diff --git a/.github/workflows/pr-ci.yml b/.github/workflows/pr-ci.yml
index 344df99..5057c84 100644
--- a/.github/workflows/pr-ci.yml
+++ b/.github/workflows/pr-ci.yml
@@ -39,12 +39,17 @@ jobs:
       - name: Sync uv
         run: uv sync --locked
           
-      - name: Create features
-        run: uv run src/create_features.py
+      - name: Test feature creation
+        run: uv run src/create_features.py --profile
 
-      - name: Train transformer
-        run: uv run src/train_transformer.py
+        # This will output model_params.json
+      - name: Test ray hyperparameter tuning
+        run: uv run src/train_transformer.py --tune --ci
 
-      - name: Make week 9 predictions
-        run: uv run src/generate_predictions.py
+        # This will use model_params.json to create best_model.pth
+      - name: Test transformer training
+        run: uv run src/train_transformer.py --profile --ci
 
+        # This will use best_model.pth for inference
+      - name: Test prediction generation
+        run: uv run src/generate_predictions.py --profile
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index e4dd250..053ad63 100644
--- a/.gitignore
+++ b/.gitignore
@@ -190,7 +190,7 @@ mlruns/
 mlflow.db
 
 # Ray
-log/
+ray_results/
 
 # Artifacts
 *.pkl
diff --git a/README.md b/README.md
index 289d41b..b7bca55 100644
--- a/README.md
+++ b/README.md
@@ -114,10 +114,16 @@ uv run src/create_features.py
 # Train the model
 uv run src/train_transformer.py
 
+# Alternatively, you can run hyper-parameter optimization which will save the best model and parameters with
+uv run src/train_transformer.py --tune
+
 # Run inference on week 9
 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
+
+# If you would like to time and of the runs you can append --profile, for example
+uv run src/train_transformer.py --profile
 ```
 
 ## Activating MLflow
@@ -133,4 +139,16 @@ mlflow ui \
   --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
+The last command will start the MLflow GUI at your local host loopback on port 5000: http://127.0.0.1:5000. Viewing MLflow will look something like this (note our results are after a 500 trial hyper-parameter optimization run with Ray Tune)
+
+![mlflow_tune](./docs/images/mlflow_tune.png)
+
+## Future Work
+
+For model improvements I'm interested in switching to a flash attention layer which can accelerate training
+
+I also want to build up per team models, do ANOVA, then highlight specific players that are giving tells on coverages
+
+Want to try out the model on a non-binary problem and try distinguishing between distinct coverage types
+
+See if I can identify blitzer (is the blitzer player identified in the dataset?)
\ No newline at end of file
diff --git a/data/training/best_model.pth b/data/training/best_model.pth
index cd7d59f..10d4210 100644
Binary files a/data/training/best_model.pth and b/data/training/best_model.pth differ
diff --git a/data/training/model_params.json b/data/training/model_params.json
index 818ed74..3772b87 100644
--- a/data/training/model_params.json
+++ b/data/training/model_params.json
@@ -1,9 +1,11 @@
 {
-    "epochs": 50,
-    "model_dim": 64,
+    "model_dim": 96,
     "num_heads": 2,
-    "num_layers": 4,
-    "dropout": 0.1,
-    "batch_size": 64,
-    "checkpoint_freq": 10
-}
+    "num_layers": 2,
+    "dropout": 0.0,
+    "multiplier": 2,
+    "lr": 0.00013702308673732994,
+    "weight_decay": 1.128209328073835e-05,
+    "batch_size": 19200,
+    "epochs": 200
+}
\ No newline at end of file
diff --git a/docs/images/mlflow_tune.png b/docs/images/mlflow_tune.png
new file mode 100644
index 0000000..59e8ae7
Binary files /dev/null and b/docs/images/mlflow_tune.png differ
diff --git a/notebooks/predictions.ipynb b/notebooks/predictions.ipynb
index c110221..55f17f1 100644
--- a/notebooks/predictions.ipynb
+++ b/notebooks/predictions.ipynb
@@ -37,28 +37,28 @@
       "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",
+      "4  2022110700      56  33131.0  Calais Campbell        1  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 01:16:09.2          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",
+      "4  2022-11-08 01:16:09.2          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_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",
