From 9fcb726e5a7974b4bcc921e758e2060913c204e1 Mon Sep 17 00:00:00 2001
From: confoundry <107474190+confoundry@users.noreply.github.com>
Date: Tue, 27 Jun 2023 10:24:15 +0100
Subject: [PATCH] Release 0.3.3 (#49)

---
 examples/__init__.py                          |    0
 examples/csuite_example.ipynb                 |  458 ++++----
 .../multi_investment_sales_attribution.ipynb  |   51 +-
 poetry.lock                                   | 1040 +++++++++--------
 pyproject.toml                                |    7 +-
 .../config/lightning/default_data.yaml        |    2 +-
 .../config/lightning/default_gaussian.yaml    |    6 +-
 .../config/lightning/default_spline.yaml      |    6 +-
 .../datasets/causica_dataset_format.py        |   48 +-
 src/causica/distributions/__init__.py         |    1 -
 .../constrained_adjacency_distributions.py    |    6 +-
 .../adjacency/directed_acyclic.py             |    4 +-
 .../adjacency/gibbs_dag_prior.py              |    6 +-
 src/causica/distributions/noise/bernoulli.py  |    2 +-
 .../noise/spline/bayesiains_nsf_rqs.py        |   30 +-
 src/causica/distributions/transforms.py       |   77 +-
 .../functional_relationships/__init__.py      |    2 +-
 .../do_functional_relationships.py            |   11 +-
 .../functional_relationships.py               |   46 +-
 src/causica/functional_relationships/icgnn.py |   21 +-
 .../linear_functional_relationships.py        |   27 +-
 src/causica/lightning/callbacks.py            |   35 +-
 src/causica/lightning/cli.py                  |    4 +-
 src/causica/lightning/loggers.py              |   60 +
 src/causica/lightning/modules/deci_module.py  |   18 +-
 .../lightning/modules/variable_spec_module.py |   17 +-
 .../sem_distribution.py                       |    0
 src/causica/sem/structural_equation_model.py  |    8 +-
 src/causica/training/auglag.py                |   88 +-
 src/causica/training/training_callbacks.py    |   38 -
 .../adjacency/test_adjacency_distributions.py |    5 +-
 test/distributions/noise/test_joint.py        |   41 +-
 test/distributions/test_sem_distribution.py   |   10 +-
 .../test_functional_relationships.py          |    2 +-
 test/lightning/test_loggers.py                |   22 +
 test/sem/test_treatment_effects.py            |    1 -
 test/training/test_auglag.py                  |   23 +-
 test/training/test_training_callbacks.py      |   18 -
 38 files changed, 1191 insertions(+), 1050 deletions(-)
 delete mode 100644 examples/__init__.py
 create mode 100644 src/causica/lightning/loggers.py
 rename src/causica/{distributions => sem}/sem_distribution.py (100%)
 delete mode 100644 src/causica/training/training_callbacks.py
 create mode 100644 test/lightning/test_loggers.py
 delete mode 100644 test/training/test_training_callbacks.py

diff --git a/examples/__init__.py b/examples/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/examples/csuite_example.ipynb b/examples/csuite_example.ipynb
index df5bb74..62dd72b 100644
--- a/examples/csuite_example.ipynb
+++ b/examples/csuite_example.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -33,17 +34,18 @@
     "    ENCOAdjacencyDistributionModule,\n",
     "    GibbsDAGPrior,\n",
     "    JointNoiseModule,\n",
-    "    SEMDistributionModule,\n",
     "    create_noise_modules,\n",
     ")\n",
     "from causica.functional_relationships import ICGNN\n",
     "from causica.graph.dag_constraint import calculate_dagness\n",
+    "from causica.sem.sem_distribution import SEMDistributionModule\n",
     "from causica.training.auglag import AugLagLossCalculator, AugLagLR, AugLagLRConfig\n",
     "\n",
     "DATASET_PATH = \"https://azuastoragepublic.blob.core.windows.net/datasets\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -68,7 +70,7 @@
     "class TrainingConfig:\n",
     "    noise_dist: ContinuousNoiseDist = ContinuousNoiseDist.SPLINE\n",
     "    batch_size: int = 128\n",
-    "    max_epoch: int = int(os.environ.get(\"MAX_EPOCH\", 2000))  # used by testing to run the notebook as a script\n",
+    "    max_epoch: int = int(os.environ.get(\"TEST_RUN\", 2000))  # used by testing to run the notebook as a script\n",
     "    gumbel_temp: float = 0.25\n",
     "    averaging_period: int = 10\n",
     "    prior_sparsity_lambda: float = 5.0\n",
@@ -86,6 +88,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -118,6 +121,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -136,6 +140,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -152,6 +157,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -165,7 +171,7 @@
    "outputs": [],
    "source": [
     "icgnn = ICGNN(\n",
-    "    variables=tensordict_shapes(dataset_train),\n",
+    "    shapes=tensordict_shapes(dataset_train),\n",
     "    embedding_size=32,\n",
     "    out_dim_g=32,\n",
     "    norm_layer=torch.nn.LayerNorm,\n",
@@ -174,6 +180,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -195,6 +202,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -213,6 +221,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -239,6 +248,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -258,6 +268,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -276,235 +287,215 @@
    "execution_count": 11,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/joel/.cache/pypoetry/virtualenvs/causica-internal-E39znP0s-py3.8/lib/python3.8/site-packages/torch/distributions/distribution.py:45: UserWarning: <class 'causica.distributions.noise.joint.JointNoise'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
-      "  warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "epoch:0 loss:7.0666 nll:7.0575 dagness:1.08616 num_edges:5 alpha:0 rho:1 step:0|1 num_lr_updates:0\n",
-      "epoch:10 loss:1.8787 nll:1.8676 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:0|161 num_lr_updates:0\n",
-      "epoch:20 loss:1.1229 nll:1.1088 dagness:0.50418 num_edges:6 alpha:0 rho:1 step:0|321 num_lr_updates:0\n",
-      "epoch:30 loss:1.0539 nll:1.0448 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|481 num_lr_updates:0\n",
-      "epoch:40 loss:1.8009 nll:1.7915 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|641 num_lr_updates:0\n",
-      "epoch:50 loss:1.8899 nll:1.878 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:0|801 num_lr_updates:1\n",
+      "epoch:10 loss:1.8691 nll:1.8574 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:0|161 num_lr_updates:0\n",
+      "epoch:20 loss:1.1163 nll:1.102 dagness:1.63362 num_edges:6 alpha:0 rho:1 step:0|321 num_lr_updates:0\n",
+      "epoch:30 loss:1.1561 nll:1.149 dagness:0.00000 num_edges:3 alpha:0 rho:1 step:0|481 num_lr_updates:0\n",
+      "epoch:40 loss:1.2811 nll:1.2715 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|641 num_lr_updates:0\n",
+      "epoch:50 loss:1.4415 nll:1.4294 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:0|801 num_lr_updates:0\n",
+      "epoch:60 loss:1.08 nll:1.0678 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:0|961 num_lr_updates:0\n",
+      "epoch:70 loss:1.0654 nll:1.0557 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|1121 num_lr_updates:0\n",
+      "epoch:80 loss:1.1433 nll:1.1336 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|1281 num_lr_updates:0\n",
+      "epoch:90 loss:1.2957 nll:1.2859 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|1441 num_lr_updates:0\n",
+      "epoch:100 loss:1.4104 nll:1.4006 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|1601 num_lr_updates:0\n",
+      "epoch:110 loss:1.1757 nll:1.1658 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|1761 num_lr_updates:1\n",
+      "epoch:120 loss:1.1064 nll:1.0965 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|1921 num_lr_updates:1\n",
+      "epoch:130 loss:1.296 nll:1.2861 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|2081 num_lr_updates:1\n",
+      "epoch:140 loss:1.0077 nll:0.99777 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|2241 num_lr_updates:1\n",
+      "epoch:150 loss:1.3671 nll:1.3572 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|2401 num_lr_updates:1\n",
+      "epoch:160 loss:1.2566 nll:1.2467 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|2561 num_lr_updates:2\n",
+      "epoch:170 loss:1.1839 nll:1.174 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|2721 num_lr_updates:2\n",
+      "epoch:180 loss:1.3817 nll:1.3718 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:0|2881 num_lr_updates:2\n",
       "Updating alpha to: 0.0\n",
-      "epoch:60 loss:1.453 nll:1.4412 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:1|23 num_lr_updates:0\n",
-      "epoch:70 loss:1.5669 nll:1.5572 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|183 num_lr_updates:0\n",
-      "epoch:80 loss:1.605 nll:1.5928 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:1|343 num_lr_updates:0\n",
-      "epoch:90 loss:1.6326 nll:1.6228 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|503 num_lr_updates:0\n",
-      "epoch:100 loss:1.8064 nll:1.7968 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|663 num_lr_updates:0\n",
-      "epoch:110 loss:3.2911 nll:3.2814 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|823 num_lr_updates:0\n",
-      "epoch:120 loss:2.3284 nll:2.3186 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|983 num_lr_updates:1\n",
-      "epoch:130 loss:2.528 nll:2.5182 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|1143 num_lr_updates:1\n",
+      "epoch:190 loss:1.0483 nll:1.0384 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|120 num_lr_updates:0\n",
+      "epoch:200 loss:1.2778 nll:1.2679 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|280 num_lr_updates:0\n",
+      "epoch:210 loss:1.2488 nll:1.2391 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|440 num_lr_updates:0\n",
+      "epoch:220 loss:0.98975 nll:0.97988 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|600 num_lr_updates:1\n",
+      "epoch:230 loss:1.2537 nll:1.2413 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:1|760 num_lr_updates:1\n",
+      "epoch:240 loss:1.1325 nll:1.1226 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|920 num_lr_updates:1\n",
+      "epoch:250 loss:1.2526 nll:1.2427 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|1080 num_lr_updates:2\n",
+      "epoch:260 loss:1.0034 nll:0.99099 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:1|1240 num_lr_updates:2\n",
+      "epoch:270 loss:1.0245 nll:1.0147 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:1|1400 num_lr_updates:2\n",
       "Updating alpha to: 0.0\n",
-      "epoch:140 loss:2.2907 nll:2.2811 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|81 num_lr_updates:0\n",
-      "epoch:150 loss:3.2521 nll:3.24 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:2|241 num_lr_updates:0\n",
-      "epoch:160 loss:2.7455 nll:2.7358 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|401 num_lr_updates:0\n",
-      "epoch:170 loss:2.6453 nll:2.6356 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|561 num_lr_updates:1\n",
+      "epoch:280 loss:1.2746 nll:1.2648 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|59 num_lr_updates:0\n",
+      "epoch:290 loss:1.3697 nll:1.3598 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|219 num_lr_updates:0\n",
+      "epoch:300 loss:1.3922 nll:1.3822 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|379 num_lr_updates:0\n",
+      "epoch:310 loss:1.3235 nll:1.3135 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|539 num_lr_updates:1\n",
+      "epoch:320 loss:1.0165 nll:1.0065 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|699 num_lr_updates:1\n",
+      "epoch:330 loss:1.297 nll:1.287 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|859 num_lr_updates:1\n",
+      "epoch:340 loss:1.1976 nll:1.1876 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|1019 num_lr_updates:1\n",
+      "epoch:350 loss:1.1771 nll:1.1671 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|1179 num_lr_updates:1\n",
+      "epoch:360 loss:1.2251 nll:1.2151 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|1339 num_lr_updates:1\n",
+      "epoch:370 loss:1.2856 nll:1.2756 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:2|1499 num_lr_updates:2\n",
       "Updating alpha to: 0.0\n",
-      "epoch:180 loss:2.6657 nll:2.656 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|150 num_lr_updates:0\n",
-      "epoch:190 loss:2.3219 nll:2.3121 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|310 num_lr_updates:0\n",
-      "epoch:200 loss:2.6876 nll:2.6777 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|470 num_lr_updates:0\n",
-      "epoch:210 loss:2.7428 nll:2.7329 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|630 num_lr_updates:1\n",
+      "epoch:380 loss:1.2299 nll:1.2199 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|37 num_lr_updates:0\n",
+      "epoch:390 loss:1.445 nll:1.4351 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|197 num_lr_updates:0\n",
+      "epoch:400 loss:1.4194 nll:1.4094 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|357 num_lr_updates:0\n",
+      "epoch:410 loss:1.3013 nll:1.2913 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|517 num_lr_updates:0\n",
+      "epoch:420 loss:1.2598 nll:1.2499 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|677 num_lr_updates:0\n",
+      "epoch:430 loss:1.1204 nll:1.1104 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|837 num_lr_updates:0\n",
+      "epoch:440 loss:1.0382 nll:1.0282 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|997 num_lr_updates:1\n",
+      "epoch:450 loss:1.2114 nll:1.2014 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|1157 num_lr_updates:1\n",
+      "epoch:460 loss:1.0294 nll:1.0194 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|1317 num_lr_updates:1\n",
+      "epoch:470 loss:1.4288 nll:1.4188 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|1477 num_lr_updates:2\n",
+      "epoch:480 loss:1.0675 nll:1.0575 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:3|1637 num_lr_updates:2\n",
       "Updating alpha to: 0.0\n",
-      "epoch:220 loss:2.4358 nll:2.4259 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|131 num_lr_updates:0\n",
-      "epoch:230 loss:2.7663 nll:2.754 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:4|291 num_lr_updates:0\n",
-      "epoch:240 loss:2.7367 nll:2.7271 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|451 num_lr_updates:0\n",
-      "epoch:250 loss:2.6815 nll:2.6718 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|611 num_lr_updates:1\n",
-      "epoch:260 loss:2.4212 nll:2.409 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:4|771 num_lr_updates:1\n",
-      "epoch:270 loss:2.4044 nll:2.3922 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:4|931 num_lr_updates:1\n",
-      "epoch:280 loss:2.5659 nll:2.5562 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|1091 num_lr_updates:2\n",
-      "epoch:290 loss:2.6539 nll:2.6442 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|1251 num_lr_updates:2\n",
-      "epoch:300 loss:2.8798 nll:2.8701 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|1411 num_lr_updates:2\n",
+      "epoch:490 loss:1.0751 nll:1.0651 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|130 num_lr_updates:0\n",
+      "epoch:500 loss:1.0996 nll:1.0896 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|290 num_lr_updates:0\n",
+      "epoch:510 loss:1.1234 nll:1.1134 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|450 num_lr_updates:0\n",
+      "epoch:520 loss:1.0372 nll:1.0272 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|610 num_lr_updates:1\n",
+      "epoch:530 loss:1.321 nll:1.311 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|770 num_lr_updates:1\n",
+      "epoch:540 loss:1.1517 nll:1.1417 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|930 num_lr_updates:1\n",
+      "epoch:550 loss:1.1097 nll:1.0997 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|1090 num_lr_updates:1\n",
+      "epoch:560 loss:1.0469 nll:1.0369 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|1250 num_lr_updates:2\n",
+      "epoch:570 loss:1.2655 nll:1.2555 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|1410 num_lr_updates:2\n",
+      "epoch:580 loss:1.2546 nll:1.2446 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:4|1570 num_lr_updates:2\n",
       "Updating alpha to: 0.0\n",
-      "epoch:310 loss:3.663 nll:3.6508 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:5|70 num_lr_updates:0\n",
-      "epoch:320 loss:2.9153 nll:2.9055 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|230 num_lr_updates:0\n",
-      "epoch:330 loss:3.2 nll:3.1878 dagness:0.00000 num_edges:5 alpha:0 rho:1 step:5|390 num_lr_updates:0\n",
-      "epoch:340 loss:2.8881 nll:2.8783 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|550 num_lr_updates:1\n",
-      "epoch:350 loss:2.9219 nll:2.9122 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|710 num_lr_updates:1\n",
-      "epoch:360 loss:3.0444 nll:3.0346 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|870 num_lr_updates:1\n",
-      "epoch:370 loss:3.0787 nll:3.0689 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|1030 num_lr_updates:1\n",
-      "epoch:380 loss:2.8592 nll:2.8494 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|1190 num_lr_updates:1\n",
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+      "epoch:1770 loss:1.0643 nll:1.0543 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|18933 num_lr_updates:2\n",
+      "epoch:1780 loss:1.0757 nll:1.0657 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|19093 num_lr_updates:2\n",
+      "epoch:1790 loss:1.0231 nll:1.0132 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|19253 num_lr_updates:2\n",
+      "epoch:1800 loss:1.4205 nll:1.4105 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|19413 num_lr_updates:2\n",
+      "epoch:1810 loss:0.97389 nll:0.9639 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|19573 num_lr_updates:2\n",
+      "epoch:1820 loss:1.2062 nll:1.1962 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|19733 num_lr_updates:2\n",
+      "epoch:1830 loss:1.2867 nll:1.2767 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|19893 num_lr_updates:2\n",
+      "epoch:1840 loss:1.3977 nll:1.3877 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|20053 num_lr_updates:2\n",
+      "epoch:1850 loss:1.1351 nll:1.1251 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|20213 num_lr_updates:2\n",
+      "epoch:1860 loss:1.0939 nll:1.0839 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|20373 num_lr_updates:2\n",
+      "epoch:1870 loss:1.0453 nll:1.0353 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|20533 num_lr_updates:2\n",
+      "epoch:1880 loss:0.93997 nll:0.92997 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|20693 num_lr_updates:2\n",
+      "epoch:1890 loss:1.0821 nll:1.0721 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|20853 num_lr_updates:2\n",
+      "epoch:1900 loss:1.2242 nll:1.2142 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|21013 num_lr_updates:2\n",
+      "epoch:1910 loss:1.1396 nll:1.1296 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|21173 num_lr_updates:2\n",
+      "epoch:1920 loss:1.176 nll:1.166 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|21333 num_lr_updates:2\n",
+      "epoch:1930 loss:1.0077 nll:0.99775 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|21493 num_lr_updates:2\n",
+      "epoch:1940 loss:1.0978 nll:1.0878 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|21653 num_lr_updates:2\n",
+      "epoch:1950 loss:1.1629 nll:1.1529 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|21813 num_lr_updates:2\n",
+      "epoch:1960 loss:1.1092 nll:1.0992 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|21973 num_lr_updates:2\n",
+      "epoch:1970 loss:1.0646 nll:1.0546 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|22133 num_lr_updates:2\n",
+      "epoch:1980 loss:1.0023 nll:0.99228 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|22293 num_lr_updates:2\n",
+      "epoch:1990 loss:1.0706 nll:1.0607 dagness:0.00000 num_edges:4 alpha:0 rho:1 step:5|22453 num_lr_updates:2\n"
      ]
     }
    ],
@@ -533,8 +524,8 @@
     "        scheduler.step(\n",
     "            optimizer=optimizer,\n",
     "            loss=auglag_loss,\n",
-    "            loss_value=loss.item(),\n",
-    "            lagrangian_penalty=constraint.item(),\n",
+    "            loss_value=loss,\n",
+    "            lagrangian_penalty=constraint,\n",
     "        )\n",
     "        # log metrics\n",
     "        if epoch % 10 == 0 and i == 0:\n",
@@ -548,6 +539,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -561,7 +553,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -573,11 +565,12 @@
    "source": [
     "true_adj_matrix = load_data(root_path, DataEnum.TRUE_ADJACENCY)\n",
     "\n",
-    "true_graph = nx.from_numpy_matrix(true_adj_matrix.cpu().numpy(), create_using=nx.DiGraph)\n",
+    "true_graph = nx.from_numpy_array(true_adj_matrix.cpu().numpy(), create_using=nx.DiGraph)\n",
     "nx.draw_networkx(true_graph, pos=nx.planar_layout(true_graph), with_labels=True, arrows=True)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -591,7 +584,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -602,9 +595,16 @@
    ],
    "source": [
     "vardist = adjacency_dist()\n",
-    "graph = nx.from_numpy_matrix(vardist.mode.cpu().numpy(), create_using=nx.DiGraph)\n",
+    "graph = nx.from_numpy_array(vardist.mode.cpu().numpy(), create_using=nx.DiGraph)\n",
     "nx.draw_networkx(graph, pos=nx.planar_layout(true_graph), with_labels=True, arrows=True)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -623,7 +623,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/examples/multi_investment_sales_attribution.ipynb b/examples/multi_investment_sales_attribution.ipynb
index e62b9c2..f525e36 100644
--- a/examples/multi_investment_sales_attribution.ipynb
+++ b/examples/multi_investment_sales_attribution.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -20,6 +21,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -55,21 +57,19 @@
     "from pytorch_lightning.callbacks import TQDMProgressBar\n",
     "from tensordict import TensorDict\n",
     "\n",
-    "from causica.distributions import (\n",
-    "    ContinuousNoiseDist,\n",
-    "    SEMDistributionModule,\n",
-    ")\n",
+    "from causica.distributions import ContinuousNoiseDist\n",
     "from causica.lightning.data_modules.basic_data_module import BasicDECIDataModule\n",
     "from causica.lightning.modules.deci_module import DECIModule\n",
-    "from causica.sem.distribution_parameters_sem import DistributionParametersSEM\n",
+    "from causica.sem.sem_distribution import SEMDistributionModule\n",
     "from causica.sem.structural_equation_model import ite\n",
     "from causica.training.auglag import AugLagLRConfig\n",
     "\n",
     "warnings.filterwarnings(\"ignore\")\n",
-    "%matplotlib inline"
+    "test_run = bool(os.environ.get(\"TEST_RUN\", False))  # used by testing to run the notebook as a script"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -110,6 +110,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -384,6 +385,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -439,7 +441,11 @@
     "fig, axis = plt.subplots(1, 1, figsize=(8, 8))\n",
     "labels = {node: i for i, node in enumerate(true_adj.nodes)}\n",
     "\n",
-    "layout = nx.nx_agraph.graphviz_layout(true_adj, prog=\"dot\")\n",
+    "try:\n",
+    "    layout = nx.nx_agraph.graphviz_layout(true_adj, prog=\"dot\")\n",
+    "except (ModuleNotFoundError, ImportError):\n",
+    "    layout = nx.layout.spring_layout(true_adj)\n",
+    "\n",
     "for node, i in labels.items():\n",
     "    axis.scatter(layout[node][0], layout[node][1], label=f\"{i}: {node}\")\n",
     "axis.legend()\n",
@@ -447,6 +453,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -454,6 +461,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -478,6 +486,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -497,6 +506,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -523,6 +533,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -542,6 +553,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -553,6 +565,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -596,7 +609,8 @@
     "\n",
     "trainer = pl.Trainer(\n",
     "    accelerator=\"auto\",\n",
-    "    max_epochs=int(os.environ.get(\"MAX_EPOCH\", 2000)),  # used by testing to run the notebook as a script\n",
+    "    max_epochs=2000,\n",
+    "    fast_dev_run=test_run,\n",
     "    callbacks=[TQDMProgressBar(refresh_rate=19)],\n",
     "    enable_checkpointing=False,\n",
     ")"
@@ -668,6 +682,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -725,6 +740,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -733,6 +749,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -742,6 +759,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -769,7 +787,7 @@
    ],
    "source": [
     "revenue_estimated_ate = {}\n",
-    "num_samples = 20000\n",
+    "num_samples = 10 if test_run else 20000\n",
     "sample_shape = torch.Size([num_samples])\n",
     "transform = data_module.normalizer.transform_modules[outcome]().inv\n",
     "\n",
@@ -791,6 +809,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -825,6 +844,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -857,13 +877,14 @@
    "source": [
     "revenue_estimated_ite = {}\n",
     "\n",
+    "base_noise = sem.sample_to_noise(data_module.dataset_train)\n",
+    "\n",
     "for treatment in treatment_columns:\n",
-    "    base_noise = sem.sample_to_noise(data_module.dataset_train)\n",
-    "    intervention_a = TensorDict({treatment: torch.tensor([1.0])}, batch_size=tuple())\n",
-    "    do_a_cfs = transform(sem.do(interventions=intervention_a).noise_to_sample(base_noise)[outcome])\n",
-    "    intervention_b = TensorDict({treatment: torch.tensor([0.0])}, batch_size=tuple())\n",
-    "    do_b_cfs = transform(sem.do(interventions=intervention_b).noise_to_sample(base_noise)[outcome])\n",
-    "    revenue_estimated_ite[treatment] = (do_a_cfs - do_b_cfs).cpu().detach().numpy()[:, 0]\n",
+    "    do_sem = sem.do(interventions=TensorDict({treatment: torch.tensor([1.0])}, batch_size=tuple()))\n",
+    "    do_a_cfs = transform(do_sem.noise_to_sample(base_noise)[outcome]).cpu().detach().numpy()[:, 0]\n",
+    "    do_sem = sem.do(interventions=TensorDict({treatment: torch.tensor([0.0])}, batch_size=tuple()))\n",
+    "    do_b_cfs = transform(do_sem.noise_to_sample(base_noise)[outcome]).cpu().detach().numpy()[:, 0]\n",
+    "    revenue_estimated_ite[treatment] = do_a_cfs - do_b_cfs\n",
     "\n",
     "revenue_estimated_ite"
    ]
diff --git a/poetry.lock b/poetry.lock
index c69c337..85b34d3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.4.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -138,14 +138,14 @@ frozenlist = ">=1.1.0"
 
