Split out ml_ops.py, add "View vector" box.

examples/Model use

@@ -41,6 +41,13 @@
       "sourceHandle": "output",
       "target": "View tables 1",
       "targetHandle": "bundle"
+    },
+    {
+      "id": "Model inference 1 View vectors 1",
+      "source": "Model inference 1",
+      "sourceHandle": "output",
+      "target": "View vectors 1",
+      "targetHandle": "bundle"
     }
   ],
   "env": "LynxKite Graph Analytics",
@@ -358,12 +365,14 @@
               },
               "df_test": {
                 "columns": [
+                  "index",
                   "x",
                   "y"
                 ]
               },
               "df_train": {
                 "columns": [
+                  "index",
                   "x",
                   "y"
                 ]
@@ -546,12 +555,14 @@
               },
               "df_test": {
                 "columns": [
+                  "index",
                   "x",
                   "y"
                 ]
               },
               "df_train": {
                 "columns": [
+                  "index",
                   "x",
                   "y"
                 ]
@@ -610,7 +621,7 @@
               "default": null,
               "name": "input_mapping",
               "type": {
-                "type": "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelTrainingInputMapping'>"
+                "type": "<class 'lynxkite_graph_analytics.ml_ops.ModelTrainingInputMapping'>"
               }
             },
             "model_name": {
@@ -624,7 +635,7 @@
           "type": "basic"
         },
         "params": {
-          "epochs": "501",
+          "epochs": "151",
           "input_mapping": "{\"map\":{\"Input__tensor_1_output\":{\"df\":\"df_train\",\"column\":\"x\"},\"Input__tensor_3_output\":{\"df\":\"df_train\",\"column\":\"y\"}}}",
           "model_name": "model"
         },
@@ -764,12 +775,14 @@
               },
               "df_test": {
                 "columns": [
+                  "index",
                   "x",
                   "y"
                 ]
               },
               "df_train": {
                 "columns": [
+                  "index",
                   "x",
                   "y"
                 ]
@@ -826,7 +839,7 @@
               "default": null,
               "name": "input_mapping",
               "type": {
-                "type": "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelInferenceInputMapping'>"
+                "type": "<class 'lynxkite_graph_analytics.ml_ops.ModelInferenceInputMapping'>"
               }
             },
             "model_name": {
@@ -840,7 +853,7 @@
               "default": null,
               "name": "output_mapping",
               "type": {
-                "type": "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelOutputMapping'>"
+                "type": "<class 'lynxkite_graph_analytics.ml_ops.ModelOutputMapping'>"
               }
             }
           },
@@ -1462,507 +1475,157 @@
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@@ -1989,12 +1652,14 @@
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@@ -2471,426 +2136,528 @@
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+            "model": "ModelConfig(model=Sequential(\n  (0) - Identity(): Input__tensor_1_output -> START_Repeat_1_output\n  (1) - Linear(4, 4, bias=True): START_Repeat_1_output -> Linear_1_output\n  (2) - <function leaky_relu at 0x7620a30c54e0>: Linear_1_output -> Activation_1_output\n  (3) - Identity(): Activation_1_output -> START_Repeat_1_output\n  (4) - Linear(4, 4, bias=True): START_Repeat_1_output -> Linear_1_output\n  (5) - <function leaky_relu at 0x7620a30c54e0>: Linear_1_output -> Activation_1_output\n  (6) - Identity(): Activation_1_output -> END_Repeat_1_output\n  (7) - Identity(): END_Repeat_1_output -> Output_1_x\n  (8) - Identity(): Output_1_x -> Output_1_x\n), model_inputs=['Input__tensor_1_output'], model_outputs=['Output_1_x'], loss_inputs=['Input__tensor_3_output', 'Output_1_x'], loss=Sequential(\n  (0) - <function constant_vector.<locals>.<lambda> at 0x761ebc727740>: nothing -> Constant_vector_1_output\n  (1) - <built-in method add of type object at 0x76209f79ef00>: Input__tensor_3_output, Constant_vector_1_output -> Add_1_output\n  (2) - <function mse_loss at 0x7620a30c6fc0>: Output_1_x, Add_1_output -> MSE_loss_2_output\n  (3) - Identity(): MSE_loss_2_output -> loss\n), optimizer_parameters={'lr': 0.1, 'type': <OptionsFor_type.SGD: 4>}, optimizer=SGD (\nParameter Group 0\n    dampening: 0\n    differentiable: False\n    foreach: None\n    fused: None\n    lr: 0.1\n    maximize: False\n    momentum: 0\n    nesterov: False\n    weight_decay: 0\n), source_workspace='Model definition', trained=True)"
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@@ -3221,6 +2988,7 @@
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@@ -3228,6 +2996,7 @@
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@@ -3300,6 +3069,229 @@
