diff --git a/.github/workflows/create-docs.yml b/.github/workflows/create-docs.yml
new file mode 100644
index 0000000..efe34a6
--- /dev/null
+++ b/.github/workflows/create-docs.yml
@@ -0,0 +1,32 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: Python package
+
+on:
+  push:
+    branches: [ "main" ]
+
+jobs:
+  build:
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.11"]
+        platform: [macos-latest] # TODO: change add ubuntu-latest
+    runs-on: ${{ matrix.platform }}
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip poetry flake8
+        poetry install
+    - name: Publish to GitHub Pages
+      uses: peaceiris/actions-gh-pages@v3
+      with:
+        github_token: ${{ secrets.GITHUB_TOKEN }}
+        publish_dir: docs/build
diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-tests.yml
similarity index 85%
rename from .github/workflows/python-package.yml
rename to .github/workflows/python-tests.yml
index a1cdae0..1de0a48 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-tests.yml
@@ -5,19 +5,18 @@ name: Python package
 
 on:
   push:
-    branches: [ "main" ]
+    branches: [ "*" ]
   pull_request:
-    branches: [ "main" ]
+    branches: [ "*" ]
 
 jobs:
   build:
-
-    runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.11", "3.12"]
-
+        python-version: ["3.11"]
+        platform: [macos-latest, windows-latest] # TODO: change add ubuntu-latest
+    runs-on: ${{ matrix.platform }}
     steps:
     - uses: actions/checkout@v3
     - name: Set up Python ${{ matrix.python-version }}
diff --git a/README.md b/README.md
index 34de638..83c765a 100644
--- a/README.md
+++ b/README.md
@@ -36,20 +36,21 @@ poetry run causy execute tests/fixtures/toy_data_larger.json --pipeline pc.json
 ### Usage via Code
 
 Use a default algorithm
+
 ```python
 from causy.algorithms import PC
-from causy.utils import show_edges
+from causy.utils import retrieve_edges
 
 model = PC()
 model.create_graph_from_data(
     [
-        {"a": 1, "b": 0.3}, 
+        {"a": 1, "b": 0.3},
         {"a": 0.5, "b": 0.2}
     ]
 )
 model.create_all_possible_edges()
 model.execute_pipeline_steps()
-edges = show_edges(model.graph)
+edges = retrieve_edges(model.graph)
 
 for edge in edges:
     print(
diff --git a/causy/algorithms.py b/causy/algorithms.py
index 17a93dc..3dfd76f 100644
--- a/causy/algorithms.py
+++ b/causy/algorithms.py
@@ -1,4 +1,5 @@
 from causy.exit_conditions import ExitOnNoActions
+from causy.generators import PairsWithNeighboursGenerator
 from causy.graph import graph_model_factory, Loop
 from causy.independence_tests import (
     CalculateCorrelations,
@@ -6,6 +7,7 @@
     PartialCorrelationTest,
     ExtendedPartialCorrelationTestMatrix,
 )
+from causy.interfaces import AS_MANY_AS_FIELDS, ComparisonSettings
 from causy.orientation_tests import (
     ColliderTest,
     NonColliderTest,
@@ -32,3 +34,34 @@
         ),
     ]
 )
+
+
+ParallelPC = graph_model_factory(
+    pipeline_steps=[
+        CalculateCorrelations(),
+        CorrelationCoefficientTest(threshold=0.01),
+        PartialCorrelationTest(
+            threshold=0.01, parallel=True, chunk_size_parallel_processing=10000
+        ),
+        ExtendedPartialCorrelationTestMatrix(
+            threshold=0.01,
+            chunk_size_parallel_processing=1000,
+            parallel=True,
+            generator=PairsWithNeighboursGenerator(
+                chunked=False,
+                shuffle_combinations=True,
+                comparison_settings=ComparisonSettings(min=4, max=AS_MANY_AS_FIELDS),
+            ),
+        ),
+        ColliderTest(),
+        Loop(
+            pipeline_steps=[
+                NonColliderTest(),
+                FurtherOrientTripleTest(),
+                OrientQuadrupleTest(),
+                FurtherOrientQuadrupleTest(),
+            ],
+            exit_condition=ExitOnNoActions(),
+        ),
+    ]
+)
diff --git a/causy/cli.py b/causy/cli.py
index 538ddab..959047c 100644
--- a/causy/cli.py
+++ b/causy/cli.py
@@ -1,18 +1,18 @@
 import importlib
 import json
+from datetime import datetime
 from json import JSONEncoder
+import logging
 
 import typer
 
 from causy.graph import graph_model_factory
 from causy.utils import (
-    load_pipeline_artefact_by_definition,
     load_pipeline_steps_by_definition,
-    show_edges,
+    retrieve_edges,
 )
 
 app = typer.Typer()
-import logging
 
 
 def load_json(pipeline_file: str):
@@ -58,7 +58,7 @@ def execute(
     data_file: str,
     pipeline: str = None,
     algorithm: str = None,
-    graph_actions_save_file: str = None,
+    output_file: str = None,
     render_save_file: str = None,
     log_level: str = "ERROR",
 ):
@@ -68,9 +68,17 @@ def execute(
         pipeline_config = load_json(pipeline)
         pipeline = create_pipeline(pipeline_config)
         model = graph_model_factory(pipeline_steps=pipeline)()
+        algorithm_reference = {
+            "type": "pipeline",
+            "reference": pipeline,  # TODO: how to reference pipeline in a way that it can be loaded?
+        }
     elif algorithm:
         typer.echo(f"💾 Creating pipeline from algorithm {algorithm}")
         model = load_algorithm(algorithm)()
+        algorithm_reference = {
+            "type": "default",
+            "reference": algorithm,
+        }
 
     else:
         raise ValueError("Either pipeline_file or algorithm must be specified")
@@ -83,18 +91,31 @@ def execute(
 
     typer.echo("🕵🏻‍♀  Executing pipeline steps...")
