5ff4b8a2eb
Port LEM scoring/training pipeline into CoreGo as pkg/ml with: - Inference abstraction with HTTP, llama-server, and Ollama backends - 3-tier scoring engine (heuristic, exact, LLM judge) - Capability and content probes for model evaluation - GGUF/safetensors format converters, MLX to PEFT adapter conversion - DuckDB integration for training data pipeline - InfluxDB metrics for lab dashboard - Training data export (JSONL + Parquet) - Expansion generation pipeline with distributed workers - 10 CLI commands under 'core ml' (score, probe, export, expand, status, gguf, convert, agent, worker) - 5 MCP tools (ml_generate, ml_score, ml_probe, ml_status, ml_backends) All 37 ML tests passing. Binary builds at 138MB with all commands. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
80 lines
2.7 KiB
Go
80 lines
2.7 KiB
Go
package ml
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import (
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"time"
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"forge.lthn.ai/core/cli/pkg/cli"
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"forge.lthn.ai/core/cli/pkg/ml"
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)
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var (
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workerAPIBase string
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workerID string
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workerName string
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workerAPIKey string
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workerGPU string
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workerVRAM int
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workerLangs string
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workerModels string
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workerInferURL string
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workerTaskType string
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workerBatchSize int
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workerPoll time.Duration
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workerOneShot bool
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workerDryRun bool
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)
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var workerCmd = &cli.Command{
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Use: "worker",
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Short: "Run a distributed worker node",
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Long: "Polls the LEM API for tasks, runs local inference, and submits results.",
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RunE: runWorker,
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}
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func init() {
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workerCmd.Flags().StringVar(&workerAPIBase, "api", ml.EnvOr("LEM_API", "https://infer.lthn.ai"), "LEM API base URL")
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workerCmd.Flags().StringVar(&workerID, "id", ml.EnvOr("LEM_WORKER_ID", ml.MachineID()), "Worker ID")
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workerCmd.Flags().StringVar(&workerName, "name", ml.EnvOr("LEM_WORKER_NAME", ml.Hostname()), "Worker display name")
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workerCmd.Flags().StringVar(&workerAPIKey, "key", ml.EnvOr("LEM_API_KEY", ""), "API key")
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workerCmd.Flags().StringVar(&workerGPU, "gpu", ml.EnvOr("LEM_GPU", ""), "GPU type")
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workerCmd.Flags().IntVar(&workerVRAM, "vram", ml.IntEnvOr("LEM_VRAM_GB", 0), "GPU VRAM in GB")
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workerCmd.Flags().StringVar(&workerLangs, "languages", ml.EnvOr("LEM_LANGUAGES", ""), "Comma-separated language codes")
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workerCmd.Flags().StringVar(&workerModels, "models", ml.EnvOr("LEM_MODELS", ""), "Comma-separated model names")
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workerCmd.Flags().StringVar(&workerInferURL, "infer", ml.EnvOr("LEM_INFER_URL", "http://localhost:8090"), "Local inference endpoint")
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workerCmd.Flags().StringVar(&workerTaskType, "type", "", "Filter by task type")
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workerCmd.Flags().IntVar(&workerBatchSize, "batch", 5, "Tasks per poll")
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workerCmd.Flags().DurationVar(&workerPoll, "poll", 30*time.Second, "Poll interval")
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workerCmd.Flags().BoolVar(&workerOneShot, "one-shot", false, "Process one batch and exit")
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workerCmd.Flags().BoolVar(&workerDryRun, "dry-run", false, "Fetch tasks but don't run inference")
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}
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func runWorker(cmd *cli.Command, args []string) error {
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if workerAPIKey == "" {
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workerAPIKey = ml.ReadKeyFile()
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}
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cfg := &ml.WorkerConfig{
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APIBase: workerAPIBase,
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WorkerID: workerID,
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Name: workerName,
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APIKey: workerAPIKey,
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GPUType: workerGPU,
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VRAMGb: workerVRAM,
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InferURL: workerInferURL,
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TaskType: workerTaskType,
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BatchSize: workerBatchSize,
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PollInterval: workerPoll,
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OneShot: workerOneShot,
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DryRun: workerDryRun,
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}
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if workerLangs != "" {
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cfg.Languages = ml.SplitComma(workerLangs)
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}
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if workerModels != "" {
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cfg.Models = ml.SplitComma(workerModels)
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}
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ml.RunWorkerLoop(cfg)
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return nil
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}
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