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:
- c8a7034bb982cd274504773b695ffa6c7e03d07b3073fe0fa29bce00317e586b
- Size of remote file:
- 49.1 MB
- SHA256:
- c163e4524187619c8485c251ae67ce26697d7fdb27e1d9168ab0615eef597c58
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