     model.execute_pipeline_steps()
-    edges = show_edges(model.graph)
-    for edge in edges:
+    edges = []
+    for edge in retrieve_edges(model.graph):
         print(
-            f"{edge[0].name} -> {edge[1].name}: {model.graph.edges[edge[0]][edge[1]]}"
+            f"{model.graph.nodes[edge[0]].name} -> {model.graph.nodes[edge[1]].name}: {model.graph.edges[edge[0]][edge[1]]}"
+        )
+        edges.append(
+            {
+                "from": model.graph.nodes[edge[0]].to_dict(),
+                "to": model.graph.nodes[edge[1]].to_dict(),
+                "value": model.graph.edges[edge[0]][edge[1]],
+            }
         )
 
-    if graph_actions_save_file:
-        typer.echo(f"💾 Saving graph actions to {graph_actions_save_file}")
-        with open(graph_actions_save_file, "w") as file:
-            file.write(
-                json.dumps(model.graph.action_history, cls=MyJSONEncoder, indent=4)
-            )
+    if output_file:
+        typer.echo(f"💾 Saving graph actions to {output_file}")
+        with open(output_file, "w") as file:
+            export = {
+                "name": algorithm,
+                "created_at": datetime.now().isoformat(),
+                "algorithm": algorithm_reference,
+                "steps": model.graph.action_history,
+                "nodes": model.graph.nodes,
+                "edges": edges,
+            }
+            file.write(json.dumps(export, cls=MyJSONEncoder, indent=4))
 
     if render_save_file:
         # I'm just a hacky rendering function, pls replace me with causy ui 🙄
@@ -105,7 +126,7 @@ def execute(
         n_graph = nx.DiGraph()
         for u in model.graph.edges:
             for v in model.graph.edges[u]:
-                n_graph.add_edge(u.name, v.name)
+                n_graph.add_edge(model.graph.nodes[u].name, model.graph.nodes[v].name)
         fig = plt.figure(figsize=(10, 10))
         nx.draw(n_graph, with_labels=True, ax=fig.add_subplot(111))
         fig.savefig(render_save_file)
diff --git a/causy/generators.py b/causy/generators.py
index 218ca5d..08cbb74 100644
--- a/causy/generators.py
+++ b/causy/generators.py
@@ -1,3 +1,4 @@
+import copy
 import itertools
 import logging
 
@@ -9,7 +10,6 @@
     AS_MANY_AS_FIELDS,
 )
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -43,6 +43,24 @@ def generate(
 
 
 class PairsWithNeighboursGenerator(GeneratorInterface):
+    shuffle_combinations = True
+    chunked = True
+
+    def __init__(
+        self,
+        comparison_settings: ComparisonSettings,
+        chunked: bool = None,
+        shuffle_combinations: bool = None,
+    ):
+        super().__init__(comparison_settings, chunked)
+        if shuffle_combinations is not None:
+            self.shuffle_combinations = shuffle_combinations
+
+    def to_dict(self):
+        result = super().to_dict()
+        result["params"]["shuffle_combinations"] = self.shuffle_combinations
+        return result
+
     def generate(
         self, graph: BaseGraphInterface, graph_model_instance_: GraphModelInterface
     ):
@@ -71,20 +89,36 @@ def generate(
         for i in range(start, stop):
             logger.debug(f"PairsWithNeighboursGenerator: i={i}")
             checked_combinations = set()
-            for node in graph.edges:
-                for neighbour in graph.edges[node]:
+            local_edges = copy.deepcopy(graph.edges)
+            for node in local_edges:
+                for neighbour in local_edges[node]:
                     if (node, neighbour) in checked_combinations:
                         continue
 
                     checked_combinations.add((node, neighbour))
                     if i == 2:
-                        yield (node.name, neighbour.name)
+                        yield (node, neighbour)
                         continue
 
                     other_neighbours = set(graph.edges[node])
-                    other_neighbours.remove(neighbour)
+                    if neighbour in other_neighbours:
+                        other_neighbours.remove(neighbour)
+                    else:
+                        continue
                     if len(other_neighbours) + 2 < i:
                         continue
-
-                    for k in itertools.combinations(other_neighbours, i):
-                        yield [node.name, neighbour.name] + [ks.name for ks in k]
+                    combinations = itertools.combinations(other_neighbours, i)
+                    if self.shuffle_combinations:
+                        combinations = list(combinations)
+                        import random
+
+                        random.shuffle(combinations)
+
+                    if self.chunked:
+                        chunk = []
+                        for k in combinations:
+                            chunk.append([node, neighbour] + [ks for ks in k])
+                        yield chunk
+                    else:
+                        for k in combinations:
+                            yield [node, neighbour] + [ks for ks in k]
diff --git a/causy/graph.py b/causy/graph.py
index 0b6b5c6..086a772 100644
--- a/causy/graph.py
+++ b/causy/graph.py
@@ -1,15 +1,15 @@
-import importlib
-import itertools
-import json
 from abc import ABC
 from dataclasses import dataclass
-from typing import List, Optional, Dict, Set
-import multiprocessing as mp
+from typing import List, Optional, Dict, Set, Tuple
+from uuid import uuid4
+import logging
+
+import torch
+import torch.multiprocessing as mp
 
 from causy.interfaces import (
     IndependenceTestInterface,
 )
-
 from causy.interfaces import (
     BaseGraphInterface,
     NodeInterface,
@@ -19,9 +19,6 @@
     LogicStepInterface,
     ExitConditionInterface,
 )
-
-import logging
-
 from causy.utils import (
     load_pipeline_artefact_by_definition,
     load_pipeline_steps_by_definition,
@@ -33,10 +30,11 @@
 @dataclass
 class Node(NodeInterface):
     name: str
-    values: List[float]
+    id: str
+    values: torch.Tensor
 
     def __hash__(self):
-        return hash(self.name)
+        return hash(self.id)
 
 
 class UndirectedGraphError(Exception):
@@ -45,8 +43,8 @@ class UndirectedGraphError(Exception):
 
 class UndirectedGraph(BaseGraphInterface):
     nodes: Dict[str, Node]
-    edges: Dict[Node, Dict[Node, Dict]]
-    edge_history: Dict[Set[Node], List[TestResult]]
+    edges: Dict[str, Dict[str, Dict]]
+    edge_history: Dict[Tuple[str, str], List[TestResult]]
     action_history: List[Dict[str, List[TestResult]]]
 
     def __init__(self):
@@ -62,20 +60,20 @@ def add_edge(self, u: Node, v: Node, value: Dict):
         :param v: v node
         :return:
         """
-        if u.name not in self.nodes:
+        if u.id not in self.nodes:
             raise UndirectedGraphError(f"Node {u} does not exist")
-        if v.name not in self.nodes:
+        if v.id not in self.nodes:
