ahsi 0.1.4

One logger, every destination — send ML training events to Discord, Slack, Telegram, W&B, MLflow, Comet, Neptune, TensorBoard, Aim, and Console simultaneously

ASHi

One logger. Every destination.

ASHi fans out ML training events to every platform you care about — notification channels and experiment trackers alike — from a single, uniform API. Stop wiring up each platform separately; call on_epoch_end() once and every backend receives it simultaneously.

Features

• Notification channels — Discord, Slack, Telegram, Console
• Experiment tracking — Weights & Biases, MLflow, Comet ML, Neptune, TensorBoard, Aim
• Framework callbacks — HuggingFace Transformers, PyTorch Lightning (zero extra dependencies)
• AI summaries — GPT-powered one-sentence training summaries posted directly to all backends
• Fan-out by default — every event goes to every backend in one call
• Resilient — a failing backend never crashes training; each returns bool success
• Composable — mix and match any backends; add more at runtime with add_backend()

Installation

pip install ahsi

Note: The PyPI package is ahsi but the Python import name is ashi — so you always write from ashi import ... in your code.

Install optional experiment-tracking backends:

pip install ahsi[wandb]       # Weights & Biases
pip install ahsi[mlflow]      # MLflow
pip install ahsi[comet]       # Comet ML
pip install ahsi[neptune]     # Neptune.ai
pip install ahsi[tensorboard] # TensorBoard
pip install ahsi[aim]         # Aim

pip install ahsi[all]         # everything at once

Quick Start

from ashi import TrainLogger, DiscordBackend, WandbBackend, ConsoleBackend

logger = TrainLogger(
    DiscordBackend(webhook_url="https://discord.com/api/webhooks/..."),
    WandbBackend(project="my-project"),
    ConsoleBackend(),
)

logger.on_train_start("my-experiment", config={"epochs": 100, "lr": 3e-4})

for epoch in range(1, 101):
    # ... train ...
    logger.on_epoch_end(epoch, {"loss": 0.42, "val_loss": 0.38}, total_epochs=100, step=epoch * 500)

    if new_best:
        logger.on_best_metric("val_loss", best_val_loss, step=global_step)

logger.on_train_end(total_steps=50000, best_val_loss=0.31)

Every call fans out to Discord, W&B, and the console simultaneously.

Backends

Notification Channels

Backend	Setup
DiscordBackend(webhook_url)	Server Settings → Integrations → Webhooks
SlackBackend(webhook_url)	Slack Incoming Webhooks
TelegramBackend(token, chat_id)	Create a bot via @BotFather, get chat_id from @userinfobot
ConsoleBackend()	No setup — prints to stdout with ANSI colors

Experiment Tracking Platforms

Backend	Install	What gets logged
WandbBackend(project)	ahsi[wandb]	metrics, config, run summary, alerts
MLflowBackend(experiment_name)	ahsi[mlflow]	metrics, params, tags
CometBackend(project_name)	ahsi[comet]	metrics, parameters
NeptuneBackend(project)	ahsi[neptune]	metrics, metadata
TensorBoardBackend(log_dir)	ahsi[tensorboard]	scalars, text
AimBackend(experiment)	ahsi[aim]	metrics, metadata

Mix backends freely:

logger = TrainLogger(
    DiscordBackend(webhook_url="..."),
    WandbBackend(project="..."),
    MLflowBackend(experiment_name="..."),
    ConsoleBackend(),
)

Or add them after construction:

logger = TrainLogger()
logger.add_backend(DiscordBackend(webhook_url="..."))
logger.add_backend(WandbBackend(project="..."))

API Reference

TrainLogger(*backends, summarizer=None)

Method	Description
on_train_start(experiment, config)	Training started — logs config to trackers, posts to notification channels
on_epoch_end(epoch, metrics, total_epochs, step)	Metrics + Unicode progress bar
on_step_end(step, metrics, total_steps)	Step-level metrics + progress bar
on_best_metric(metric_name, value, step)	New best metric alert
on_checkpoint_saved(step, path)	Checkpoint saved notification
on_train_end(total_steps, best_val_loss)	Training complete summary
on_error(error, context)	Exception with context — sends alert to all backends
send(message, color)	Custom one-line message
send_file(file_path, message, filename)	File attachment (images, plots, model weights, etc.)
catch_errors(context)	Context manager — auto-calls on_error on exception, then re-raises
summarize()	Call the AI summarizer and post the result to all backends

Available colors for send(): green, red, yellow, blue, gray, orange

catch_errors context manager

with logger.catch_errors("eval_loop"):
    metrics = model.evaluate(val_dataloader)
# Any exception → on_error posted to all backends, exception re-raised

---

Experiment Tracking Details

Weights & Biases

from ashi import TrainLogger, WandbBackend

logger = TrainLogger(WandbBackend(project="my-project"))
# wandb.init() is called automatically on on_train_start

Reuse an existing run:

import wandb
wandb.init(project="my-project", name="run-1")
logger = TrainLogger(WandbBackend(run=wandb.run))

Event	wandb action
on_train_start	wandb.init() with config, or updates existing run config
on_epoch_end / on_step_end	wandb.log(metrics, step=step)
on_best_metric	wandb.log({"best/<name>": value}) + run.summary update
on_checkpoint_saved	run.summary — last checkpoint path and step
on_train_end	run.summary update, then wandb.finish()
on_error	wandb.alert(level=ERROR)
send / summarize	wandb.alert(level=INFO)

Pass finish_on_end=False to keep the run open after on_train_end.

