diff --git a/.env.docker b/.env.docker
index 2626c5a..c29e5fe 100644
--- a/.env.docker
+++ b/.env.docker
@@ -1,4 +1,3 @@
 # .env.docker  (container)
 CONTAINER_WORKSPACE=/app
-PYTHONPATH_CONTAINER=/app/src
-NFL_HOME_CONTAINER=/app
\ No newline at end of file
+PYTHONPATH_CONTAINER=/app/src
\ No newline at end of file
diff --git a/.github/workflows/pr-ci.yml b/.github/workflows/pr-ci.yml
index 953e958..344df99 100644
--- a/.github/workflows/pr-ci.yml
+++ b/.github/workflows/pr-ci.yml
@@ -37,16 +37,14 @@ jobs:
           version: "latest"
 
       - name: Sync uv
-        run: |
-          uv sync --locked
+        run: uv sync --locked
           
       - name: Create features
-        run: |
-          source environment.sh
-          uv run src/create_features.py
+        run: uv run src/create_features.py
 
       - name: Train transformer
-        run: |
-          source environment.sh
-          uv run src/train_transformer.py
+        run: uv run src/train_transformer.py
+
+      - name: Make week 9 predictions
+        run: uv run src/generate_predictions.py
 
diff --git a/.gitignore b/.gitignore
index 4c82df0..e4dd250 100644
--- a/.gitignore
+++ b/.gitignore
@@ -187,6 +187,10 @@ output/**/*.txt
 
 # MLflow
 mlruns/
+mlflow.db
+
+# Ray
+log/
 
 # Artifacts
 *.pkl
diff --git a/.vscode/launch.json b/.vscode/launch.json
index 6c2d502..2611be3 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -12,7 +12,6 @@
       "justMyCode": false,
       "subProcess": true,
       "env": {
-        "NFL_HOME": "${env:NFL_HOME}",
         "PYTHONPATH": "${workspaceFolder}"
       },
       "envFile": "${workspaceFolder}/.env",
@@ -24,14 +23,12 @@
       "request": "launch",
       "program": "${workspaceFolder}/src/main.py",
       "args": [
-        "--data-root", "${env:NFL_HOME}"
       ],
       "console": "integratedTerminal",
       "cwd": "${workspaceFolder}",
       "justMyCode": false,
       "subProcess": true,
       "env": {
-        "NFL_HOME": "${env:NFL_HOME}",
         "PYTHONPATH": "${workspaceFolder}"
       },
       "envFile": "${workspaceFolder}/.env",
@@ -48,7 +45,6 @@
       "justMyCode": false,
       "subProcess": true,
       "env": {
-        "NFL_HOME": "${env:NFL_HOME}",
         "PYTHONPATH": "${workspaceFolder}"
       },
       "envFile": "${workspaceFolder}/.env",
@@ -63,7 +59,6 @@
       "cwd": "${workspaceFolder}",
       "justMyCode": false,
       "env": {
-        "NFL_HOME": "${env:NFL_HOME}",
         "PYTHONPATH": "${workspaceFolder}"
       },
       "envFile": "${workspaceFolder}/.env",
diff --git a/Dockerfile b/Dockerfile
index d3527cf..219e644 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -30,8 +30,5 @@ RUN uv sync --frozen --no-dev --no-install-project
 # Copy the rest of your repo
 COPY . .
 
-# Default envs helpful for your project
-ENV NFL_HOME=/app
-
 # Set a sensible default; override in docker-compose.yml
 CMD ["bash"]
\ No newline at end of file
diff --git a/README.md b/README.md
index 32dc87a..289d41b 100644
--- a/README.md
+++ b/README.md
@@ -103,22 +103,19 @@ curl -LsSf https://astral.sh/uv/install.sh | sh
 To create and activate the virtual environment, run the following:
 ```bash
 uv sync --locked
-source .venv/bin/activate
 ```
 
 ## Run instructions
 ```bash
-# Source the environmental variables
-source environment.sh
 
 # Digest the raw data and create features
-uv src/create_features.py
+uv run src/create_features.py
 
 # Train the model
-uv src/train_transformer.py
+uv run src/train_transformer.py
 
 # Run inference on week 9
-uv src/generate_predictions.py
+uv run src/generate_predictions.py
 
 # From this point you can explore the notebooks/predictions.ipynb, adjust the data path as neccesary and see the animations and plots
 ```
@@ -126,11 +123,14 @@ uv src/generate_predictions.py
 ## Activating MLflow
 This project uses MLflow to manage ML models and track experiments from hyper paremter optimization. To start MLflow simply:
 ```bash
-# Make sure your virtual environment is started if you opened a new terminal
+# Make sure your virtual environment is sourced so the terminal recognizes mlflow
 source .venv/bin/activate
 
 # Start the tracking server
-mlflow ui --backend-store-uri ./mlruns
+mlflow ui \
+  --backend-store-uri sqlite:///mlflow.db \
+  --host 0.0.0.0 \
+  --port 5000
 ```
 