             raise UndirectedGraphError(f"Node {v} does not exist")
-        if u not in self.edges:
-            self.edges[u] = {}
-        if v not in self.edges:
-            self.edges[v] = {}
+        if u.id not in self.edges:
+            self.edges[u.id] = {}
+        if v.id not in self.edges:
+            self.edges[v.id] = {}
 
-        self.edges[u][v] = value
-        self.edges[v][u] = value
+        self.edges[u.id][v.id] = value
+        self.edges[v.id][u.id] = value
 
-        self.edge_history[(u, v)] = []
-        self.edge_history[(v, u)] = []
+        self.edge_history[(u.id, v.id)] = []
+        self.edge_history[(v.id, u.id)] = []
 
     def retrieve_edge_history(
         self, u, v, action: TestResultAction = None
@@ -88,14 +86,14 @@ def retrieve_edge_history(
         :return:
         """
         if action is None:
-            return self.edge_history[(u, v)]
+            return self.edge_history[(u.id, v.id)]
 
-        return [i for i in self.edge_history[(u, v)] if i.action == action]
+        return [i for i in self.edge_history[(u.id, v.id)] if i.action == action]
 
     def add_edge_history(self, u, v, action: TestResultAction):
-        if (u, v) not in self.edge_history:
-            self.edge_history[(u, v)] = []
-        self.edge_history[(u, v)].append(action)
+        if (u.id, v.id) not in self.edge_history:
+            self.edge_history[(u.id, v.id)] = []
+        self.edge_history[(u.id, v.id)].append(action)
 
     def remove_edge(self, u: Node, v: Node):
         """
@@ -104,22 +102,22 @@ def remove_edge(self, u: Node, v: Node):
         :param v: v node
         :return:
         """
-        if u.name not in self.nodes:
+        if u.id not in self.nodes:
             raise UndirectedGraphError(f"Node {u} does not exist")
-        if v.name not in self.nodes:
+        if v.id not in self.nodes:
             raise UndirectedGraphError(f"Node {v} does not exist")
-        if u not in self.edges:
+        if u.id not in self.edges:
             raise UndirectedGraphError(f"Node {u} does not have any nodes")
-        if v not in self.edges:
+        if v.id not in self.edges:
             raise UndirectedGraphError(f"Node {v} does not have any nodes")
 
-        if v not in self.edges[u]:
+        if v.id not in self.edges[u.id]:
             return
-        del self.edges[u][v]
+        del self.edges[u.id][v.id]
 
-        if u not in self.edges[v]:
+        if u.id not in self.edges[v.id]:
             return
-        del self.edges[v][u]
+        del self.edges[v.id][u.id]
 
     def remove_directed_edge(self, u: Node, v: Node):
         """
@@ -128,18 +126,18 @@ def remove_directed_edge(self, u: Node, v: Node):
         :param v: v node
         :return:
         """
-        if u.name not in self.nodes:
+        if u.id not in self.nodes:
             raise UndirectedGraphError(f"Node {u} does not exist")
-        if v.name not in self.nodes:
+        if v.id not in self.nodes:
             raise UndirectedGraphError(f"Node {v} does not exist")
-        if u not in self.edges:
+        if u.id not in self.edges:
             raise UndirectedGraphError(f"Node {u} does not have any nodes")
-        if v not in self.edges:
+        if v.id not in self.edges:
             raise UndirectedGraphError(f"Node {v} does not have any nodes")
 
-        if v not in self.edges[u]:
+        if v.id not in self.edges[u.id]:
             return
-        del self.edges[u][v]
+        del self.edges[u.id][v.id]
 
     def update_edge(self, u: Node, v: Node, value: Dict):
         """
@@ -148,17 +146,17 @@ def update_edge(self, u: Node, v: Node, value: Dict):
         :param v: v node
         :return:
         """
-        if u.name not in self.nodes:
+        if u.id not in self.nodes:
             raise UndirectedGraphError(f"Node {u} does not exist")
-        if v.name not in self.nodes:
+        if v.id not in self.nodes:
             raise UndirectedGraphError(f"Node {v} does not exist")
-        if u not in self.edges:
+        if u.id not in self.edges:
             raise UndirectedGraphError(f"Node {u} does not have any edges")
-        if v not in self.edges:
+        if v.id not in self.edges:
             raise UndirectedGraphError(f"Node {v} does not have any edges")
 
-        self.edges[u][v] = value
-        self.edges[v][u] = value
+        self.edges[u.id][v.id] = value
+        self.edges[v.id][u.id] = value
 
     def update_directed_edge(self, u: Node, v: Node, value: Dict):
         """
@@ -185,13 +183,13 @@ def edge_exists(self, u: Node, v: Node):
         :param v: node v
         :return: True if any edge exists, False otherwise
         """
-        if u.name not in self.nodes:
+        if u.id not in self.nodes:
             return False
-        if v.name not in self.nodes:
+        if v.id not in self.nodes:
             return False
-        if u in self.edges and v in self.edges[u]:
+        if u.id in self.edges and v.id in self.edges[u.id]:
             return True
-        if v in self.edges and u in self.edges[v]:
+        if v.id in self.edges and u.id in self.edges[v.id]:
             return True
         return False
 
@@ -202,13 +200,13 @@ def directed_edge_exists(self, u: Node, v: Node):
         :param v: node v
         :return: True if a directed edge exists, False otherwise
         """
-        if u.name not in self.nodes:
+        if u.id not in self.nodes:
             return False
-        if v.name not in self.nodes:
+        if v.id not in self.nodes:
             return False
-        if u not in self.edges:
+        if u.id not in self.edges:
             return False
-        if v not in self.edges[u]:
+        if v.id not in self.edges[u.id]:
             return False
         return True
 
@@ -254,18 +252,23 @@ def bidirected_edge_exists(self, u: Node, v: Node):
         return False
 
     def edge_value(self, u: Node, v: Node):
-        return self.edges[u][v]
+        return self.edges[u.id][v.id]
 
-    def add_node(self, name: str, values: List[float]):
+    def add_node(self, name: str, values: List[float], id_: str = None):
         """
         Add a node to the graph
         :param name: name of the node
         :param values: values of the node
+        :param id_: id_ of the node
         :param : node
 
         :return:
         """
-        self.nodes[name] = Node(name, values)
+        if id_ is None:
+            id_ = str(uuid4())
+        self.nodes[id_] = Node(
+            name=name, id=id_, values=torch.tensor(values, dtype=torch.float64)
+        )
 
     def directed_path_exists(self, u: Node, v: Node):
         """
@@ -276,8 +279,8 @@ def directed_path_exists(self, u: Node, v: Node):
         """
         if self.directed_edge_exists(u, v):
             return True
-        for w in self.edges[u]:
-            if self.directed_path_exists(w, v):
