Add Training System

.gitignore

@@ -1,6 +1,18 @@
 .env
+__pycache__
 
 # Development
 /sandbox
 /staging
-/data
+/data
+/original
+og
+
+# Non-Docker Training Outputs
+/src/trainer/outputs
+/src/trainer/mlruns
+
+# Temp uv setup
+.python-version
+pyproject.toml
+uv.lock

configs/trainer.yaml

@@ -0,0 +1,122 @@
+data:
+  manifest_dir: "../../../ad_data/manifests"
+  dataset_root: "../../../ad_data/data/dataset"
+  extra_val_file: "rruff.jsonl"
+  auto_generate_manifests: true
+  train_ratio: 0.8
+  val_ratio: 0.1
+  test_ratio: 0.1
+  seed: 42
+
+  loader:
+    # --- DataLoader ---
+    batch_size: 64                  # match OG run (64 per process)
+    num_workers: 8
+    pin_memory: true
+    persistent_workers: true
+    prefetch_factor: 2
+    train_file: "train.jsonl"
+    val_file: "val.jsonl"
+    test_file: "test.jsonl"
+
+  preprocessing:
+    validate_paths: false
+    extract_labels: true
+    allow_pickle: true
+    labels_key_map:
+      x: "dp"
+      cs: "cs"
+      sg: "sg"
+      lattice_params: null
+      lp_a: "_cell_length_a"
+      lp_b: "_cell_length_b"
+      lp_c: "_cell_length_c"
+      lp_alpha: "_cell_angle_alpha"
+      lp_beta: "_cell_angle_beta"
+      lp_gamma: "_cell_angle_gamma"
+    dtype: "float32"
+    mmap_mode: null
+    floor_at_zero: true
+    normalize_log1p: False
+    shift_labels: true
+
+  augmentation:
+    noise_poisson_range: [1.0, 100.0]
+    noise_gaussian_range: [0.001, 0.1]
+    standardize_to: [0.0, 100.0]
+
+model:
+  type: "multiscale"
+
+  backbone:
+    dim_in: 8192
+    dims: [80, 80, 80]
+    kernel_sizes: [100, 50, 25]
+    strides: [5, 5, 5]
+    dropout_rate: 0.3
+    layer_scale_init_value: 0.0
+    drop_path_rate: 0.3
+    ramped_dropout_rate: false
+    block_type: "convnext"
+    pooling_type: "average"
+    final_pool: true
+    use_batchnorm: false
+    activation: "leaky_relu"
+    output_type: "flatten"
+
+  heads:
+    head_dropout: 0.5
+    cs_hidden: [2300, 1150]
+    sg_hidden: [2300, 1150]
+    lp_hidden: [512, 256]
+
+  tasks:
+    num_cs_classes: 7
+    num_sg_classes: 230
+    num_lp_outputs: 6
+
+    lp_bounds_min: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+    lp_bounds_max: [300.0, 300.0, 300.0, 180.0, 180.0, 180.0]
+    bound_lp_with_sigmoid: true
+
+  loss:
+    lambda_cs: 1.0
+    lambda_sg: 1.0
+    lambda_lp: 1.0
+
+    gemd_mu: 0.0
+    gemd_distance_matrix_path: null
+
+optimizer:
+  lr: 0.0002
+  weight_decay: 0.01
+  use_adamw: true
+  gradient_clip_val: 1.0
+  gradient_clip_algorithm: "norm"
+
+trainer:
+  default_root_dir: "outputs/convnext_paper"
+  max_epochs: 100
+  accumulate_grad_batches: 1
+  precision: "32"                # match OG (AMP disabled)
+  accelerator: "gpu"
+  devices: 1
+  log_every_n_steps: 200
+  deterministic: false
+  benchmark: true
+
+logging:
+  logger: "mlflow"
+  csv_logger_name: "model_logs_convnext_paper"
+  mlflow_experiment_name: "AlphaDiffract_Paper_ConvNeXt"
+  mlflow_tracking_uri: null
+  mlflow_run_name: "ConvNeXt_Paper_Run"
+
+checkpointing:
+  monitor: "val/loss"
+  mode: "min"
+  save_top_k: 1
+  every_n_epochs: 1
+
+  resume_from: null
+  test_after_train: true

src/trainer/dataset/__init__.py

@@ -0,0 +1,39 @@
+"""
+Dataset package for training.
+
+Assumptions:
+- Import paths use the local 'dataset' package.
+- Manifests are JSON Lines and may include an optional first-line meta header:
+    {"__meta__": {"version": 1, "base_dir": "<path to dataset root>"}}
+  When present, non-absolute file paths in records are resolved relative to base_dir.
+  base_dir itself may be relative to the manifest file's directory. This makes manifests
+  independent of the current working directory.
+- Legacy manifests without the meta header remain supported; their file paths are used as-is.
+
+Exports:
+- NpyManifestDataset: Map-style dataset loading .npy files listed in JSONL manifests.
+- NpyDataModule: LightningDataModule wiring datasets and DataLoaders.
+- generate_manifests: Utility to create train/val/test manifests split by material ID.
+- ManifestStats: Summary dataclass for manifest generation.
+"""
+
+from .dataset import NpyManifestDataset, default_manifest_paths
+from .datamodule import NpyDataModule
+from .manifest_utils import (
+    generate_manifests,
+    ManifestStats,
+    scan_dataset_root,
+    split_materials,
+    write_jsonl_manifest,
+)
+
+__all__ = [
+    "NpyManifestDataset",
+    "default_manifest_paths",
+    "NpyDataModule",
+    "generate_manifests",
+    "ManifestStats",
+    "scan_dataset_root",
+    "split_materials",
+    "write_jsonl_manifest",
+]