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+                [
+                  775.7841796875,
+                  -987.1351318359375,
+                  "",
+                  -7.914520263671875
+                ],
+                [
+                  -834.7413330078125,
+                  -1010.576904296875,
+                  "",
+                  -11.352218627929688
+                ],
+                [
+                  1341.696044921875,
+                  327.52203369140625,
+                  "",
+                  -12.275350570678711
+                ],
+                [
+                  2031.8687744140625,
+                  1949.9718017578125,
+                  "",
+                  -11.710403442382812
+                ],
+                [
+                  506.062255859375,
+                  1029.9742431640625,
+                  "",
+                  -8.997444152832031
+                ]
+              ],
+              "symbolSize": 15.811388300841896,
+              "type": "scatter"
+            }
+          ],
+          "title": {
+            "text": "UMAP projection of pred"
+          },
+          "tooltip": {
+            "show": false
+          },
+          "visualMap": {
+            "calculable": true,
+            "dimension": 3,
+            "inRange": {
+              "color": [
+                "#440154",
+                "#482777",
+                "#3E4989",
+                "#31688E",
+                "#26828E",
+                "#1F9E89",
+                "#35B779",
+                "#6CCE59",
+                "#B4DE2C",
+                "#FDE725"
+              ]
+            },
+            "max": -7.914520263671875,
+            "min": -12.964160919189453,
+            "right": 10,
+            "top": "center"
+          },
+          "xAxis": [
+            {
+              "type": "value"
+            }
+          ],
+          "yAxis": [
+            {
+              "type": "value"
+            }
+          ]
+        },
+        "error": null,
+        "input_metadata": [
+          {
+            "dataframes": {
+              "df": {
+                "columns": [
+                  "x",
+                  "y"
+                ]
+              },
+              "df_test": {
+                "columns": [
+                  "index",
+                  "pred",
+                  "x",
+                  "y"
+                ]
+              },
+              "df_train": {
+                "columns": [
+                  "index",
+                  "x",
+                  "y"
+                ]
+              },
+              "training": {
+                "columns": [
+                  "training_loss"
+                ]
+              }
+            },
+            "other": {
+              "model": {
+                "model": {
+                  "inputs": [
+                    "Input__tensor_1_output"
+                  ],
+                  "loss_inputs": [
+                    "Input__tensor_3_output",
+                    "Output_1_x"
+                  ],
+                  "outputs": [
+                    "Output_1_x"
+                  ],
+                  "trained": true
+                },
+                "type": "model"
+              }
+            },
+            "relations": []
+          }
+        ],
+        "meta": {
+          "inputs": {
+            "bundle": {
+              "name": "bundle",
+              "position": "left",
+              "type": {
+                "type": "<class 'lynxkite_graph_analytics.core.Bundle'>"
+              }
+            }
+          },
+          "name": "View vectors",
+          "outputs": {},
+          "params": {
+            "label_column": {
+              "default": "",
+              "name": "label_column",
+              "type": {
+                "type": "<class 'str'>"
+              }
+            },
+            "table_name": {
+              "default": "nodes",
+              "name": "table_name",
+              "type": {
+                "type": "<class 'str'>"
+              }
+            },
+            "vector_column": {
+              "default": "",
+              "name": "vector_column",
+              "type": {
+                "type": "<class 'str'>"
+              }
+            }
+          },
+          "position": {
+            "x": 943.0,
+            "y": 315.0
+          },
+          "type": "visualization"
+        },
+        "params": {
+          "label_column": "",
+          "table_name": "df_test",
+          "vector_column": "pred"
+        },
+        "status": "done",
+        "title": "View vectors"
+      },
+      "dragHandle": ".bg-primary",
+      "height": 668.0,
+      "id": "View vectors 1",
+      "position": {
+        "x": 3091.4609779299844,
+        "y": 659.497108066971
+      },
+      "type": "visualization",
+      "width": 613.0
     }
   ]
 }