+      "4  2022110700_56  2022110700_56_1         117              -116   0.832631   \n",
       "\n",
       "   man_prob  pred  actual  \n",
       "0  0.224613     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",
+      "4  0.167369     0       0  \n",
       "\n",
       "[5 rows x 42 columns]\n",
       "Finished processing week 9...\n",
@@ -67,28 +67,28 @@
       "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",
+      "4  2022110700      56  33131.0  Calais Campbell        1  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 01:16:09.2          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",
+      "4  2022-11-08 01:16:09.2          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_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",
+      "4  2022110700_56  2022110700_56_1         117              -116   0.832631   \n",
       "\n",
       "   man_prob pred  actual  base_correct  \n",
       "0  0.224613    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",
+      "4  0.167369    0       0          True  \n",
       "\n",
       "[5 rows x 43 columns]\n"
      ]
@@ -154,7 +154,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x720 with 1 Axes>"
       ]
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "id": "a1575c4b",
    "metadata": {},
    "outputs": [
@@ -252,56 +252,56 @@
      "output_type": "stream",
      "text": [
       "         gameId  playId  man_prob_start  man_prob_end  man_prob_change\n",
-      "462  2022110606    2868        0.088892      0.856261         0.767369\n",
-      "742  2022110610    3836        0.113602      0.872288         0.758687\n",
-      "119  2022110601     394        0.105599      0.841861         0.736262\n",
-      "766  2022110700     567        0.136234      0.871605         0.735371\n",
-      "224  2022110603     250        0.211062      0.944661         0.733599\n",
-      "607  2022110609     582        0.191812      0.923659         0.731848\n",
-      "598  2022110609     105        0.151426      0.882540         0.731114\n",
-      "701  2022110610    1617        0.117710      0.826161         0.708451\n",
-      "124  2022110601     802        0.135883      0.838525         0.702642\n",
-      "699  2022110610    1461        0.129331      0.821083         0.691753\n",
-      "539  2022110608     247        0.165391      0.856897         0.691506\n",
-      "702  2022110610    1740        0.136020      0.820747         0.684727\n",
-      "167  2022110602     124        0.234146      0.914454         0.680309\n",
-      "104  2022110600    3242        0.191162      0.869154         0.677992\n",
-      "522  2022110607    2827        0.263628      0.906988         0.643360\n",
-      "71   2022110600    1233        0.348300      0.944168         0.595868\n",
-      "746  2022110610    3991        0.276203      0.866461         0.590258\n",
-      "396  2022110605    2744        0.335488      0.922082         0.586594\n",
-      "458  2022110606    2411        0.331336      0.914600         0.583264\n",
-      "171  2022110602     369        0.290061      0.847524         0.557463\n",
-      "116  2022110601     220        0.342098      0.898106         0.556008\n",
-      "434  2022110606    1107        0.343870      0.887801         0.543931\n",
-      "454  2022110606    2257        0.311255      0.841236         0.529981\n",
-      "125  2022110601     845        0.374516      0.903648         0.529132\n",
-      "129  2022110601    1048        0.331571      0.815322         0.483751\n",
-      "248  2022110603    1919        0.343870      0.821279         0.477409\n",
-      "154  2022110601    2953        0.473764      0.923501         0.449736\n",
-      "644  2022110609    2495        0.492139      0.937178         0.445039\n",
-      "345  2022110604    3388        0.492391      0.927774         0.435383\n",
-      "755  2022110610    4428        0.533211      0.928780         0.395569\n",
-      "795  2022110700    2307        0.420379      0.804075         0.383696\n",
-      "157  2022110601    3188        0.488436      0.866897         0.378461\n",
-      "146  2022110601    2379        0.553914      0.931213         0.377299\n",