 The last command will start the MLflow GUI at your local host loopback on port 5000: http://127.0.0.1:5000
\ No newline at end of file
diff --git a/data/training/best_model.pth b/data/training/best_model.pth
new file mode 100644
index 0000000..cd7d59f
Binary files /dev/null and b/data/training/best_model.pth differ
diff --git a/data/training/model_params.json b/data/training/model_params.json
new file mode 100644
index 0000000..818ed74
--- /dev/null
+++ b/data/training/model_params.json
@@ -0,0 +1,9 @@
+{
+    "epochs": 50,
+    "model_dim": 64,
+    "num_heads": 2,
+    "num_layers": 4,
+    "dropout": 0.1,
+    "batch_size": 64,
+    "checkpoint_freq": 10
+}
diff --git a/environment.sh b/environment.sh
deleted file mode 100644
index 0f1be83..0000000
--- a/environment.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-#!/bin/bash
-
-# Set NFL_HOME to the repo root
-export NFL_HOME="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
\ No newline at end of file
diff --git a/notebooks/eda.ipynb b/notebooks/eda.ipynb
index 466410d..55494b2 100644
--- a/notebooks/eda.ipynb
+++ b/notebooks/eda.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "0fe17705",
    "metadata": {},
    "outputs": [],
@@ -15,7 +15,6 @@
     "\n",
     "from load_data import RawDataLoader\n",
     "\n",
-    "os.environ[\"NFL_HOME\"] = \"/home/sam/repos/hobby-repos/nfl/\"\n",
     "rawLoader = RawDataLoader()\n",
     "games_df, plays_df, players_df, location_data_df = rawLoader.get_data(weeks=[i for i in range(1, 10)])"
    ]
diff --git a/notebooks/predictions.ipynb b/notebooks/predictions.ipynb
index 0b97aed..c110221 100644
--- a/notebooks/predictions.ipynb
+++ b/notebooks/predictions.ipynb
@@ -34,61 +34,61 @@
      "text": [
       "       gameId  playId    nflId      displayName  frameId    frameType  \\\n",
       "0  2022110700      56  33131.0  Calais Campbell        1  BEFORE_SNAP   \n",
-      "1  2022110700      56  33131.0  Calais Campbell        2  BEFORE_SNAP   \n",
-      "2  2022110700      56  33131.0  Calais Campbell        3  BEFORE_SNAP   \n",
-      "3  2022110700      56  33131.0  Calais Campbell        4  BEFORE_SNAP   \n",
-      "4  2022110700      56  33131.0  Calais Campbell        5  BEFORE_SNAP   \n",
+      "1  2022110700      56  33131.0  Calais Campbell        1  BEFORE_SNAP   \n",
+      "2  2022110700      56  33131.0  Calais Campbell        1  BEFORE_SNAP   \n",
+      "3  2022110700      56  33131.0  Calais Campbell        1  BEFORE_SNAP   \n",
+      "4  2022110700      56  33131.0  Calais Campbell        2  BEFORE_SNAP   \n",
       "\n",
       "                    time  jerseyNumber club playDirection  ...  defense  week  \\\n",
       "0  2022-11-08 01:16:09.2          93.0  BAL          left  ...        1     9   \n",
-      "1  2022-11-08 01:16:09.3          93.0  BAL          left  ...        1     9   \n",
-      "2  2022-11-08 01:16:09.4          93.0  BAL          left  ...        1     9   \n",
-      "3  2022-11-08 01:16:09.5          93.0  BAL          left  ...        1     9   \n",
-      "4  2022-11-08 01:16:09.6          93.0  BAL          left  ...        1     9   \n",
+      "1  2022-11-08 01:16:09.2          93.0  BAL          left  ...        1     9   \n",
+      "2  2022-11-08 01:16:09.2          93.0  BAL          left  ...        1     9   \n",
+      "3  2022-11-08 01:16:09.2          93.0  BAL          left  ...        1     9   \n",
+      "4  2022-11-08 01:16:09.3          93.0  BAL          left  ...        1     9   \n",
       "\n",
       "        uniqueId    frameUniqueId  snap_frame  frames_from_snap  zone_prob  \\\n",
       "0  2022110700_56  2022110700_56_1         117              -116   0.775387   \n",
-      "1  2022110700_56  2022110700_56_2         117              -115   0.783555   \n",
-      "2  2022110700_56  2022110700_56_3         117              -114   0.793051   \n",
-      "3  2022110700_56  2022110700_56_4         117              -113   0.809845   \n",
-      "4  2022110700_56  2022110700_56_5         117              -112   0.820785   \n",
+      "1  2022110700_56  2022110700_56_1         117              -116   0.781979   \n",
+      "2  2022110700_56  2022110700_56_1         117              -116   0.781979   \n",
+      "3  2022110700_56  2022110700_56_1         117              -116   0.781979   \n",
+      "4  2022110700_56  2022110700_56_2         117              -115   0.783555   \n",
       "\n",
       "   man_prob  pred  actual  \n",
       "0  0.224613     0       0  \n",
-      "1  0.216445     0       0  \n",
-      "2  0.206949     0       0  \n",
-      "3  0.190155     0       0  \n",
-      "4  0.179215     0       0  \n",
+      "1  0.218021     0       0  \n",
+      "2  0.218021     0       0  \n",
+      "3  0.218021     0       0  \n",
+      "4  0.216445     0       0  \n",
       "\n",
       "[5 rows x 42 columns]\n",
       "Finished processing week 9...\n",
       "       gameId  playId    nflId      displayName  frameId    frameType  \\\n",
       "0  2022110700      56  33131.0  Calais Campbell        1  BEFORE_SNAP   \n",
-      "1  2022110700      56  33131.0  Calais Campbell        2  BEFORE_SNAP   \n",
-      "2  2022110700      56  33131.0  Calais Campbell        3  BEFORE_SNAP   \n",
-      "3  2022110700      56  33131.0  Calais Campbell        4  BEFORE_SNAP   \n",
-      "4  2022110700      56  33131.0  Calais Campbell        5  BEFORE_SNAP   \n",
+      "1  2022110700      56  33131.0  Calais Campbell        1  BEFORE_SNAP   \n",
+      "2  2022110700      56  33131.0  Calais Campbell        1  BEFORE_SNAP   \n",
+      "3  2022110700      56  33131.0  Calais Campbell        1  BEFORE_SNAP   \n",
+      "4  2022110700      56  33131.0  Calais Campbell        2  BEFORE_SNAP   \n",
       "\n",
       "                    time  jerseyNumber club playDirection  ...  week  \\\n",
       "0  2022-11-08 01:16:09.2          93.0  BAL          left  ...     9   \n",
-      "1  2022-11-08 01:16:09.3          93.0  BAL          left  ...     9   \n",
-      "2  2022-11-08 01:16:09.4          93.0  BAL          left  ...     9   \n",
-      "3  2022-11-08 01:16:09.5          93.0  BAL          left  ...     9   \n",
-      "4  2022-11-08 01:16:09.6          93.0  BAL          left  ...     9   \n",
+      "1  2022-11-08 01:16:09.2          93.0  BAL          left  ...     9   \n",
+      "2  2022-11-08 01:16:09.2          93.0  BAL          left  ...     9   \n",
+      "3  2022-11-08 01:16:09.2          93.0  BAL          left  ...     9   \n",
+      "4  2022-11-08 01:16:09.3          93.0  BAL          left  ...     9   \n",
       "\n",
       "        uniqueId    frameUniqueId  snap_frame  frames_from_snap  zone_prob  \\\n",
       "0  2022110700_56  2022110700_56_1         117              -116   0.775387   \n",
-      "1  2022110700_56  2022110700_56_2         117              -115   0.783555   \n",
-      "2  2022110700_56  2022110700_56_3         117              -114   0.793051   \n",
-      "3  2022110700_56  2022110700_56_4         117              -113   0.809845   \n",
-      "4  2022110700_56  2022110700_56_5         117              -112   0.820785   \n",
+      "1  2022110700_56  2022110700_56_1         117              -116   0.781979   \n",
+      "2  2022110700_56  2022110700_56_1         117              -116   0.781979   \n",
+      "3  2022110700_56  2022110700_56_1         117              -116   0.781979   \n",
+      "4  2022110700_56  2022110700_56_2         117              -115   0.783555   \n",
       "\n",
       "   man_prob pred  actual  base_correct  \n",
       "0  0.224613    0       0          True  \n",
-      "1  0.216445    0       0          True  \n",
-      "2  0.206949    0       0          True  \n",
-      "3  0.190155    0       0          True  \n",
-      "4  0.179215    0       0          True  \n",
+      "1  0.218021    0       0          True  \n",
+      "2  0.218021    0       0          True  \n",
+      "3  0.218021    0       0          True  \n",
+      "4  0.216445    0       0          True  \n",
       "\n",
       "[5 rows x 43 columns]\n"
      ]
@@ -148,13 +148,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "85892cf2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x720 with 1 Axes>"
       ]
diff --git a/notebooks/train_lstm.ipynb b/notebooks/train_lstm.ipynb
index 7ebb763..98e9235 100644
--- a/notebooks/train_lstm.ipynb
+++ b/notebooks/train_lstm.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "1df8838e",
    "metadata": {},
    "outputs": [
@@ -54,8 +54,6 @@
     "#################################################################\n",
     "\n",
     "import os\n",
-    "os.environ[\"NFL_HOME\"] = \"/home/sam/repos/hobby-repos/nfl/\"\n",
-    "\n",
     "from common.data_loader import RawDataLoader\n",
     "\n",
     "# Get raw data\n",
diff --git a/pyproject.toml b/pyproject.toml
index 90a3840..ba270e1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,6 +37,7 @@ dependencies = [
   "pandas>=2.3.2",
   "pip-tools>=7.5.0",
   "polars>=1.35.1",
+  "ray[train,tune]>=2.51.1",
   "scikit-learn>=1.7.2",
   "seaborn>=0.13.2",
   "torch>=2.8.0",
@@ -44,35 +45,9 @@ dependencies = [
   "xgboost>=3.0.5",
 ]
 
