Merge pull request google-research#8 from kashif/hub

[Hub] Download weights automatically from hub

README.md

@@ -78,7 +78,7 @@ tfm = timesfm.TimesFm(
     model_dims=1280,
     backend=<backend>,
 )
-tfm.load_from_checkpoint(<checkpoint_path>)
+tfm.load_from_checkpoint(repo_id="google/timesfm-1.0-200m")
 ```
 
 Note that the four parameters are fixed to load the 200m model

src/timesfm.py

@@ -16,14 +16,16 @@
 
 import logging
 import multiprocessing
+from os import path
 import time
-from typing import Any, Literal, Sequence
+from typing import Any, Literal, Optional, Sequence
 
 import einshape as es
 import jax
 import jax.numpy as jnp
 import numpy as np
 import pandas as pd
+from huggingface_hub import snapshot_download
 from paxml import checkpoints
 from paxml import tasks_lib
 from praxis import base_hyperparams
@@ -222,17 +224,23 @@ def _get_sample_inputs(self):
 
   def load_from_checkpoint(
       self,
-      checkpoint_path: str,
+      checkpoint_path: Optional[str] = None,
+      repo_id: str = "google/timesfm-1.0-200m",
       checkpoint_type: checkpoints.CheckpointType = checkpoints.CheckpointType.FLAX,
       step: int | None = None,
   ) -> None:
     """Loads a checkpoint and compiles the decoder.
 
     Args:
-      checkpoint_path: path to the checkpoint directory.
+      checkpoint_path: Optional path to the checkpoint directory.
+      repo_id: Hugging Face Hub repo id.
       checkpoint_type: type of PAX checkpoint
       step: step of the checkpoint to load. If `None`, load latest checkpoint.
     """
+    # Download the checkpoint from Hugging Face Hub if not given
+    if checkpoint_path is None:
+      checkpoint_path = path.join(snapshot_download(repo_id), "checkpoints")
+
     #  Initialize the model weights.
     self._logging("Constructing model weights.")
     start_time = time.time()