doyensahoo gorold commited on
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1 Parent(s): 48bf805

Update README for PyTorchModelHubMixin (#4)

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- Update README for PyTorchModelHubMixin (f42c49a9cbca3ec6b51173f78779a846a28dca94)


Co-authored-by: Gerald Woo <gorold@users.noreply.huggingface.co>

Files changed (1) hide show
  1. README.md +4 -6
README.md CHANGED
@@ -45,13 +45,13 @@ A simple example to get started:
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  ```python
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  import torch
 
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  import pandas as pd
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  from gluonts.dataset.pandas import PandasDataset
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  from gluonts.dataset.split import split
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- from huggingface_hub import hf_hub_download
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  from uni2ts.eval_util.plot import plot_single
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- from uni2ts.model.moirai import MoiraiForecast
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  SIZE = "small" # model size: choose from {'small', 'base', 'large'}
@@ -85,9 +85,7 @@ test_data = test_template.generate_instances(
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  # Prepare pre-trained model by downloading model weights from huggingface hub
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  model = MoiraiForecast.load_from_checkpoint(
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- checkpoint_path=hf_hub_download(
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- repo_id=f"Salesforce/moirai-R-{SIZE}", filename="model.ckpt"
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- ),
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  prediction_length=PDT,
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  context_length=CTX,
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  patch_size=PSZ,
@@ -95,7 +93,6 @@ model = MoiraiForecast.load_from_checkpoint(
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  target_dim=1,
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  feat_dynamic_real_dim=ds.num_feat_dynamic_real,
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  past_feat_dynamic_real_dim=ds.num_past_feat_dynamic_real,
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- map_location="cuda:0" if torch.cuda.is_available() else "cpu",
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  )
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  predictor = model.create_predictor(batch_size=BSZ)
@@ -117,6 +114,7 @@ plot_single(
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  name="pred",
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  show_label=True,
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  )
 
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  ```
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  ## The Moirai Family
 
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  ```python
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  import torch
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+ import matplotlib.pyplot as plt
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  import pandas as pd
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  from gluonts.dataset.pandas import PandasDataset
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  from gluonts.dataset.split import split
 
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  from uni2ts.eval_util.plot import plot_single
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+ from uni2ts.model.moirai import MoiraiForecast, MoiraiModule
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  SIZE = "small" # model size: choose from {'small', 'base', 'large'}
 
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  # Prepare pre-trained model by downloading model weights from huggingface hub
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  model = MoiraiForecast.load_from_checkpoint(
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+ module=MoiraiModule.from_pretrained(f"Salesforce/moirai-1.0-R-{SIZE}"),
 
 
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  prediction_length=PDT,
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  context_length=CTX,
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  patch_size=PSZ,
 
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  target_dim=1,
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  feat_dynamic_real_dim=ds.num_feat_dynamic_real,
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  past_feat_dynamic_real_dim=ds.num_past_feat_dynamic_real,
 
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  )
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  predictor = model.create_predictor(batch_size=BSZ)
 
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  name="pred",
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  show_label=True,
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  )
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+ plt.show()
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  ```
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  ## The Moirai Family