MosaicML Composer
3 minute read
Composer is a library for training neural networks better, faster, and cheaper. It contains many state-of-the-art methods for accelerating neural network training and improving generalization, along with an optional Trainer API that makes composing many different enhancements easy.
W&B provides a lightweight wrapper for logging your ML experiments. But you don’t need to combine the two yourself: W&B is incorporated directly into the Composer library via the WandBLogger.
Start logging to W&B
from composer import Trainer
from composer.loggers import WandBLogger
trainer = Trainer(..., logger=WandBLogger())
Use Composer’s WandBLogger
The Composer library uses WandBLogger class in the Trainer to log metrics to Weights and Biases. It is a simple as instantiating the logger and passing it to the Trainer
wandb_logger = WandBLogger(project="gpt-5", log_artifacts=True)
trainer = Trainer(logger=wandb_logger)
Logger arguments
Below the parameters for WandbLogger, see the Composer documentation for a full list and description
| Parameter | Description |
|---|---|
project |
W&B project name (str, optional) |
group |
W&B group name (str, optional) |
name |
W&B run name. If not specified, the State.run_name is used (str, optional) |
entity |
W&B entity name, such as your username or W&B Team name (str, optional) |
tags |
W&B tags (List[str], optional) |
log_artifacts |
Whether to log checkpoints to wandb, default: false (bool, optional) |
rank_zero_only |
Whether to log only on the rank-zero process. When logging artifacts, it is highly recommended to log on all ranks. Artifacts from ranks ≥1 are not stored, which may discard pertinent information. For example, when using Deepspeed ZeRO, it would be impossible to restore from checkpoints without artifacts from all ranks, default: True (bool, optional) |
init_kwargs |
Params to pass to wandb.init such as your wandb config etc See here for the full list wandb.init accepts |
A typical usage would be:
init_kwargs = {"notes":"Testing higher learning rate in this experiment",
"config":{"arch":"Llama",
"use_mixed_precision":True
}
}
wandb_logger = WandBLogger(log_artifacts=True, init_kwargs=init_kwargs)
Log prediction samples
You can use Composer’s Callbacks system to control when you log to Weights & Biases via the WandBLogger, in this example a sample of the validation images and predictions is logged:
import wandb
from composer import Callback, State, Logger
class LogPredictions(Callback):
def __init__(self, num_samples=100, seed=1234):
super().__init__()
self.num_samples = num_samples
self.data = []
def eval_batch_end(self, state: State, logger: Logger):
"""Compute predictions per batch and stores them on self.data"""
if state.timer.epoch == state.max_duration: #on last val epoch
if len(self.data) < self.num_samples:
n = self.num_samples
x, y = state.batch_pair
outputs = state.outputs.argmax(-1)
data = [[wandb.Image(x_i), y_i, y_pred] for x_i, y_i, y_pred in list(zip(x[:n], y[:n], outputs[:n]))]
self.data += data
def eval_end(self, state: State, logger: Logger):
"Create a wandb.Table and logs it"
columns = ['image', 'ground truth', 'prediction']
table = wandb.Table(columns=columns, data=self.data[:self.num_samples])
wandb.log({'sample_table':table}, step=int(state.timer.batch))
...
trainer = Trainer(
...
loggers=[WandBLogger()],
callbacks=[LogPredictions()]
)
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