 [[package]]
 name = "alembic"
-version = "1.10.4"
+version = "1.11.1"
 description = "A database migration tool for SQLAlchemy."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "alembic-1.10.4-py3-none-any.whl", hash = "sha256:43942c3d4bf2620c466b91c0f4fca136fe51ae972394a0cc8b90810d664e4f5c"},
-    {file = "alembic-1.10.4.tar.gz", hash = "sha256:295b54bbb92c4008ab6a7dcd1e227e668416d6f84b98b3c4446a2bc6214a556b"},
+    {file = "alembic-1.11.1-py3-none-any.whl", hash = "sha256:dc871798a601fab38332e38d6ddb38d5e734f60034baeb8e2db5b642fccd8ab8"},
+    {file = "alembic-1.11.1.tar.gz", hash = "sha256:6a810a6b012c88b33458fceb869aef09ac75d6ace5291915ba7fae44de372c01"},
 ]
 
 [package.dependencies]
@@ -212,14 +212,14 @@ files = [
 
 [[package]]
 name = "azure-core"
-version = "1.26.4"
+version = "1.27.1"
 description = "Microsoft Azure Core Library for Python"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "azure-core-1.26.4.zip", hash = "sha256:075fe06b74c3007950dd93d49440c2f3430fd9b4a5a2756ec8c79454afc989c6"},
-    {file = "azure_core-1.26.4-py3-none-any.whl", hash = "sha256:d9664b4bc2675d72fba461a285ac43ae33abb2967014a955bf136d9703a2ab3c"},
+    {file = "azure-core-1.27.1.zip", hash = "sha256:5975c20808fa388243f01a8b79021bfbe114f503a27c543f002c5fc8bbdd73dd"},
+    {file = "azure_core-1.27.1-py3-none-any.whl", hash = "sha256:1b4b19f455eb7b4332c6f92adc2c669353ded07c2722eb436165f0c253737792"},
 ]
 
 [package.dependencies]
@@ -232,20 +232,20 @@ aio = ["aiohttp (>=3.0)"]
 
 [[package]]
 name = "azure-identity"
-version = "1.12.0"
+version = "1.13.0"
 description = "Microsoft Azure Identity Library for Python"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "azure-identity-1.12.0.zip", hash = "sha256:7f9b1ae7d97ea7af3f38dd09305e19ab81a1e16ab66ea186b6579d85c1ca2347"},
-    {file = "azure_identity-1.12.0-py3-none-any.whl", hash = "sha256:2a58ce4a209a013e37eaccfd5937570ab99e9118b3e1acf875eed3a85d541b92"},
+    {file = "azure-identity-1.13.0.zip", hash = "sha256:c931c27301ffa86b07b4dcf574e29da73e3deba9ab5d1fe4f445bb6a3117e260"},
+    {file = "azure_identity-1.13.0-py3-none-any.whl", hash = "sha256:bd700cebb80cd9862098587c29d8677e819beca33c62568ced6d5a8e5e332b82"},
 ]
 
 [package.dependencies]
 azure-core = ">=1.11.0,<2.0.0"
 cryptography = ">=2.5"
-msal = ">=1.12.0,<2.0.0"
+msal = ">=1.20.0,<2.0.0"
 msal-extensions = ">=0.3.0,<2.0.0"
 six = ">=1.12.0"
 
@@ -283,13 +283,13 @@ msrest = ">=0.6.21"
 
 [[package]]
 name = "azureml-mlflow"
-version = "1.50.0"
+version = "1.51.0"
 description = "Contains the integration code of AzureML with Mlflow."
 category = "main"
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
-    {file = "azureml_mlflow-1.50.0-py3-none-any.whl", hash = "sha256:ef8de12e8c6bb606aeb011ea326851f425d3a02593f94b8c7065ffb0dd1a7f4f"},
+    {file = "azureml_mlflow-1.51.0-py3-none-any.whl", hash = "sha256:69161d709dcfdcb7b4c6aa59ad2d4c37c807b149d8be9dc11754ea5ac27af472"},
 ]
 