+        for w in self.edges[u.id]:
+            if self.directed_path_exists(self.nodes[w], v):
                 return True
         return False
 
@@ -291,8 +294,8 @@ def directed_paths(self, u: Node, v: Node):
         if self.directed_edge_exists(u, v):
             return [[(u, v)]]
         paths = []
-        for w in self.edges[u]:
-            for path in self.directed_paths(w, v):
+        for w in self.edges[u.id]:
+            for path in self.directed_paths(self.nodes[w], v):
                 paths.append([(u, w)] + path)
         return paths
 
@@ -310,7 +313,8 @@ def inducing_path_exists(self, u: Node, v: Node):
             for i in range(1, len(path) - 1):
                 r, w = path[i]
                 if not self.bidirected_edge_exists(r, w):
-                    return False
+                    # TODO: check if this is correct (@sof)
+                    return True
         return False
 
 
@@ -332,7 +336,7 @@ def __init__(
     ):
         self.graph = graph
         self.pipeline_steps = pipeline_steps or []
-        self.pool = mp.Pool(mp.cpu_count() * 2)
+        self.pool = mp.Pool(round(mp.cpu_count() / 2))
 
     def create_graph_from_data(self, data: List[Dict[str, float]]):
         """
@@ -474,14 +478,23 @@ def execute_pipeline_step(self, test_fn: IndependenceTestInterface):
                     result = [result]
                 actions_taken.extend(self._take_action(result))
         else:
-            iterator = [
-                unpack_run(i)
-                for i in [
-                    [test_fn, [*i], self.graph]
-                    for i in test_fn.GENERATOR.generate(self.graph, self)
+            if test_fn.GENERATOR.chunked:
+                for chunk in test_fn.GENERATOR.generate(self.graph, self):
+                    iterator = [
+                        unpack_run(i)
+                        for i in [[test_fn, [*c], self.graph] for c in chunk]
+                    ]
+                    actions_taken.extend(self._take_action(iterator))
+            else:
+                iterator = [
+                    unpack_run(i)
+                    for i in [
+                        [test_fn, [*i], self.graph]
+                        for i in test_fn.GENERATOR.generate(self.graph, self)
+                    ]
                 ]
-            ]
-            actions_taken.extend(self._take_action(iterator))
+                actions_taken.extend(self._take_action(iterator))
+
         self.graph.action_history.append(
             {"step": type(test_fn).__name__, "actions": actions_taken}
         )
@@ -530,11 +543,11 @@ def __init__(
     ):
         super().__init__()
         # TODO check if this is a good idea
-        if type(exit_condition) == dict:
+        if isinstance(exit_condition, dict):
             exit_condition = load_pipeline_artefact_by_definition(exit_condition)
 
         # TODO: check if this is a good idea
-        if len(pipeline_steps) > 0 and type(pipeline_steps[0]) == dict:
+        if len(pipeline_steps) > 0 and isinstance(pipeline_steps[0], dict):
             pipeline_steps = load_pipeline_steps_by_definition(pipeline_steps)
 
         self.pipeline_steps = pipeline_steps or []
diff --git a/causy/independence_tests.py b/causy/independence_tests.py
index 30675b3..a8965ea 100644
--- a/causy/independence_tests.py
+++ b/causy/independence_tests.py
@@ -1,22 +1,11 @@
 import itertools
-from statistics import correlation, covariance  # , linear_regression
-from typing import Tuple, List
-import math
-
-from causy.generators import AllCombinationsGenerator, PairsWithNeighboursGenerator
-
-# Use cupy for GPU support - if available - otherwise use numpy
-try:
-    import cupy as np
-except ImportError:
-    import numpy as np
-
-from causy.utils import get_t_and_critial_t, get_correlation
-
+from typing import Tuple, List, Optional
 import logging
 
-logger = logging.getLogger(__name__)
+import torch
 
+from causy.generators import AllCombinationsGenerator, PairsWithNeighboursGenerator
+from causy.utils import get_t_and_critical_t, get_correlation, pearson_correlation
 from causy.interfaces import (
     IndependenceTestInterface,
     BaseGraphInterface,
@@ -27,7 +16,7 @@
     ComparisonSettings,
 )
 
-# TODO: make tests configurable (choosing different generators for different algorithms)
+logger = logging.getLogger(__name__)
 
 
 class CalculateCorrelations(IndependenceTestInterface):
@@ -46,8 +35,10 @@ def test(self, nodes: Tuple[str], graph: BaseGraphInterface) -> TestResult:
         x = graph.nodes[nodes[0]]
         y = graph.nodes[nodes[1]]
         edge_value = graph.edge_value(graph.nodes[nodes[0]], graph.nodes[nodes[1]])
-        edge_value["correlation"] = correlation(x.values, y.values)
-        # edge_value["covariance"] = covariance(x.values, y.values)
+        edge_value["correlation"] = pearson_correlation(
+            x.values,
+            y.values,
+        ).item()
         return TestResult(
             x=x,
             y=y,
@@ -63,7 +54,7 @@ class CorrelationCoefficientTest(IndependenceTestInterface):
     CHUNK_SIZE_PARALLEL_PROCESSING = 1
     PARALLEL = False
 
-    def test(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
+    def test(self, nodes: List[str], graph: BaseGraphInterface) -> Optional[TestResult]:
         """
         Test if x and y are independent and delete edge in graph if they are.
         :param nodes: list of nodes
@@ -76,7 +67,7 @@ def test(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
         sample_size = len(x.values)
         nb_of_control_vars = 0
         corr = graph.edge_value(x, y)["correlation"]
-        t, critical_t = get_t_and_critial_t(
+        t, critical_t = get_t_and_critical_t(
             sample_size, nb_of_control_vars, corr, self.threshold
         )
         if abs(t) < critical_t:
@@ -88,8 +79,6 @@ def test(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
                 data={},
             )
 
-        return
-
 
 class PartialCorrelationTest(IndependenceTestInterface):
     GENERATOR = AllCombinationsGenerator(
@@ -98,7 +87,9 @@ class PartialCorrelationTest(IndependenceTestInterface):
     CHUNK_SIZE_PARALLEL_PROCESSING = 1
     PARALLEL = False
 
-    def test(self, nodes: Tuple[str], graph: BaseGraphInterface) -> TestResult:
+    def test(
+        self, nodes: Tuple[str], graph: BaseGraphInterface
+    ) -> Optional[List[TestResult]]:
         """
         Test if nodes x,y are independent given node z based on a partial correlation test.
         We use this test for all combinations of 3 nodes because it is faster than the extended test (which supports combinations of n nodes). We can
@@ -109,6 +100,7 @@ def test(self, nodes: Tuple[str], graph: BaseGraphInterface) -> TestResult:
         TODO: we are testing (C and E given B) and (E and C given B), we just need one of these, remove redundant tests.
