Instructions to use Omarelrayes/mlflow-artifacts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Omarelrayes/mlflow-artifacts with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Omarelrayes/mlflow-artifacts") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39609590f2cdc8aa170b654a57934362ab3cd095ba351ae3d981eb355a423755
- Size of remote file:
- 7.22 MB
- SHA256:
- b53c31ac0a501330ce7a21911152446aa7e98001c59fb189d5ac1aeebc61a15d
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