0ce879aa44
- Implemented HTML pages for datasets, models, training, testing, and results. - Created API endpoints for managing repositories, results, tests, and training sessions. - Added functionality for streaming training progress via Server-Sent Events (SSE). - Introduced a Dockerfile for the ML runner with necessary dependencies. - Developed an SDK for user code execution within the runner container. - Enhanced CSS styles for improved UI layout and navigation. - Established a layout template for consistent HTML structure across pages. - Added JavaScript for dynamic interactions on the models page. - Implemented WebSocket handling for real-time communication with kiosk devices and controllers. - Implemented model registration and management API at /api/models - Added Gitea proxy API for repository interactions at /api/repos - Created results API for listing and comparing training results at /api/results - Developed training management API for enqueueing and retrieving training jobs at /api/trainings - Introduced SSE endpoint for live training progress updates - Added HTML pages for models, datasets, and training management - Created a Dockerfile for the ML runner with necessary dependencies - Developed SDK for user code execution within the runner container - Enhanced CSS styles for improved UI/UX - Implemented WebSocket communication for real-time device and controller interactions in the kiosk system
46 lines
767 B
Plaintext
46 lines
767 B
Plaintext
PORT=3007
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# Auth condiviso
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JWT_SECRET=change-me
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INTERNAL_API_KEY=change-me
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AUTH_LOGIN_URL=https://auth.mebboat.it/login
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# Postgres (db ml)
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PG_HOST=meb-postgres
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PG_PORT=5432
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DB_USER=meb
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DB_PASSWORD=meb
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ML_DB=ml
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# Redis
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REDIS_HOST=meb-redis
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REDIS_PORT=6379
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# MinIO (bucket unico)
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MINIO_ENDPOINT=minio
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MINIO_PORT=9000
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MINIO_USE_SSL=false
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MINIO_ACCESS_KEY=
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MINIO_SECRET_KEY=
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MINIO_BUCKET=ml
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# InfluxDB
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INFLUX_URL=http://meb-influx:8086
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INFLUX_TOKEN=
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INFLUX_ORG=meb
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INFLUX_BUCKET=ml_metrics
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# Gitea (self-hosted esterno)
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GITEA_URL=https://git.mebboat.it
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GITEA_TOKEN=
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# API service
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API_URL=http://api:3003
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# Training runtime
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ML_TRAIN_CONCURRENCY=1
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ML_RUNNER_IMAGE=meb-ml-runner:latest
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ML_RUNNER_TMP=/var/ml/tmp
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ML_GITCACHE_DIR=/var/ml/gitcache
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ML_MAX_UPLOAD_MB=500
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