         """
         results = []
+        already_deleted_edges = set()
         for nodes in itertools.permutations(nodes):
             x: NodeInterface = graph.nodes[nodes[0]]
             y: NodeInterface = graph.nodes[nodes[1]]
@@ -117,6 +109,10 @@ def test(self, nodes: Tuple[str], graph: BaseGraphInterface) -> TestResult:
             # Avoid division by zero
             if x is None or y is None or z is None:
                 return
+
+            if not graph.edge_exists(x, y) or (y, x) in already_deleted_edges:
+                continue
+
             try:
                 cor_xy = graph.edge_value(x, y)["correlation"]
                 cor_xz = graph.edge_value(x, z)["correlation"]
@@ -136,7 +132,7 @@ def test(self, nodes: Tuple[str], graph: BaseGraphInterface) -> TestResult:
             # make t test for independency of x and y given z
             sample_size = len(x.values)
             nb_of_control_vars = len(nodes) - 2
-            t, critical_t = get_t_and_critial_t(
+            t, critical_t = get_t_and_critical_t(
                 sample_size, nb_of_control_vars, par_corr, self.threshold
             )
 
@@ -144,6 +140,7 @@ def test(self, nodes: Tuple[str], graph: BaseGraphInterface) -> TestResult:
                 logger.debug(
                     f"Nodes {x.name} and {y.name} are uncorrelated given {z.name}"
                 )
+
                 results.append(
                     TestResult(
                         x=x,
@@ -152,6 +149,7 @@ def test(self, nodes: Tuple[str], graph: BaseGraphInterface) -> TestResult:
                         data={"separatedBy": [z]},
                     )
                 )
+                already_deleted_edges.add((x, y))
         return results
 
 
@@ -159,10 +157,12 @@ class ExtendedPartialCorrelationTestLinearRegression(IndependenceTestInterface):
     GENERATOR = AllCombinationsGenerator(
         comparison_settings=ComparisonSettings(min=4, max=AS_MANY_AS_FIELDS)
     )
-    CHUNK_SIZE_PARALLEL_PROCESSING = 1
-    PARALLEL = False
+    CHUNK_SIZE_PARALLEL_PROCESSING = 1000
+    PARALLEL = True
 
-    def test(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
+    def test(
+        self, nodes: List[str], graph: BaseGraphInterface
+    ) -> Optional[List[TestResult]]:
         """
         Test if nodes x,y are independent given Z (set of nodes) based on partial correlation using linear regression and a correlation test on the residuals.
         We use this test for all combinations of more than 3 nodes because it is slower.
@@ -190,7 +190,7 @@ def test(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
                 par_corr = get_correlation(x, y, other_nodes)
                 logger.debug(f"par_corr {par_corr}")
                 # make t test for independence of a and y given other nodes
-                t, critical_t = get_t_and_critial_t(
+                t, critical_t = get_t_and_critical_t(
                     sample_size, nb_of_control_vars, par_corr, self.threshold
                 )
 
@@ -211,10 +211,10 @@ class ExtendedPartialCorrelationTestMatrix(IndependenceTestInterface):
     GENERATOR = PairsWithNeighboursGenerator(
         comparison_settings=ComparisonSettings(min=4, max=AS_MANY_AS_FIELDS)
     )
-    CHUNK_SIZE_PARALLEL_PROCESSING = 200
+    CHUNK_SIZE_PARALLEL_PROCESSING = 1000
     PARALLEL = False
 
-    def test(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
+    def test(self, nodes: List[str], graph: BaseGraphInterface) -> Optional[TestResult]:
         """
         Test if nodes x,y are independent given Z (set of nodes) based on partial correlation using the inverted covariance matrix (precision matrix).
         https://en.wikipedia.org/wiki/Partial_correlation#Using_matrix_inversion
@@ -222,56 +222,46 @@ def test(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
         :param nodes: the nodes to test
         :return: A TestResult with the action to take
         """
+
         if not graph.edge_exists(graph.nodes[nodes[0]], graph.nodes[nodes[1]]):
             return
 
-        other_neighbours = set(graph.edges[graph.nodes[nodes[0]]])
-
-        other_neighbours.remove(graph.nodes[nodes[1]])
+        other_neighbours = set(graph.edges[nodes[0]])
+        other_neighbours.remove(graph.nodes[nodes[1]].id)
 
-        if not set(nodes[2:]).issubset(set([on.name for on in list(other_neighbours)])):
+        if not set(nodes[2:]).issubset(set([on for on in list(other_neighbours)])):
             return
 
-        covariance_matrix = [
-            [None for _ in range(len(nodes))] for _ in range(len(nodes))
-        ]
-        for i in range(len(nodes)):
-            for k in range(i, len(nodes)):
-                if covariance_matrix[i][k] is None:
-                    covariance_matrix[i][k] = covariance(
-                        graph.nodes[nodes[i]].values, graph.nodes[nodes[k]].values
-                    )
-                    covariance_matrix[k][i] = covariance_matrix[i][k]
+        inverse_cov_matrix = torch.inverse(
+            torch.cov(torch.stack([graph.nodes[node].values for node in nodes]))
+        )
+        n = inverse_cov_matrix.size(0)
+        diagonal = torch.diag(inverse_cov_matrix)
+        diagonal_matrix = torch.zeros((n, n), dtype=torch.float64)
+        for i in range(n):
+            diagonal_matrix[i, i] = diagonal[i]
 
-        cov_matrix = np.array(covariance_matrix)
-        inverse_cov_matrix = np.linalg.inv(cov_matrix)
-        n = len(inverse_cov_matrix)
-        diagonal = np.diagonal(inverse_cov_matrix)
-        diagonal_matrix = np.zeros((n, n))
-        np.fill_diagonal(diagonal_matrix, diagonal)
-        helper = np.dot(np.sqrt(diagonal_matrix), inverse_cov_matrix)
-        precision_matrix = np.dot(helper, np.sqrt(diagonal_matrix))
+        helper = torch.mm(torch.sqrt(diagonal_matrix), inverse_cov_matrix)
+        precision_matrix = torch.mm(helper, torch.sqrt(diagonal_matrix))
 
         sample_size = len(graph.nodes[nodes[0]].values)
         nb_of_control_vars = len(nodes) - 2
-        results = []
 
-        nodes_set = set([graph.nodes[n] for n in nodes])
-
-        t, critical_t = get_t_and_critial_t(
+        t, critical_t = get_t_and_critical_t(
             sample_size,
             nb_of_control_vars,
             (
                 (-1 * precision_matrix[0][1])
-                / (math.sqrt(precision_matrix[0][0] * precision_matrix[1][1]))
-            ),
+                / torch.sqrt(precision_matrix[0][0] * precision_matrix[1][1])
+            ).item(),
             self.threshold,
         )
 
         if abs(t) < critical_t:
             logger.debug(