-[project.optional-dependencies]
-dev = [
-  "pytest>=7.0",
-  "black>=24.0",
-  "flake8>=6.0",
-  "mypy>=1.0"
-]
-# you can define other "extras", e.g.:
-# docs = ["sphinx", "sphinx-rtd-theme"]
-
 [project.scripts]
 # command = "module:function"
 myproject-cli = "myproject.cli:main"
 
 [tool.setuptools.packages.find]
 where = ["src"]
-
-# Tool-specific configuration examples:
-
-[tool.black]
-line-length = 88
-target-version = ["py39", "py310", "py311", "py312"]
-
-[tool.isort]
-profile = "black"
-
-[tool.flake8]
-max-line-length = 88
-extend-ignore = ["E203"]
-
-[tool.mypy]
-strict = true
diff --git a/src/common/args.py b/src/common/args.py
new file mode 100644
index 0000000..57a6a1a
--- /dev/null
+++ b/src/common/args.py
@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+
+"""
+Parses input arguments to then act on in code
+"""
+
+import argparse
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--tune",
+        action="store_true",
+        help="Use Ray Tune to search hyperparameters instead of a single training run",
+    )
+    parser.add_argument(
+        "--profile",
+        action="store_true",
+        help="Enable @time_fcn timing decorators for profiling",
+    )
+    return parser.parse_args()
\ No newline at end of file
diff --git a/src/common/csv_to_parquet.py b/src/common/csv_to_parquet.py
index fedab7b..bbfdc7d 100644
--- a/src/common/csv_to_parquet.py
+++ b/src/common/csv_to_parquet.py
@@ -4,9 +4,9 @@
 Transforms raw csv data to raw parquet data
 
 Requires:
-- Raw location tracking data in NFL_HOME/data/parquet
+- Raw location tracking data in nfl/data/parquet
 
-Outputs parquet data in NFL_HOME/data/parquet that is stored in git LFS
+Outputs parquet data in nfl/data/parquet that is stored in git LFS
 """
 
 import sys
diff --git a/src/common/decorators.py b/src/common/decorators.py
index efa70fb..f53b0b5 100644
--- a/src/common/decorators.py
+++ b/src/common/decorators.py
@@ -4,14 +4,34 @@
 Contains common use decorators
 """
 
-def time_fcn(func):
-    import time
-    from functools import wraps
+import time
+import functools
+import logging
 
-    @wraps(func)
+_TIME_DECORATORS_ENABLED = False
+
+def set_time_decorators_enabled(enabled: bool) -> None:
+    """
+    Enable/disable time_fcn decorators globally, called at startup
+    """
+    global _TIME_DECORATORS_ENABLED
+    _TIME_DECORATORS_ENABLED = enabled
+
+def time_fcn(fn):
+    """
+    Decorator that measures wall-clock time of a function and logs it.
+    When _TIME_DECORATORS_ENABLED is False, it becomes a simple passthrough.
+    """
+    @functools.wraps(fn)
     def wrapper(*args, **kwargs):
+        # Fast exit when disabled (minimal overhead)
+        if not _TIME_DECORATORS_ENABLED:
+            return fn(*args, **kwargs)
+
         start = time.perf_counter()
-        result = func(*args, **kwargs)
-        print(f"{func.__name__} took {time.perf_counter() - start:.4f}s")
-        return result
-    return wrapper
\ No newline at end of file
+        try:
+            return fn(*args, **kwargs)
+        finally:
+            elapsed = time.perf_counter() - start
+            logging.info("Function %s took %.4f s", fn.__name__, elapsed)
+    return wrapper
diff --git a/src/common/paths.py b/src/common/paths.py
new file mode 100644
index 0000000..18aed02
--- /dev/null
+++ b/src/common/paths.py
@@ -0,0 +1,12 @@
+from pathlib import Path
+
+def project_root() -> Path:
+    # 2 since this is at src/commmon/path.py
+    return Path(__file__).resolve().parents[2]
+
+# Set project root
+PROJECT_ROOT = project_root()
+
+# Create processing dirs
+SAVE_DIR = PROJECT_ROOT / "data" / "processed"
+SAVE_DIR.mkdir(parents=True, exist_ok=True)
\ No newline at end of file
diff --git a/src/create_features.py b/src/create_features.py
index d58ca48..4bcccea 100644
--- a/src/create_features.py
+++ b/src/create_features.py
@@ -4,9 +4,9 @@
 Creates features for model training
 
 Requires:
-- Raw location tracking data in NFL_HOME/data/parquet
+- Raw location tracking data in nfl/data/parquet
 
-Cleans data and creates the following in NFL_HOME/data/processed:
+Cleans data and creates the following in nfl/data/processed:
 - features_training.pt
 - features_val.pt
 - targets_training.pt
@@ -27,7 +27,9 @@
 
 from load_data import RawDataLoader
 from clean_data import *
-from common.decorators import time_fcn
+from common.decorators import *
+from common.paths import SAVE_DIR
+from common.args import parse_args
 
 def clean_df(location_df: pd.DataFrame, plays_df: pd.DataFrame, game_df: pd.DataFrame):
     
@@ -84,14 +86,27 @@ def _process_df(location_df: pd.DataFrame, plays_df: pd.DataFrame, games_df: pd.
   return combined_location_df
 
 
-def _load_weeks(weeks, prefix, save_dir):
-    tensors = [torch.load(os.path.join(save_dir, f"{prefix}_w{w}.pt")) for w in weeks]
+def _load_weeks(weeks, prefix, SAVE_DIR):
+    tensors = [torch.load(SAVE_DIR / f"{prefix}_w{w}.pt") for w in weeks]
     return torch.cat(tensors, dim=0) if len(tensors) > 1 else tensors[0]
 
+
 @time_fcn
 def main() -> None:
+    # Logging
     logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
+    # Get input args
+    args = parse_args()
+
+    # Enable/disable timing decorators
+    if args.profile:
+        set_time_decorators_enabled(True)
+        logging.info("Timing decorators enabled")
+    else:
+        set_time_decorators_enabled(False)
+        logging.info("Timing decorators disabled")
+
     # Specify constants
     all_weeks = list(range(1, 10))
     train_weeks = list(range(1, 9))
@@ -99,10 +114,6 @@ def main() -> None:
     features = ["x_clean", "y_clean", "v_x", "v_y", "defense"]
     target_column = "pff_manZone"
 
-    # Create processing dirs
-    save_dir = os.path.join(os.getenv("NFL_HOME"), "data", "processed")
-    os.makedirs(save_dir, exist_ok=True)
-
     # Load data once
     raw = RawDataLoader()
     games_df, plays_df, players_df, _ = raw.get_data(weeks=all_weeks)
@@ -133,8 +144,8 @@ def main() -> None:
         features_tensor_w, targets_tensor_w = prepare_frame_data(combined_loc_df_w, features=features, target_column=target_column)
 
         # Save per-week artifacts
-        torch.save(features_tensor_w,  os.path.join(save_dir, f"features_w{w}.pt"))
-        torch.save(targets_tensor_w,  os.path.join(save_dir, f"targets_w{w}.pt"))
+        torch.save(features_tensor_w,  SAVE_DIR / f"features_w{w}.pt")
+        torch.save(targets_tensor_w,  SAVE_DIR / f"targets_w{w}.pt")
 
