InMemoryLogger

class composer.loggers.InMemoryLogger

Logs metrics to dictionary objects that persist in memory throughout training.

Useful for collecting and plotting data inside notebooks.

Example usage:

from composer.loggers import InMemoryLogger
from composer.trainer import Trainer
logger = InMemoryLogger(
)
trainer = Trainer(
    model=model,
    train_dataloader=train_dataloader,
    eval_dataloader=eval_dataloader,
    max_duration="1ep",
    optimizers=[optimizer],
    loggers=[logger]
)
# Get data from logger. If you are using multiple loggers, be sure to confirm
# which index in trainer.logger.destinations contains your desired logger.
logged_data = trainer.logger.destinations[0].data

data

Mapping of a logged key to a (Timestamp, logged value) tuple. This dictionary contains all logged data.

Type

Dict[str, List[Tuple[Timestamp, Any]]]

most_recent_values

Mapping of a key to the most recent value for that key.

Type

Dict[str, Any]

most_recent_timestamps

Mapping of a key to the Timestamp of the last logging call for that key.

Type

Dict[str, Timestamp]

hyperparameters

Dictionary of all hyperparameters.

Type

Dict[str, Any]

get_timeseries(metric)

Returns logged data as dict containing values of a desired metric over time.

Parameters

metric (str) – Metric of interest. Must be present in self.data.keys().

Returns

timeseries (Dict[str, Any]) – Dictionary in which one key is metric, and the associated value is a list of values of that metric. The remaining keys are each a unit of time, and the associated values are each a list of values of that time unit for the corresponding index of the metric. For example: >>> InMemoryLogger.get_timeseries(metric=”accuracy/val”) {“accuracy/val”: [31.2, 45.6, 59.3, 64.7, “epoch”: [1, 2, 3, 4, …], …], “batch”: [49, 98, 147, 196, …], …}

Example

import matplotlib.pyplot as plt

from composer.loggers import InMemoryLogger
from composer.core.time import Time, Timestamp

in_mem_logger = InMemoryLogger()
trainer = Trainer(
    model=model,
    train_dataloader=train_dataloader,
    eval_dataloader=eval_dataloader,
    max_duration="1ep",
    optimizers=[optimizer],
    loggers=[in_mem_logger]
)

# Populate the logger with data
for b in range(0,3):
    datapoint = b * 3
    in_mem_logger.log_metrics({"accuracy/val": datapoint})

timeseries = in_mem_logger.get_timeseries("accuracy/val")
plt.plot(timeseries["batch"], timeseries["accuracy/val"])
plt.xlabel("Batch")
plt.ylabel("Validation Accuracy")