Graph Machine Learning
AnemoI
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@@ -78,13 +78,13 @@ pip install flash-attn --no-build-isolation
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  ai-models anemoi --checkpoint aifs_single_v0.2.1.ckpt --file example_20241107_12_n320.grib
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  ```
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- **Note** we train AIFS using `flash_attention` (https://github.com/Dao-AILab/flash-attention).
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  There are currently some issues when trying to install flash attention with the latest PyTorch version 2.5 and CUDA 12.4 (https://github.com/Dao-AILab/flash-attention/issues/1330).
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  For that reason, we recommend you install PyTorch 2.4.
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  Additonally the use of 'Flash Attention' package also imposes certain requirements in terms of software and hardware. Those can be found under #Installation and Features in https://github.com/Dao-AILab/flash-attention
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  After running the `ai-models` command the output of the forecast should be written into `anemoi.grib`
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  Below you can find an example to read that file and load it as numpy array or xarray.
 
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  ai-models anemoi --checkpoint aifs_single_v0.2.1.ckpt --file example_20241107_12_n320.grib
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  ```
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+ 🚨 **Note** we train AIFS using `flash_attention` (https://github.com/Dao-AILab/flash-attention).
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  There are currently some issues when trying to install flash attention with the latest PyTorch version 2.5 and CUDA 12.4 (https://github.com/Dao-AILab/flash-attention/issues/1330).
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  For that reason, we recommend you install PyTorch 2.4.
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  Additonally the use of 'Flash Attention' package also imposes certain requirements in terms of software and hardware. Those can be found under #Installation and Features in https://github.com/Dao-AILab/flash-attention
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+ 🚨 **Note** the `aifs_single_v0.2.1.ckpt` checkpoint just contains the model’s weights.
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+ That file does not contain any information about the optimizer states, lr-scheduler states, etc.
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  After running the `ai-models` command the output of the forecast should be written into `anemoi.grib`
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  Below you can find an example to read that file and load it as numpy array or xarray.