-      "53   2022110600     326        0.580506      0.945217         0.364711\n",
-      "325  2022110604    2242        0.516376      0.864839         0.348462\n",
-      "177  2022110602     633        0.527294      0.841911         0.314617\n",
-      "231  2022110603     558        0.659318      0.953999         0.294682\n",
-      "201  2022110602    2548        0.528147      0.808738         0.280592\n",
-      "470  2022110606    3396        0.694953      0.952630         0.257676\n",
-      "445  2022110606    1701        0.668404      0.893735         0.225331\n",
-      "273  2022110603    3322        0.733775      0.946481         0.212706\n",
-      "651  2022110609    2943        0.711699      0.914848         0.203149\n",
-      "693  2022110610     931        0.703451      0.900434         0.196983\n",
-      "708  2022110610    1983        0.670604      0.863170         0.192567\n",
-      "128  2022110601    1003        0.764499      0.916109         0.151611\n",
-      "79   2022110600    1682        0.808978      0.954098         0.145120\n",
-      "130  2022110601    1083        0.801070      0.940372         0.139302\n",
-      "271  2022110603    3274        0.697031      0.827441         0.130410\n",
-      "265  2022110603    2874        0.726167      0.852143         0.125976\n",
-      "50   2022110600     172        0.821084      0.926996         0.105913\n"
+      "742  2022110610    3836        0.075986      0.826214         0.750228\n",
+      "109  2022110600    3521        0.291647      0.934054         0.642406\n",
+      "701  2022110610    1617        0.198709      0.838289         0.639580\n",
+      "171  2022110602     369        0.273909      0.909861         0.635952\n",
+      "131  2022110601    1211        0.275959      0.910532         0.634573\n",
+      "396  2022110605    2744        0.238280      0.856732         0.618451\n",
+      "805  2022110700    2843        0.234192      0.849649         0.615457\n",
+      "766  2022110700     567        0.203215      0.811886         0.608670\n",
+      "607  2022110609     582        0.250615      0.854990         0.604375\n",
+      "515  2022110607    2409        0.235214      0.830188         0.594974\n",
+      "116  2022110601     220        0.322917      0.913313         0.590396\n",
+      "71   2022110600    1233        0.368813      0.957681         0.588868\n",
+      "278  2022110603    3614        0.232425      0.816197         0.583772\n",
+      "462  2022110606    2868        0.357498      0.914679         0.557181\n",
+      "100  2022110600    3074        0.359848      0.911483         0.551635\n",
+      "614  2022110609    1000        0.282125      0.830036         0.547911\n",
+      "119  2022110601     394        0.361745      0.909308         0.547563\n",
+      "21   2022110300    1229        0.275545      0.817196         0.541651\n",
+      "70   2022110600    1162        0.301591      0.838405         0.536814\n",
+      "466  2022110606    3040        0.280015      0.804513         0.524497\n",
+      "258  2022110603    2370        0.306106      0.829438         0.523332\n",
+      "112  2022110600    3772        0.406569      0.908079         0.501510\n",
+      "248  2022110603    1919        0.303070      0.800845         0.497775\n",
+      "699  2022110610    1461        0.405270      0.894969         0.489698\n",
+      "125  2022110601     845        0.458622      0.946454         0.487832\n",
+      "702  2022110610    1740        0.321101      0.808342         0.487240\n",
+      "368  2022110605    1125        0.359468      0.845988         0.486520\n",
+      "111  2022110600    3574        0.390580      0.870376         0.479796\n",
+      "422  2022110606     330        0.351204      0.819465         0.468261\n",
+      "88   2022110600    2223        0.394877      0.848171         0.453294\n",
+      "795  2022110700    2307        0.416444      0.867382         0.450938\n",
+      "442  2022110606    1531        0.406420      0.856893         0.450473\n",
+      "161  2022110601    3394        0.452907      0.900946         0.448039\n",
+      "690  2022110610     763        0.430892      0.853569         0.422677\n",