 [package.dependencies]
@@ -321,26 +321,26 @@ files = [
 
 [[package]]
 name = "cachetools"
-version = "5.3.0"
+version = "5.3.1"
 description = "Extensible memoizing collections and decorators"
 category = "main"
 optional = false
-python-versions = "~=3.7"
+python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.3.0-py3-none-any.whl", hash = "sha256:429e1a1e845c008ea6c85aa35d4b98b65d6a9763eeef3e37e92728a12d1de9d4"},
-    {file = "cachetools-5.3.0.tar.gz", hash = "sha256:13dfddc7b8df938c21a940dfa6557ce6e94a2f1cdfa58eb90c805721d58f2c14"},
+    {file = "cachetools-5.3.1-py3-none-any.whl", hash = "sha256:95ef631eeaea14ba2e36f06437f36463aac3a096799e876ee55e5cdccb102590"},
+    {file = "cachetools-5.3.1.tar.gz", hash = "sha256:dce83f2d9b4e1f732a8cd44af8e8fab2dbe46201467fc98b3ef8f269092bf62b"},
 ]
 
 [[package]]
 name = "certifi"
-version = "2022.12.7"
+version = "2023.5.7"
 description = "Python package for providing Mozilla's CA Bundle."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2022.12.7-py3-none-any.whl", hash = "sha256:4ad3232f5e926d6718ec31cfc1fcadfde020920e278684144551c91769c7bc18"},
-    {file = "certifi-2022.12.7.tar.gz", hash = "sha256:35824b4c3a97115964b408844d64aa14db1cc518f6562e8d7261699d1350a9e3"},
+    {file = "certifi-2023.5.7-py3-none-any.whl", hash = "sha256:c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716"},
+    {file = "certifi-2023.5.7.tar.gz", hash = "sha256:0f0d56dc5a6ad56fd4ba36484d6cc34451e1c6548c61daad8c320169f91eddc7"},
 ]
 
 [[package]]
@@ -534,33 +534,33 @@ files = [
 
 [[package]]
 name = "cmake"
-version = "3.26.3"
+version = "3.26.4"
 description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
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 [package.dependencies]
 numpy = ">=1.16"
 
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 ]
 
 [package.dependencies]
@@ -718,31 +711,31 @@ toml = ["tomli"]
 
 [[package]]
 name = "cryptography"
-version = "40.0.2"
+version = "41.0.1"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -751,12 +744,12 @@ cffi = ">=1.12"
 [package.extras]
 docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"]
 docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
-pep8test = ["black", "check-manifest", "mypy", "ruff"]
-sdist = ["setuptools-rust (>=0.11.4)"]
+nox = ["nox"]
+pep8test = ["black", "check-sdist", "mypy", "ruff"]
+sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["iso8601", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-shard (>=0.1.2)", "pytest-subtests", "pytest-xdist"]
+test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
-tox = ["tox"]
 
 [[package]]
 name = "cycler"
@@ -772,14 +765,14 @@ files = [
 
 [[package]]
 name = "databricks-cli"
-version = "0.17.6"
+version = "0.17.7"
 description = "A command line interface for Databricks"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "databricks-cli-0.17.6.tar.gz", hash = "sha256:7fea8b4e47ac38bd4eaad8a76e38a6916419df930ad1c615a6b43feb427672c4"},
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+    {file = "databricks-cli-0.17.7.tar.gz", hash = "sha256:5a545063449f3b9ad904644c0f251058485e29e564dedf8d4e4a7b45caf9549b"},
+    {file = "databricks_cli-0.17.7-py2-none-any.whl", hash = "sha256:5b025943c70bbd374415264d38bfaddfb34ce070fadb083d851aec311e0f8901"},
 ]
 
 [package.dependencies]
@@ -789,17 +782,18 @@ pyjwt = ">=1.7.0"
 requests = ">=2.17.3"
 six = ">=1.10.0"
 tabulate = ">=0.7.7"
+urllib3 = ">=1.26.7,<2.0.0"
 
 [[package]]
 name = "dataclasses-json"
-version = "0.5.7"
+version = "0.5.8"
 description = "Easily serialize dataclasses to and from JSON"
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "dataclasses-json-0.5.7.tar.gz", hash = "sha256:c2c11bc8214fbf709ffc369d11446ff6945254a7f09128154a7620613d8fda90"},
-    {file = "dataclasses_json-0.5.7-py3-none-any.whl", hash = "sha256:bc285b5f892094c3a53d558858a88553dd6a61a11ab1a8128a0e554385dcc5dd"},
+    {file = "dataclasses-json-0.5.8.tar.gz", hash = "sha256:6572ac08ad9340abcb74fd8c4c8e9752db2a182a402c8e871d0a8aa119e3804e"},
+    {file = "dataclasses_json-0.5.8-py3-none-any.whl", hash = "sha256:65b167c15fdf9bde27569c09ac18dd39bf1cc5b7998525024cb4678d2653946c"},
 ]
 
 [package.dependencies]
@@ -808,18 +802,18 @@ marshmallow-enum = ">=1.5.1,<2.0.0"
 typing-inspect = ">=0.4.0"
 
 [package.extras]
-dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (>=6.2.3)", "simplejson", "types-dataclasses"]
+dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest (>=7.2.0)", "simplejson", "types-dataclasses"]
 
 [[package]]
 name = "docker"
-version = "6.0.1"
+version = "6.1.3"
 description = "A Python library for the Docker Engine API."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "docker-6.0.1-py3-none-any.whl", hash = "sha256:dbcb3bd2fa80dca0788ed908218bf43972772009b881ed1e20dfc29a65e49782"},
-    {file = "docker-6.0.1.tar.gz", hash = "sha256:896c4282e5c7af5c45e8b683b0b0c33932974fe6e50fc6906a0a83616ab3da97"},
+    {file = "docker-6.1.3-py3-none-any.whl", hash = "sha256:aecd2277b8bf8e506e484f6ab7aec39abe0038e29fa4a6d3ba86c3fe01844ed9"},
+    {file = "docker-6.1.3.tar.gz", hash = "sha256:aa6d17830045ba5ef0168d5eaa34d37beeb113948c413affe1d5991fc11f9a20"},
 ]
 
 [package.dependencies]
@@ -873,30 +867,30 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.12.0"
+version = "3.12.2"
 description = "A platform independent file lock."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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+docs = ["furo (>=2023.5.20)", "sphinx (>=7.0.1)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
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 [[package]]
 name = "flask"
-version = "2.3.1"
+version = "2.3.2"
 description = "A simple framework for building complex web applications."
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -904,7 +898,7 @@ blinker = ">=1.6.2"
 click = ">=8.1.3"
 itsdangerous = ">=2.1.2"
 Jinja2 = ">=3.1.2"
-Werkzeug = ">=2.3.0"
+Werkzeug = ">=2.3.3"
 
 [package.extras]
 async = ["asgiref (>=3.2)"]
@@ -912,14 +906,46 @@ dotenv = ["python-dotenv"]
 
 [[package]]
 name = "fonttools"
-version = "4.39.3"
+version = "4.40.0"
 description = "Tools to manipulate font files"
 category = "main"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.extras]
@@ -1022,14 +1048,14 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.4.0"
+version = "2023.6.0"
 description = "File-system specification"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1092,28 +1118,29 @@ gitdb = ">=4.0.1,<5"
 
 [[package]]
 name = "google-auth"
-version = "2.17.3"
+version = "2.20.0"
 description = "Google Authentication Library"
 category = "main"
 optional = false
-python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*"
+python-versions = ">=3.6"
 files = [
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 [package.dependencies]
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 pyasn1-modules = ">=0.2.1"
-rsa = {version = ">=3.1.4,<5", markers = "python_version >= \"3.6\""}
+rsa = ">=3.1.4,<5"
 six = ">=1.9.0"
+urllib3 = "<2.0"
 
 [package.extras]
-aiohttp = ["aiohttp (>=3.6.2,<4.0.0dev)", "requests (>=2.20.0,<3.0.0dev)"]
+aiohttp = ["aiohttp (>=3.6.2,<4.0.0.dev0)", "requests (>=2.20.0,<3.0.0.dev0)"]
 enterprise-cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"]
 pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
 reauth = ["pyu2f (>=0.1.5)"]
-requests = ["requests (>=2.20.0,<3.0.0dev)"]
+requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-auth-oauthlib"
@@ -1210,61 +1237,61 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "grpcio"
-version = "1.54.0"
+version = "1.56.0"
 description = "HTTP/2-based RPC framework"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 name = "gunicorn"
@@ -1318,14 +1345,14 @@ files = [
 
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 [package.dependencies]
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 [[package]]
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 [[package]]
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 [[package]]
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 [[package]]
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 [package.dependencies]
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 profiling = ["gprof2dot"]
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 testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
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 [[package]]
@@ -1857,14 +1884,14 @@ files = [
 
 [[package]]
 name = "mlflow"
-version = "2.3.1"
+version = "2.4.1"
 description = "MLflow: A Platform for ML Development and Productionization"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1888,7 +1915,7 @@ numpy = "<2"
 packaging = "<24"
 pandas = "<3"
 protobuf = ">=3.12.0,<5"
-pyarrow = ">=4.0.0,<12"
+pyarrow = ">=4.0.0,<13"
 pytz = "<2024"
 pyyaml = ">=5.1,<7"
 querystring-parser = "<2"
@@ -1903,17 +1930,19 @@ waitress = {version = "<3", markers = "platform_system == \"Windows\""}
 aliyun-oss = ["aliyunstoreplugin"]
 databricks = ["azure-storage-file-datalake (>12)", "boto3 (>1)", "google-cloud-storage (>=1.30.0)"]
 extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0,!=1.3.1)", "mlserver-mlflow (>=1.2.0,!=1.3.1)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "virtualenv"]
+gateway = ["fastapi (<1)", "pydantic (>=1.0,<3)", "uvicorn (<1)"]
 sqlserver = ["mlflow-dbstore"]
 
 [[package]]
 name = "mlflow-skinny"
-version = "2.3.1"
+version = "2.4.1"
 description = "MLflow: A Platform for ML Development and Productionization"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1934,6 +1963,7 @@ sqlparse = ">=0.4.0,<1"
 aliyun-oss = ["aliyunstoreplugin"]
 databricks = ["azure-storage-file-datalake (>12)", "boto3 (>1)", "google-cloud-storage (>=1.30.0)"]
 extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0,!=1.3.1)", "mlserver-mlflow (>=1.2.0,!=1.3.1)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "virtualenv"]
+gateway = ["fastapi (<1)", "pydantic (>=1.0,<3)", "uvicorn (<1)"]
 sqlserver = ["mlflow-dbstore"]
 
 [[package]]
@@ -2552,14 +2582,14 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 
 [[package]]
 name = "pluggy"
-version = "1.0.0"
+version = "1.2.0"
 description = "plugin and hook calling mechanisms for python"
 category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -2588,60 +2618,60 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
 
 [[package]]
 name = "protobuf"
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+version = "4.23.3"
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 category = "main"
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 python-versions = ">=3.7"
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 [[package]]
 name = "pyarrow"
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 description = "Python library for Apache Arrow"
 category = "main"
 optional = false
 python-versions = ">=3.7"
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 ]
 
 [package.dependencies]
@@ -2703,14 +2733,14 @@ plugins = ["importlib-metadata"]
 
 [[package]]
 name = "pyjwt"
-version = "2.6.0"
+version = "2.7.0"
 description = "JSON Web Token implementation in Python"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2724,14 +2754,14 @@ tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.0.9"
+version = "3.1.0"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 category = "main"
 optional = false
 python-versions = ">=3.6.8"
 files = [
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 [package.extras]
@@ -2739,14 +2769,14 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "7.3.1"
+version = "7.4.0"
 description = "pytest: simple powerful testing with Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2758,7 +2788,7 @@ pluggy = ">=0.12,<2.0"
 tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
+testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-cov"
@@ -2939,21 +2969,21 @@ six = "*"
 
 [[package]]
 name = "requests"
-version = "2.29.0"
+version = "2.31.0"
 description = "Python HTTP for Humans."
 category = "main"
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 python-versions = ">=3.7"
 files = [
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 certifi = ">=2017.4.17"
 charset-normalizer = ">=2,<4"
 idna = ">=2.5,<4"
-urllib3 = ">=1.21.1,<1.27"
+urllib3 = ">=1.21.1,<3"
 
 [package.extras]
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
@@ -2980,18 +3010,18 @@ rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
 
 [[package]]
 name = "rich"
-version = "13.3.5"
+version = "13.4.2"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
 category = "main"
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 [package.dependencies]
-markdown-it-py = ">=2.2.0,<3.0.0"
+markdown-it-py = ">=2.2.0"
 pygments = ">=2.13.0,<3.0.0"
 
 [package.extras]
@@ -3057,58 +3087,56 @@ tests = ["black (>=22.3.0)", "flake8 (>=3.8.2)", "matplotlib (>=3.1.3)", "mypy (
 
 [[package]]
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+version = "1.11.0"
 description = "Fundamental algorithms for scientific computing in Python"
 category = "main"
 optional = false
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 [package.dependencies]
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+numpy = ">=1.21.6,<1.28.0"
 
 [package.extras]
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-doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
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 test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "setuptools"
-version = "67.7.2"
+version = "68.0.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.extras]
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 [[package]]
@@ -3137,53 +3165,53 @@ files = [
 
 [[package]]
 name = "sqlalchemy"
-version = "2.0.11"
+version = "2.0.17"
 description = "Database Abstraction Library"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
@@ -3210,6 +3238,7 @@ postgresql-pg8000 = ["pg8000 (>=1.29.1)"]
 postgresql-psycopg = ["psycopg (>=3.0.7)"]
 postgresql-psycopg2binary = ["psycopg2-binary"]
 postgresql-psycopg2cffi = ["psycopg2cffi"]
+postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3-binary"]
 
@@ -3232,14 +3261,14 @@ test = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "sympy"
-version = "1.11.1"
+version = "1.12"
 description = "Computer algebra system (CAS) in Python"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3262,13 +3291,13 @@ widechars = ["wcwidth"]
 
 [[package]]
 name = "tensorboard"
-version = "2.12.2"
+version = "2.13.0"
 description = "TensorBoard lets you watch Tensors Flow"
 category = "main"
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 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -3282,60 +3311,48 @@ protobuf = ">=3.19.6"
 requests = ">=2.21.0,<3"
 setuptools = ">=41.0.0"
 tensorboard-data-server = ">=0.7.0,<0.8.0"
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 werkzeug = ">=1.0.1"
 wheel = ">=0.26"
 
 [[package]]
 name = "tensorboard-data-server"
-version = "0.7.0"
+version = "0.7.1"
 description = "Fast data loading for TensorBoard"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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-
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-description = "What-If Tool TensorBoard plugin."
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-python-versions = "*"
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 ]
 
