224),
    )
    wandb.init("fastai_ddp", entity="capecape")
    cb = WandbCallback()
    learn = vision_learner(dls, resnet34, metrics=error_rate, cbs=cb).to_fp16()
    with learn.distrib_ctx(sync_bn=False):
        learn.fit(1)

if __name__ == "__main__":
    train()

Then, in your terminal you will execute:

$ torchrun --nproc_per_node 2 train.py

in this case, the machine has 2 GPUs.

Log only on the main process

In the examples above, wandb launches one run per process. At the end of the training, you will end up with two runs. This can sometimes be confusing, and you may want to log only on the main process. To do so, you will have to detect in which process you are manually and avoid creating runs (calling wandb.init in all other processes)

Script

import wandb
from fastai.vision.all import *
from fastai.distributed import *
from fastai.callback.wandb import WandbCallback

wandb.require(experiment="service")
path = rank0_first(lambda: untar_data(URLs.PETS) / "images")

def train():
    cb = []
    dls = ImageDataLoaders.from_name_func(
        path,
        get_image_files(path),
        valid_pct=0.2,
        label_func=lambda x: x[0].isupper(),
        item_tfms=Resize(224),
    )
    if rank_distrib() == 0:
        run = wandb.init("fastai_ddp", entity="capecape")
        cb = WandbCallback()
    learn = vision_learner(dls, resnet34, metrics=error_rate, cbs=cb).to_fp16()
    with learn.distrib_ctx(sync_bn=False):
        learn.fit(1)

if __name__ == "__main__":
    train()

in your terminal call:

$ torchrun --nproc_per_node 2 train.py

Examples

• Visualize, track, and compare Fastai models: A thoroughly documented walkthrough.
• Image Segmentation on CamVid: A sample use case of the integration.