-                f"Nodes {graph.nodes[nodes[0]].name} and {graph.nodes[nodes[1]].name} are uncorrelated given nodes {','.join([on.name for on in other_neighbours])}"
+                f"Nodes {graph.nodes[nodes[0]].name} and {graph.nodes[nodes[1]].name} are uncorrelated given nodes {','.join([graph.nodes[on].name for on in other_neighbours])}"
             )
+            nodes_set = set([graph.nodes[n] for n in nodes])
             return TestResult(
                 x=graph.nodes[nodes[0]],
                 y=graph.nodes[nodes[1]],
@@ -283,8 +273,6 @@ def test(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
                 },
             )
 
-        return results
-
 
 class PlaceholderTest(IndependenceTestInterface):
     NUM_OF_COMPARISON_ELEMENTS = 2
diff --git a/causy/interfaces.py b/causy/interfaces.py
index 75cbebf..6aa3cf6 100644
--- a/causy/interfaces.py
+++ b/causy/interfaces.py
@@ -2,7 +2,7 @@
 import multiprocessing
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import List, Dict
+from typing import List, Dict, Optional
 import logging
 
 from causy.utils import serialize_module_name, load_pipeline_artefact_by_definition
@@ -12,7 +12,6 @@
 DEFAULT_THRESHOLD = 0.01
 
 AS_MANY_AS_FIELDS = 0
-import sys
 
 
 @dataclass
@@ -32,10 +31,11 @@ def serialize(self):
 
 class NodeInterface:
     name: str
+    id: str
     values: List[float]
 
     def to_dict(self):
-        return self.name
+        return {"id": self.id, "name": self.name}
 
 
 class TestResultAction(enum.StrEnum):
@@ -51,7 +51,7 @@ class TestResult:
     x: NodeInterface
     y: NodeInterface
     action: TestResultAction
-    data: Dict = None
+    data: Optional[Dict] = None
 
     def to_dict(self):
         return {
@@ -63,7 +63,7 @@ def to_dict(self):
 
 class BaseGraphInterface(ABC):
     nodes: Dict[str, NodeInterface]
-    edges: Dict[NodeInterface, Dict[NodeInterface, Dict]]
+    edges: Dict[str, Dict[str, Dict]]
 
     @abstractmethod
     def retrieve_edge_history(self, u, v, action: TestResultAction) -> List[TestResult]:
@@ -128,6 +128,7 @@ def execute_pipeline_step(self, step):
 
 class GeneratorInterface(ABC):
     comparison_settings: ComparisonSettings
+    chunked: bool = False
 
     @abstractmethod
     def generate(self, graph: BaseGraphInterface, graph_model_instance_: dict):
@@ -138,42 +139,46 @@ def serialize(self) -> dict:
             "name": serialize_module_name(self),
             "params": {
                 "comparison_settings": self.comparison_settings.serialize(),
+                "chunked": self.chunked,
             },
         }
 
-    def __init__(self, comparison_settings: ComparisonSettings):
-        if type(comparison_settings) == dict:
+    def __init__(self, comparison_settings: ComparisonSettings, chunked: bool = None):
+        if isinstance(comparison_settings, dict):
             comparison_settings = load_pipeline_artefact_by_definition(
                 comparison_settings
             )
 
+        if chunked is not None:
+            self.chunked = chunked
+
         self.comparison_settings = comparison_settings
 
 
 class IndependenceTestInterface(ABC):
-    NUM_OF_COMPARISON_ELEMENTS = 0
-    GENERATOR: GeneratorInterface = None
+    NUM_OF_COMPARISON_ELEMENTS: int = 0
+    GENERATOR: Optional[GeneratorInterface] = None
 
-    CHUNK_SIZE_PARALLEL_PROCESSING = 1
+    CHUNK_SIZE_PARALLEL_PROCESSING: int = 1
 
-    PARALLEL = True
+    PARALLEL: bool = True
 
     def __init__(
         self,
         threshold: float = DEFAULT_THRESHOLD,
-        generator: GeneratorInterface = None,
+        generator: Optional[GeneratorInterface] = None,
         num_of_comparison_elements: int = None,
         chunk_size_parallel_processing: int = None,
         parallel: bool = None,
     ):
         if generator:
-            if type(generator) == dict:
+            if isinstance(generator, dict):
                 self.GENERATOR = load_pipeline_artefact_by_definition(generator)
             else:
                 self.GENERATOR = generator
 
         if num_of_comparison_elements:
-            if type(num_of_comparison_elements) == dict:
+            if isinstance(num_of_comparison_elements, dict):
                 self.NUM_OF_COMPARISON_ELEMENTS = load_pipeline_artefact_by_definition(
                     num_of_comparison_elements
                 )
@@ -189,7 +194,7 @@ def __init__(
         self.threshold = threshold
 
     @abstractmethod
-    def test(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
+    def test(self, nodes: List[str], graph: BaseGraphInterface) -> Optional[TestResult]:
         """
         Test if x and y are independent
         :param x: x values
@@ -198,7 +203,9 @@ def test(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
         """
         pass
 
-    def __call__(self, nodes: List[str], graph: BaseGraphInterface) -> TestResult:
+    def __call__(
+        self, nodes: List[str], graph: BaseGraphInterface
+    ) -> Optional[TestResult]:
         return self.test(nodes, graph)
 
     def serialize(self) -> dict:
diff --git a/causy/orientation_tests.py b/causy/orientation_tests.py
index a1ebaab..793cb1d 100644
--- a/causy/orientation_tests.py
+++ b/causy/orientation_tests.py
@@ -43,13 +43,14 @@ def test(
             return TestResult(x=x, y=y, action=TestResultAction.DO_NOTHING, data={})
 
         # if x and y are NOT adjacent, store all shared adjacent nodes
-        potential_zs = set(graph.edges[x].keys()).intersection(
-            set(graph.edges[y].keys())
+        potential_zs = set(graph.edges[x.id].keys()).intersection(
+            set(graph.edges[y.id].keys())
         )
 
         # if x and y are not independent given z, safe action: make z a collider
         results = []
         for z in potential_zs:
+            z = graph.nodes[z]
             actions = graph.retrieve_edge_history(
                 x, y, TestResultAction.REMOVE_EDGE_UNDIRECTED
             )
@@ -57,9 +58,9 @@ def test(
             separators = []
             for action in actions:
                 if "separatedBy" in action.data:
-                    separators += action.data["separatedBy"]
+                    separators += [a.id for a in action.data["separatedBy"]]
 
-            if z not in separators:
+            if z.id not in separators:
                 results += [
                     TestResult(
                         x=z,
@@ -103,12 +104,13 @@ def test(
             return
 
         # if x and y are NOT adjacent, store all shared adjacent nodes
-        potential_zs = set(graph.edges[x].keys()).intersection(
-            set(graph.edges[y].keys())
+        potential_zs = set(graph.edges[x.id].keys()).intersection(
+            set(graph.edges[y.id].keys())
         )