 [[package]]
 name = "tensorboardx"
-version = "2.5"
+version = "2.6.1"
 description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
 numpy = "*"
-protobuf = ">=3.8.0"
-six = "*"
+packaging = "*"
+protobuf = ">=4.22.3"
 
 [[package]]
 name = "tensordict"
-version = "0.1.0"
+version = "0.1.2"
 description = ""
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "tensordict-0.1.0-py3-none-any.whl", hash = "sha256:25ab7bcda69dae77186b75b2da279763ef1d8fdcb676a5b9098433ff3e863150"},
+    {file = "tensordict-0.1.2-py3-none-any.whl", hash = "sha256:09723530487cc223a15baeebcf5bc15f7be7027849386f4c36c8df8df9a6de5e"},
 ]
 
 [package.dependencies]
@@ -3345,7 +3362,8 @@ torch = "*"
 
 [package.extras]
 checkpointing = ["torchsnapshot-nightly"]
-tests = ["pytest", "pytest-instafail", "pytest-rerunfailures", "pyyaml"]
+h5 = ["h5py (>=3.8)"]
+tests = ["pytest", "pytest-benchmark", "pytest-instafail", "pytest-rerunfailures", "pyyaml"]
 
 [[package]]
 name = "threadpoolctl"
@@ -3514,26 +3532,26 @@ tutorials = ["matplotlib", "pandas", "tabulate"]
 
 [[package]]
 name = "types-pyyaml"
-version = "6.0.12.9"
+version = "6.0.12.10"
 description = "Typing stubs for PyYAML"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "types-PyYAML-6.0.12.9.tar.gz", hash = "sha256:c51b1bd6d99ddf0aa2884a7a328810ebf70a4262c292195d3f4f9a0005f9eeb6"},
-    {file = "types_PyYAML-6.0.12.9-py3-none-any.whl", hash = "sha256:5aed5aa66bd2d2e158f75dda22b059570ede988559f030cf294871d3b647e3e8"},
+    {file = "types-PyYAML-6.0.12.10.tar.gz", hash = "sha256:ebab3d0700b946553724ae6ca636ea932c1b0868701d4af121630e78d695fc97"},
+    {file = "types_PyYAML-6.0.12.10-py3-none-any.whl", hash = "sha256:662fa444963eff9b68120d70cda1af5a5f2aa57900003c2006d7626450eaae5f"},
 ]
 
 [[package]]
 name = "typeshed-client"
-version = "2.2.0"
+version = "2.3.0"
 description = "A library for accessing stubs in typeshed."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "typeshed_client-2.2.0-py3-none-any.whl", hash = "sha256:6f5a5841a0cf664ac66ea05f1ed3372a191d307b146f8b9aaa09719d1156626e"},
-    {file = "typeshed_client-2.2.0.tar.gz", hash = "sha256:57baebdd5312486e438393e2971043d487c0c331404b73fe72d1f22ba0a5992d"},
+    {file = "typeshed_client-2.3.0-py3-none-any.whl", hash = "sha256:27976287e0682be3005233f08f6f3cedf0c496413bcb213378f46e08798988c4"},
+    {file = "typeshed_client-2.3.0.tar.gz", hash = "sha256:e2734755f93a0b6c1da8daad11670c75a36583d889d4d7b110eb882765b8bced"},
 ]
 
 [package.dependencies]
@@ -3541,26 +3559,26 @@ importlib-resources = ">=1.4.0"
 
 [[package]]
 name = "typing-extensions"
-version = "4.5.0"
+version = "4.6.3"
 description = "Backported and Experimental Type Hints for Python 3.7+"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "typing_extensions-4.5.0-py3-none-any.whl", hash = "sha256:fb33085c39dd998ac16d1431ebc293a8b3eedd00fd4a32de0ff79002c19511b4"},
-    {file = "typing_extensions-4.5.0.tar.gz", hash = "sha256:5cb5f4a79139d699607b3ef622a1dedafa84e115ab0024e0d9c044a9479ca7cb"},
+    {file = "typing_extensions-4.6.3-py3-none-any.whl", hash = "sha256:88a4153d8505aabbb4e13aacb7c486c2b4a33ca3b3f807914a9b4c844c471c26"},
+    {file = "typing_extensions-4.6.3.tar.gz", hash = "sha256:d91d5919357fe7f681a9f2b5b4cb2a5f1ef0a1e9f59c4d8ff0d3491e05c0ffd5"},
 ]
 
 [[package]]
 name = "typing-inspect"
-version = "0.8.0"
+version = "0.9.0"
 description = "Runtime inspection utilities for typing module."
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "typing_inspect-0.8.0-py3-none-any.whl", hash = "sha256:5fbf9c1e65d4fa01e701fe12a5bca6c6e08a4ffd5bc60bfac028253a447c5188"},
-    {file = "typing_inspect-0.8.0.tar.gz", hash = "sha256:8b1ff0c400943b6145df8119c41c244ca8207f1f10c9c057aeed1560e4806e3d"},
+    {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
+    {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
 ]
 
 [package.dependencies]
@@ -3569,14 +3587,14 @@ typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "urllib3"
-version = "1.26.15"
+version = "1.26.16"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
 files = [
-    {file = "urllib3-1.26.15-py2.py3-none-any.whl", hash = "sha256:aa751d169e23c7479ce47a0cb0da579e3ede798f994f5816a74e4f4500dcea42"},
-    {file = "urllib3-1.26.15.tar.gz", hash = "sha256:8a388717b9476f934a21484e8c8e61875ab60644d29b9b39e11e4b9dc1c6b305"},
+    {file = "urllib3-1.26.16-py2.py3-none-any.whl", hash = "sha256:8d36afa7616d8ab714608411b4a3b13e58f463aee519024578e062e141dce20f"},
+    {file = "urllib3-1.26.16.tar.gz", hash = "sha256:8f135f6502756bde6b2a9b28989df5fbe87c9970cecaa69041edcce7f0589b14"},
 ]
 
 [package.extras]
@@ -3602,14 +3620,14 @@ testing = ["coverage (>=5.0)", "pytest", "pytest-cover"]
 
 [[package]]
 name = "websocket-client"
-version = "1.5.1"
+version = "1.6.1"
 description = "WebSocket client for Python with low level API options"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "websocket-client-1.5.1.tar.gz", hash = "sha256:3f09e6d8230892547132177f575a4e3e73cfdf06526e20cc02aa1c3b47184d40"},
-    {file = "websocket_client-1.5.1-py3-none-any.whl", hash = "sha256:cdf5877568b7e83aa7cf2244ab56a3213de587bbe0ce9d8b9600fc77b455d89e"},
+    {file = "websocket-client-1.6.1.tar.gz", hash = "sha256:c951af98631d24f8df89ab1019fc365f2227c0892f12fd150e935607c79dd0dd"},
+    {file = "websocket_client-1.6.1-py3-none-any.whl", hash = "sha256:f1f9f2ad5291f0225a49efad77abf9e700b6fef553900623060dad6e26503b9d"},
 ]
 
 [package.extras]
@@ -3619,14 +3637,14 @@ test = ["websockets"]
 
 [[package]]
 name = "werkzeug"
-version = "2.3.1"
+version = "2.3.6"
 description = "The comprehensive WSGI web application library."
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "Werkzeug-2.3.1-py3-none-any.whl", hash = "sha256:69a4b8fcbb30a4c3fa81a5cebd961541273e4d222a4c08593b0b18312e14f64a"},
-    {file = "Werkzeug-2.3.1.tar.gz", hash = "sha256:2d35a28a75ae03727eae14ea7d13627c0f77aed6b9998441e6dae0b387f697fc"},
+    {file = "Werkzeug-2.3.6-py3-none-any.whl", hash = "sha256:935539fa1413afbb9195b24880778422ed620c0fc09670945185cce4d91a8890"},
+    {file = "Werkzeug-2.3.6.tar.gz", hash = "sha256:98c774df2f91b05550078891dee5f0eb0cb797a522c757a2452b9cee5b202330"},
 ]
 
 [package.dependencies]
@@ -3757,4 +3775,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
 [metadata]
 lock-version = "2.0"
 python-versions = "~3.10"
-content-hash = "4e75da1d01bae2bd1675a51b22aa54241002acfc7634aef13e7b2f039d6154ed"
+content-hash = "155fa74e9e6d900d908ec85d3951fd04d594a03af259a60312f2f9506b1c4de9"
diff --git a/pyproject.toml b/pyproject.toml
index 536d619..99d60a6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,12 +1,11 @@
 [tool.poetry]
 name = "causica"
-version = "0.3.2"
+version = "0.3.3"
 description = ""
 readme = "README.md"
 authors = []
 packages = [
-    { include = "causica", from = "src" },
-    { include = "examples", from = "." },
+    { include = "causica", from = "src" }
 ]
 license = "MIT"
 
@@ -22,7 +21,7 @@ jsonargparse = "<4.21.0"  # 4.21.0 breaks lightning cli
 dataclasses-json = "^0.5.7"
 types-PyYAML = "^6.0.12.2"
 tensordict = "^0.1.0"
-torch = "^2.0.0"
+torch = "2.0.0"
 numba = "^0.56.0"  # needed to make the build work
 
 [tool.poetry.dev-dependencies]
diff --git a/src/causica/config/lightning/default_data.yaml b/src/causica/config/lightning/default_data.yaml
index 1c46fed..58a0e9b 100644
--- a/src/causica/config/lightning/default_data.yaml
+++ b/src/causica/config/lightning/default_data.yaml
@@ -1,4 +1,4 @@
-class_path: causica.lightning.data_modules.CSuiteDataModule
+class_path: causica.lightning.data_modules.variable_spec_data.CSuiteDataModule
 init_args:
   dataset_name: csuite_nonlingauss
   batch_size: 128
diff --git a/src/causica/config/lightning/default_gaussian.yaml b/src/causica/config/lightning/default_gaussian.yaml
index 0e5f5df..ff2c42e 100644
--- a/src/causica/config/lightning/default_gaussian.yaml
+++ b/src/causica/config/lightning/default_gaussian.yaml
@@ -1,6 +1,6 @@
 seed_everything: 234
 model:
-  class_path: causica.lightning.deci_module.DECIModule
+  class_path: causica.lightning.modules.deci_module.DECIModule
   init_args:
     noise_dist: "GAUSSIAN"
     embedding_size: 32
@@ -39,8 +39,8 @@ best_checkpoint_callback:
   filename: "best_model"
   save_top_k: 1
   mode: "max"
-  monitor: "average_batch_log_prob"
-  every_n_train_steps: 1
+  monitor: "batch_log_prob"
+  every_n_epochs: 1
 last_checkpoint_callback:
   save_last: true
   filename: "last_model"
diff --git a/src/causica/config/lightning/default_spline.yaml b/src/causica/config/lightning/default_spline.yaml
index 4fb784e..73a1ec2 100644
--- a/src/causica/config/lightning/default_spline.yaml
+++ b/src/causica/config/lightning/default_spline.yaml
@@ -1,6 +1,6 @@
 seed_everything: 234
 model:
-  class_path: causica.lightning.deci_module.DECIModule
+  class_path: causica.lightning.modules.deci_module.DECIModule
   init_args:
     noise_dist: "SPLINE"
     embedding_size: 32
@@ -39,8 +39,8 @@ best_checkpoint_callback:
   filename: "best_model"
   save_top_k: 1
   mode: "max"
-  monitor: "average_batch_log_prob"
-  every_n_train_steps: 1
+  monitor: "batch_log_prob"
+  every_n_epochs: 1
 last_checkpoint_callback:
   save_last: true
   filename: "last_model"
diff --git a/src/causica/datasets/causica_dataset_format.py b/src/causica/datasets/causica_dataset_format.py
index e1bc446..435542a 100644
--- a/src/causica/datasets/causica_dataset_format.py
+++ b/src/causica/datasets/causica_dataset_format.py
@@ -73,7 +73,7 @@ def load_data(
 
     fsspec_open = partial(fsspec.open, mode="r", encoding="utf-8", **storage_options)
 
-    logger.debug(f"Loading {data_enum} from {path_name} with storage options {storage_options}")
+    logger.debug("Loading %s from %s with storage options %s", data_enum, path_name, storage_options)
 
     if data_enum == DataEnum.TRUE_ADJACENCY:
         with fsspec_open(path_name) as f:
@@ -89,19 +89,20 @@ def load_data(
         variables_metadata = load_data(root_path, data_enum=DataEnum.VARIABLES_JSON)
 
     with fsspec_open(path_name) as f:
-        if data_enum in {DataEnum.TRAIN, DataEnum.TEST}:
-            arr = np.loadtxt(f, delimiter=",")
-            categorical_sizes = _get_categorical_sizes(variables_list=variables_metadata["variables"])
-            return convert_one_hot(
-                tensordict_from_variables_metadata(arr, variables_metadata["variables"]),
-                one_hot_sizes=categorical_sizes,
-            )
-        elif data_enum == DataEnum.INTERVENTIONS:
-            return _load_interventions(json_object=json.load(f), metadata=variables_metadata)
-        elif data_enum == DataEnum.COUNTERFACTUALS:
-            return _load_counterfactuals(json_object=json.load(f), metadata=variables_metadata)
-        else:
-            raise RuntimeError("Unrecognized data type")
+        match data_enum:
+            case (DataEnum.TRAIN | DataEnum.TEST):
+                arr = np.loadtxt(f, delimiter=",")
+                categorical_sizes = _get_categorical_sizes(variables_list=variables_metadata["variables"])
+                return convert_one_hot(
+                    tensordict_from_variables_metadata(arr, variables_metadata["variables"]),
+                    one_hot_sizes=categorical_sizes,
+                )
+            case DataEnum.INTERVENTIONS:
+                return _load_interventions(json_object=json.load(f), metadata=variables_metadata)
+            case DataEnum.COUNTERFACTUALS:
+                return _load_counterfactuals(json_object=json.load(f), metadata=variables_metadata)
+
+        raise RuntimeError("Unrecognized data type")
 
 
 def _load_interventions(json_object: dict[str, Any], metadata: dict[str, Any]) -> list[InterventionWithEffects]:
@@ -307,3 +308,22 @@ def tensordict_to_tensor(tensor_dict: TensorDict) -> torch.Tensor:
 def _intersect_dicts_left(dict_1: dict, dict_2: dict) -> dict:
     """Select the keys that are in both dictionaries, with values from the first."""
     return {key: dict_1[key] for key in dict_1.keys() & dict_2.keys()}
+
+
+def get_group_names(variables_dict: dict[str, Any]) -> list[str]:
+    """Get the names of the groups in the variables dict."""
+    return list(dict.fromkeys([var["group_name"] for var in variables_dict["variables"]]))
+
+
+def get_group_idxs(variables_dict: dict[str, Any]) -> list[list[int]]:
+    """Get the indices of the nodes/groups in each group."""
+    group_names = get_group_names(variables_dict)
+    return [
+        [idx for idx, var in enumerate(variables_dict["variables"]) if var["group_name"] == group_name]
+        for group_name in group_names
+    ]
+
+
+def get_name_to_idx(variables_dict: dict[str, Any]) -> dict[str, int]:
+    """Get a dictionary mapping node/group names to their index in the variables dict."""
+    return {var["name"]: idx for idx, var in enumerate(variables_dict["variables"])}
diff --git a/src/causica/distributions/__init__.py b/src/causica/distributions/__init__.py
index 7d0e6ce..af35e7d 100644
--- a/src/causica/distributions/__init__.py
+++ b/src/causica/distributions/__init__.py
@@ -28,4 +28,3 @@
     create_noise_modules,
     create_spline_dist_params,
 )
-from causica.distributions.sem_distribution import SEMDistribution, SEMDistributionModule
diff --git a/src/causica/distributions/adjacency/constrained_adjacency_distributions.py b/src/causica/distributions/adjacency/constrained_adjacency_distributions.py
index c2c7226..48968b9 100644
--- a/src/causica/distributions/adjacency/constrained_adjacency_distributions.py
+++ b/src/causica/distributions/adjacency/constrained_adjacency_distributions.py
@@ -129,8 +129,10 @@ def get_graph_constraint(graph_constraint_matrix: torch.Tensor) -> tuple[torch.T
     # Mask self-edges
     mask = ~torch.eye(graph_constraint_matrix.shape[0], dtype=torch.bool, device=graph_constraint_matrix.device)
 
