File size: 4,160 Bytes
bdd9175 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | """TinyCast: an attention-free, 146,505-parameter dilated-convolution
time-series foundation model.
Forecast:
>>> from tinycast import TinyCastPredictor, load_checkpoint
>>> predictor = TinyCastPredictor(
... prediction_length=48,
... checkpoint_path="model.safetensors",
... freq="H", domain="Energy", device="cpu",
... force_flip_invariance=True,
... )
>>> forecasts = predictor.predict(gluonts_test_input)
Evaluate:
>>> from tinycast import summarize_by_freq_bin
>>> summarize_by_freq_bin("all_results.csv")["overall"]["ncrps"]
Train, then release:
>>> from tinycast import (TinyCastConfig, train, average_checkpoints,
... export_safetensors)
>>> result = train(TinyCastConfig(), data=windows, max_steps=1000,
... output_dir="run/", batch_size=32, checkpoint_every=100)
>>> weights = average_checkpoints(result.checkpoints[-8:])
>>> export_safetensors(weights, "release/", expect_parameters=146_505)
Rebuild the synthetic corpus (needs CUDA):
>>> from tinycast import build_shard, verify_shard
>>> build_shard() # published shard 0
>>> verify_shard("synth4096_0", shard=0)
``tinycast.train`` is the training function, not the module: the two share a
name and the function wins. Module-level recipe constants are reachable as
``from tinycast.train import AR_CHUNKS``. A submodule the package does not
import itself, ``tinycast.backbone`` and ``tinycast.periodogram`` among them,
becomes an attribute only after ``import tinycast.backbone``.
"""
from .config import TinyCastConfig
from .model import TinyCastForPrediction, TinyCastBackbone, PredictionOutput
from .checkpoint import load_checkpoint, load_model
from .predictor import TinyCastPredictor, ARRolloutPredictor
# Training and the objectives it optimizes.
from .losses import committing_loss, pinball_loss, seasonal_copy_baseline
from .train import TrainResult, train, training_window_width
# The synthetic pretraining corpus generators.
from .synth import generate_gp, generate_spikes, generate_tsi
# eval, export and corpus each carry a ``python -m`` entry point, so the package
# must not import them eagerly: that puts them in sys.modules before runpy runs
# them as __main__, which warns and executes the module body twice. Resolving
# them on first attribute access (PEP 562) keeps ``from tinycast import
# evaluate`` working and leaves the command line quiet.
_LAZY_MODULES = ("eval", "export", "corpus")
_LAZY_EXPORTS = {
"evaluate": "eval",
"summarize_by_freq_bin": "eval",
"export_safetensors": "export",
"average_checkpoints": "export",
"check_export_roundtrip": "export",
"ExportError": "export",
"build_shard": "corpus",
"verify_shard": "corpus",
"iter_shard_series": "corpus",
}
def __getattr__(name: str):
from importlib import import_module
if name in _LAZY_MODULES:
value = import_module(f".{name}", __name__)
else:
module = _LAZY_EXPORTS.get(name)
if module is None:
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
value = getattr(import_module(f".{module}", __name__), name)
globals()[name] = value # resolve once, then it is a plain global
return value
def __dir__() -> list:
return sorted(set(globals()) | set(_LAZY_EXPORTS) | set(_LAZY_MODULES))
__all__ = [
# model and inference
"TinyCastConfig",
"TinyCastForPrediction",
"TinyCastBackbone",
"PredictionOutput",
"load_checkpoint",
"load_model",
"TinyCastPredictor",
"ARRolloutPredictor",
# training
"train",
"TrainResult",
"training_window_width",
"pinball_loss",
"committing_loss",
"seasonal_copy_baseline",
# export
"export_safetensors",
"average_checkpoints",
"check_export_roundtrip",
"ExportError",
# evaluation
"evaluate",
"summarize_by_freq_bin",
# synthetic corpus
"build_shard",
"verify_shard",
"iter_shard_series",
"generate_gp",
"generate_spikes",
"generate_tsi",
]
__version__ = "1.0.0"
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