+      "245  2022110603    1473        0.515904      0.923859         0.407955\n",
+      "428  2022110606     796        0.449007      0.854154         0.405147\n",
+      "124  2022110601     802        0.441437      0.828420         0.386983\n",
+      "458  2022110606    2411        0.535048      0.916955         0.381907\n",
+      "434  2022110606    1107        0.466888      0.848738         0.381851\n",
+      "128  2022110601    1003        0.554024      0.933162         0.379138\n",
+      "157  2022110601    3188        0.545932      0.873865         0.327933\n",
+      "345  2022110604    3388        0.530769      0.851481         0.320712\n",
+      "376  2022110605    1796        0.559705      0.866196         0.306491\n",
+      "402  2022110605    3054        0.589068      0.885620         0.296552\n",
+      "801  2022110700    2637        0.646215      0.918210         0.271994\n",
+      "146  2022110601    2379        0.675562      0.946161         0.270599\n",
+      "224  2022110603     250        0.690393      0.955999         0.265605\n",
+      "244  2022110603    1442        0.640401      0.905547         0.265147\n",
+      "231  2022110603     558        0.699621      0.945901         0.246280\n",
+      "755  2022110610    4428        0.626490      0.868448         0.241958\n"
      ]
     }
    ],
@@ -342,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "id": "87e40720",
    "metadata": {},
    "outputs": [
@@ -384,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "id": "03b48cc7",
    "metadata": {},
    "outputs": [
@@ -438,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "id": "58d18852",
    "metadata": {},
    "outputs": [],
@@ -542,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "341bae72",
    "metadata": {},
    "outputs": [
@@ -550,31 +550,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gameId: 2022110610, playId: 3836, Q: 4, (1:47) (Shotgun) P.Mahomes pass short left to J.McKinnon to KC 40 for 4 yards (A.Adams).\n",
-      "gameId: 2022110601, playId: 394, Q: 1, (8:28) (Shotgun) T.Tagovailoa pass deep middle to T.Sherfield to CHI 33 for 18 yards (E.Jackson).\n",
-      "gameId: 2022110609, playId: 582, Q: 1, (5:55) (Shotgun) M.Stafford pass short right to Al.Robinson to LA 36 for 4 yards (C.Davis).\n",
-      "gameId: 2022110601, playId: 802, Q: 1, (:18) (Shotgun) J.Fields pass short right to D.Mooney pushed ob at MIA 18 for 10 yards (K.Kohou).\n",
-      "gameId: 2022110610, playId: 1461, Q: 2, (6:51) (Shotgun) M.Willis pass deep left to A.Hooper to KC 2 for 18 yards (J.Thornhill).\n",
-      "gameId: 2022110602, playId: 124, Q: 1, (13:41) (Shotgun) J.Burrow pass short middle to H.Hurst to CIN 23 for 5 yards (M.Hartsfield).\n",
-      "gameId: 2022110607, playId: 2827, Q: 4, (14:20) T.Heinicke pass short right to D.Milne for 6 yards, TOUCHDOWN.\n",
-      "gameId: 2022110600, playId: 1233, Q: 2, (5:53) (Shotgun) J.Herbert pass short right to M.Bandy to ATL 11 for 4 yards (I.Oliver).\n",
-      "gameId: 2022110602, playId: 369, Q: 1, (8:26) (Shotgun) P.Walker pass short right to S.Brown to CAR 31 for 1 yard (T.Hendrickson).\n",
-      "gameId: 2022110606, playId: 1107, Q: 2, (11:41) (Shotgun) Z.Wilson pass short right to J.Robinson to BUF 45 for -2 yards (T.Edmunds, J.Johnson).\n",
-      "gameId: 2022110606, playId: 2257, Q: 3, (7:44) (Shotgun) Z.Wilson pass short left to G.Wilson to BUF 19 for 8 yards (K.Elam).\n",
-      "gameId: 2022110601, playId: 845, Q: 2, (15:00) J.Fields pass short right to C.Kmet for 18 yards, TOUCHDOWN.\n",
-      "gameId: 2022110601, playId: 1048, Q: 2, (11:05) (Shotgun) T.Tagovailoa pass short right to J.Wilson to CHI 6 for 4 yards (N.Morrow). PENALTY on CHI-D.Robinson, Roughing the Passer, 3 yards, enforced at CHI 6.\n",
-      "gameId: 2022110601, playId: 2953, Q: 4, (11:42) J.Fields pass short right to C.Kmet for 4 yards, TOUCHDOWN.\n",
-      "gameId: 2022110604, playId: 3388, Q: 4, (4:18) (Shotgun) T.Lawrence pass short left to C.Kirk pushed ob at 50 for 10 yards (A.Averett).\n",
-      "gameId: 2022110601, playId: 2379, Q: 3, (6:07) (Shotgun) T.Tagovailoa pass short left to J.Wilson for 10 yards, TOUCHDOWN.\n",