         # Free memory for next loop
         del location_df_w, combined_loc_df_w, features_tensor_w, targets_tensor_w
@@ -142,22 +153,22 @@ def main() -> None:
 
     # Aggregrate train/val from saved weekly tensors
     logging.info("Aggregrating training set (Week 1 through 8)")
-    train_features = _load_weeks(train_weeks, "features", save_dir)
-    train_targets  = _load_weeks(train_weeks, "targets", save_dir)
+    train_features = _load_weeks(train_weeks, "features", SAVE_DIR)
+    train_targets  = _load_weeks(train_weeks, "targets", SAVE_DIR)
 
     logging.info("Aggregrating validation set (Week 9)")
-    val_features = _load_weeks(val_weeks, "features", save_dir)
-    val_targets  = _load_weeks(val_weeks, "targets", save_dir)
+    val_features = _load_weeks(val_weeks, "features", SAVE_DIR)
+    val_targets  = _load_weeks(val_weeks, "targets", SAVE_DIR)
 
     # Optional: ensure float32
     train_features = train_features.to(torch.float32)
     val_features   = val_features.to(torch.float32)
 
     # Save pooled artifacts
-    torch.save(train_features, os.path.join(save_dir, "features_training.pt"))
-    torch.save(train_targets, os.path.join(save_dir, "targets_training.pt"))
-    torch.save(val_features, os.path.join(save_dir, "features_val.pt"))
-    torch.save(val_targets, os.path.join(save_dir, "targets_val.pt"))
+    torch.save(train_features, SAVE_DIR / "features_training.pt")
+    torch.save(train_targets, SAVE_DIR / "targets_training.pt")
+    torch.save(val_features, SAVE_DIR / "features_val.pt")
+    torch.save(val_targets, SAVE_DIR / "targets_val.pt")
 
     logging.info("Train features: %s", getattr(train_features, "shape", None))
     logging.info("Val features:   %s", getattr(val_features, "shape", None))
diff --git a/src/generate_predictions.py b/src/generate_predictions.py
index c522008..a4241ab 100644
--- a/src/generate_predictions.py
+++ b/src/generate_predictions.py
@@ -4,10 +4,10 @@
 Trains LSTM model on location tracking data
 
 Requires:
-- Raw location tracking data in NFL_HOME/data/parquet
-- Trained model in NFL_HOME/data/processed/model.pth
+- Raw location tracking data in nfl/data/parquet
+- Trained model in nfl/data/processed/model.pth
 
-Performs inference on each week and saves to NFL_HOME/data/processed/week{n}_predictions.csv
+Performs inference on each week and saves to nfl/data/processed/week{n}_predictions.csv
 """
 
 import math
@@ -15,6 +15,8 @@
 import random
 import os
 import logging
+import json
+from pathlib import Path
 
 import pandas as pd
 pd.options.mode.chained_assignment = None
@@ -29,50 +31,57 @@
 from models.transformer import ManZoneTransformer
 from load_data import RawDataLoader
 from clean_data import *
+from common.paths import PROJECT_ROOT, SAVE_DIR
+from common.decorators import *
+from common.args import parse_args
+
 
 def process_week_data_preds(week_number, plays):
-  file_path = os.path.join(os.getenv("NFL_HOME"), "data", "parquet", f"tracking_week_{week_number}.parquet")
-  week = pd.read_parquet(file_path)
-  logging.info(f"Finished reading Week {week_number} data")
+    if os.getenv("CI_DATA_ROOT"):
+        WEEK_PARQUET_PATH = Path(os.getenv("CI_DATA_ROOT")) / f"tracking_week_{week_number}.parquet"
+    else:
+        WEEK_PARQUET_PATH = PROJECT_ROOT /  "data" / "parquet" / f"tracking_week_{week_number}.parquet"
+    week = pd.read_parquet(WEEK_PARQUET_PATH)
+    logging.info(f"Finished reading Week {week_number} data")
 
-  # applying cleaning functions
-  week = rotate_direction_and_orientation(week)
-  week = make_plays_left_to_right(week)
-  week = calculate_velocity_components(week)
-  week = pass_attempt_merging(week, plays)
-  # week = label_offense_defense_coverage(week, plays)  # for specific coverage... currently set to man/zone only
-  week = label_offense_defense_manzone(week, plays)
+    # applying cleaning functions
+    week = rotate_direction_and_orientation(week)
+    week = make_plays_left_to_right(week)
+    week = calculate_velocity_components(week)
+    week = pass_attempt_merging(week, plays)
+    # week = label_offense_defense_coverage(week, plays)  # for specific coverage... currently set to man/zone only
+    week = label_offense_defense_manzone(week, plays)
 
-  week['week'] = week_number
-  week['uniqueId'] = week['gameId'].astype(str) + "_" + week['playId'].astype(str)
-  week['frameUniqueId'] = (
-      week['gameId'].astype(str) + "_" +
-      week['playId'].astype(str) + "_" +
-      week['frameId'].astype(str))
+    week['week'] = week_number
+    week['uniqueId'] = week['gameId'].astype(str) + "_" + week['playId'].astype(str)
+    week['frameUniqueId'] = (
+        week['gameId'].astype(str) + "_" +
+        week['playId'].astype(str) + "_" +
+        week['frameId'].astype(str))
 
-  # adding frames_from_snap (to do: make this a function but fine for now)
-  snap_frames = week[week['frameType'] == 'SNAP'].groupby('uniqueId')['frameId'].first()
-  week = week.merge(snap_frames.rename('snap_frame'), on='uniqueId', how='left')
-  week['frames_from_snap'] = week['frameId'] - week['snap_frame']
+    # adding frames_from_snap (to do: make this a function but fine for now)
+    snap_frames = week[week['frameType'] == 'SNAP'].groupby('uniqueId')['frameId'].first()
+    week = week.merge(snap_frames.rename('snap_frame'), on='uniqueId', how='left')
+    week['frames_from_snap'] = week['frameId'] - week['snap_frame']
 
-  # filtering only for even frames
-  # week = week[week['frameId'] % 2 == 0]
+    # filtering only for even frames
+    # week = week[week['frameId'] % 2 == 0]
 
-  # ridding of any potential outliers (25 seconds after the snap)
-  week = week[(week['frames_from_snap'] >= -150) & (week['frames_from_snap'] <= 30)]
+    # ridding of any potential outliers (25 seconds after the snap)
+    week = week[(week['frames_from_snap'] >= -150) & (week['frames_from_snap'] <= 30)]
 
-  # applying data augmentation to increase training size (centered around 0-4 seconds presnap!)
-  # -- 1/3rd of the current num of frames... specifically selecting for frames around the snap
+    # applying data augmentation to increase training size (centered around 0-4 seconds presnap!)
+    # -- 1/3rd of the current num of frames... specifically selecting for frames around the snap
 
-  # num_unique_frames = len(set(week['frameUniqueId']))
-  # selected_frames = select_augmented_frames(week, int(num_unique_frames / 3), sigma=5)
-  # week_aug = data_augmentation(week, selected_frames)
+    # num_unique_frames = len(set(week['frameUniqueId']))
+    # selected_frames = select_augmented_frames(week, int(num_unique_frames / 3), sigma=5)
+    # week_aug = data_augmentation(week, selected_frames)
 
-  # week = pd.concat([week, week_aug])
+    # week = pd.concat([week, week_aug])
 
