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+# Using quickrun
+
+## The main idea
+
+The `quickrun` mode is a way to quickly run an FL experiment
+without needing to understand the details of `declearn`.
+
+Once you have `declearn` installed, try running `declearn-quickrun`
+to get run the MNIST example.
+
+This mode requires two files and some data :
+
+* A TOML file, to store your experiment configurations.
+* A model file, to store your model wrapped in a `declearn` object
+* A folder with your data, either already split between clients or
+to be split usiong our utility function
+
+To run your own expriments, these three elements needs to be either
+organizzed in a specific way, or referenced in the TOML file. See details
+in the last section.
+
+## The TOML file
+
+TOML is a minimal, human-readable configuration file format.
+We use is to store all the configurations of an FL experiment.
+
+This file is your main entry point to everythiong else.
+If you write your own, the absolute path to this file should be given
+as an argument :  `declearn-quickrun --config <path_to_toml_file>`
+
+For a minimal example of what it looks like in `declearn`, see
+`./config.toml`. You can use it as a template
+to write your own.
+
+The TOML is parsed to python as dictionnary with each `[header]`
+as a key. If you are a first time user, you just need to
+understand how to write dictionnaries and lists. This :
+
+```
+[key1]
+sub_key = ["a","b"]
+[key2]
+sub_key = 1
+```
+
+will be parsed as :
+
+```python
+{"key1":{"sub_key":["a","b"]},"key2":{"sub_key":1}}
+```
+
+Note the = sign and the absence of quotes around keys.
+
+For more details, see the full doc : <https://toml.io/en/>
+
+For a more detailed templates, with all options used
+See `./custom/config_custom.toml`
+
+## The Model file
+
+The model file should just contain the model you built for
+your data, e.g. a `torch` model, wrapped in a declearn object.
+See `./model.py` for an example.
+
+The wrapped model should be named "MyModel" by default. If you use
+any other name, you'll need to mention it in the TOML file, as done
+in `./custom/config_custom.toml`
+
+## The data
+
+Your data, in a standard tabular format. This data can either require
+splitting or be already split by client
+
+Requires splitting:
+
+* You have a single dataset and want to use provided utils to split it
+* In which case you need to mention your data source in the TOML file,
+as well as details on how to split your data. See
+`./custom/config_custom.toml` for details.
+* Note that our data splitting util currently has a limited scope,
+only dealing with classification tasks, excluding multi-label. If your
+use case falls outside of that, you can write custom splitting code
+and refer to the next paragraph
+
+Already split:
+
+* If your data is already split between clients, you will need to
+add details in the TOML file on where to find this data. See
+`./custom/config_custom.toml` for details.
+
+## Organizing your files
+
+The quickrun mode expects a `config` path as an argument. This can be the path to :
+
+* A folder, expected to be structured a certain way
+* A TOML file, where the location of every other object is mentionned
+
+In both cases, the default is to check the folder provided, or the TOML
+parent folder, is structured as follows:
+
+```
+    folder/
+    │    config.toml - the config file
+    │    model.py - the model
+    └─── data*/
+        └─── client*/
+        │      train_data.* - training data
+        │      train_target.* - training labels
+        │      valid_data.* - validation data
+        │      valid_target.* - validation labels
+        └─── client*/
+        │    ...
+```
+
+Any changes to this structure should be referenced in the TOML file, as
+shown in `./custom/config_custom.toml`.