-      "gameId: 2022110604, playId: 2242, Q: 3, (10:20) (Shotgun) T.Lawrence pass short right to C.Kirk for 7 yards, TOUCHDOWN.\n",
-      "gameId: 2022110602, playId: 633, Q: 1, (4:53) (Shotgun) J.Burrow pass short right to T.Higgins to CIN 42 for 11 yards (J.Horn).\n",
-      "gameId: 2022110606, playId: 1701, Q: 2, (:42) (Shotgun) Z.Wilson pass short right to Mi.Carter pushed ob at BUF 6 for 10 yards (D.Hamlin).\n",
-      "gameId: 2022110610, playId: 931, Q: 2, (15:00) (Shotgun) P.Mahomes pass short right to M.Hardman for 7 yards, TOUCHDOWN.\n",
-      "gameId: 2022110601, playId: 1003, Q: 2, (12:28) (Shotgun) T.Tagovailoa pass short left to C.Wilson ran ob at CHI 14 for 14 yards (E.Jackson).\n",
-      "gameId: 2022110600, playId: 1682, Q: 2, (:36) J.Herbert pass short right to A.Ekeler for 1 yard, TOUCHDOWN.\n",
-      "gameId: 2022110601, playId: 1083, Q: 2, (10:42) (Shotgun) T.Tagovailoa pass short right to T.Hill for 3 yards, TOUCHDOWN.\n",
-      "gameId: 2022110603, playId: 3274, Q: 4, (8:04) (Shotgun) A.Rodgers pass short right to S.Toure to DET 12 for 2 yards (W.Harris).\n",
-      "gameId: 2022110603, playId: 2874, Q: 4, (14:56) (Shotgun) J.Goff pass short middle to J.Mitchell for 3 yards, TOUCHDOWN.\n"
+      "gameId: 2022110610, playId: 3836, Q: 4, (1:47) (Shotgun) P.Mahomes pass short left to J.McKinnon to KC 40 for 4 yards (A.Adams).\n"
      ]
     }
    ],
diff --git a/src/common/args.py b/src/common/args.py
index 57a6a1a..9ce7940 100644
--- a/src/common/args.py
+++ b/src/common/args.py
@@ -18,4 +18,10 @@ def parse_args():
         action="store_true",
         help="Enable @time_fcn timing decorators for profiling",
     )
+
+    parser.add_argument(
+        "--ci",
+        action="store_true",
+        help="Enable CI mode with reduced epochs for faster training during pipeline",
+    )
     return parser.parse_args()
\ No newline at end of file
diff --git a/src/generate_predictions.py b/src/generate_predictions.py
index a4241ab..72c650f 100644
--- a/src/generate_predictions.py
+++ b/src/generate_predictions.py
@@ -85,20 +85,19 @@ def process_week_data_preds(week_number, plays):
 
 
 def prepare_tensor(play, num_players=22, num_features=5):
+    features = ['x_clean', 'y_clean', 'v_x', 'v_y', 'defense']
+    play_data = play[features + ['frameId']]
+    play_data = play_data.sort_values(by='frameId')
 
-  features = ['x_clean', 'y_clean', 'v_x', 'v_y', 'defense']
-  play_data = play[features + ['frameId']]
-  play_data = play_data.sort_values(by='frameId')
-
-  frames = (
+    frames = (
     play_data
     .groupby('frameId')[features]
     .apply(lambda g: g.to_numpy())
-  )
-  all_frames_tensor = np.stack(frames.to_list())  # Shape: [num_frames, num_players, num_features]
-  all_frames_tensor = torch.tensor(all_frames_tensor, dtype=torch.float32)
+    )
+    all_frames_tensor = np.stack(frames.to_list())  # Shape: [num_frames, num_players, num_features]
+    all_frames_tensor = torch.tensor(all_frames_tensor, dtype=torch.float32)
 
-  return all_frames_tensor  # Shape: [num_frames, num_players, num_features]
+    return all_frames_tensor  # Shape: [num_frames, num_players, num_features]
 
 
 @time_fcn
@@ -117,35 +116,52 @@ def main():
         set_time_decorators_enabled(False)
         logging.info("Timing decorators disabled")
 
+    # Set TensorFloat-32 (TF32) mode for matmul and cudnn (speeds up training on Ampere+ GPUs with minimal impact on accuracy)
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
     # 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)
+        config = json.load(file)
     model = ManZoneTransformer(
-        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)
+        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["multiplier"]),   # from ray tune or loaded
+        dropout=float(config["dropout"]),                                       # from ray tune or loaded
+        output_dim=2                                                            # man or zone classification
+    )
+    # Move model to device (GPU)
+    model = model.to(device)
+
+    # Compile with fullgraph
+    model = torch.compile(
+        model,
+        mode="default",        # default, reduces overhead, generally stable
+        fullgraph=True         # enable full graph fusion, helps reduce kernel launch overhead