         # if one edge has an arrowhead at z, orient the other one pointing away from z.
         # It cannot be a collider because we have already oriented all unshielded triples that contain colliders.
         for z in potential_zs:
+            z = graph.nodes[z]
             breakflag = False
             if graph.only_directed_edge_exists(x, z) and graph.undirected_edge_exists(
                 z, y
@@ -161,12 +163,13 @@ def test(
         x = graph.nodes[nodes[0]]
         y = graph.nodes[nodes[1]]
 
-        potential_zs = set(graph.edges[x].keys()).intersection(
-            set(graph.edges[y].keys())
+        potential_zs = set(graph.edges[x.id].keys()).intersection(
+            set(graph.edges[y.id].keys())
         )
 
         results = []
         for z in potential_zs:
+            z = graph.nodes[z]
             if (
                 graph.undirected_edge_exists(x, y)
                 and graph.only_directed_edge_exists(x, z)
@@ -216,14 +219,14 @@ def test(
         x = graph.nodes[nodes[0]]
         y = graph.nodes[nodes[1]]
 
-        potential_zs = set(graph.edges[x].keys()).intersection(
-            set(graph.edges[y].keys())
+        potential_zs = set(graph.edges[x.id].keys()).intersection(
+            set(graph.edges[y.id].keys())
         )
 
         results = []
         for zs in itertools.combinations(potential_zs, 2):
-            z = zs[0]
-            w = zs[1]
+            z = graph.nodes[zs[0]]
+            w = graph.nodes[zs[1]]
             if (
                 not graph.undirected_edge_exists(x, y)
                 and graph.only_directed_edge_exists(x, z)
@@ -279,14 +282,14 @@ def test(
         x = graph.nodes[nodes[0]]
         y = graph.nodes[nodes[1]]
 
-        potential_zs = set(graph.edges[x].keys()).intersection(
-            set(graph.edges[y].keys())
+        potential_zs = set(graph.edges[x.id].keys()).intersection(
+            set(graph.edges[y.id].keys())
         )
 
         results = []
         for zs in itertools.combinations(potential_zs, 2):
-            z = zs[0]
-            w = zs[1]
+            z = graph.nodes[zs[0]]
+            w = graph.nodes[zs[1]]
             if (
                 not graph.undirected_edge_exists(x, y)
                 and graph.only_directed_edge_exists(x, z)
diff --git a/causy/utils.py b/causy/utils.py
index 21b3e97..6a264cd 100644
--- a/causy/utils.py
+++ b/causy/utils.py
@@ -1,7 +1,8 @@
 import importlib
 import logging
 
-from sympy import transpose, Matrix, solve_linear_system, symbols, Symbol, shape
+import torch
+from sympy import transpose, Matrix, Symbol, shape
 from scipy import stats as scipy_stats
 import math
 from statistics import correlation
@@ -24,7 +25,7 @@ def backward_substituion(R, b, n):
     :param n: int, dimension
     :return: Matrix, n-dimensional regression coefficient vector
     """
-
+    # TODO: rewrite this function with torch or remove it
     # Define the symbolic variables
     my_symbols = []
     for z in range(n + 1):
@@ -52,6 +53,7 @@ def get_regression_coefficients(x, Z):
     :param Z: list of lists (length of outer list = # of samples, length of inner list = # of variables in Z)
     :return: sympy matrix, regression coefficients from regressing x on Z
     """
+    # TODO: rewrite this function with torch or remove it
     z_matrix = Matrix(Z)
     (n, m) = shape(z_matrix)
     logger.debug(f"(n,m)={(n, m)}")
@@ -60,7 +62,7 @@ def get_regression_coefficients(x, Z):
             "Z must have at most as many rows as columns. (Otherwise you have more variables than samples - which seems to be the case here)"
         )
     Q, R = z_matrix.QRdecomposition()
-    if not shape(R) == (m, m):
+    if shape(R) != (m, m):
         raise Exception("The matrix of data we regress on (Z) must have full rank.")
     q_transposed = transpose(Q)
     logger.debug(f"Q_transposed shape = {shape(q_transposed)}")
@@ -84,12 +86,15 @@ def get_residuals(x, Z):
     CAUTION: Z must have full rank
     TODO: add optional check
     """
+    # TODO: rewrite this function with torch or remove it
+
     n = len(x)
     res_x = Matrix(n, 1, x) - Matrix(Z) @ get_regression_coefficients(x, Z)
     return list(res_x)
 
 
 def get_correlation(x, y, other_nodes):
+    # TODO: rewrite this function with torch or remove it
     other_nodes_transposed = [list(i) for i in zip(*other_nodes)]
     residuals_x = get_residuals(x.values, other_nodes_transposed)
     residuals_y = get_residuals(y.values, other_nodes_transposed)
@@ -97,14 +102,15 @@ def get_correlation(x, y, other_nodes):
     return corr
 
 
-def get_t_and_critial_t(sample_size, nb_of_control_vars, par_corr, threshold):
+def get_t_and_critical_t(sample_size, nb_of_control_vars, par_corr, threshold):
+    # TODO: rewrite this function with torch
     # check if we have to normalize data
     deg_of_freedom = sample_size - 2 - nb_of_control_vars
     if abs(round(par_corr, 4)) == 1:
         return (1, 0)
     critical_t = scipy_stats.t.ppf(1 - threshold / 2, deg_of_freedom)
-    t = par_corr * math.sqrt((deg_of_freedom) / (1 - par_corr**2))
-    return (t, critical_t)
+    t = par_corr * math.sqrt(deg_of_freedom / (1 - par_corr**2))
+    return t, critical_t
 
 
 def serialize_module_name(cls):
@@ -134,9 +140,16 @@ def load_pipeline_steps_by_definition(steps):
     return pipeline
 
 
-def show_edges(graph):
+def retrieve_edges(graph):
     edges = []
     for u in graph.edges:
         for v in graph.edges[u]:
             edges.append((u, v))
     return edges
+
+
+def pearson_correlation(x, y):
+    cov_xy = torch.mean((x - x.mean()) * (y - y.mean()))
+    std_x = x.std()
+    std_y = y.std()
+    return cov_xy / (std_x * std_y)
diff --git a/poetry.lock b/poetry.lock
index 52e81ae..a4b02fd 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,33 +2,29 @@
 
 [[package]]
