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https://github.com/amazon-science/chronos-forecasting
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b4e8085c7f
*Description of changes:* Add training script and config files. Can be used for pre-training, or adapted for fine-tuning chronos models. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Abdul Fatir <Abdulfatirs@gmail.com>
35 lines
757 B
YAML
35 lines
757 B
YAML
training_data_paths:
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- "/home/ubuntu/tsmixup-data.arrow"
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- "/home/ubuntu/kernelsynth-data.arrow"
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probability:
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- 0.9
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- 0.1
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context_length: 512
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prediction_length: 64
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min_past: 60
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max_steps: 200_000
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save_steps: 100_000
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log_steps: 500
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per_device_train_batch_size: 32
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learning_rate: 0.001
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optim: adamw_torch_fused
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num_samples: 20
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shuffle_buffer_length: 100_000
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gradient_accumulation_steps: 1
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model_id: google/t5-efficient-tiny
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model_type: seq2seq
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random_init: true
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tie_embeddings: true
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output_dir: ./output/
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tf32: true
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torch_compile: true
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tokenizer_class: "MeanScaleUniformBins"
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tokenizer_kwargs:
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low_limit: -15.0
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high_limit: 15.0
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n_tokens: 4096
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lr_scheduler_type: linear
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warmup_ratio: 0.0
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dataloader_num_workers: 1
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max_missing_prop: 0.9
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use_eos_token: true
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