+    )
+    
+    # Load model
     model.load_state_dict(torch.load(PROJECT_ROOT / "data" / "training" / "best_model.pth", map_location=device))
+
+    # Set to eval mode
     model.eval()
 
     # Load data
     rawLoader = RawDataLoader()
-    games_df, plays_df, players_df, location_data_df = rawLoader.get_data(weeks=[i for i in range(1, 10)])
+    _, plays_df, _, _ = rawLoader.get_data(weeks=list(range(1, 10)))
 
     # Process + predict one week at a time (keeps RAM low)
     for week_eval in [9]:
         week_df = process_week_data_preds(week_eval, plays_df)
 
-        # filter early to shrink memory
+        # Filtering early to shrink memory
         week_df = week_df[(week_df['club'] != 'football') & (week_df['passAttempt'] == 1)].copy()
 
-        # Polars convert optional; if you like the speed, keep it—otherwise skip to use pandas only
-        tracking_df_polars = pl.DataFrame(week_df)  # or comment this out and use pandas below
+        # Polars convert optional for speed
+        tracking_df_polars = pl.DataFrame(week_df)
 
         # Stream predictions to CSV in batches
         weekly_predictions_path_csv = SAVE_DIR / f"week{week_eval}_preds.csv"
diff --git a/src/train_transformer.py b/src/train_transformer.py
index 5bc5868..8cf7648 100644
--- a/src/train_transformer.py
+++ b/src/train_transformer.py
@@ -17,23 +17,24 @@
 import math
 from pathlib import Path
 import random
-import random
 import json
+import joblib
 
+# Util imports
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
+
+# Torch imports
 import torch
 import torch.nn as nn
 from torch.utils.data import TensorDataset, DataLoader
 from torch.optim import AdamW
+from torch.amp import autocast, GradScaler
 
 # 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
@@ -43,11 +44,13 @@
 from ray.tune import Tuner, RunConfig, TuneConfig, FailureConfig
 from ray.tune.schedulers import ASHAScheduler
 
+# Local imports
 from models.transformer import ManZoneTransformer
 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) -> torch.Generator:
     # Python & NumPy
     random.seed(seed)
@@ -75,51 +78,90 @@ def set_seed(seed: int = 42) -> torch.Generator:
 
 
 @time_fcn
-def train_epoch(train_loader: DataLoader, model, optimizer, loss_fn, device) -> float:
-        # Training
-        model.train()
-        running_loss = 0.0
-        for features, targets in train_loader:
-            features, targets = features.to(device), targets.to(device)
-            optimizer.zero_grad()
+def train_epoch(train_loader: DataLoader, model, optimizer, loss_fn, device, amp_dtype) -> float:
+    # Training
+    model.train()
+    running_loss = 0.0
+    for features, targets in train_loader:
+        
+        # Transfer from CPU to GPU, non_blocking for pinned memory
+        features = features.to(device, non_blocking=True)
+        targets = targets.to(device, non_blocking=True)
+
+        # Zero gradients
+        optimizer.zero_grad(set_to_none=True)
+
+        # For forward pass, allow mixed precision for speed in some operations
+        # NOTE: PyTorch keeps some sensitive ops in FP32 for stability
+        with autocast(device_type="cuda", dtype=amp_dtype):
             outputs = model(features)
             loss = loss_fn(outputs, targets)
-            loss.backward()
-            optimizer.step()
-            running_loss += loss.item() * features.size(0)
-        avg_train_loss = running_loss / len(train_loader.dataset)
 
-        # Return losses
-        return avg_train_loss
+        # Backpropagate and optimize
+        # NOTE: We don't want the gradients in less than FP32, PyTorch would handle this for us but being explicit
+        loss.backward()
+        optimizer.step()
+
+        # Record running loss
+        running_loss += loss.detach().item() * features.size(0)
+
+    # Record running metrics
+    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
-        correct = 0
-        with torch.no_grad():
-            for val_features_batch, val_targets_batch in val_loader:
-                val_features_batch, val_targets_batch = val_features_batch.to(device), val_targets_batch.to(device)
+def validate_epoch(val_loader: DataLoader, model, loss_fn, device, amp_dtype) -> tuple[float, float]:
