> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-docs-sandboxes-integrations-placement.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
> State of the art algorithms to train your neural networks
# MosaicML Composer
export const ColabLink = ({url}) =>
Try in Colab
;
[Composer](https://github.com/mosaicml/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](https://docs.mosaicml.com/projects/composer/en/stable/trainer/using_the_trainer.html) 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](https://docs.mosaicml.com/projects/composer/en/stable/trainer/file_uploading.html#weights-biases-artifacts).
## Start logging to W\&B
```python theme={null}
from composer import Trainer
from composer.loggers import WandBLogger
trainer = Trainer(..., logger=WandBLogger())
```
## Use Composer's `WandBLogger`
The Composer library uses [WandBLogger](https://docs.mosaicml.com/projects/composer/en/stable/trainer/file_uploading.html#weights-biases-artifacts) class in the `Trainer` to log metrics to W\&B. It is as simple as instantiating the logger and passing it to the `Trainer`.
```python theme={null}
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](https://docs.mosaicml.com/projects/composer/en/stable/api_reference/generated/composer.loggers.WandBLogger.html) 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 the [`wandb.init()` parameters](/models/ref/python/functions/init) for parameters that `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](https://docs.mosaicml.com/projects/composer/en/stable/trainer/callbacks.html) system to control when you log to W\&B via the `WandBLogger`, in this example a sample of the validation images and predictions is logged:
```python theme={null}
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):
with wandb.init() as run:
"Create a wandb.Table and logs it"
columns = ['image', 'ground truth', 'prediction']
table = wandb.Table(columns=columns, data=self.data[:self.num_samples])
run.log({'sample_table':table}, step=int(state.timer.batch))
...
trainer = Trainer(
...
loggers=[WandBLogger()],
callbacks=[LogPredictions()]
)
```
## Use Composer's `WandBLogger`
The Composer library uses [WandBLogger](https://docs.mosaicml.com/projects/composer/en/stable/trainer/file_uploading.html#weights-biases-artifacts) class in the `Trainer` to log metrics to W\&B. It is as simple as instantiating the logger and passing it to the `Trainer`.
```python theme={null}
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](https://docs.mosaicml.com/projects/composer/en/stable/api_reference/generated/composer.loggers.WandBLogger.html) 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 the [`wandb.init()` parameters](/models/ref/python/functions/init) for parameters that `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](https://docs.mosaicml.com/projects/composer/en/stable/trainer/callbacks.html) system to control when you log to W\&B via the `WandBLogger`, in this example a sample of the validation images and predictions is logged:
```python theme={null}
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):
with wandb.init() as run:
"Create a wandb.Table and logs it"
columns = ['image', 'ground truth', 'prediction']
table = wandb.Table(columns=columns, data=self.data[:self.num_samples])
run.log({'sample_table':table}, step=int(state.timer.batch))
...
trainer = Trainer(
...
loggers=[WandBLogger()],
callbacks=[LogPredictions()]
)
```