a few final tweaks for marin runs (#755)

docs/Configuration-Guide.md

@@ -111,7 +111,6 @@ The following table lists some of the parameters that you might want to change.
 | Parameter | Description | Default |
 |----------------|-------------------------------------------------------------------------------|---------|
 | `log_dir` | Where to save logs (python logger). `$run_id` will be appended | `logs/` |
-| `run_base_dir` | where to save run artifacts. not really used much. `$run_id` will be appended | `runs/` |
 
 
 

src/levanter/checkpoint.py

@@ -549,8 +549,12 @@ class CheckpointerConfig:
  default_factory=lambda: [dict(every=10000)]
  ) # list of dicts with two keys: every and until
 
+ append_run_id_to_base_path: bool = True
+
  def expanded_path(self, run_id) -> str:
- return os.path.expanduser(os.path.join(self.base_path, run_id))
+ if self.append_run_id_to_base_path:
+ return os.path.expanduser(os.path.join(self.base_path, run_id))
+ return os.path.expanduser(self.base_path)
 
  def create(self, run_id) -> Checkpointer:
  keeps = [CheckpointInterval(**k) for k in self.keep]

src/levanter/data/text.py

@@ -577,6 +577,8 @@ def tagged_eval_sets(
 class LMDatasetConfig(LMDatasetSourceConfig, LMTaskConfig):
  """This class supports loading data both from HF Datasets and from a raw dataset of jsonl urls"""
 
+ cache_dir: Optional[str] = "cache/"
+
  def train_set(
  self, seq_len: int, monitors: Union[bool, List[MetricsMonitor]] = True, *, key: Optional[PRNGKeyArray] = None
  ) -> AsyncDataset[np.ndarray]:
@@ -705,6 +707,8 @@ def _convert_id_to_token(self, index: int) -> str:
 class LMMixtureDatasetConfig(LMTaskConfig):
  """This class represents a mixture of datasets with their associated weights."""
 
+ cache_dir: Optional[str] = "cache/"
+
  # data source configs and weights
  configs: Dict[str, LMDatasetSourceConfig] = field(default_factory=dict)
  """ configuration of each dataset source (urls, hf dataset id, etc.) """

src/levanter/main/train_lm.py

@@ -188,7 +188,11 @@ def main(config: TrainLmConfig):
  callbacks.log_performance_stats(Pos.size, trainer.config.train_batch_size, flops_per_example), every=1
  )
  if config.hf_save_path is not None:
- full_save_path = os.path.join(config.hf_save_path, trainer.run_id)
+ # bit gross to reach this far into the config, but it's fine
+ if config.trainer.checkpointer.append_run_id_to_base_path:
+ full_save_path = os.path.join(config.hf_save_path, trainer.run_id)
+ else:
+ full_save_path = config.hf_save_path
 
  trainer.add_hook(
  save_hf_checkpoint_callback(full_save_path, converter, upload_to_hf=config.hf_upload or False),

src/levanter/trainer.py

@@ -519,7 +519,6 @@ class TrainerConfig:
 
  wandb: Optional[tracker.wandb.WandbConfig] = None
  log_dir: Path = Path("logs/")
- run_base_dir: Path = Path("runs/")
  id: Optional[str] = None # run id. if None, will be set to a random string
 
  tracker: TrackerConfig | Tuple[TrackerConfig, ...] = field(default_factory=tracker.wandb.WandbConfig)