-  logging.info(f"Finished processing Week {week_number} data")
+    logging.info(f"Finished processing Week {week_number} data")
 
-  return week
+    return week
 
 
 def prepare_tensor(play, num_players=22, num_features=5):
@@ -92,22 +101,36 @@ def prepare_tensor(play, num_players=22, num_features=5):
   return all_frames_tensor  # Shape: [num_frames, num_players, num_features]
 
 
+@time_fcn
 def main():
+    # Set logging
     logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
-    save_path = os.path.join(os.getenv('NFL_HOME'), 'data', 'processed')
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    # Get input args
+    args = parse_args()
 
+    # Enable/disable timing decorators
+    if args.profile:
+        set_time_decorators_enabled(True)
+        logging.info("Timing decorators enabled")
+    else:
+        set_time_decorators_enabled(False)
+        logging.info("Timing decorators disabled")
+
+    # Get model params and set device
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    with open(PROJECT_ROOT / "data" / "training" / "model_params.json", 'r') as file:
+        model_params_map = json.load(file)
     model = ManZoneTransformer(
-        feature_len=5,    # num of input features (x, y, v_x, v_y, defense)
-        model_dim=64,     # experimented with 96 & 128... seems best
-        num_heads=2,      # 2 seems best (but may have overfit when tried 4... may be worth iterating)
-        num_layers=4,
-        dim_feedforward=64 * 4,
-        dropout=0.1,      # 10% dropout to prevent overfitting... iterate as model becomes more complex (industry std is higher, i believe)
+        feature_len=5,                  # num of input features (x, y, v_x, v_y, defense)
+        model_dim=model_params_map["model_dim"],
+        num_heads=model_params_map["num_heads"],
+        num_layers=model_params_map["num_layers"],
+        dim_feedforward=int(model_params_map["model_dim"]) * int(model_params_map["num_layers"]),
+        dropout=model_params_map["dropout"],
         output_dim=2      # man or zone classification
     ).to(device)
-    model.load_state_dict(torch.load(os.path.join(save_path, 'model.pth'), weights_only=True, map_location=device))
+    model.load_state_dict(torch.load(PROJECT_ROOT / "data" / "training" / "best_model.pth", map_location=device))
     model.eval()
 
     # Load data
@@ -125,7 +148,7 @@ def main():
         tracking_df_polars = pl.DataFrame(week_df)  # or comment this out and use pandas below
 
         # Stream predictions to CSV in batches
-        out_pred_csv = os.path.join(save_path, f"week{week_eval}_preds.csv")
+        weekly_predictions_path_csv = SAVE_DIR / f"week{week_eval}_preds.csv"
         wrote_header = False
         batch = []
         BATCH_SIZE = 5000  # tune (smaller => lower peak RAM)
@@ -173,21 +196,21 @@ def main():
 
             # Flush batch to CSV to keep RAM low
             if len(batch) >= BATCH_SIZE:
-                pd.DataFrame(batch).to_csv(out_pred_csv, mode='a', header=not wrote_header, index=False)
+                pd.DataFrame(batch).to_csv(weekly_predictions_path_csv, mode='a', header=not wrote_header, index=False)
                 wrote_header = True
                 batch.clear()
 
         # Flush tail
         if batch:
-            pd.DataFrame(batch).to_csv(out_pred_csv, mode='a', header=not wrote_header, index=False)
+            pd.DataFrame(batch).to_csv(weekly_predictions_path_csv, mode='a', header=not wrote_header, index=False)
             batch.clear()
 
         logging.info(f"Finished week {week_eval}... saved to week{week_eval}_preds.csv\n")
 
         # Merge week_df with preds (per-week, small)
-        preds_week = pd.read_csv(out_pred_csv, usecols=['frameUniqueId','zone_prob','man_prob','pred', 'actual'])
+        preds_week = pd.read_csv(weekly_predictions_path_csv, usecols=['frameUniqueId','zone_prob','man_prob','pred', 'actual'])
         tracking_preds = week_df.merge(preds_week, on='frameUniqueId', how='left')
-        tracking_preds.to_csv(os.path.join(save_path, f"tracking_week_{week_eval}_preds.csv"), index=False)
+        tracking_preds.to_csv(SAVE_DIR / f"tracking_week_{week_eval}_preds.csv", index=False)
 
         # Free RAM before next week
         del week_df, tracking_df_polars, preds_week, tracking_preds
diff --git a/src/load_data.py b/src/load_data.py
index 22fb985..54dceaf 100644
--- a/src/load_data.py
+++ b/src/load_data.py
@@ -7,13 +7,17 @@
 import os
 import pandas as pd
 from typing import Tuple
+from pathlib import Path
+
+from common.paths import PROJECT_ROOT
+
 
 class RawDataLoader:
-    def __init__(self, nfl_home=None):
+    def __init__(self):
         if os.getenv("CI_DATA_ROOT"):
-            self.data_path = os.getenv("CI_DATA_ROOT")
+            self.DATA_PATH = Path(os.getenv("CI_DATA_ROOT"))
         else:
-            self.data_path = os.path.join(os.getenv('NFL_HOME'), 'data', 'parquet')
+            self.DATA_PATH = PROJECT_ROOT / "data" / "parquet"
         self.games_df = None
         self.plays_df = None
         self.players_df = None
@@ -29,16 +33,16 @@ def _load_parquet(self, filepath):
 
     def _load_base_data(self):
         """Load games, plays, and players data"""
-        self.games_df = self._load_parquet(os.path.join(self.data_path, 'games.parquet'))
-        self.plays_df = self._load_parquet(os.path.join(self.data_path, 'plays.parquet'))
-        self.players_df = self._load_parquet(os.path.join(self.data_path, 'players.parquet'))
+        self.games_df = self._load_parquet(self.DATA_PATH / 'games.parquet')
+        self.plays_df = self._load_parquet(self.DATA_PATH / 'plays.parquet')
+        self.players_df = self._load_parquet(self.DATA_PATH / 'players.parquet')
 
     def _load_tracking_data(self, weeks=list[int]):
         """Load tracking data for specified weeks"""
         weekly_dfs = []
         
         for week in weeks:
-            filepath = os.path.join(self.data_path, f'tracking_week_{week}.parquet')
+            filepath = self.DATA_PATH / f'tracking_week_{week}.parquet'
             df = self._load_parquet(filepath)
             if df is not None:
                 weekly_dfs.append(df)
diff --git a/src/old/train_xgb.py b/src/old/train_xgb.py
index d6a4223..4ccbaa6 100644
--- a/src/old/train_xgb.py
+++ b/src/old/train_xgb.py
@@ -43,14 +43,15 @@
 #from common.models.svc import SVC
 from common.models.rfr import RFRModel
 from common.models.mlp import MLPModel
+from common.paths import PROJECT_ROOT
 
 def load_data() -> dict[str]:
-    base_path = os.path.join(os.getenv('NFL_HOME'), 'data', 'coverage')
+    base_path = PROJECT_ROOT / "data" / "coverage"
     data_file_list = ['x', 'y']
     data_dict = {}
     for file in data_file_list:
         file_name = file + '.pkl'
-        file_path = os.path.join(base_path, file_name)
+        file_path = base_path / file_name
         if not os.path.exists(file_path):
             raise FileNotFoundError(f"Required data file {file_name} not found in {base_path}")
         else:
@@ -70,7 +71,7 @@ def plot_roc(y_test, y_proba) -> None:
     plt.legend(loc='lower right')
     plt.grid(True)
     image_name = f'man_zone_roc_auc.png'
-    image_path = os.path.join(os.getenv('NFL_HOME'), 'output', 'coverage', image_name)
+    image_path = PROJECT_ROOT / "output" / "coverage" / image_name
     plt.savefig(image_path, dpi=200)
     mlflow.log_artifact(image_path, artifact_path="plots")
 