-    positive_constraints = mask * torch.nan_to_num(graph_constraint_matrix, nan=0).to(dtype=torch.bool)
-    negative_constraints = torch.nan_to_num(graph_constraint_matrix, nan=1).to(dtype=torch.bool)
+    positive_constraints = mask * torch.nan_to_num(graph_constraint_matrix, nan=0).to(
+        dtype=torch.bool, non_blocking=True
+    )
+    negative_constraints = torch.nan_to_num(graph_constraint_matrix, nan=1).to(dtype=torch.bool, non_blocking=True)
     return positive_constraints, negative_constraints
 
 
diff --git a/src/causica/distributions/adjacency/directed_acyclic.py b/src/causica/distributions/adjacency/directed_acyclic.py
index 92474ea..fc7e6b7 100644
--- a/src/causica/distributions/adjacency/directed_acyclic.py
+++ b/src/causica/distributions/adjacency/directed_acyclic.py
@@ -50,7 +50,9 @@ def sample(self, sample_shape: torch.Size = torch.Size()) -> torch.Tensor:
         aranges = np.tile(np.arange(self.num_nodes), sample_shape + self.probs.shape + (1,))  # shape [..., n]
         np_perms = torch.tensor(self.np_rng.permuted(aranges, axis=-1))  # a batch of rearranged [0, 1, 2... n]
         # one hot the last dimension to create a tensor of shape [..., n, n]
-        perms = torch.nn.functional.one_hot(np_perms, num_classes=self.num_nodes).to(dtype=low_tri.dtype)
+        perms = torch.nn.functional.one_hot(np_perms, num_classes=self.num_nodes).to(
+            dtype=low_tri.dtype, non_blocking=True
+        )
 
         return torch.einsum("...ij,...jk,...lk->...il", perms, low_tri, perms)
 
diff --git a/src/causica/distributions/adjacency/gibbs_dag_prior.py b/src/causica/distributions/adjacency/gibbs_dag_prior.py
index 7da3d35..4110e40 100644
--- a/src/causica/distributions/adjacency/gibbs_dag_prior.py
+++ b/src/causica/distributions/adjacency/gibbs_dag_prior.py
@@ -18,8 +18,10 @@ def __init__(self, dag: torch.Tensor, mask: torch.Tensor, confidence: float, sca
 
         self.dag = torch.nn.Parameter(dag, requires_grad=False)
         self.mask = torch.nn.Parameter(mask, requires_grad=False)
-        self.confidence = confidence
-        self.scale = scale
+        self.confidence: torch.Tensor
+        self.scale: torch.Tensor
+        self.register_buffer("confidence", torch.tensor(confidence, dtype=torch.float))
+        self.register_buffer("scale", torch.tensor(scale, dtype=torch.float))
 
 
 class GibbsDAGPrior(td.Distribution):
diff --git a/src/causica/distributions/noise/bernoulli.py b/src/causica/distributions/noise/bernoulli.py
index 8693e1b..a227c1f 100644
--- a/src/causica/distributions/noise/bernoulli.py
+++ b/src/causica/distributions/noise/bernoulli.py
@@ -66,7 +66,7 @@ def mode(self):
 
         We favour sparseness, so if logit == 0, set the mode to be zero.
         """
-        return (self.logits > 0).to(self.logits)
+        return (self.logits > 0).to(self.logits, non_blocking=True)
 
 
 class BernoulliNoiseModule(NoiseModule[IndependentNoise[BernoulliNoise]]):
diff --git a/src/causica/distributions/noise/spline/bayesiains_nsf_rqs.py b/src/causica/distributions/noise/spline/bayesiains_nsf_rqs.py
index 2ce9cda..5d34205 100644
--- a/src/causica/distributions/noise/spline/bayesiains_nsf_rqs.py
+++ b/src/causica/distributions/noise/spline/bayesiains_nsf_rqs.py
@@ -161,21 +161,21 @@ def rational_quadratic_spline(
         logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
 
         return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
 
-        numerator = input_heights * (input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta) * theta_one_minus_theta
-        )
-        outputs = input_cumheights + numerator / denominator
+    theta = (inputs - input_cumwidths) / input_bin_widths
+    theta_one_minus_theta = theta * (1 - theta)
 
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * theta.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - theta).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+    numerator = input_heights * (input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta)
+    denominator = input_delta + (
+        (input_derivatives + input_derivatives_plus_one - 2 * input_delta) * theta_one_minus_theta
+    )
+    outputs = input_cumheights + numerator / denominator
 
-        return outputs, logabsdet
+    derivative_numerator = input_delta.pow(2) * (
+        input_derivatives_plus_one * theta.pow(2)
+        + 2 * input_delta * theta_one_minus_theta
+        + input_derivatives * (1 - theta).pow(2)
+    )
+    logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+    return outputs, logabsdet
diff --git a/src/causica/distributions/transforms.py b/src/causica/distributions/transforms.py
index d950028..a14a1cc 100644
--- a/src/causica/distributions/transforms.py
+++ b/src/causica/distributions/transforms.py
@@ -55,7 +55,7 @@ def log_abs_det_jacobian(self, x: TensorDict, y: TensorDict) -> torch.Tensor:
             {
                 key: self.transformations[key].log_abs_det_jacobian(x[key], y[key])
                 if key in self.transformations
-                else torch.ones_like(x[key])
+                else torch.zeros_like(x[key])
                 for key in x.keys()
             }
         )
@@ -71,3 +71,78 @@ def domain(self):
     @property
     def codomain(self):
         return {key: t.codomain for key, t in self.transformations.items()}
+
+
+class TensorToTensorDictTransform(td.Transform):
+    """
+    A transform for converting a torch tensor to a TensorDict.
+
+    It extracts the slices from the last dimension of the tensor and assigns them to the correct key.
+    """
+
+    bijective = True
+
+    def __init__(self, shapes: dict[str, torch.Size]):
+        """
+        Args:
+            shapes: the shapes of each of the keys
+        """
+        super().__init__()
+        self.shapes = shapes
+        self.num_keys = len(shapes)
+        self.output_shape, self.slices = shapes_to_slices(self.shapes)
+
+    def _call(self, x: torch.Tensor) -> TensorDict:
+        """Create a Tensordict by retrieving the slice associated with each key."""
+        return TensorDict({name: x[..., slice_] for name, slice_ in self.slices.items()}, batch_size=x.shape[:-1])
+
+    def _inverse(self, y: TensorDict) -> torch.Tensor:
+        """
+        Create a tensor by stacking the slice associated with each key.
+
+        Args:
+            y: Tensordict with batch_shape
+        Returns:
+            A tensor with shape batch_shape + [output_shape]
+        """
+        return torch.cat([y[name] for name in self.slices], dim=-1)
+
+    def log_abs_det_jacobian(self, _: torch.Tensor, y: TensorDict) -> TensorDict:
+        """This transformation doesn't affect the log det jacobian"""
+        return y.apply(torch.zeros_like)
+
+    def stacked_key_masks(self) -> torch.Tensor:
+        """
+        Create a binary of matrix of where each key is in the tensor.
+
+        Returns:
+            A matrix of shape [num_keys, output_shape] with 1 if the index of the tensor
+            belongs to the key corresponding to that row
+        """
+        stacked_key_masks = torch.zeros((self.num_keys, self.output_shape), dtype=torch.float)
+        for i, slice_ in enumerate(self.slices.values()):
+            stacked_key_masks[i, slice_] = 1.0
+        return stacked_key_masks
+
+
+def shapes_to_slices(shapes: dict[str, torch.Size]) -> tuple[int, dict[str, slice]]:
+    """
+    Convert a dictionary of shapes to a dictionary of masks by stacking the shapes
+
+    Each mask corresponds to the embedded location in the tensor
+
+    Args:
+        shapes: A dict of key names to shapes
+    Returns:
+        The shape of the stacked tensor and a dictionary of each key to the mask
+    """
+    assert all(len(shape) == 1 for shape in shapes.values())
+
+    slices: dict[str, slice] = {}
+    idx = 0
+    for name, shape in shapes.items():
+        next_idx = idx + shape[-1]
+        slices[name] = slice(idx, next_idx)
+        idx = next_idx
+
+    return next_idx, slices
diff --git a/src/causica/functional_relationships/__init__.py b/src/causica/functional_relationships/__init__.py
index 150f633..b55432b 100644
--- a/src/causica/functional_relationships/__init__.py
+++ b/src/causica/functional_relationships/__init__.py
@@ -1,4 +1,4 @@
 from .do_functional_relationships import DoFunctionalRelationships, create_do_functional_relationship
-from .functional_relationships import FunctionalRelationships, sample_dict_to_tensor, tensor_to_sample_dict
+from .functional_relationships import FunctionalRelationships
 from .icgnn import ICGNN
 from .linear_functional_relationships import LinearFunctionalRelationships
diff --git a/src/causica/functional_relationships/do_functional_relationships.py b/src/causica/functional_relationships/do_functional_relationships.py
index 4f52e98..44eb96d 100644
--- a/src/causica/functional_relationships/do_functional_relationships.py
+++ b/src/causica/functional_relationships/do_functional_relationships.py
@@ -18,15 +18,15 @@ def __init__(self, func: FunctionalRelationships, do: TensorDict, submatrix: tor
         """
         assert all(val.ndim == 1 for val in do.values()), "Intervention is only supported for 1 vector per variable"
 
-        new_variables = {key: value for key, value in func.variables.items() if key not in do.keys()}
-        super().__init__(new_variables)
+        new_shapes = {key: shape for key, shape in func.shapes.items() if key not in do.keys()}
+        super().__init__(new_shapes)
 
         self.func = func
         self.do = do  # dict of key to vectors
         self.submatrix = submatrix
 
         self.do_nodes_mask = torch.tensor(
-            [(name in self.do.keys()) for name in self.func.variables.keys()], dtype=torch.bool
+            [(name in self.do.keys()) for name in self.func.shapes.keys()], dtype=torch.bool
         )
 
     def pad_intervened_graphs(self, graphs: torch.Tensor) -> torch.Tensor:
@@ -38,7 +38,8 @@ def pad_intervened_graphs(self, graphs: torch.Tensor) -> torch.Tensor:
         Returns:
             A tensor of shape batch_shape_g + (func_n, func_n)
         """
-        target_shape = graphs.shape[:-2] + (self.func.num_nodes, self.func.num_nodes)
+        num_nodes = self.func.tensor_to_td.num_keys
+        target_shape = graphs.shape[:-2] + (num_nodes, num_nodes)
 
         output_graphs = torch.zeros(target_shape, dtype=graphs.dtype, device=graphs.device)
         assign_submatrix(output_graphs, graphs, ~self.do_nodes_mask, ~self.do_nodes_mask)
@@ -93,7 +94,7 @@ def create_do_functional_relationship(
     Return:
         A tuple with the intervened functional relationship and the intervened graph
     """
-    node_names = list(func.variables.keys())
+    node_names = list(func.shapes.keys())
     do_nodes_mask = torch.zeros(len(node_names), dtype=torch.bool)
     for i, name in enumerate(node_names):
         if name in interventions.keys():
diff --git a/src/causica/functional_relationships/functional_relationships.py b/src/causica/functional_relationships/functional_relationships.py
index 9638abd..0227f28 100644
--- a/src/causica/functional_relationships/functional_relationships.py
+++ b/src/causica/functional_relationships/functional_relationships.py
@@ -4,40 +4,30 @@
 import torch
 from tensordict import TensorDict
 
+from causica.distributions.transforms import TensorToTensorDictTransform
+
 
 class FunctionalRelationships(abc.ABC, torch.nn.Module):
-    def __init__(self, variables: dict[str, torch.Size]) -> None:
+    def __init__(self, shapes: dict[str, torch.Size]) -> None:
         """_summary_
 
         Args:
-            variables: Dict of node shapes (how many dimensions a variable has)
+            shapes: Dict of node shapes (how many dimensions a node has)
                 Order corresponds to the order in graph(s).
         """
         super().__init__()
-        self.num_nodes = len(variables)
-        self.variables = variables
-        self.output_shape = sum(variable.numel() for variable in variables.values())
-
-        self.variable_masks = {}
-        last_idx = 0
-        for name, shape in variables.items():
-            mask = torch.zeros(self.output_shape, dtype=torch.bool)
-            mask[last_idx : last_idx + shape.numel()] = True
-
-            self.variable_masks[name] = mask
-            last_idx += shape.numel()
+        self.shapes = shapes
+        # create a transform for mapping tensors to tensordicts
+        self.tensor_to_td = TensorToTensorDictTransform(shapes)
+        # this needs to be registered to the module, and register buffer doesn't work
+        self.stacked_key_masks = torch.nn.Parameter(self.tensor_to_td.stacked_key_masks(), requires_grad=False)
 
     def set_extra_state(self, state: dict[str, Any]):
-        self.num_nodes = state.pop("num_nodes")
-        self.variables = state.pop("variables")
-        self.output_shape = state.pop("output_shape")
+        self.shapes = state.pop("shapes")
+        self.tensor_to_td = TensorToTensorDictTransform(self.shapes)
 
     def get_extra_state(self) -> dict[str, Any]:
-        return {
-            "num_nodes": self.num_nodes,
-            "variables": self.variables,
-            "output_shape": self.output_shape,
-        }
+        return {"shapes": self.shapes}
 
