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.

  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:

  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
    
pip install wandb
import wandb
wandb.login()
!pip install wandb

import 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())

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:

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.

You can now run distributed training directly inside a notebook.

import wandb
from fastai.vision.all import *

from accelerate import notebook_launcher
from fastai.distributed import *
from fastai.callback.wandb import WandbCallback

wandb.require(experiment="service")
path = 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(in_notebook=True, sync_bn=False):
        learn.fit(1)

notebook_launcher(train, num_processes=2)

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)

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
import wandb
from fastai.vision.all import *

from accelerate import notebook_launcher
from fastai.distributed import *
from fastai.callback.wandb import WandbCallback

wandb.require(experiment="service")
path = 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(in_notebook=True, sync_bn=False):
        learn.fit(1)

notebook_launcher(train, num_processes=2)

Examples


Last modified February 20, 2025: c1984ba