@@ -159,10 +160,10 @@ def model_man_vs_zone() -> None:
         })
 
         # Save scaler
-        artifact_path = os.path.join(os.getenv("NFL_HOME"), "output", "coverage")
-        os.makedirs(artifact_path, exist_ok=True)
-        joblib.dump(scaler, os.path.join(artifact_path, "standard_scaler.pkl"))
-        mlflow.log_artifact(os.path.join(artifact_path, "standard_scaler.pkl"), artifact_path="preprocessing")
+        artifact_path = PROJECT_ROOT / "output" / "coverage"
+        artifact_path.mkdir(parents=True, exist_ok=True)
+        joblib.dump(scaler, artifact_path / "standard_scaler.pkl")
+        mlflow.log_artifact(artifact_path / "standard_scaler.pkl", artifact_path="preprocessing")
 
         # Enable autologging so the fitted model is captured
         mlflow.xgboost.autolog(log_model_signatures=True, log_input_examples=True)
@@ -228,10 +229,10 @@ def train_xgb(model, skf, x_train, y_train, x_test, y_test) -> None:
 
     # Save classification report as an artifact
     report_txt = classification_report(y_test, y_pred, target_names=['Zone', 'Man'])
-    artifact_path = os.path.join(os.getenv("NFL_HOME"), "output", "coverage")
-    with open(os.path.join(artifact_path, "classification_report.txt"), "w") as f:
+    artifact_path = PROJECT_ROOT / "output" / "coverage"
+    with open(artifact_path / "classification_report.txt", "w") as f:
         f.write(report_txt)
-    mlflow.log_artifact(os.path.join(artifact_path, "classification_report.txt"), artifact_path="reports")
+    mlflow.log_artifact(artifact_path / "classification_report.txt", artifact_path="reports")
 
     # Plot & log ROC curve
     plot_roc(y_test, y_proba)
diff --git a/src/train_lstm.py b/src/train_lstm.py
index 06e8d82..45a401f 100644
--- a/src/train_lstm.py
+++ b/src/train_lstm.py
@@ -3,7 +3,7 @@
 """
 Trains LSTM model on location tracking data
 
-Requires raw location tracking data in NFL_HOME/data/parquet
+Requires raw location tracking data in nfl/data/parquet
 
 Will produce classification report, confusion matrix, and ROC curve for best model
 """
diff --git a/src/train_transformer.py b/src/train_transformer.py
index 791f891..5bc5868 100644
--- a/src/train_transformer.py
+++ b/src/train_transformer.py
@@ -9,16 +9,18 @@
 - targets_training.pt
 - targets_val.pt
 
-Will train 30 epochs (unless it early stops) and produce model.pth
+Will train 50 epochs (unless it early stops) and produce model.pth
 """
 
 import os
 import logging
 import math
+from pathlib import Path
 import random
+import random
+import json
 
 import pandas as pd
-pd.options.mode.chained_assignment = None
 import numpy as np
 import matplotlib.pyplot as plt
 import torch
@@ -26,13 +28,34 @@
 from torch.utils.data import TensorDataset, DataLoader
 from torch.optim import AdamW
 
+# ML imports
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+import torch
+from torch.utils.data import TensorDataset, DataLoader
+import joblib
+
+# Ray Tune
+import ray
+from ray import train, tune, air
+from ray.air.integrations.mlflow import MLflowLoggerCallback, setup_mlflow
+from ray.tune import Tuner, RunConfig, TuneConfig, FailureConfig, Checkpoint
+from ray.tune import Tuner, RunConfig, TuneConfig, FailureConfig
+from ray.tune.schedulers import ASHAScheduler
+
 from models.transformer import ManZoneTransformer
-from common.decorators import time_fcn
+from common.decorators import set_time_decorators_enabled, time_fcn
+from common.paths import PROJECT_ROOT, SAVE_DIR
+from common.args import parse_args
 
-def set_seed(seed: int = 42):
+def set_seed(seed: int = 42) -> torch.Generator:
     # Python & NumPy
     random.seed(seed)
     np.random.seed(seed)
+    torch.manual_seed(seed)
+    g = torch.Generator()    # Creates a generator that fixes the shuffle in torch Dataloader
+    g.manual_seed(seed)
+
     os.environ["PYTHONHASHSEED"] = str(seed)
 
     # PyTorch CPU & CUDA
@@ -48,8 +71,11 @@ def set_seed(seed: int = 42):
     # DO NOT enforce deterministic algorithms globally
     # torch.use_deterministic_algorithms(False)
 
+    return g
+
+
 @time_fcn
-def train_epoch(train_loader: DataLoader, val_loader: DataLoader, model, optimizer, loss_fn, device) -> tuple[float, float]:
+def train_epoch(train_loader: DataLoader, model, optimizer, loss_fn, device) -> float:
         # Training
         model.train()
         running_loss = 0.0
@@ -63,6 +89,12 @@ def train_epoch(train_loader: DataLoader, val_loader: DataLoader, model, optimiz
             running_loss += loss.item() * features.size(0)
         avg_train_loss = running_loss / len(train_loader.dataset)
 
+        # Return losses
+        return avg_train_loss
+
+
+@time_fcn
+def validate_epoch(val_loader: DataLoader, model, loss_fn, device) -> tuple[float, float]:
         # Validation
         model.eval()
         val_running_loss = 0.0
@@ -79,88 +111,242 @@ def train_epoch(train_loader: DataLoader, val_loader: DataLoader, model, optimiz
         val_accuracy = correct / len(val_loader.dataset)
 
         # Return losses
-        return avg_train_loss, avg_val_loss, val_accuracy
+        return avg_val_loss, val_accuracy
 
 
 @time_fcn
-def main():
+def train_trial(config, args):
 
-    # Init logging and save path
-    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-    save_path = os.path.join(os.getenv('NFL_HOME'), 'data', 'processed')
+    # Set seeds
+    g = set_seed(42)
 
-    # Define model and device
-    set_seed(42)
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    ######################################################################
+    # Create model, loss, optimizer
+    ######################################################################
+    device = torch.device("cuda")   # this is the Ray-assigned GPU (Ray sets CUDA_VISIBLE_DEVICES)
+
+    # Defining ManZoneTransformer params, initializing optimizer and loss_fn
     model = ManZoneTransformer(
-        feature_len=5,          # num of input features (x, y, v_x, v_y, defense)
-        model_dim=64,           # TBD with tune
-        num_heads=2,            # TBD with tune
-        num_layers=4,           # TBD with tune
-        dim_feedforward=64 * 4, # model_dim * number of layers
-        dropout=0.1,            # TBD with tune
-        output_dim=2            # man or zone classification
+        feature_len=5,                                                          # num of input features (x, y, v_x, v_y, defense)
+        model_dim=int(config["model_dim"]),                                     # from ray tune or loaded
+        num_heads=int(config["num_heads"]),                                     # from ray tune or loaded
+        num_layers=int(config["num_layers"]),                                   # from ray tune or loaded
+        dim_feedforward=int(config["model_dim"]) * int(config["num_layers"]),   # from ray tune or loaded
+        dropout=float(config["dropout"]),                                       # 10% dropout to prevent overfitting... iterate as model becomes more complex (industry std is higher, i believe)
+        output_dim=2                                                            # man or zone classification
     ).to(device)
 