+    # Validation
+    model.eval()
+    val_running_loss = 0.0
+    correct = 0
+    with torch.no_grad():
+        for val_features_batch, val_targets_batch in val_loader:
+
+            # Transfer from CPU to GPU, non_blocking for pinned memory
+            val_features_batch = val_features_batch.to(device, non_blocking=True)
+            val_targets_batch = val_targets_batch.to(device, non_blocking=True)
+
+            # AMP
+            with autocast(device_type="cuda", dtype=amp_dtype):
                 val_outputs = model(val_features_batch)
                 val_loss = loss_fn(val_outputs, val_targets_batch)
-                val_running_loss += val_loss.item() * val_features_batch.size(0)
                 _, predicted = torch.max(val_outputs, 1)
-                correct += (predicted == val_targets_batch).sum().item()
-        avg_val_loss = val_running_loss / len(val_loader.dataset)
-        val_accuracy = correct / len(val_loader.dataset)
 
-        # Return losses
-        return avg_val_loss, val_accuracy
+            # Grab metrics
+            val_running_loss += val_loss.item() * val_features_batch.size(0)
+            correct += (predicted == val_targets_batch).sum().item()
+
+    # Record running metrics
+    avg_val_loss = val_running_loss / len(val_loader.dataset)
+    val_accuracy = correct / len(val_loader.dataset)
+
+    # Return losses
+    return avg_val_loss, val_accuracy
 
 
 @time_fcn
-def train_trial(config, args):
+def run_trial(config, args):
 
     # Set seeds
     g = set_seed(42)
 
+    ######################################################################
+    # Set things that allow for speed-up in training (valid on Ampere+ GPUs)
+    ######################################################################
+    # NOTE: Importing torch here because Ray serializes the fcn, if we allow the global scope torch it fails in trying to serialize the global chain
+    import torch
+
+    # Use mixed precision with FP16 for speed for forward pass (see train_epoch)
+    amp_dtype = torch.float16 
+
+    # Set TensorFloat-32 (TF32) mode for matmul and cudnn (speeds up training on Ampere+ GPUs with minimal impact on accuracy)
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
     ######################################################################
     # Create model, loss, optimizer
     ######################################################################
@@ -131,13 +173,27 @@ def train_trial(config, args):
         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)
+        dim_feedforward=int(config["model_dim"]) * int(config["multiplier"]),   # from ray tune or loaded
+        dropout=float(config["dropout"]),                                       # from ray tune or loaded
         output_dim=2                                                            # man or zone classification
-    ).to(device)
-
+    )
+    # Move model to device (GPU)
+    model = model.to(device)
+
+    # Compile with fullgraph
+    model = torch.compile(
+        model,
+        mode="default",        # default, reduces overhead, generally stable
+        fullgraph=True         # enable full graph fusion, helps reduce kernel launch overhead
+    )
+    
     # Set optimizer and loss fcn
-    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
+    optimizer = torch.optim.AdamW(
+        model.parameters(),
+        lr=float(config["lr"]),
+        weight_decay=float(config["weight_decay"]),
+        fused=True,                                 # All operations on single CUDA kernel for speed
+    )
     loss_fn = nn.CrossEntropyLoss()
 
     ######################################################################
@@ -175,18 +231,22 @@ def train_trial(config, args):
     train_losses = []
     val_losses = []
     val_accuracies = []
-    early_stopping_patience = 5
+    early_stopping_patience = 20
     best_val_loss = float('inf')
     epochs_no_improve = 0
 
+    # If running in CI mode, reduce epochs for speed, we just want to ensure it can actually train, not train a whole model in testing here (for pipelines later)
+    if args.ci:
+        config["epochs"] = 5
+
     for epoch in range(int(config["epochs"])):
 
         # Train
