Update README.md

README.md

@@ -54,28 +54,20 @@ and direct observational data.
 
 ## How to Get Started with the Model
 
-To be able to run AIFS to generate a new forecast, you can use ai-models https://github.com/ecmwf-lab/ai-models.
-`ai-models` command can be used to run different models, since in this case we are looking at using AIFS we need to specify
-`anemoi` as `model-name` and then pass the path to the checkpoint (`aifs_single_v0.2.1.ckpt`) and the initial conditions. 
-You can find an example of a set of initial conditions in the GRIB file `example_20241107_12_n320.grib`.
+To generate a new forecast using AIFS, you can use [anemoi-inference](https://github.com/ecmwf/anemoi-inference). In the [following notebook](run_AIFS_v0_2_1.ipynb), a 
+step-by-step workflow is specified to run the AIFS using the HuggingFace model:
 
-Use the code below to get started with the model. 
+- Install the required packages
+- Select a date
+- Get the data from the [ECMWF Open Data API](https://www.ecmwf.int/en/forecasts/datasets/open-data)
+- Get input fields
+- Add the single levels fields and pressure levels fields
+- Convert geopotential height into greopotential
+- Create the initial state
+- Create a runner
+- Run the forecast
+- Plot a field
 
-```
-# 1st - create the conda environment
-export CONDA_ENV=aifs-env
-conda create -n ${CONDA_ENV} python=3.10
-conda activate ${CONDA_ENV}
-
-pip install torch=2.4
-pip install anemoi-inference[plugin] anemoi-models==0.2
-pip install ninja
-pip install flash-attn --no-build-isolation
-
-# 2nd - Run ai-models to generate weather forecast
-
-ai-models anemoi --checkpoint aifs_single_v0.2.1.ckpt --file example_20241107_12_n320.grib
-```
 
 🚨  **Note** we train AIFS using `flash_attention` (https://github.com/Dao-AILab/flash-attention).
 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).
@@ -85,51 +77,6 @@ Additonally the use of 'Flash Attention' package also imposes certain requiremen
 🚨 **Note** the `aifs_single_v0.2.1.ckpt` checkpoint just contains the model’s weights. 
 That file does not contain any information about the optimizer states, lr-scheduler states, etc.
 
-**Note** By default, when running `ai-models` the model will be run for a 10-day lead time (240 hours). 
-It is possible to extend or modify the lead time to for example 15 days by doing `ai-models --lead-time 360`
-Please refer to `ai-models` documentation for more information regarding defaults and available command line options.
-
-
-After running the `ai-models` command the output of the forecast should be written into `anemoi.grib` 
-Below you can find an example to read that file and load it as numpy array or xarray. 
-
-```
-
-import earthkit.data as ekd
-import cartopy.crs as ccrs
-import numpy as np
-from plotting_utils import map_scatter
-
-source_filename='anemoi.grib'
-aifs_forecast = ekd.from_source('file',source_filename)
-
-# to load a variable as a numpy array
-aifs_forecast.sel(param="2t").to_numpy() 
-
-# to load a variables as an xarray array
-aifs_forecast.sel(param="2t").to_xarray()
-
-aifs_forecast_2t_xr = aifs_forecast.sel(param="2t").to_xarray()
-
-fig, axes = plt.subplots(subplot_kw={'projection': ccrs.Robinson()}, figsize=(16,6))
-
-map_scatter(data_v021_2t_xr.latitude, data_v021_2t_xr.longitude, data_v021_2t_xr.t2m[0,1,0,:],
-            axes=axes, 
-            proj=ccrs.Robinson(),
-            vmin=np.percentile(data_v021_2t_xr.t2m[0,1,0,:], 2),
-            vmax=np.percentile(data_v021_2t_xr.t2m[0,1,0,:], 98),
-            cmap="RdYlBu_r",
-            mask_ocean=False,
-            plot_title='2-Meter Temperature Forecast',
-            cmap_label="Temperature (K)"
-            )
-
-```
-<div style="display: flex; justify-content: center;">
-  <img src="2t_aifs_v021.png" alt="AIFS 2m Temperature" style="width: 50%;"/>
-</div>
-
-
 
 ## Training Details