     @abc.abstractmethod
     def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
@@ -50,15 +40,3 @@ def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
         Returns:
             Dictionary of torch.Tensors of shape sample_shape + batch_shape + [node shape]
         """
-
-
-def sample_dict_to_tensor(sample_dict: TensorDict, variable_masks: dict[str, torch.Tensor]) -> torch.Tensor:
-    """Converts a sample dictionary to a tensor."""
-    return torch.cat([sample_dict[name] for name in variable_masks.keys()], dim=-1)
-
-
-def tensor_to_sample_dict(sample_tensor: torch.Tensor, variable_masks: dict[str, torch.Tensor]) -> TensorDict:
-    """Converts a tensor to a sample dictionary."""
-    return TensorDict(
-        {name: sample_tensor[..., mask] for name, mask in variable_masks.items()}, batch_size=sample_tensor.shape[:-1]
-    )
diff --git a/src/causica/functional_relationships/icgnn.py b/src/causica/functional_relationships/icgnn.py
index 0b3ee07..cc927a8 100644
--- a/src/causica/functional_relationships/icgnn.py
+++ b/src/causica/functional_relationships/icgnn.py
@@ -4,11 +4,7 @@
 from tensordict import TensorDict
 from torch import nn
 
-from causica.functional_relationships.functional_relationships import (
-    FunctionalRelationships,
-    sample_dict_to_tensor,
-    tensor_to_sample_dict,
-)
+from causica.functional_relationships.functional_relationships import FunctionalRelationships
 
 
 class ICGNN(FunctionalRelationships):
@@ -22,25 +18,18 @@ class ICGNN(FunctionalRelationships):
 
     def __init__(
         self,
-        variables: dict[str, torch.Size],
+        shapes: dict[str, torch.Size],
         embedding_size: Optional[int] = None,
         out_dim_g: Optional[int] = None,
         norm_layer: Optional[Type[nn.LayerNorm]] = None,
         res_connection: bool = False,
     ) -> None:
-        super().__init__(variables)
+        super().__init__(shapes=shapes)
 
-        # this needs to be a parameter so it is registered to the module
-        self.stacked_variable_masks = torch.nn.Parameter(
-            torch.stack(list(self.variable_masks.values())).float(), requires_grad=False
-        )
-
-        self.nn = FGNNI(self.stacked_variable_masks, embedding_size, out_dim_g, norm_layer, res_connection)
+        self.nn = FGNNI(self.stacked_key_masks, embedding_size, out_dim_g, norm_layer, res_connection)
 
     def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
-        return tensor_to_sample_dict(
-            self.nn(sample_dict_to_tensor(samples, self.variable_masks), graphs), self.variable_masks
-        )
+        return self.tensor_to_td(self.nn(self.tensor_to_td.inv(samples), graphs))
 
 
 class FGNNI(nn.Module):
diff --git a/src/causica/functional_relationships/linear_functional_relationships.py b/src/causica/functional_relationships/linear_functional_relationships.py
index 1cb7bb6..e46b390 100644
--- a/src/causica/functional_relationships/linear_functional_relationships.py
+++ b/src/causica/functional_relationships/linear_functional_relationships.py
@@ -1,11 +1,7 @@
 import torch
 from tensordict import TensorDict
 
-from causica.functional_relationships.functional_relationships import (
-    FunctionalRelationships,
-    sample_dict_to_tensor,
-    tensor_to_sample_dict,
-)
+from causica.functional_relationships.functional_relationships import FunctionalRelationships
 
 
 class LinearFunctionalRelationships(FunctionalRelationships):
@@ -15,24 +11,21 @@ class LinearFunctionalRelationships(FunctionalRelationships):
 
     def __init__(
         self,
-        variables: dict[str, torch.Size],
+        shapes: dict[str, torch.Size],
         initial_linear_coefficient_matrix: torch.Tensor,
         trainable: bool = False,
     ) -> None:
         """
         Args:
-            variables: Dict of node shapes (how many dimensions a variable has)
+            shapes: Dict of node shapes (how many dimensions a variable has)
                 Order corresponds to the order in graph(s).
             initial_linear_coefficient_matrix: the linear coefficients [output_shape, output_shape]
             trainable: whether the coefficient matrix should be learnable
         """
-        super().__init__(variables)
+        super().__init__(shapes=shapes)
 
-        self.stacked_variable_masks = torch.nn.Parameter(
-            torch.stack(list(self.variable_masks.values())).float(), requires_grad=False
-        )
-
-        assert initial_linear_coefficient_matrix.shape == (self.output_shape, self.output_shape)
+        shape = self.tensor_to_td.output_shape
+        assert initial_linear_coefficient_matrix.shape == (shape, shape)
         self.linear_coefficients = torch.nn.Parameter(initial_linear_coefficient_matrix, requires_grad=trainable)
 
     def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
@@ -43,9 +36,7 @@ def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
         Returns:
             A Dict of tensors of shape batch_shape_x + batch_shape_g + (processed_dim_all)
         """
-        return tensor_to_sample_dict(
-            self.linear_map(sample_dict_to_tensor(samples, self.variable_masks), graphs), self.variable_masks
-        )
+        return self.tensor_to_td(self.linear_map(self.tensor_to_td.inv(samples), graphs))
 
     def linear_map(self, samples: torch.Tensor, graph: torch.Tensor) -> torch.Tensor:
         """
@@ -60,9 +51,7 @@ def linear_map(self, samples: torch.Tensor, graph: torch.Tensor) -> torch.Tensor
         batch_shape_x = samples.shape[:-1]
         batch_shape_g = graph.shape[:-2]
 
-        masked_graph = torch.einsum(
-            "ji,...jk,kl->...il", self.stacked_variable_masks, graph, self.stacked_variable_masks
-        )
+        masked_graph = torch.einsum("ji,...jk,kl->...il", self.stacked_key_masks, graph, self.stacked_key_masks)
 
         graph_broad = masked_graph.expand(*(batch_shape_x + tuple([-1] * len(graph.shape))))
         target_shape = batch_shape_x + batch_shape_g + samples.shape[-1:]
diff --git a/src/causica/lightning/callbacks.py b/src/causica/lightning/callbacks.py
index 5847cb4..654fba0 100644
--- a/src/causica/lightning/callbacks.py
+++ b/src/causica/lightning/callbacks.py
@@ -15,12 +15,14 @@
 class AuglagLRCallback(pl.Callback):
     """Wrapper Class to make the Auglag Learning Rate Scheduler compatible with Pytorch Lightning"""
 
-    def __init__(self, scheduler: AugLagLR):
+    def __init__(self, scheduler: AugLagLR, log_auglag: bool = False):
         """
         Args:
-            scheduler: The auglag learning rate scheduler to wrap
+            scheduler: The auglag learning rate scheduler to wrap.
+            log_auglag: Whether to log the auglag state as metrics at the end of each epoch.
         """
         self.scheduler = scheduler
+        self._log_auglag = log_auglag
 
     def on_train_batch_end(
         self, trainer: pl.Trainer, pl_module: pl.LightningModule, outputs: STEP_OUTPUT, batch: Any, batch_idx: int
@@ -36,25 +38,28 @@ def on_train_batch_end(
         is_converged = self.scheduler.step(
             optimizer=optimizer,
             loss=auglag_loss,
-            loss_value=outputs["loss"].item(),
-            lagrangian_penalty=outputs["constraint"].item(),
+            loss_value=outputs["loss"],
+            lagrangian_penalty=outputs["constraint"],
         )
 
-        auglag_logging_attributes = [
-            "num_lr_updates",
-            "outer_opt_counter",
-            "step_counter",
-            "outer_below_penalty_tol",
-            "outer_max_rho",
-            "last_best_step",
-            "last_lr_update_step",
-        ]
-        pl_module.log_dict({f"auglag/{k}": getattr(self.scheduler, k) for k in auglag_logging_attributes}, on_step=True)
-
         # Notify trainer to stop if the auglag algorithm has converged
         if is_converged:
             trainer.should_stop = True
 
+    def on_train_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+        _ = trainer
+        if self._log_auglag:
+            auglag_state = {
+                "num_lr_updates": self.scheduler.num_lr_updates,
+                "outer_opt_counter": self.scheduler.outer_opt_counter,
+                "step_counter": self.scheduler.step_counter,
+                "outer_below_penalty_tol": self.scheduler.outer_below_penalty_tol,
+                "outer_max_rho": self.scheduler.outer_max_rho,
+                "last_best_step": self.scheduler.last_best_step,
+                "last_lr_update_step": self.scheduler.last_lr_update_step,
+            }
+            pl_module.log_dict(auglag_state, on_epoch=True, rank_zero_only=True, prog_bar=False)
+
 
 class MLFlowSaveConfigCallback(SaveConfigCallback):
     """Logs the config using MLFlow if there is an active run, otherwise saves locally as the superclass."""
diff --git a/src/causica/lightning/cli.py b/src/causica/lightning/cli.py
index 0cae556..5ee381c 100644
--- a/src/causica/lightning/cli.py
+++ b/src/causica/lightning/cli.py
@@ -6,8 +6,8 @@
 
 class LightningCLIWithDefaults(LightningCLI):
     default_logger = {
-        "class_path": "pytorch_lightning.loggers.MLFlowLogger",
-        "init_args": {"run_id": os.environ.get("AZUREML_RUN_ID", None)},
+        "class_path": "causica.lightning.loggers.BufferingMlFlowLogger",
+        "init_args": {"run_id": os.environ.get("AZUREML_RUN_ID", None), "buffer_size": 2000},
     }
 
     def add_arguments_to_parser(self, parser):
diff --git a/src/causica/lightning/loggers.py b/src/causica/lightning/loggers.py
new file mode 100644
index 0000000..b2e0917
--- /dev/null
+++ b/src/causica/lightning/loggers.py
@@ -0,0 +1,60 @@
+from typing import Sequence
+
+from lightning_utilities.core.rank_zero import rank_zero_only
+from mlflow.entities import Metric, Param, RunTag
+from pytorch_lightning.loggers import MLFlowLogger
+
+
+class BufferingMlFlowLogger(MLFlowLogger):
+    """MlFlowLogger that buffers metrics on logging and flushes on finalize or when the buffer is full."""
+
+    def __init__(self, buffer_size: int, *args, **kwargs):
+        """
+        Args:
+            buffer_size: The maximum number of metrics to buffer before flushing
+            *args: Passed to `MLFlowLogger`
+            **kwargs: Passed to `MLFlowLogger`
+        """
+        super().__init__(*args, **kwargs)
+        self._buffer_size = buffer_size
+        self._buffer: list[Metric] = []
+        self._original_log_batch = self.experiment.log_batch
+        self.experiment.log_batch = self._buffer_log_batch_metrics(self.experiment.log_batch)
+
+    @rank_zero_only
+    def _buffer_log_batch_metrics(self, original_log_batch):
+        """Returns a decorated `log_batch` that buffers metrics and flushes them when the buffer is full."""
+
+        def log_batch(
+            run_id: str,
+            metrics: Sequence[Metric] = (),
+            params: Sequence[Param] = (),
+            tags: Sequence[RunTag] = (),
+        ) -> None:
+            if metrics:
+                self._buffer.extend(metrics)
+                if len(self._buffer) >= self._buffer_size:
+                    self.flush()
+            if params or tags:
+                original_log_batch(run_id=run_id, params=params, tags=tags)
+
+        return log_batch
+
+    def get_buffer_count(self) -> int:
+        """Return the current number of buffered messages."""
+        return len(self._buffer)
+
+    @rank_zero_only
+    def flush(self):
+        if self._buffer:
+            self._original_log_batch(run_id=self.run_id, metrics=self._buffer)
+            self._buffer.clear()
+
+    @rank_zero_only
+    def finalize(self, *args, **kwargs) -> None:
+        self.flush()
+        return super().finalize(*args, **kwargs)
+
+    @rank_zero_only
+    def __del__(self) -> None:
+        self.flush()
diff --git a/src/causica/lightning/modules/deci_module.py b/src/causica/lightning/modules/deci_module.py
index 5676643..54cd4f4 100644
--- a/src/causica/lightning/modules/deci_module.py
+++ b/src/causica/lightning/modules/deci_module.py
@@ -18,7 +18,6 @@
     ExpertGraphContainer,
     GibbsDAGPrior,
     JointNoiseModule,
-    SEMDistributionModule,
     create_noise_modules,
 )
 from causica.distributions.noise.joint import ContinuousNoiseDist
@@ -29,6 +28,7 @@
 from causica.lightning.callbacks import AuglagLRCallback
 from causica.lightning.data_modules.deci_data_module import DECIDataModule
 from causica.lightning.modules.variable_spec_module import VariableSpecModule
+from causica.sem.sem_distribution import SEMDistributionModule
 from causica.sem.structural_equation_model import SEM
 from causica.training.auglag import AugLagLossCalculator, AugLagLR, AugLagLRConfig
 from causica.training.evaluation import (
@@ -38,7 +38,6 @@
     list_logsumexp,
     list_mean,
 )
-from causica.training.training_callbacks import AverageMetricTracker
 
 logging.basicConfig(level=logging.INFO)
 
@@ -132,7 +131,7 @@ def infer_missing_state_from_dataset(self):
     def setup(self, stage: Optional[str] = None):
         if self.is_setup:
             return  # Already setup
-        elif stage not in {TrainerFn.TESTING, TrainerFn.FITTING}:
+        if stage not in {TrainerFn.TESTING, TrainerFn.FITTING}:
             raise ValueError(f"Model can only be setup during the {TrainerFn.FITTING} and {TrainerFn.TESTING} stages.")
 