MLflow

pip install ahsi[mlflow]

from ashi import TrainLogger, MLflowBackend

# Local ./mlruns directory
logger = TrainLogger(MLflowBackend())

# Remote tracking server
logger = TrainLogger(
    MLflowBackend(experiment_name="my-project", tracking_uri="http://localhost:5000")
)

on_train_start starts a new run and logs config as params. on_train_end calls mlflow.end_run().

Comet ML

pip install ahsi[comet]

from ashi import TrainLogger, CometBackend

logger = TrainLogger(
    CometBackend(api_key="...", project_name="my-project", workspace="my-workspace")
)

# Reuse an existing experiment
import comet_ml
exp = comet_ml.Experiment(api_key="...", project_name="...")
logger = TrainLogger(CometBackend(experiment=exp))

Neptune

pip install ahsi[neptune]

from ashi import TrainLogger, NeptuneBackend

logger = TrainLogger(
    NeptuneBackend(project="workspace/project", api_token="...")
)

# Reuse an existing run
import neptune
run = neptune.init_run(project="workspace/project")
logger = TrainLogger(NeptuneBackend(run=run))

Metrics are logged via run["metrics/<name>"].append(value, step=step).

TensorBoard

pip install ahsi[tensorboard]

from ashi import TrainLogger, TensorBoardBackend

logger = TrainLogger(TensorBoardBackend(log_dir="runs/my-experiment"))

# Reuse an existing SummaryWriter
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter("runs/my-experiment")
logger = TrainLogger(TensorBoardBackend(writer=writer))

Works with torch.utils.tensorboard (no extra install if PyTorch is already present) or tensorboardX.

Aim

pip install ahsi[aim]

from ashi import TrainLogger, AimBackend

logger = TrainLogger(AimBackend(experiment="my-experiment", repo="."))

# Reuse an existing run
from aim import Run
run = Run(experiment="my-experiment")
logger = TrainLogger(AimBackend(run=run))

---

Framework Callbacks

HuggingFace Transformers

No extra dependencies — pass the callback to Trainer.

from transformers import Trainer
from ashi import TrainLogger, DiscordBackend, HuggingFaceCallback

logger = TrainLogger(DiscordBackend(webhook_url="..."))
trainer = Trainer(
    model=model,
    args=training_args,
    callbacks=[HuggingFaceCallback(logger, experiment="my-run")],
)
trainer.train()

Hooks: on_train_begin → on_log (each loss log) → on_save → on_train_end

PyTorch Lightning

import pytorch_lightning as pl
from ashi import TrainLogger, DiscordBackend, LightningCallback

logger = TrainLogger(DiscordBackend(webhook_url="..."))
trainer = pl.Trainer(
    max_epochs=100,
    callbacks=[LightningCallback(logger, experiment="my-run")],
)
trainer.fit(model)

Hooks: on_train_start → on_train_epoch_end → on_save_checkpoint → on_train_end → on_exception

---

AI Summary (OpenAI)

Pass an AISummarizer to get a 1–2 sentence natural-language analysis of your training run, posted to all backends automatically.

from ashi import TrainLogger, DiscordBackend, AISummarizer

logger = TrainLogger(
    DiscordBackend(webhook_url="..."),
    summarizer=AISummarizer(api_key="sk-...", model="gpt-4o-mini"),
)

logger.on_train_start("my-exp", config={"lr": 3e-4, "epochs": 100})
for epoch in range(1, 101):
    logger.on_epoch_end(epoch, {"loss": 0.42, "val_loss": 0.38}, total_epochs=100)

# Posts an orange message like:
# "Training is converging steadily — val_loss dropped from 0.89 to 0.38
#  over 100 epochs with no signs of overfitting."
logger.summarize()

AI summaries are English by default. Set language to receive summaries in another language:

logger = TrainLogger(
    DiscordBackend(webhook_url="..."),
    summarizer=AISummarizer(api_key="sk-...", language="Korean"),
)

# Example output:
# "학습은 안정적으로 수렴 중이며, val_loss가 0.89에서 0.38까지 감소했습니다."
logger.summarize()

You can pass any language instruction supported by the model, such as "Korean", "Japanese", or "Spanish".

Call summarize() at any point — mid-training, on checkpoint, or at the end. No extra packages required beyond requests.

---

License

MIT