Real-time notifications for any Python pipeline

Run anything.
Know instantly. Stay anywhere.

slackker sends real-time notifications to Slack or Telegram for any Python script, data pipeline, ML training run, or automation workflow — complete with metrics, outputs, and optional plots.

pip install slackker View on GitHub

main.py

from slackker.callbacks.basic import SlackUpdate

slackker = SlackUpdate(token="xoxb-...", channel="A04AAB77ABC")

@slackker.notifier
def train_model():
    return "done"

slackker.notify(event="training_complete", status="completed")

Why slackker

Stay informed without staying glued to your screen

Any Python Function

Decorator-based notifications for script execution with returned output summaries.

Keras Callback

Attach to model.fit and receive metric progress during epoch training.

Lightning Callback

Track train and validation logs with monitor-based best epoch updates.

Plot Sharing

Export and send history plots so you can check model behavior quickly on mobile.

Detailed callback usage

Drop-in examples for real projects

pipeline.py

from slackker.callbacks.basic import SlackUpdate

notify = SlackUpdate(
    token="xoxb-your-bot-token",
    channel="A04AAB77ABC",
    verbose=1
)

@notify.notifier
def run_data_pipeline(source_path: str):
    rows_processed = 12500
    status = "success"
    return rows_processed, status

if __name__ == "__main__":
    rows, status = run_data_pipeline("./data/train.csv")
    notify.notify(
        event="pipeline_finished",
        rows_processed=rows,
        status=status,
        attachment="./artifacts/summary.txt"
    )

train_keras.py

from slackker.callbacks.keras import SlackUpdate

slackker_cb = SlackUpdate(
    token="xoxb-your-bot-token",
    channel="A04AAB77ABC",
    ModelName="ImageClassifierV1",
    export="png",
    SendPlot=True,
    verbose=1
)

history = model.fit(
    x_train,
    y_train,
    epochs=20,
    batch_size=32,
    validation_data=(x_val, y_val),
    callbacks=[slackker_cb]
)

train_lightning.py

from lightning.pytorch import Trainer
from slackker.callbacks.lightning import SlackUpdate

slackker_cb = SlackUpdate(
    token="xoxb-your-bot-token",
    channel="A04AAB77ABC",
    ModelName="LightningClassifier",
    TrackLogs=["train_loss", "train_acc", "val_loss", "val_acc"],
    monitor="val_loss",
    export="png",
    SendPlot=True,
    verbose=1
)

trainer = Trainer(max_epochs=12, callbacks=[slackker_cb])
trainer.fit(model, train_loader, val_loader)

Quick start

From install to updates in four steps

1Install package: pip install slackker
2Setup Slack app token and channel, or Telegram token.
3Create callback object and attach notifier/decorator.
4Run training and receive updates plus optional exported plots.

Run anything.Know instantly. Stay anywhere.