         self.infer_missing_state_from_dataset()
@@ -150,7 +149,7 @@ def setup(self, stage: Optional[str] = None):
             adjacency_dist = ConstrainedAdjacency(adjacency_dist, self.constraint_matrix)
 
         icgnn = ICGNN(
-            variables=self.variable_group_shapes,
+            shapes=self.variable_group_shapes,
             embedding_size=self.embedding_size,
             out_dim_g=self.out_dim_g,
             norm_layer=None if self.norm_layer is False else torch.nn.LayerNorm,
@@ -167,14 +166,12 @@ def setup(self, stage: Optional[str] = None):
             sparsity_lambda=self.prior_sparsity_lambda,
             expert_graph_container=self.expert_graph_container,
         )
-        # TODO: Set a more reasonable averaging period
-        self.average_batch_log_prob_tracker: AverageMetricTracker = AverageMetricTracker(averaging_period=10)
         self.is_setup = True
 
     def training_step(self, *args, **kwargs) -> STEP_OUTPUT:
         _ = kwargs
         batch, *_ = args
-        batch = batch.apply(lambda t: t.to(torch.float32))
+        batch = batch.apply(lambda t: t.to(torch.float32, non_blocking=True))
 
         sem_distribution = self.sem_module()
         sem, *_ = sem_distribution.relaxed_sample(torch.Size([]), temperature=self.gumbel_temp)  # soft sample
@@ -184,13 +181,11 @@ def training_step(self, *args, **kwargs) -> STEP_OUTPUT:
         prior_term = self.prior.log_prob(sem.graph)
         objective = (-sem_distribution_entropy - prior_term) / self.num_samples - batch_log_prob
         constraint = calculate_dagness(sem.graph)
-        self.average_batch_log_prob_tracker.step(batch_log_prob.item())
         step_output = {
             "loss": self.auglag_loss(objective, constraint / self.num_samples),
             "batch_log_prob": batch_log_prob,
             "constraint": constraint,
             "num_edges": (sem.graph > 0.0).count_nonzero(),
-            "average_batch_log_prob": self.average_batch_log_prob_tracker.average,
             "vardist_entropy": sem_distribution_entropy,
             "prior_term": prior_term,
         }
@@ -218,11 +213,12 @@ def configure_optimizers(self):
 
     def configure_callbacks(self) -> Union[Sequence[pl.Callback], pl.Callback]:
         """Create a callback for the auglag callback."""
-        return [AuglagLRCallback(AugLagLR(config=self.auglag_config))]
+        lr_scheduler = AugLagLR(config=self.auglag_config)
+        return [AuglagLRCallback(lr_scheduler, log_auglag=True)]
 
     def test_step_observational(self, batch: TensorDict, *args, **kwargs):
         """Evaluate the log prob of the model on the test set using multiple graph samples."""
-        batch = batch.apply(lambda t: t.to(torch.float32))
+        batch = batch.apply(lambda t: t.to(torch.float32, non_blocking=True))
         sems = self.sem_module().sample(torch.Size([NUM_GRAPH_SAMPLES]))
         dataset_size = self.trainer.datamodule.dataset_test.batch_size  # type: ignore
         assert len(dataset_size) == 1, "Only one batch size is supported"
diff --git a/src/causica/lightning/modules/variable_spec_module.py b/src/causica/lightning/modules/variable_spec_module.py
index 4c560e8..cac23eb 100644
--- a/src/causica/lightning/modules/variable_spec_module.py
+++ b/src/causica/lightning/modules/variable_spec_module.py
@@ -30,14 +30,15 @@ def test_step(self, batch, batch_idx, dataloader_idx=0):
 
         See the superclass for *args and **kwargs conventions.
         """
-        if dataloader_idx == 0:
-            return self.test_step_observational(batch, batch_idx)
-        elif dataloader_idx == 1:
-            return self.test_step_graph(batch, batch_idx)
-        elif dataloader_idx == 2:
-            return self.test_step_interventions(batch, batch_idx)
-        elif dataloader_idx == 3:
-            return self.test_step_counterfactuals(batch, batch_idx)
+        match dataloader_idx:
+            case 0:
+                return self.test_step_observational(batch, batch_idx)
+            case 1:
+                return self.test_step_graph(batch, batch_idx)
+            case 2:
+                return self.test_step_interventions(batch, batch_idx)
+            case 3:
+                return self.test_step_counterfactuals(batch, batch_idx)
 
     @abc.abstractmethod
     def test_step_observational(self, batch: TensorDict, *args, **kwargs):
diff --git a/src/causica/distributions/sem_distribution.py b/src/causica/sem/sem_distribution.py
similarity index 100%
rename from src/causica/distributions/sem_distribution.py
rename to src/causica/sem/sem_distribution.py
diff --git a/src/causica/sem/structural_equation_model.py b/src/causica/sem/structural_equation_model.py
index b5f3a7a..e2837a4 100644
--- a/src/causica/sem/structural_equation_model.py
+++ b/src/causica/sem/structural_equation_model.py
@@ -7,6 +7,12 @@
 
 
 class SEM(dist.Distribution, abc.ABC):
+    """A structural equation model (SEM).
+
+    An SEM defines the causal relationships amongst a given set of nodes. This class provides methods to sample data
+    from the observational and interventional distributions of the SEM.
+    """
+
     arg_constraints: dict = {}
 
     def __init__(
@@ -137,8 +143,6 @@ def counterfactual(sem: SEM, factual_data: TensorDict, intervention: TensorDict)
     Returns:
        TensorDict: Dictionary holding the counterfactual values for all samples.
     """
-    # TODO: do we want to average over multiple "sample_to_noise" values for the discrete variables
-    # where this is not a 1:1 mapping?
     return sem.do(interventions=intervention).noise_to_sample(sem.sample_to_noise(factual_data))
 
 
diff --git a/src/causica/training/auglag.py b/src/causica/training/auglag.py
index cee0ea7..a69286c 100644
--- a/src/causica/training/auglag.py
+++ b/src/causica/training/auglag.py
@@ -1,19 +1,21 @@
 from collections import deque
 from dataclasses import dataclass, field
-from typing import Union
+from typing import Optional, Union
 
-import numpy as np
 import torch
 from dataclasses_json import dataclass_json
 from torch.optim import Optimizer
 
 
-class AugLagLossCalculator:
+class AugLagLossCalculator(torch.nn.Module):
     def __init__(self, init_alpha: float, init_rho: float):
-        self.alpha = init_alpha
-        self.rho = init_rho
+        super().__init__()
+        self.alpha: torch.Tensor
+        self.rho: torch.Tensor
+        self.register_buffer("alpha", torch.tensor(init_alpha, dtype=torch.float))
+        self.register_buffer("rho", torch.tensor(init_rho, dtype=torch.float))
 
-    def __call__(self, objective: torch.Tensor, constraint: torch.Tensor) -> torch.Tensor:
+    def forward(self, objective: torch.Tensor, constraint: torch.Tensor) -> torch.Tensor:
         return objective + self.alpha * constraint + self.rho * constraint * constraint / 2
 
 
@@ -75,26 +77,21 @@ def __init__(self, config: AugLagLRConfig) -> None:
         self.outer_opt_counter = 0
         self.outer_below_penalty_tol = 0
         self.outer_max_rho = 0
-        self._prev_lagrangian_penalty = np.inf
-        self._cur_lagrangian_penalty = np.inf
+        self._prev_lagrangian_penalty = torch.tensor(torch.inf)
+        self._cur_lagrangian_penalty = torch.tensor(torch.inf)
 
-        self.best_loss = np.inf
-        self.last_lr_update_step = 0
-        self.num_lr_updates = 0
-        self.last_best_step = 0
-        self.loss_tracker: deque = deque([], maxlen=config.aggregation_period)
-        self.step_counter = 0
-        self.epoch_counter = 0
+        self.loss_tracker: deque[torch.Tensor] = deque([], maxlen=config.aggregation_period)
+        self._init_new_inner_optimisation()
 
-    def _init_new_inner_optimisation(self):
+    def _init_new_inner_optimisation(self) -> None:
         """Init the hyperparameters for a new inner loop optimization."""
-        self.best_loss = np.inf
+        self.best_loss = torch.tensor(torch.inf)
         self.last_lr_update_step = 0
         self.num_lr_updates = 0
         self.last_best_step = 0
         self.loss_tracker.clear()
+        self.loss_tracker_sum: Optional[torch.Tensor] = None
         self.step_counter = 0
-        self.epoch_counter = 0
 
     def _is_inner_converged(self) -> bool:
         """Check if the inner optimization loop has converged, based on maximum number of inner steps, number of lr updates.
@@ -156,7 +153,7 @@ def _update_lr(self, optimizer: Union[Optimizer, list[Optimizer]]):
             for param_group in optimizer.param_groups:
                 param_group["lr"] *= self.config.lr_factor
 
-    def _reset_lr(self, optimizer: Union[Optimizer, list[Optimizer]]):
+    def reset_lr(self, optimizer: Union[Optimizer, list[Optimizer]]):
         """Reset the learning rate of individual param groups from lr init dictionary.
 
         Args:
@@ -179,7 +176,7 @@ def _update_lagrangian_params(self, loss: AugLagLossCalculator):
         Args:
             loss: loss with lagrangian attributes rho and alpha to be updated.
         """
-        if self._cur_dag_penalty < self.config.penalty_tolerance:
+        if self._cur_lagrangian_penalty < self.config.penalty_tolerance:
             self.outer_below_penalty_tol += 1
         else:
             self.outer_below_penalty_tol = 0
@@ -187,20 +184,21 @@ def _update_lagrangian_params(self, loss: AugLagLossCalculator):
         if loss.rho > self.config.safety_rho:
             self.outer_max_rho += 1
 
-        if self._cur_dag_penalty > self._prev_lagrangian_penalty * self.config.penalty_progress_rate:
+        if self._cur_lagrangian_penalty > self._prev_lagrangian_penalty * self.config.penalty_progress_rate:
             print(f"Updating rho, dag penalty prev: {self._prev_lagrangian_penalty: .10f}")
             loss.rho *= 10.0
         else:
-            self._prev_lagrangian_penalty = self._cur_dag_penalty
-            loss.alpha += loss.rho * self._cur_dag_penalty
-            if self._cur_dag_penalty == 0.0:
+            self._prev_lagrangian_penalty = self._cur_lagrangian_penalty
+            loss.alpha += loss.rho * self._cur_lagrangian_penalty
+            if self._cur_lagrangian_penalty == 0.0:
                 loss.alpha *= 5
             print(f"Updating alpha to: {loss.alpha}")
         if loss.rho >= self.config.safety_rho:
             loss.alpha *= 5
 
-        loss.rho = min([loss.rho, self.config.safety_rho])
-        loss.alpha = min([loss.alpha, self.config.safety_alpha])
+        # Update parameters and make sure to maintain the dtype and device
+        loss.alpha = torch.min(loss.alpha, torch.full_like(loss.alpha, self.config.safety_alpha))
+        loss.rho = torch.min(loss.rho, torch.full_like(loss.rho, self.config.safety_rho))
 
     def _is_auglag_converged(self, optimizer: Union[Optimizer, list[Optimizer]], loss: AugLagLossCalculator) -> bool:
         """Checks if the inner and outer loops have converged. If inner loop is converged,
@@ -216,20 +214,34 @@ def _is_auglag_converged(self, optimizer: Union[Optimizer, list[Optimizer]], los
         if self._is_inner_converged():
             if self._is_outer_converged():
                 return True
-            else:
-                self._update_lagrangian_params(loss)
-                self.outer_opt_counter += 1
-                self._init_new_inner_optimisation()
-                self._reset_lr(optimizer)
+
+            self._update_lagrangian_params(loss)
+            self.outer_opt_counter += 1
+            self._init_new_inner_optimisation()
+            self.reset_lr(optimizer)
         elif self._enough_steps_since_last_lr_update() and self._enough_steps_since_best_model():
             self._update_lr(optimizer)
 
         return False
 
+    def _update_loss_tracker(self, loss_value: torch.Tensor):
+        """Update the loss tracker with the current loss value.
+
+        Args:
+            loss_value: The current loss value.
+        """
+        if self.loss_tracker_sum is None:
+            self.loss_tracker_sum = torch.zeros_like(loss_value)
+
+        if len(self.loss_tracker) == self.loss_tracker.maxlen:
+            self.loss_tracker_sum -= self.loss_tracker.popleft()
+        self.loss_tracker.append(loss_value)
+        self.loss_tracker_sum += loss_value
+
     def _check_best_loss(self):
         """Update the best loss based on the average loss over an aggregation period."""
-        if len(self.loss_tracker) == self.config.aggregation_period:
-            avg_loss = np.mean(self.loss_tracker)
+        if len(self.loss_tracker) == self.loss_tracker.maxlen and self.loss_tracker_sum is not None:
+            avg_loss = self.loss_tracker_sum / self.loss_tracker.maxlen
             if avg_loss < self.best_loss:
                 self.best_loss = avg_loss
                 self.last_best_step = self.step_counter
@@ -238,8 +250,8 @@ def step(
         self,
         optimizer: Union[Optimizer, list[Optimizer]],
         loss: AugLagLossCalculator,
-        loss_value: float,
-        lagrangian_penalty: float,
+        loss_value: torch.Tensor,
+        lagrangian_penalty: torch.Tensor,
     ) -> bool:
         """The main update method to take one auglag inner step.
 
@@ -252,9 +264,9 @@ def step(
         Returns:
             bool: if the auglag has converged (False) or not (True)
         """
-        assert lagrangian_penalty >= 0, "auglag penalty must be non-negative"
-        self.loss_tracker.append(loss_value)
-        self._cur_dag_penalty = lagrangian_penalty
+        assert torch.all(lagrangian_penalty >= 0), "auglag penalty must be non-negative"
+        self._update_loss_tracker(loss_value.detach())
+        self._cur_lagrangian_penalty = lagrangian_penalty.detach()
         self.step_counter += 1
         self._check_best_loss()
         return self._is_auglag_converged(optimizer=optimizer, loss=loss)
diff --git a/src/causica/training/training_callbacks.py b/src/causica/training/training_callbacks.py
deleted file mode 100644
index 516e906..0000000
--- a/src/causica/training/training_callbacks.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from typing import Deque
-
-import numpy as np
-
-
-class AverageMetricTracker:
-    """A class to keep the smallest value of the rolling average of a metric over time.
-
-    Args:
-        averaging_period: Number of steps to average over.
-    """
-
-    def __init__(self, averaging_period: int = 10):
-        self._averaging_period = averaging_period
-        self.min_value = np.inf
-        self.queue: Deque = Deque([], maxlen=self._averaging_period)
-        self.rolling_sum = 0.0
-
-    @property
-    def average(self):
-        return self.rolling_sum / len(self.queue)
-
-    def step(self, value: float) -> bool:
-        """Add a new value to the tracker."""
-        removed_value = self.queue.popleft() if len(self.queue) == self._averaging_period else 0.0
-        self.queue.append(value)
-        self.rolling_sum += value - removed_value
-
-        if (current_average := self.average) < self.min_value:
-            self.min_value = current_average
-            return True
-        return False
-
-    def reset(self):
-        """Reset the tracker."""
-        self.min_value = np.inf
-        self.queue = Deque([], maxlen=self._averaging_period)
-        self.rolling_sum = 0.0
diff --git a/test/distributions/adjacency/test_adjacency_distributions.py b/test/distributions/adjacency/test_adjacency_distributions.py
index bed2dc5..db6c083 100644
--- a/test/distributions/adjacency/test_adjacency_distributions.py
+++ b/test/distributions/adjacency/test_adjacency_distributions.py
@@ -26,14 +26,13 @@ def _distribution_factory(
         return ConstrainedAdjacencyDistribution(
             inner_dist, positive_constraints=positive_constraints, negative_constraints=negative_constraints
         )
-
-    elif dist_class is ENCOAdjacencyDistribution:
+    if dist_class is ENCOAdjacencyDistribution:
         length = (num_nodes * (num_nodes - 1)) // 2
         return ENCOAdjacencyDistribution(
             logits_exist=torch.randn(batch_shape + (num_nodes, num_nodes)),
             logits_orient=torch.randn(batch_shape + (length,)),
         )
-    elif dist_class is ThreeWayAdjacencyDistribution:
+    if dist_class is ThreeWayAdjacencyDistribution:
         logits = torch.randn(batch_shape + ((num_nodes * (num_nodes - 1)) // 2, 3))
         return ThreeWayAdjacencyDistribution(logits=logits)
     raise ValueError("Unrecognised Class")
diff --git a/test/distributions/noise/test_joint.py b/test/distributions/noise/test_joint.py
index c0cb409..00c3d9a 100644
--- a/test/distributions/noise/test_joint.py
+++ b/test/distributions/noise/test_joint.py
@@ -2,11 +2,14 @@
 import torch
 from tensordict import TensorDict
 
