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 →b) Install the wandb library on your machine in a Python 3 environment using
pipc) Login to the wandb library on your machine. You will find your API key here: https://wandb.ai/authorize.
Notebook
Command Line
!pip install wandb
import wandb
wandb.login()
pip install wandb
wandb login
Then 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 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 |
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.
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.A minimal example is shown below:
Notebook
Script
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)
train.py
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.
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)Notebook
Script
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)
train.py
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
Last modified 5mo ago