9c3d741a08
Move 17 library crates into crates/, CLI binary into bin/fxt, consolidate 10 test crates into testing/, split config crate from deployment config files. Root directory reduced from 38+ to ~17 directories. All Cargo.toml paths and build.rs proto refs updated. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
125 lines
3.3 KiB
JSON
125 lines
3.3 KiB
JSON
{
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"timestamp": "2025-10-17T08:21:24.753360416+00:00",
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"gpu_info": {
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"device_name": "NVIDIA RTX 3050 Ti (4GB)",
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"device_available": true,
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"vram_total_mb": 4096.0,
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"cuda_version": "12.8"
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},
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"data_info": {
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"source": "Databento DBN files (6E.FUT - Euro Futures)",
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"symbols": [
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"6E.FUT"
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],
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"total_bars": 10000,
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"date_range": "2024-01 to 2024-12"
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},
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"dqn_results": {
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"model_name": "WorkingDQN",
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"total_epochs": 10,
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"statistics": {
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"mean_seconds": 0.0010403124285714286,
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"std_dev": 0.00008561190370864938,
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"confidence_interval_95": [
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0.0009611346234210708,
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0.0011194902337217864
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],
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"p50_median": 0.001008157,
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"p95": 0.0011756355,
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"p99": 0.0012128127,
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"coefficient_of_variation": 0.08229441594407637,
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"num_samples": 7,
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"outliers_removed": 0
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},
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"memory_peak_mb": 143.0,
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"stability": {
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"is_stable": false,
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"has_nan_inf": false,
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"gradient_health": "Healthy",
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"loss_trend": "Diverging",
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"warnings": [
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"Loss diverging: increased from 4.203730 to 4.946043"
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]
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},
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"batch_config": {
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"batch_size": 230,
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"gradient_accumulation_steps": 1,
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"effective_batch_size": 230
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},
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"training_losses": [
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4.985262870788574,
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5.132430553436279,
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4.777216911315918,
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4.060189247131348,
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6.4390106201171875,
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3.9263854026794434,
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5.328873634338379,
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3.3559303283691406,
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5.588294982910156,
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4.303791046142578
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],
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"avg_loss": 4.789738559722901
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},
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"ppo_results": {
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"model_name": "PPO",
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"total_epochs": 10,
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"statistics": {
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"mean_seconds": 0.168184648125,
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"std_dev": 0.005084581863015776,
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"confidence_interval_95": [
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0.16393383130977984,
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0.17243546494022016
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],
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"p50_median": 0.1682371755,
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"p95": 0.17534651304999999,
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"p99": 0.17768017781,
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"coefficient_of_variation": 0.03023214020840213,
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"num_samples": 8,
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"outliers_removed": 0
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},
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"memory_peak_mb": 145.0,
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"stability": {
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"is_stable": true,
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"has_nan_inf": false,
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"gradient_health": "Healthy",
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"loss_trend": "Converging",
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"warnings": []
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},
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"batch_config": {
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"batch_size": 230,
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"gradient_accumulation_steps": 1,
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"effective_batch_size": 230
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},
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"total_training_time_ms": 1697.802204,
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"epoch_times_ms": [
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186.184101,
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165.997634,
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163.163858,
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161.157464,
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167.998949,
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167.34253099999998,
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178.26359399999998,
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169.929077,
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168.475402,
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169.14631
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],
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"avg_policy_loss": 0.08274438,
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"avg_value_loss": 0.5487219
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},
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"aggregate_metrics": {
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"total_training_time_hours": 0.09372489129960317,
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"total_memory_peak_mb": 145.0,
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"all_stable": false,
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"models_tested": [
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"DQN",
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"PPO"
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]
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},
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"decision": {
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"recommendation": "local_gpu",
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"rationale": "Local GPU training is highly viable. Total time 0.1h (<24h threshold), cost $0.00 vs $0.05 cloud. Local GPU provides faster iteration cycles and zero network latency.",
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"estimated_local_hours": 0.09372489129960317,
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"estimated_cost_local_usd": 0.0021088100542410713,
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"estimated_cost_cloud_usd": 0.049299292823591266
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}
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} |