3de1e6e9ab
- Add development and production configuration templates - Include Docker build files for containerized deployment - Add Nginx configuration with SSL/TLS setup - Include environment configuration examples - Add SSL certificate setup and management - Configure application schemas and validation - Support for both local and production deployment scenarios Provides flexible deployment options from development to production with proper security, monitoring, and configuration management.
39 lines
859 B
TOML
39 lines
859 B
TOML
worker_id = "worker-prod-01"
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base_path = "/data/ml-experiments"
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max_workers = 4
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# Redis connection
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redis_addr = "localhost:6379"
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redis_password = "JZVd2Y6IDaLNaYLBOFgQ7ae4Ox5t37NTIyPMQlLJD4k="
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redis_db = 0
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# SSH connection (for remote operations)
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host = "localhost"
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user = "ml-user"
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port = 22
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ssh_key = "~/.ssh/id_rsa"
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# Podman configuration
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podman_image = "ml-training:latest"
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gpu_access = true
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container_workspace = "/workspace"
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container_results = "/results"
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train_script = "train.py"
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# Dataset management
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auto_fetch_data = true
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data_dir = "/data/datasets"
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data_manager_path = "/usr/local/bin/data_manager"
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dataset_cache_ttl = "24h"
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# Task management
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task_lease_duration = "1h"
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heartbeat_interval = "30s"
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graceful_timeout = "5m"
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poll_interval = "100ms"
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metrics_flush_interval = "10s"
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# Metrics exporter
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[metrics]
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enabled = true
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listen_addr = ":9090"
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