runyard (Python client)
See the main README.
Optional extras: images (numpy, Pillow), system (GPU, CPU and RAM usage), video (imageio-ffmpeg: frames
become an H.264 mp4 instead of a GIF) and tensorboard (for runyard watch DIR, which sends TensorBoard event
files: one run per directory, --once to import and exit). runyard --help lists the commands.
Framework adapters
runyard.integrations.<module> imports its framework, which you install yourself. Keyword arguments other than
project and name go to runyard.init (tags, notes, system, ...).
| Module | One line | Run, config, metrics |
|---|---|---|
hf |
Trainer(..., callbacks=[RunyardCallback()]) |
per training; TrainingArguments and model config; train/, eval/ by global step |
accelerate |
Accelerator(log_with=[RunyardTracker(project=...)]) |
per init_trackers; its config; accelerator.log() |
lightning |
Trainer(logger=RunyardLogger(project=...)) |
per Trainer call; hyperparameters; logged metrics by global step |
keras |
model.fit(..., callbacks=[RunyardCallback(project=...)]) |
per fit; fit params and parameter count; train/, val/ by epoch, batch/ with log_every_n_batches |
xgboost |
xgb.train(..., callbacks=[RunyardCallback(project=...)]) |
per training; booster training params; <eval name>/<metric> by iteration |
lightgbm |
lgb.train(..., callbacks=[runyard_callback(project=...)]) |
per training; params; <valid name>/<metric> by iteration |
sklearn |
autolog(project=...); autolog(disable=True) undoes it |
per outer fit(), named after the class; get_params(deep=False); fit_seconds, train/score |
torchtune |
metric_logger: {_component_: runyard.integrations.torchtune.RunyardLogger, project: ...} |
per recipe; resolved recipe config; the recipe's metrics by step |
On macOS, xgboost and lightgbm need brew install libomp, and they crash if torch was imported first: import them
before torch.
1 maintainer
Metadata
1 maintainer
Release files for runyard 0.5.0
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