Time Series Forecasting
Keras
English
time-series
stock-forecasting
LSTM
ARIMA
Prophet
machine-learning
deep-learning
forecasting
Instructions to use Hiruni2207/DataSynthis_ML_JobTask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Hiruni2207/DataSynthis_ML_JobTask with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Hiruni2207/DataSynthis_ML_JobTask") - Notebooks
- Google Colab
- Kaggle
| import argparse | |
| import os | |
| from huggingface_hub import HfApi, HfFolder, Repository | |
| def upload_to_hf(repo_id, path, token=None): | |
| api = HfApi() | |
| if token is None: | |
| token = os.getenv("HF_TOKEN") | |
| if token is None: | |
| raise ValueError("HF token not provided. Set HF_TOKEN environment variable or pass --token") | |
| # Create the repo if it doesn't exist | |
| api.create_repo(repo_id=repo_id, exist_ok=True, token=token) | |
| # Upload all files from path | |
| for root, _, files in os.walk(path): | |
| for file in files: | |
| file_path = os.path.join(root, file) | |
| rel_path = os.path.relpath(file_path, path) | |
| print(f"Uploading {rel_path}...") | |
| api.upload_file( | |
| path_or_fileobj=file_path, | |
| path_in_repo=rel_path, | |
| repo_id=repo_id, | |
| repo_type="model", | |
| token=token | |
| ) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--repo_id", type=str, required=True, help="Hugging Face repo id (username/repo_name)") | |
| parser.add_argument("--path", type=str, required=True, help="Path to upload") | |
| parser.add_argument("--token", type=str, default=None, help="Hugging Face token") | |
| args = parser.parse_args() | |
| upload_to_hf(args.repo_id, args.path, args.token) | |