megh
MeghCloud metrics tracking SDK for ML training runs.
Installation
pip install megh[huggingface]
Usage with Hugging Face Trainer
import megh
from transformers import Trainer, TrainingArguments
# Register megh_metrics with HuggingFace Trainer
megh.init(framework="hf_trainer")
# Use Trainer as usual — megh_metrics is automatically enabled
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=8,
logging_steps=10,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
)
trainer.train()
Metrics, hyperparameters, and model config are automatically tracked.
Metadata
Release files for megh 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| megh-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Release files / megh-0.0.2-py3-none-any.whl
| Download URL | megh-0.0.2-py3-none-any.whl |
|---|---|
| Size | 7.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
bb5977d97f3e49d1f2f7b8c68b524028a3386d17d44d45f37468bac2c6c73765
|
|
BLAKE2b-256 checksum How to use checksums |
2f5de2d4d27c60ad89930d8ec628a91dd13e58b7838e5b647e14b67c4e3be843
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.15
|