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HACK: Dynamically import the AITrainer when it's needed to avoid crashes when the AVX instruction set isn't available

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This commit is contained in:
Starbeamrainbowlabs 2019-07-22 16:04:49 +01:00
parent a1516a67bc
commit ebbc96ec8c
2 changed files with 14 additions and 2 deletions
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14
server/bootstrap/cli.mjs
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@ -2,6 +2,10 @@
import show_help from '../help.mjs'; import show_help from '../help.mjs';
import { decode_payload } from '../ttn-app-server/DecodePayload.mjs'; import { decode_payload } from '../ttn-app-server/DecodePayload.mjs';
import path from 'path';
// HACK
import awilix from 'awilix';
export default async function(c) { export default async function(c) {
let { ansi: a, log: l, settings } = c.cradle; let { ansi: a, log: l, settings } = c.cradle;
@ -78,6 +82,16 @@ export default async function(c) {
break; break;
case "train-ai": case "train-ai":
// Do ninja dynamic import
// This avoid loading TensorFlow when it's not needed, as it causes
// a crash if the AVX2 CPU instruction set isn't available for some
// reason. Hrm.
let AITrainer = (await import(path.join(c.cradle.root_dir, "train-ai/AITrainer.mjs"))).default;
c.register({
AITrainer: awilix.asClass(AITrainer)
});
l.log(`${a.fgreen}${a.hicol}Training AIs${a.reset}`); l.log(`${a.fgreen}${a.hicol}Training AIs${a.reset}`);
let ai_trainer = c.cradle.AITrainer; let ai_trainer = c.cradle.AITrainer;
await ai_trainer.train_all(); await ai_trainer.train_all();

2
server/bootstrap/container.mjs
View file

@ -13,7 +13,6 @@ import MessageHandler from '../ttn-app-server/MessageHandler.mjs';
import DataProcessor from '../process-data/DataProcessor.mjs'; import DataProcessor from '../process-data/DataProcessor.mjs';
import AITrainer from '../train-ai/AITrainer.mjs';
import DatasetFetcher from '../train-ai/DatasetFetcher.mjs'; import DatasetFetcher from '../train-ai/DatasetFetcher.mjs';
import settings from './settings.mjs'; import settings from './settings.mjs';
@ -37,7 +36,6 @@ export default async function setup() {
DataProcessor: a.asClass(DataProcessor), DataProcessor: a.asClass(DataProcessor),
AITrainer: a.asClass(AITrainer),
DatasetFetcher: a.asClass(DatasetFetcher) DatasetFetcher: a.asClass(DatasetFetcher)
}); });