-        avg_train_loss = train_epoch(train_loader, model, optimizer, loss_fn, device)
+        avg_train_loss = train_epoch(train_loader, model, optimizer, loss_fn, device, amp_dtype)
         train_losses.append(avg_train_loss)
 
         # Validate
-        avg_val_loss, val_accuracy = validate_epoch(val_loader, model, loss_fn, device)
+        avg_val_loss, val_accuracy = validate_epoch(val_loader, model, loss_fn, device, amp_dtype)
 
         # Info
         logging.info(f"Epoch [{epoch + 1}/{int(config['epochs'])}]")
@@ -197,29 +257,13 @@ def train_trial(config, args):
             # 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),
+                "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)
-        
+            tune.report(metrics)
         else:
             # Record metrics
             val_losses.append(avg_val_loss)
@@ -239,7 +283,7 @@ def train_trial(config, args):
 
 
 @time_fcn       
-def run_HPO(args):
+def run_hpo(args) -> None:
 
     ######################################################################
     # Start parent HPO, MLflow session
@@ -252,45 +296,46 @@ def run_HPO(args):
     ######################################################################
     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]) ,
+        "model_dim": tune.choice([32, 64, 96, 128]),
+        "num_heads": tune.choice([2, 4, 8]),
+        "num_layers": tune.choice([2, 3, 4, 6]),
+        "dropout": tune.choice([0.0, 0.1, 0.2, 0.3]),
+        "multiplier": tune.choice([2, 3, 4]),
 
         # Training
-        "batch_size": tune.choice([32, 64, 128]),
+        "lr": tune.loguniform(1e-5, 5e-3),
+        "weight_decay": tune.loguniform(1e-6, 1e-2),
+        "batch_size": 19200,        # Configured for L4 GPU utilization
 
-        # Epochs / checkpointing
-        "epochs": 50,
-        "checkpoint_freq": 10,
+        # Epochs
+        "epochs": 100,
     }
-
     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
+        grace_period=20,                            # Allow for 20 epochs before pruning (so each trial gets at least this many epochs)
         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
+    trainable = tune.with_parameters(run_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
+        tune.with_resources(trainable, resources={"cpu": 16, "gpu": 1}),    # Gives 16 CPU and one GPU per trial
         param_space=params,
-        tune_config=TuneConfig( 
+        tune_config=TuneConfig(
             metric="val_accuracy",
             mode="max",
             scheduler=scheduler,
-            num_samples=10,             # total trials
+            num_samples=1,                                                  # total trials, set to 1 for CI, modify here for HPO runs
         ),
         run_config=RunConfig(
             name="transformer_hpo",
-            storage_path=os.path.abspath("./log/ray_results"),
+            storage_path=os.path.abspath("./ray_results"),
             failure_config=FailureConfig(fail_fast=True),
             callbacks=[
                 MLflowLoggerCallback(
@@ -308,10 +353,16 @@ def run_HPO(args):
     results = tuner.fit()
     best = results.get_best_result(metric="val_accuracy", mode="max")
     print("Best config:", best.config)
+    
+    # Save file to best config
+    output_file = PROJECT_ROOT / "data" / "training" / "model_params.json"
+    with open(output_file, 'w') as json_file:
+        json.dump(best.config, json_file, indent=4) # indent for pretty-printing
 
 
 @time_fcn
-def main():
+def main() -> None:
+    """Main function to run HPO or single trial."""
 
     # Set logging
     logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -329,23 +380,23 @@ def main():
 
     # 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(
+            # Set the system config for the metrics exporter to pick a free port
+            _metrics_export_port=5001,
+            # Set the runtime to only bundle the src dir
             runtime_env={
                 "working_dir": str(script_dir),
-            }
+            },
         )
-        run_HPO(args)
+        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)
+        run_trial(model_params_map, args)
 
 
 if __name__ == "__main__":