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{ | |
"model_name": "Crossformer", | |
"use_decoder": true, | |
"model_type": "PyTorch", | |
"model_params": { | |
"n_time_series": 4, | |
"forecast_history":6, | |
"forecast_length": 4, | |
"seg_len": 6 | |
}, |
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wandb_version: 1 | |
GCS: | |
desc: null | |
value: true | |
_wandb: | |
desc: null | |
value: | |
cli_version: 0.10.17 | |
framework: torch |
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import torch | |
class TransformerXCBasic(torch.nn.Module): | |
""" Transformer model """ | |
def __init__(self, n_time_series, out_seq_len, device, d_model=128, dropout=.5, n_head=8): | |
super(TransformerXCBasic, self).__init__() | |
self.input_dim = n_time_series | |
self.n_head = n_head |
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model = model_class.from_pretrained(self.args.model_path, | |
config=config, | |
# SpERT model parameters | |
cls_token=self._tokenizer.convert_tokens_to_ids('[CLS]'), | |
relation_types=input_reader.relation_type_count - 1, | |
entity_types=input_reader.entity_type_count, | |
max_pairs=self.args.max_pairs, | |
prop_drop=self.args.prop_drop, | |
size_embedding=self.args.size_embedding, | |
freeze_transformer=self.args.freeze_transformer) |
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_column_definition = [ | |
('id', DataTypes.REAL_VALUED, InputTypes.ID), | |
('hours_from_start', DataTypes.REAL_VALUED, InputTypes.TIME), | |
('power_usage', DataTypes.REAL_VALUED, InputTypes.TARGET), | |
('hour', DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT), | |
('day_of_week', DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT), | |
('hours_from_start', DataTypes.REAL_VALUED, InputTypes.KNOWN_INPUT), | |
('categorical_id', DataTypes.CATEGORICAL, InputTypes.STATIC_INPUT), | |
] |
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label = conll04_eval | |
model_type = spert | |
model_path = data/models/ade | |
tokenizer_path = data/models/ade | |
dataset_path = data/datasets/ade/ade_split_0_test.json | |
types_path = data/datasets/ade/ade_types.json | |
eval_batch_size = 1 | |
rel_filter_threshold = 0.4 | |
size_embedding = 25 | |
prop_drop = 0.1 |
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label = ade_eval | |
model_type = spert | |
model_path = data/models/ade | |
tokenizer_path = data/models/ade | |
dataset_path = data/datasets/conll04/conll04_test.json | |
types_path = data/datasets/conll04/conll04_types.json | |
eval_batch_size = 1 | |
rel_filter_threshold = 0.4 | |
size_embedding = 25 | |
prop_drop = 0.1 |
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def train_epoch_loop(data_loader:DataLoader, opt:torch.optim, model:PyTorchForecast, takes_target:bool, forward_params={}) | |
i = 0 | |
running_loss = 0.0 | |
for src, trg in data_loader: | |
opt.zero_grad() | |
# Convert to CPU/GPU/TPU | |
src = src.to(model.device) | |
trg = trg.to(model.device) | |
# TODO figure how to avoid | |
if takes_target: |
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FROM pytorch/pytorch:1.4-cuda10.1-cudnn7-devel | |
COPY requirements.txt /tmp/ | |
RUN pip install -r /tmp/requirements.txt | |
ARG url | |
RUN git clone -n https://github.com/example/example_repo | |
RUN git checkout 543231 |
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@TrainerBase.register('metatrainer') | |
class MetaTrainer(Trainer): | |
def __init__(self, | |
model: Model, | |
meta_model: MetaModel, | |
optimizer: torch.optim.Optimizer, | |
iterator: DataIterator, | |
train_datasets: List[Iterable[Instance]], | |
validation_datasets: Optional[Iterable[Instance]] = None, |
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