     # Set optimizer and loss fcn
     optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
     loss_fn = nn.CrossEntropyLoss()
 
-    early_stopping_patience = 5
-    best_val_loss = float('inf')
-    epochs_no_improve = 0
-    num_epochs = 50
+    ######################################################################
+    # Create dataloaders
+    ######################################################################
+    # Load in data and create tensor datasets
+    train_features = torch.load(SAVE_DIR / f"features_training.pt")
+    train_targets = torch.load(SAVE_DIR / f"targets_training.pt")
 
-    # Init random
-    g = torch.Generator()    # Creates a generator that fixes the shuffle in torch Dataloader
-    g.manual_seed(42)
+    val_features = torch.load(SAVE_DIR / f"features_val.pt")
+    val_targets = torch.load(SAVE_DIR / f"targets_val.pt")
 
-    # Create data loaders
-    train_features = torch.load(os.path.join(save_path, f"features_training.pt"))
-    train_targets = torch.load(os.path.join(save_path, f"targets_training.pt"))
+    # Create data loaders for batching
     train_loader = DataLoader(
         TensorDataset(train_features, train_targets),
-        batch_size=64,
+        batch_size=int(config["batch_size"]),
         shuffle=True,                       # We want random mini batches so GD doesn't overfit to specific ordering patterns, lets shuffle
         generator=g,                        # Fixes the shuffle
         num_workers=0,                      # Eliminate worker non-determinism
         pin_memory=True,                    # Batches are allocated on page-locked ("pinned") memory on the host, allows GPU driver to perform faster async DMA
-    )  
+        )  
     
-    val_features = torch.load(os.path.join(save_path, f"features_val.pt"))
-    val_targets = torch.load(os.path.join(save_path, f"targets_val.pt"))
     val_loader = DataLoader(
         TensorDataset(val_features, val_targets),
-        batch_size=64,
+        batch_size=int(config["batch_size"]),
         shuffle=False,                      # In eval we aren't updating the weights, so it doesn't really matter if we imply ordering or not
         num_workers=0,                      # Eliminate worker non-determinism
         pin_memory=True,                    # Batches are allocated on page-locked ("pinned") memory on the host, allows GPU driver to perform faster async DMA
-    )       
+        )       
 
-    # Train with early stoppping
+    ######################################################################
+    # Train model (and evaluate)
+    ######################################################################
+    # Init
     train_losses = []
     val_losses = []
     val_accuracies = []
-    for epoch in range(num_epochs):
+    early_stopping_patience = 5
+    best_val_loss = float('inf')
+    epochs_no_improve = 0
 
-        avg_train_loss, avg_val_loss, val_accuracy = train_epoch(train_loader, val_loader, model, optimizer, loss_fn, device)
+    for epoch in range(int(config["epochs"])):
+
+        # Train
+        avg_train_loss = train_epoch(train_loader, model, optimizer, loss_fn, device)
         train_losses.append(avg_train_loss)
-        val_losses.append(avg_val_loss)
-        val_accuracies.append(val_accuracy)
-        logging.info(f"Epoch [{epoch+1}/{num_epochs}]")
+
+        # Validate
+        avg_val_loss, val_accuracy = validate_epoch(val_loader, model, loss_fn, device)
+
+        # Info
+        logging.info(f"Epoch [{epoch + 1}/{int(config['epochs'])}]")
         logging.info(f"Train Loss: {avg_train_loss:.4f}, Val Loss: {avg_val_loss:.4f}, Val Accuracy: {val_accuracy:.4f}")
 
-        # Early stopping
-        if avg_val_loss < best_val_loss:
-            best_val_loss = avg_val_loss
-            epochs_no_improve = 0
-            # saving the best model
-            torch.save(model.state_dict(), os.path.join(save_path, f"model.pth"))
+        if args.tune:
+            ######################################################################
+            # Tune logging (with MLflow callback this is all mirrored there as well)
+            # NOTE: By calling tune.report here effectively once per epoch, that becomes our time scale!
+            ######################################################################
+            session   = tune.get_context()
+            trial_dir = Path(session.get_trial_dir())
+            best_model_path = trial_dir / "best_model.pth"
+
+            # Record metrics
+            metrics = {
+                "val_accuracy": val_accuracy,
+                # "val_loss": float(avg_val_loss),
+                # "train_loss": float(avg_train_loss),
+            }
+
+            # Report metrics and save checkpoint if applicable (checkpoint every n epochs and don't have redudant checkpoints if using workers via train)
+            checkpoint = None
+            should_checkpoint = epoch % config.get("checkpoint_freq", 1) == 0
+            if should_checkpoint:
+                ckpt_dir  = trial_dir / f"ckpt_e{epoch:04d}"
+                ckpt_dir.mkdir(parents=True, exist_ok=True)
+                torch.save(model.state_dict(), ckpt_dir / "model.pth")
+                checkpoint = Checkpoint.from_directory(str(ckpt_dir))
+                tune.report(metrics, checkpoint=checkpoint)
+            else:
+                tune.report(metrics)
+        
         else:
-            epochs_no_improve += 1
-            if epochs_no_improve >= early_stopping_patience:
-                logging.info(f"Early stopping triggered")
-                break
+            # Record metrics
+            val_losses.append(avg_val_loss)
+            val_accuracies.append(val_accuracy)
+
+            # Adding early stopping check (effort to prevent overfitting)
+            best_model_path = PROJECT_ROOT / "data" / "training" / "best_model.pth"
+            if avg_val_loss < best_val_loss:
+                best_val_loss = avg_val_loss
+                epochs_no_improve = 0
+                torch.save(model.state_dict(), best_model_path)
+            else:
+                epochs_no_improve += 1
+                if epochs_no_improve >= early_stopping_patience:
+                    logging.info(f"Early stopping triggered")
+                    break
+
+
+@time_fcn       
+def run_HPO(args):
+
+    ######################################################################
+    # Start parent HPO, MLflow session
+    ######################################################################
+    mlflow_tracking_uri = "sqlite:///mlflow.db"
+    experiment   = "transformer"
+
+    ######################################################################
+    # Define search space and scheduler
+    ######################################################################
+    transformer_params = {
+        # Varying model shape
+        "model_dim": tune.choice([32, 64, 128, 256]),
+        "num_heads": tune.choice([2, 4]),
+        "num_layers": tune.choice([1, 2, 3, 4]),
+        "dropout": tune.choice([0.0, 0.1, 0.2]) ,
+
+        # Training
+        "batch_size": tune.choice([32, 64, 128]),
+
+        # Epochs / checkpointing
+        "epochs": 50,
+        "checkpoint_freq": 10,
+    }
+
+    params=transformer_params
+
+    # Async Successive Halfing Scheduler (ASHA)
+    # Instead of running all trials for all epochs, it allocates more resources to promising ones and kills of bad ones early
+    scheduler = ASHAScheduler(
+        max_t=params["epochs"],                     # Max amount of "things" on our whatever our scale is (since we call tune.report once per epoch this max epochs per trial)
+        grace_period=params["checkpoint_freq"]+1,   # Allow for 6 epochs each trial until we kill it
+        reduction_factor=2,                         # ASHA keeps about 50% of the top trials each time it prunes
+    )
+    
+    ######################################################################
+    # Build tuner; pass MLflow context and PARENT RUN ID to workers via env vars
+    ######################################################################
+    trainable = tune.with_parameters(train_trial, args=args)  # Allows each training run to have any specific params
+    tuner = Tuner(
+        tune.with_resources(trainable, resources={"cpu": 4, "gpu": 1}), # Gives 4 CPU and one GPU per trial
+        param_space=params,
+        tune_config=TuneConfig( 
+            metric="val_accuracy",
+            mode="max",
+            scheduler=scheduler,
+            num_samples=10,             # total trials
+        ),
+        run_config=RunConfig(
+            name="transformer_hpo",
+            storage_path=os.path.abspath("./log/ray_results"),
+            failure_config=FailureConfig(fail_fast=True),
+            callbacks=[
+                MLflowLoggerCallback(
+                    tracking_uri=mlflow_tracking_uri,
+                    experiment_name=experiment,
+                    save_artifact=True,
+                )
+            ],
+        ),
+    )
+
+    ######################################################################
+    # Execute HPO
+    ######################################################################
+    results = tuner.fit()
+    best = results.get_best_result(metric="val_accuracy", mode="max")
+    print("Best config:", best.config)
+
+
+@time_fcn
+def main():
+
+    # Set logging
+    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+    # Get input args
+    args = parse_args()
+
+    # Enable/disable timing decorators
+    if args.profile:
+        set_time_decorators_enabled(True)
+        logging.info("Timing decorators enabled")
+    else:
+        set_time_decorators_enabled(False)
+        logging.info("Timing decorators disabled")
+
+    # We are doing HPO with Ray
+    if args.tune:
+        # Disable some warnings about GPUs if we don't use them (we are always using GPUs for training here)
+        os.environ["RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO"] = "0"
+
+        # Set the runtime env to only ship src/, the whole repo that has a bunch of data
+        script_dir = Path(__file__).resolve().parent         # Directory containing train_transformer.py (i.e., src/)
+        ray.init(
+            runtime_env={
+                "working_dir": str(script_dir),
+            }
+        )
+        run_HPO(args)
+
+    # We are doing a single run with fixed model parameters located at nfl/data/training/model_params.json
+    else:
+        with open(PROJECT_ROOT / "data" / "training" / "model_params.json", 'r') as file:
+            model_params_map = json.load(file)
+        train_trial(model_params_map, args)
+
 
