74 lines
2.4 KiB
Markdown
74 lines
2.4 KiB
Markdown
# Kronos Finetuning on Your Custom csv Dataset
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Supports fine-tuning training with custom CSV data using configuration files
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## 1. Quick Start
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### Configuration Setup
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First edit the `config.yaml` file to set the correct paths and parameters:
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```yaml
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# Data configuration
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data:
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data_path: "/path/to/your/data.csv"
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lookback_window: 512
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predict_window: 48
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# ... other parameters
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# Model path configuration
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model_paths:
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pretrained_tokenizer: "/path/to/pretrained/tokenizer"
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pretrained_predictor: "/path/to/pretrained/predictor"
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base_save_path: "/path/to/save/models"
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# ... other paths
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```
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### Run Training
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Using train_sequential
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```bash
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# Complete training
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python train_sequential.py --config configs/config_ali09988_candle-5min.yaml
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# Skip existing models
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python train_sequential.py --config configs/config_ali09988_candle-5min.yaml --skip-existing
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# Only train tokenizer
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python train_sequential.py --config configs/config_ali09988_candle-5min.yaml --skip-basemodel
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# Only train basemodel
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python train_sequential.py --config configs/config_ali09988_candle-5min.yaml --skip-tokenizer
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```
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Run each stage separately
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```bash
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# Only train tokenizer
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python finetune_tokenizer.py --config configs/config_ali09988_candle-5min.yaml
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# Only train basemodel (requires fine-tuned tokenizer first)
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python finetune_base_model.py --config configs/config_ali09988_candle-5min.yaml
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```
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DDP Training
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```bash
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# Choose communication protocol yourself, nccl can be replaced with gloo
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DIST_BACKEND=nccl \
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torchrun --standalone --nproc_per_node=8 train_sequential.py --config configs/config_ali09988_candle-5min.yaml
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```
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## 2. Training Results
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**Data Format**: Ensure CSV file contains the following columns: `timestamps`, `open`, `high`, `low`, `close`, `volume`, `amount`
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