-from causica.distributions.noise.bernoulli import BernoulliNoise
-from causica.distributions.noise.categorical import CategoricalNoise
-from causica.distributions.noise.joint import JointNoise
-from causica.distributions.noise.noise import IndependentNoise, Noise
-from causica.distributions.noise.univariate_normal import UnivariateNormalNoise
+from causica.distributions import (
+    BernoulliNoise,
+    CategoricalNoise,
+    IndependentNoise,
+    JointNoise,
+    Noise,
+    UnivariateNormalNoise,
+)
 
 NOISE_DISTRIBUTIONS = [
     IndependentNoise(BernoulliNoise(torch.randn(3), torch.randn(3)), 1),
@@ -24,9 +27,9 @@ def test_joint_noise_passthrough(noise: Noise):
     joint_noise = JointNoise({"a": noise})
 
     # Distribution properties
-    torch.testing.assert_allclose(joint_noise.entropy(), noise.entropy())
-    torch.testing.assert_allclose(joint_noise.mode.get("a"), noise.mode)
-    torch.testing.assert_allclose(joint_noise.mean.get("a"), noise.mean)
+    torch.testing.assert_close(joint_noise.entropy(), noise.entropy())
+    torch.testing.assert_close(joint_noise.mode.get("a"), noise.mode)
+    torch.testing.assert_close(joint_noise.mean.get("a"), noise.mean)
 
 
 @pytest.mark.parametrize("noise_a", NOISE_DISTRIBUTIONS)
@@ -36,11 +39,11 @@ def test_joint_noise_properties(noise_a: Noise, noise_b: Noise):
     joint_noise = JointNoise({"a": noise_a, "b": noise_b})
 
     # Distribution properties
-    torch.testing.assert_allclose(joint_noise.entropy(), noise_a.entropy() + noise_b.entropy())
-    torch.testing.assert_allclose(joint_noise.mode.get("a"), noise_a.mode)
-    torch.testing.assert_allclose(joint_noise.mode.get("b"), noise_b.mode)
-    torch.testing.assert_allclose(joint_noise.mean.get("a"), noise_a.mean)
-    torch.testing.assert_allclose(joint_noise.mean.get("b"), noise_b.mean)
+    torch.testing.assert_close(joint_noise.entropy(), noise_a.entropy() + noise_b.entropy())
+    torch.testing.assert_close(joint_noise.mode.get("a"), noise_a.mode)
+    torch.testing.assert_close(joint_noise.mode.get("b"), noise_b.mode)
+    torch.testing.assert_close(joint_noise.mean.get("a"), noise_a.mean)
+    torch.testing.assert_close(joint_noise.mean.get("b"), noise_b.mean)
 
 
 @pytest.mark.parametrize("noise_a", NOISE_DISTRIBUTIONS)
@@ -52,13 +55,13 @@ def test_joint_noise_sample_log_prob(noise_a: Noise, noise_b: Noise, sample_shap
     sample_a = noise_a.sample(sample_shape)
     sample_b = noise_b.sample(sample_shape)
     joint_sample = TensorDict({"a": sample_a, "b": sample_b}, batch_size=sample_shape)
-    torch.testing.assert_allclose(
+    torch.testing.assert_close(
         joint_noise.log_prob(joint_sample), noise_a.log_prob(sample_a) + noise_b.log_prob(sample_b)
     )
     joint_sample = joint_noise.sample()
     sample_a = joint_sample.get("a")
     sample_b = joint_sample.get("b")
-    torch.testing.assert_allclose(
+    torch.testing.assert_close(
         joint_noise.log_prob(joint_sample), noise_a.log_prob(sample_a) + noise_b.log_prob(sample_b)
     )
 
@@ -87,9 +90,9 @@ def test_joint_noise_empirical(noise_a: Noise, noise_b: Noise):
     mean, std = torch.mean, torch.std
     for key, value in samples.items():
         joint_value = joint_samples.get(key)
-        torch.testing.assert_allclose(mean(value, sample_dim), mean(joint_value, sample_dim), atol=0.01, rtol=0.01)
-        torch.testing.assert_allclose(std(value, sample_dim), std(joint_value, sample_dim), atol=0.01, rtol=0.01)
+        torch.testing.assert_close(mean(value, sample_dim), mean(joint_value, sample_dim), atol=0.01, rtol=0.01)
+        torch.testing.assert_close(std(value, sample_dim), std(joint_value, sample_dim), atol=0.01, rtol=0.01)
 
     # Similar log probs
-    torch.testing.assert_allclose(mean(log_probs), mean(joint_log_probs), atol=0.01, rtol=0.01)
-    torch.testing.assert_allclose(std(log_probs), std(joint_log_probs), atol=0.01, rtol=0.01)
+    torch.testing.assert_close(mean(log_probs), mean(joint_log_probs), atol=0.01, rtol=0.01)
+    torch.testing.assert_close(std(log_probs), std(joint_log_probs), atol=0.01, rtol=0.01)
diff --git a/test/distributions/test_sem_distribution.py b/test/distributions/test_sem_distribution.py
index 710454d..b4832bb 100644
--- a/test/distributions/test_sem_distribution.py
+++ b/test/distributions/test_sem_distribution.py
@@ -6,9 +6,9 @@
 from causica.distributions.noise.joint import JointNoiseModule
 from causica.distributions.noise.noise import Noise, NoiseModule
 from causica.distributions.noise.univariate_normal import UnivariateNormalNoiseModule
-from causica.distributions.sem_distribution import SEMDistribution
 from causica.functional_relationships import ICGNN
 from causica.functional_relationships.functional_relationships import FunctionalRelationships
+from causica.sem.sem_distribution import SEMDistribution
 
 
 def create_sem_params(
@@ -40,7 +40,7 @@ def test_sem_distribution_passthrough():
     shapes = {"a": torch.Size([5]), "b": torch.Size([2])}
     adjacency_dist, noise_dist, func = create_sem_params(shapes)
     sem_dist = SEMDistribution(adjacency_dist, noise_dist, func)
-    torch.testing.assert_allclose(adjacency_dist.entropy(), sem_dist.entropy())
-    torch.testing.assert_allclose(adjacency_dist.mean, sem_dist.mean.graph)
-    torch.testing.assert_allclose(adjacency_dist.mode, sem_dist.mode.graph)
-    torch.testing.assert_allclose(adjacency_dist.log_prob(adjacency_dist.mode), sem_dist.log_prob(sem_dist.mode))
+    torch.testing.assert_close(adjacency_dist.entropy(), sem_dist.entropy())
+    torch.testing.assert_close(adjacency_dist.mean, sem_dist.mean.graph)
+    torch.testing.assert_close(adjacency_dist.mode, sem_dist.mode.graph)
+    torch.testing.assert_close(adjacency_dist.log_prob(adjacency_dist.mode), sem_dist.log_prob(sem_dist.mode))
diff --git a/test/functional_relationships/test_functional_relationships.py b/test/functional_relationships/test_functional_relationships.py
index 70e837b..3becaea 100644
--- a/test/functional_relationships/test_functional_relationships.py
+++ b/test/functional_relationships/test_functional_relationships.py
@@ -27,7 +27,7 @@ def fixture_two_variable_graphs():
 def test_ICGNN_init(two_variable_dict):
     icgnn = ICGNN(two_variable_dict)
 
-    assert icgnn.output_shape == 3
+    assert icgnn.tensor_to_td.output_shape == 3
 
 
 def test_ICGNN_forward(two_variable_dict, two_variable_graph, two_variable_sample):
diff --git a/test/lightning/test_loggers.py b/test/lightning/test_loggers.py
new file mode 100644
index 0000000..85e3fa5
--- /dev/null
+++ b/test/lightning/test_loggers.py
@@ -0,0 +1,22 @@
+from pathlib import Path
+
+import mlflow
+
+from causica.lightning.loggers import BufferingMlFlowLogger
+
+
+def test_buffering_mlflow_logger(tmp_path: Path):
+    mlflow.set_tracking_uri(tmp_path)
+    client = mlflow.tracking.MlflowClient(tracking_uri=str(tmp_path))
+    experiment_id = client.create_experiment("test")
+    run = client.create_run(experiment_id=experiment_id)
+    logger = BufferingMlFlowLogger(buffer_size=3, run_id=run.info.run_id, tracking_uri=str(tmp_path))
+    logger.log_metrics({"a": 1})
+    logger.log_metrics({"a": 2})
+    assert logger.get_buffer_count() == 2
+    logger.log_metrics({"a": 3})  # Should flush due to full
+    assert logger.get_buffer_count() == 0
+    logger.log_metrics({"a": 4})
+    assert logger.get_buffer_count() == 1
+    logger.flush()
+    assert logger.get_buffer_count() == 0
diff --git a/test/sem/test_treatment_effects.py b/test/sem/test_treatment_effects.py
index 771e84c..7370048 100644
--- a/test/sem/test_treatment_effects.py
+++ b/test/sem/test_treatment_effects.py
@@ -22,7 +22,6 @@ def fixture_three_variable_dict():
 @pytest.mark.parametrize("graph", [torch.tensor([[0, 0], [1, 0.0]]), torch.tensor([[0, 1], [0, 0.0]])])
 def test_ate_ite_cf_two_node(graph, two_variable_dict):
     coef_matrix = torch.rand((3, 3))
-    # TODO: Figure out what to do about tests requiring modified scale
     sem = create_lingauss_sem(two_variable_dict, coef_matrix, graph, log_scale=math.log(1e-8))
     intervention_values_a = TensorDict({"x2": torch.tensor([1.42, 0.42])}, batch_size=tuple())
     intervention_values_b = TensorDict({"x2": torch.tensor([0.42, 1.42])}, batch_size=tuple())
diff --git a/test/training/test_auglag.py b/test/training/test_auglag.py
index bbfb3e3..5378594 100644
--- a/test/training/test_auglag.py
+++ b/test/training/test_auglag.py
@@ -1,5 +1,3 @@
-import random
-
 import torch
 from torch.nn import Parameter
 from torch.optim import Adam
@@ -33,13 +31,14 @@ def test_on_train_batch_end():
     auglag_callback = AugLagLR(_get_auglag_config(group_lr))
     optimizer = Adam(param)
     loss = AugLagLossCalculator(init_alpha=0.0, init_rho=1.0)
-    random_generator = random.Random(0)
+    random_generator = torch.Generator()
+    random_generator.manual_seed(1337)
     for _ in range(10000):
         auglag_callback.step(
             optimizer=optimizer,
             loss=loss,
-            loss_value=random_generator.random(),
-            lagrangian_penalty=random_generator.random(),
+            loss_value=torch.rand((), generator=random_generator),
+            lagrangian_penalty=torch.rand((), generator=random_generator),
         )
 
 
@@ -49,14 +48,15 @@ def test_on_train_batch_end_list_opt():
     param = [{"params": group_param[key], "name": key, "lr": val} for key, val in group_lr.items()]
     optimizer_list = [Adam(param[:1]), Adam(param[1:])]
     loss = AugLagLossCalculator(init_alpha=0.0, init_rho=1.0)
-    random_generator = random.Random(0)
+    random_generator = torch.Generator()
+    random_generator.manual_seed(1337)
     auglag_callback = AugLagLR(_get_auglag_config(group_lr))
     for _ in range(10000):
         auglag_callback.step(
             optimizer=optimizer_list,
             loss=loss,
-            loss_value=random_generator.random(),
-            lagrangian_penalty=random_generator.random(),
+            loss_value=torch.rand((), generator=random_generator),
+            lagrangian_penalty=torch.rand((), generator=random_generator),
         )
 
 
@@ -96,20 +96,21 @@ def test_solve_auglag():
     auglag_loss = AugLagLossCalculator(init_alpha=0.0, init_rho=1.0)
     step_counter = 0
     max_iter = 10000
+    constraint = torch.inf
     for _ in range(max_iter):
         optimizer.zero_grad()
         loss = x**2
-        constraint = max(3 - x, torch.zeros(()))
+        constraint = torch.max(3 - x, torch.zeros(()))
         auglag_loss_tensor = auglag_loss(loss, constraint)
         auglag_loss_tensor.backward()
         optimizer.step()
         converged = scheduler.step(
-            optimizer=optimizer, loss=auglag_loss, loss_value=loss.item(), lagrangian_penalty=constraint.item()
+            optimizer=optimizer, loss=auglag_loss, loss_value=loss, lagrangian_penalty=constraint
         )
         if converged:
             break
 
         step_counter += 1
 
-    assert constraint.item() < 1e-3
+    assert constraint < 1e-3
     assert torch.isclose(x, torch.tensor(3.0), atol=0.1)
diff --git a/test/training/test_training_callbacks.py b/test/training/test_training_callbacks.py
deleted file mode 100644
index 7d49a17..0000000
--- a/test/training/test_training_callbacks.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from causica.training.training_callbacks import AverageMetricTracker
-
-
-def test_average_metric_tracker():
-    length = 7
-    avg_metric_tracker = AverageMetricTracker(averaging_period=length)
-    values = list(reversed(range(length + 1)))
-    for index in range(length):
-        avg_metric_tracker.step(values[index])
-        assert avg_metric_tracker.min_value == sum(values[: index + 1]) / (index + 1)
-    # now we should overrun the buffer
-    avg_metric_tracker.step(values[-1])
-    assert avg_metric_tracker.min_value == sum(values[1:]) / length
-
-    # check reset
-    avg_metric_tracker.reset()
-    avg_metric_tracker.step(values[length // 2])
-    assert avg_metric_tracker.min_value == values[length // 2]