pontus-x_cli
Version:
Command Line Interface for the Pontus-X Data Space Ecosystem.
82 lines • 3.98 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
const fs_1 = require("fs");
const nautilus_1 = require("@deltadao/nautilus");
const nautilus_2 = require("@deltadao/nautilus");
const publish = async (folder, connection, provider, dryRun) => {
// ALGORITHM USER INPUT DATA
const consumerParametersBuilder = new nautilus_1.ConsumerParameterBuilder();
const consumerParameters = [];
consumerParameters.push(consumerParametersBuilder
.setName("dataset")
.setLabel("dataset")
.setDescription("Dataset parameters to train the model with, such as: separator, target_column, split, random_state and stratify.")
.setDefault(JSON.stringify(JSON.parse((0, fs_1.readFileSync)(`${folder}/dataset-default.json`, "utf8"))))
.setRequired(true)
.setType("text")
.build());
consumerParametersBuilder.reset(); // Reset the builder to create a new parameter
consumerParameters.push(consumerParametersBuilder
.setName("model")
.setLabel("model")
.setDescription("Model parameters to train the model with, such as: name, params and metrics.")
.setDefault(JSON.stringify(JSON.parse((0, fs_1.readFileSync)(`${folder}/model-default.json`, "utf8"))))
.setRequired(true)
.setType("text")
.build());
// ALGORITHM METADATA
const algoMetadata = {
language: "python",
version: "0.2",
container: {
entrypoint: "/algorithm/.venv/bin/python /algorithm/src/main.py",
image: "clopezgarcia/basic-predictor",
tag: "0.1.0",
checksum: "sha256:ee1f7f5d3dc6d3323f32f68852aaba8895482a2b4b4e7ff652045b42b7becfbe",
},
consumerParameters: consumerParameters,
};
const service = new nautilus_2.ServiceBuilder({ serviceType: nautilus_2.ServiceTypes.COMPUTE, fileType: nautilus_2.FileTypes.URL })
.setServiceEndpoint(provider) // the access controller to be in control of this asset
.setTimeout(0) // Timeout in seconds (0 means unlimited access after purchase)
.addFile({
type: "url", // there are multiple supported data source types, see https://docs.oceanprotocol.com/developers/storage
url: "https://raw.githubusercontent.com/AgrospAI/ocean-algo/refs/heads/main/basic-predictor/algorithm/src/main.py",
method: "GET", // HTTP request method
// headers: {
// Authorization: 'Basic XXX' // optional headers field e.g. for basic access control
// }
})
.setPricing(connection.pricingConfig.fixedRateEUROe(0))
.setDatatokenNameAndSymbol("UdL scikit-learn model trainer", "UDL-SKLEARN")
.build();
const asset = new nautilus_1.AssetBuilder()
.setType("algorithm")
.setName("SciKit-learn algorithm training (working?)")
.setAuthor("Universitat de Lleida (UdL)")
.setOwner(connection.wallet.address)
.setDescription((0, fs_1.readFileSync)(`${folder}/description.md`, "utf8"))
.addTags(["ml", "sklearn", "scikit-learn", "tabular-data", "pandas", "udl"])
.setLicense("MIT")
.setNftData({
name: "UdL scikit-learn model trainer",
symbol: "UDL-SKLEARN",
templateIndex: 1,
tokenURI: "https://scikit-learn.org/stable/_static/scikit-learn-logo-small.png",
transferable: false,
})
.setAlgorithm(algoMetadata)
.addService(service)
.build();
console.log(`Asset metadata: \n ${JSON.stringify(asset, null, 2)}`);
if (!dryRun) {
console.log(`Publishing asset...`);
const result = await connection.nautilus.publish(asset);
console.log(`Asset published, ` +
`transaction: ${connection.networkConfig.explorerUri}/tx/${result.setMetadataTxReceipt.transactionHash}\n`);
}
else {
console.log("\nDry run completed. Asset not published.\n");
}
};
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