asociita


Nameasociita JSON
Version 0.3.4 PyPI version JSON
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home_pagehttps://github.com/Scolpe/Asociita
SummaryAn intuitive and modular simulator for assessing the marginal value of a client's contribution in a decentralized setting.
upload_time2023-08-08 12:11:12
maintainer
docs_urlNone
authorMaciej Zuziak
requires_python>=3.10,<4.0
licenseMIT
keywords
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            ### Setting Configuration

In order to run the simulation, the `Orchestrator` instance must receive a `settings` object that contains all the necessary parameters. It is possible to store those parameters in a `JSON` format and load them as the Python dictionary by using `asociita.utils.helper.load_from_json` function. Below is an exemplary settings object embedded as a `json` file. All the elements are necessary unless stated otherwise.

```
{
    "orchestrator":{
        "iterations": int,
        "number_of_nodes": int,
        "local_warm_start": bool,
        "sample_size": int,
        "evaluation": "none" | "full"
        "save_metrics": bool,
	"save_models": bool,
	"save_path": str
	"nodes": [0,
	1,
	2]
    },
    "nodes":{
    "local_epochs": int,
    "model_settings": {
        "optimizer": "RMS",
        "batch_size": int,
        "learning_rate": float}
        }
}
```

The `settings` contains two dictionaries: `orchestrator` and `nodes`.

`orchestrator` contains all the settings necessary details of the training:

* `iterations` is the number of rounds to be performed. Example: `iterations:12`
* `number_of_nodes` is the number of nodes that will be included in the training. Example: `number_of_nodes: 10`
* `local_warm_start` allows to distribute various pre-trained weights to different local clients. Not implemeneted yet. Example: `local_warm_start: false`.
* `sample_size` is the size of the sample that will be taken each round. Example: `sample_size : 4.`
* `evaluation` allows to control the evaluation procedure across the clients.  Currently, only `none` or `full` are supported. Setting the evaluation to full will perform a full evaluation of every client included in the training. Example: `evaluation: full`
* `save_metrics` allows to control whether the metrics should be saved in a csv file. Example: `save_metrics: true.`
* `save_models` allows to control whether the models should be saved. Not implemeneted yet. Example: `save_metrics: false`.
* `save_path` is the system path that will be used when saving the model. It is possible to define a saving_path in a method call.
* `nodes` is the list containing the ids of all the nodes participating in the training. Length of `nodes` must be equal `number_of_nodes`.

`nodes` contains all the necessary configuration for nodes.

* `"local_epochs":` the number of local epochs to be performed on the local nodes.
* `"model_settings"` is a dictionary containing all the parameters for training the model.
  * `optimizer` is an optimizer that will be used during the training. Example: `optimizer: "RMS"`
  * `batch_size` is the batch size that will be used during the training. Example: `batch_size: 32`
  * `learning_rate` is the learning rate that will be used during the training. Example: `learning_rate: 0.001`

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    "description": "### Setting Configuration\n\nIn order to run the simulation, the `Orchestrator` instance must receive a `settings` object that contains all the necessary parameters. It is possible to store those parameters in a `JSON` format and load them as the Python dictionary by using `asociita.utils.helper.load_from_json` function. Below is an exemplary settings object embedded as a `json` file. All the elements are necessary unless stated otherwise.\n\n```\n{\n    \"orchestrator\":{\n        \"iterations\": int,\n        \"number_of_nodes\": int,\n        \"local_warm_start\": bool,\n        \"sample_size\": int,\n        \"evaluation\": \"none\" | \"full\"\n        \"save_metrics\": bool,\n\t\"save_models\": bool,\n\t\"save_path\": str\n\t\"nodes\": [0,\n\t1,\n\t2]\n    },\n    \"nodes\":{\n    \"local_epochs\": int,\n    \"model_settings\": {\n        \"optimizer\": \"RMS\",\n        \"batch_size\": int,\n        \"learning_rate\": float}\n        }\n}\n```\n\nThe `settings` contains two dictionaries: `orchestrator` and `nodes`.\n\n`orchestrator` contains all the settings necessary details of the training:\n\n* `iterations` is the number of rounds to be performed. Example: `iterations:12`\n* `number_of_nodes` is the number of nodes that will be included in the training. Example: `number_of_nodes: 10`\n* `local_warm_start` allows to distribute various pre-trained weights to different local clients. Not implemeneted yet. Example: `local_warm_start: false`.\n* `sample_size` is the size of the sample that will be taken each round. Example: `sample_size : 4.`\n* `evaluation` allows to control the evaluation procedure across the clients.  Currently, only `none` or `full` are supported. Setting the evaluation to full will perform a full evaluation of every client included in the training. Example: `evaluation: full`\n* `save_metrics` allows to control whether the metrics should be saved in a csv file. Example: `save_metrics: true.`\n* `save_models` allows to control whether the models should be saved. Not implemeneted yet. Example: `save_metrics: false`.\n* `save_path` is the system path that will be used when saving the model. It is possible to define a saving_path in a method call.\n* `nodes` is the list containing the ids of all the nodes participating in the training. Length of `nodes` must be equal `number_of_nodes`.\n\n`nodes` contains all the necessary configuration for nodes.\n\n* `\"local_epochs\":` the number of local epochs to be performed on the local nodes.\n* `\"model_settings\"` is a dictionary containing all the parameters for training the model.\n  * `optimizer` is an optimizer that will be used during the training. Example: `optimizer: \"RMS\"`\n  * `batch_size` is the batch size that will be used during the training. Example: `batch_size: 32`\n  * `learning_rate` is the learning rate that will be used during the training. Example: `learning_rate: 0.001`\n",
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Maciej Zuziak
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