 if __name__ == "__main__":
-  main()
\ No newline at end of file
+    main()
\ No newline at end of file
diff --git a/src/viz/draw_play_driver.py b/src/viz/draw_play_driver.py
index 8b73d29..c75b8ab 100644
--- a/src/viz/draw_play_driver.py
+++ b/src/viz/draw_play_driver.py
@@ -10,6 +10,8 @@
 from data_processing.process import load_data
 from data_processing.draw_play import animate_play
 
+from common.paths import PROJECT_ROOT
+
 
 if __name__ == "__main__":
 
@@ -17,10 +19,10 @@
     random.seed(42)
 
     # Load data
-    games_df = load_data(os.path.join(os.getenv('NFL_HOME'),'data', 'raw', 'games.csv'))
-    plays_df = load_data(os.path.join(os.getenv('NFL_HOME'),'data', 'raw', 'plays.csv'))
-    players_df = load_data(os.path.join(os.getenv('NFL_HOME'),'data','raw', 'players.csv'))
-    week1_df = load_data(os.path.join(os.getenv('NFL_HOME'),'data','raw', 'tracking_week_1.csv'))
+    games_df = load_data(PROJECT_ROOT / 'data' / 'raw' / 'games.csv')
+    plays_df = load_data(PROJECT_ROOT / 'data' / 'raw' /  'plays.csv')
+    players_df = load_data(PROJECT_ROOT / 'data' / 'raw' /  'players.csv')
+    week1_df = load_data(PROJECT_ROOT / 'data' / 'raw' /  'tracking_week_1.csv')
 
     # Draw play frame
     for i in range(0,len(games_df)):
diff --git a/uv.lock b/uv.lock
index 3cfd86d..ec4e8d5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -3,25 +3,25 @@ requires-python = ">=3.12"
 
 [[package]]
 name = "alembic"
-version = "1.17.0"
+version = "1.17.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mako" },
     { name = "sqlalchemy" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/6b/45/6f4555f2039f364c3ce31399529dcf48dd60726ff3715ad67f547d87dfd2/alembic-1.17.0.tar.gz", hash = "sha256:4652a0b3e19616b57d652b82bfa5e38bf5dbea0813eed971612671cb9e90c0fe", size = 1975526 }
+sdist = { url = "https://files.pythonhosted.org/packages/02/a6/74c8cadc2882977d80ad756a13857857dbcf9bd405bc80b662eb10651282/alembic-1.17.2.tar.gz", hash = "sha256:bbe9751705c5e0f14877f02d46c53d10885e377e3d90eda810a016f9baa19e8e", size = 1988064 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/44/1f/38e29b06bfed7818ebba1f84904afdc8153ef7b6c7e0d8f3bc6643f5989c/alembic-1.17.0-py3-none-any.whl", hash = "sha256:80523bc437d41b35c5db7e525ad9d908f79de65c27d6a5a5eab6df348a352d99", size = 247449 },
+    { url = "https://files.pythonhosted.org/packages/ba/88/6237e97e3385b57b5f1528647addea5cc03d4d65d5979ab24327d41fb00d/alembic-1.17.2-py3-none-any.whl", hash = "sha256:f483dd1fe93f6c5d49217055e4d15b905b425b6af906746abb35b69c1996c4e6", size = 248554 },
 ]
 
 [[package]]
 name = "annotated-doc"
-version = "0.0.3"
+version = "0.0.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d7/a6/dc46877b911e40c00d395771ea710d5e77b6de7bacd5fdcd78d70cc5a48f/annotated_doc-0.0.3.tar.gz", hash = "sha256:e18370014c70187422c33e945053ff4c286f453a984eba84d0dbfa0c935adeda", size = 5535 }
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/02/b7/cf592cb5de5cb3bade3357f8d2cf42bf103bbe39f459824b4939fd212911/annotated_doc-0.0.3-py3-none-any.whl", hash = "sha256:348ec6664a76f1fd3be81f43dffbee4c7e8ce931ba71ec67cc7f4ade7fbbb580", size = 5488 },
+    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303 },
 ]
 
 [[package]]
@@ -58,48 +58,32 @@ wheels = [
 
 [[package]]
 name = "asttokens"
-version = "3.0.0"
+version = "3.0.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978 }
+sdist = { url = "https://files.pythonhosted.org/packages/be/a5/8e3f9b6771b0b408517c82d97aed8f2036509bc247d46114925e32fe33f0/asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7", size = 62308 }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/d2/39/e7eaf1799466a4aef85b6a4fe7bd175ad2b1c6345066aa33f1f58d4b18d0/asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a", size = 27047 },
 ]
 
 [[package]]
-name = "black"
-version = "25.9.0"
+name = "attrs"
+version = "25.4.0"
 source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "click" },
-    { name = "mypy-extensions" },
-    { name = "packaging" },
-    { name = "pathspec" },
-    { name = "platformdirs" },
-    { name = "pytokens" },
-]
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+sdist = { url = "https://files.pythonhosted.org/packages/6b/5c/685e6633917e101e5dcb62b9dd76946cbb57c26e133bae9e0cd36033c0a9/attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11", size = 934251 }
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