 name = "black"
-version = "23.9.1"
+version = "23.10.0"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "black-23.9.1-cp310-cp310-macosx_10_16_arm64.whl", hash = "sha256:d6bc09188020c9ac2555a498949401ab35bb6bf76d4e0f8ee251694664df6301"},
-    {file = "black-23.9.1-cp310-cp310-macosx_10_16_universal2.whl", hash = "sha256:13ef033794029b85dfea8032c9d3b92b42b526f1ff4bf13b2182ce4e917f5100"},
-    {file = "black-23.9.1-cp310-cp310-macosx_10_16_x86_64.whl", hash = "sha256:75a2dc41b183d4872d3a500d2b9c9016e67ed95738a3624f4751a0cb4818fe71"},
-    {file = "black-23.9.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13a2e4a93bb8ca74a749b6974925c27219bb3df4d42fc45e948a5d9feb5122b7"},
-    {file = "black-23.9.1-cp310-cp310-win_amd64.whl", hash = "sha256:adc3e4442eef57f99b5590b245a328aad19c99552e0bdc7f0b04db6656debd80"},
-    {file = "black-23.9.1-cp311-cp311-macosx_10_16_arm64.whl", hash = "sha256:8431445bf62d2a914b541da7ab3e2b4f3bc052d2ccbf157ebad18ea126efb91f"},
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 [package.dependencies]
@@ -37,6 +33,8 @@ mypy-extensions = ">=0.4.3"
 packaging = ">=22.0"
 pathspec = ">=0.9.0"
 platformdirs = ">=2"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
@@ -156,13 +154,13 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
 [[package]]
 name = "cycler"
-version = "0.12.0"
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 description = "Composable style cycles"
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 python-versions = ">=3.8"
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 ]
 
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 unicode = ["unicodedata2 (>=15.0.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
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+dask = ["dask", "distributed"]
+devel = ["pytest", "pytest-cov"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
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+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+tqdm = ["tqdm"]
+
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 typing-extensions = ">=4.1.0"
 
 [package.extras]
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 name = "nodeenv"
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+    {file = "torch-2.1.0-cp38-none-macosx_10_9_x86_64.whl", hash = "sha256:c8bf7eaf9514465e5d9101e05195183470a6215bb50295c61b52302a04edb690"},
+    {file = "torch-2.1.0-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:05661c32ec14bc3a157193d0f19a7b19d8e61eb787b33353cad30202c295e83b"},
+    {file = "torch-2.1.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:556d8dd3e0c290ed9d4d7de598a213fb9f7c59135b4fee144364a8a887016a55"},
+    {file = "torch-2.1.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:de7d63c6ecece118684415a3dbd4805af4a4c1ee1490cccf7405d8c240a481b4"},
+    {file = "torch-2.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:2419cf49aaf3b2336c7aa7a54a1b949fa295b1ae36f77e2aecb3a74e3a947255"},
+    {file = "torch-2.1.0-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:6ad491e70dbe4288d17fdbfc7fbfa766d66cbe219bc4871c7a8096f4a37c98df"},
+    {file = "torch-2.1.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:421739685eba5e0beba42cb649740b15d44b0d565c04e6ed667b41148734a75b"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = "*"
+jinja2 = "*"
+networkx = "*"
+sympy = "*"
+typing-extensions = "*"
+
+[package.extras]
+opt-einsum = ["opt-einsum (>=3.3)"]
+
 [[package]]
 name = "typer"
 version = "0.9.0"
@@ -1025,5 +1198,5 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.11,<3.13"
-content-hash = "5c7d100865caa0954b52972e85eb4966f9458e7dda72620bb01af7590c29ae53"
+python-versions = ">=3.10,<3.13"
+content-hash = "83d7e2fd2dd57d39a01e6803b63217bd397d90495c80f894f28950d57202b359"
diff --git a/pyproject.toml b/pyproject.toml
index 1a6627f..0efb2c8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,20 +6,22 @@ authors = ["Sofia Faltenbacher <faltenbacher.sofia@gmail.com>", "Lilith Wittmann
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = ">=3.11,<3.13"
+python = ">=3.11,<3.12" # until torch is available for 3.12
 sympy = "^1.12"
-numpy = "^1.26.0"
 scipy = "^1.11.2"
-black = "^23.9.1"
 pre-commit = "^3.4.0"
 typer = "^0.9.0"
 networkx = "^3.1"
 matplotlib = "^3.8.0"
+torch = "^2.1.0"
 
 
 [tool.poetry.group.dev.dependencies]
 mypy = "^1.5.1"
 pyinstrument = "^4.5.3"
+black = "^23.9.1"
+numpy = "^1.26.0"
+
 
 [build-system]
 requires = ["poetry-core"]
diff --git a/tests/test_pc_graph.py b/tests/test_pc_graph.py
index 976fdb1..aa013fe 100644
--- a/tests/test_pc_graph.py
+++ b/tests/test_pc_graph.py
@@ -4,7 +4,7 @@
 import numpy as np
 
 from causy.algorithms import PC
-from causy.utils import show_edges
+from causy.utils import retrieve_edges
 
 
 # TODO: generate larger toy model to test quadruple orientation rules.
@@ -64,7 +64,7 @@ def test_with_rki_data(self):
         tst.create_graph_from_data(test_data)
         tst.create_all_possible_edges()
         tst.execute_pipeline_steps()
-        show_edges(tst.graph)
+        retrieve_edges(tst.graph)
 
     def test_with_minimal_toy_model(self):
         a, b, c, d, sample_size = 1.2, 1.7, 2, 1.5, 10000
@@ -73,7 +73,7 @@ def test_with_minimal_toy_model(self):
         tst.create_graph_from_data(test_data)
         tst.create_all_possible_edges()
         tst.execute_pipeline_steps()
-        show_edges(tst.graph)
+        retrieve_edges(tst.graph)
 
     def test_with_larger_toy_model(self):
         a, b, c, d, e, f, g, sample_size = 1.2, 1.7, 2, 1.5, 3, 4, 1.8, 10000
@@ -82,7 +82,7 @@ def test_with_larger_toy_model(self):
         tst.create_graph_from_data(test_data)
         tst.create_all_possible_edges()
         tst.execute_pipeline_steps()
-        show_edges(tst.graph)
+        retrieve_edges(tst.graph)
 
 
 if __name__ == "__main__":