lynxkite-app/web/src/workspace/Workspace.tsx

@@ -352,7 +352,7 @@ function LynxKiteFlow() {
             onConnect={onConnect}
             proOptions={{ hideAttribution: true }}
             maxZoom={1}
-            minZoom={0.3}
+            minZoom={0.2}
             zoomOnScroll={false}
             preventScrolling={false}
             defaultEdgeOptions={{

lynxkite-app/web/src/workspace/nodes/NodeParameter.tsx

@@ -4,10 +4,10 @@ import ArrowsHorizontal from "~icons/tabler/arrows-horizontal.jsx";
 
 const BOOLEAN = "<class 'bool'>";
 const MODEL_TRAINING_INPUT_MAPPING =
-  "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelTrainingInputMapping'>";
+  "<class 'lynxkite_graph_analytics.ml_ops.ModelTrainingInputMapping'>";
 const MODEL_INFERENCE_INPUT_MAPPING =
-  "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelInferenceInputMapping'>";
-const MODEL_OUTPUT_MAPPING = "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelOutputMapping'>";
+  "<class 'lynxkite_graph_analytics.ml_ops.ModelInferenceInputMapping'>";
+const MODEL_OUTPUT_MAPPING = "<class 'lynxkite_graph_analytics.ml_ops.ModelOutputMapping'>";
 function ParamName({ name }: { name: string }) {
   return <span className="param-name bg-base-200">{name.replace(/_/g, " ")}</span>;
 }

lynxkite-app/web/src/workspace/nodes/NodeWithVisualization.tsx

@@ -8,6 +8,10 @@ const NodeWithVisualization = (props: any) => {
   useEffect(() => {
     const opts = props.data?.display?.value;
     if (!opts || !chartsRef.current) return;
+    if (opts.tooltip?.formatter === "GET_THIRD_VALUE") {
+      // We can't pass a function from the backend, and can't get good tooltips otherwise.
+      opts.tooltip.formatter = (params: any) => params.value[2];
+    }
     chartsInstanceRef.current = echarts.init(chartsRef.current, null, {
       renderer: "canvas",
       width: "auto",

lynxkite-graph-analytics/pyproject.toml

@@ -16,6 +16,7 @@ dependencies = [
     "polars[gpu]>=1.14.0",
     "torch>=2.6.0",
     "torch-geometric>=2.6.1",
+    "cuml-cu12>=25.2.1",
 ]
 
 [project.optional-dependencies]

lynxkite-graph-analytics/src/lynxkite_graph_analytics/__init__.py

@@ -14,6 +14,7 @@ from .core import *  # noqa (easier access for core classes)
 from . import lynxkite_ops  # noqa (imported to trigger registration)
 from . import networkx_ops  # noqa (imported to trigger registration)
 from . import pytorch  # noqa (imported to trigger registration)
+from . import ml_ops  # noqa (imported to trigger registration)
 
 if os.environ.get("LYNXKITE_BIONEMO_INSTALLED", "").strip().lower() == "true":
     from . import bionemo_ops  # noqa (imported to trigger registration)

lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py

@@ -2,15 +2,11 @@
 
 import enum
 import os
-import pathlib
 import fsspec
 from lynxkite.core import ops
 from collections import deque
 
-from tqdm import tqdm
 from . import core
-from .pytorch import pytorch_core
-from lynxkite.core import workspace
 import grandcypher
 import joblib
 import matplotlib
@@ -323,127 +319,7 @@ def create_graph(bundle: core.Bundle, *, relations: str = None) -> core.Bundle:
     return ops.Result(output=bundle, display=bundle.to_dict(limit=100))
 
 
-def load_ws(model_workspace: str):
-    cwd = pathlib.Path()
-    path = cwd / model_workspace
-    assert path.is_relative_to(cwd)
-    assert path.exists(), f"Workspace {path} does not exist"
-    ws = workspace.load(path)
-    return ws
-
-
 @op("Biomedical foundation graph (PLACEHOLDER)")
 def biomedical_foundation_graph(*, filter_nodes: str):
     """Loads the gigantic Lynx-maintained knowledge graph. Includes drugs, diseases, genes, proteins, etc."""
     return None
-
-
-@op("Define model")
-def define_model(
-    bundle: core.Bundle,
-    *,
-    model_workspace: str,
-    save_as: str = "model",
-):
-    """Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
-    assert model_workspace, "Model workspace is unset."
-    ws = load_ws(model_workspace)
-    # Build the model without inputs, to get its interface.
-    m = pytorch_core.build_model(ws)
-    m.source_workspace = model_workspace
-    bundle = bundle.copy()
-    bundle.other[save_as] = m
-    return bundle
-
-
-# These contain the same mapping, but they get different UIs.
-# For inputs, you select existing columns. For outputs, you can create new columns.
-class ModelInferenceInputMapping(pytorch_core.ModelMapping):
-    pass
-
-
-class ModelTrainingInputMapping(pytorch_core.ModelMapping):
-    pass
-
-
-class ModelOutputMapping(pytorch_core.ModelMapping):
-    pass
-
-
-@op("Train model")
-@ops.slow
-def train_model(
-    bundle: core.Bundle,
-    *,
-    model_name: str = "model",
-    input_mapping: ModelTrainingInputMapping,
-    epochs: int = 1,
-):
-    """Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
-    m = bundle.other[model_name].copy()
-    inputs = pytorch_core.to_tensors(bundle, input_mapping)
-    t = tqdm(range(epochs), desc="Training model")
-    losses = []
-    for _ in t:
-        loss = m.train(inputs)
-        t.set_postfix({"loss": loss})
-        losses.append(loss)
-    m.trained = True
-    bundle = bundle.copy()
-    bundle.dfs["training"] = pd.DataFrame({"training_loss": losses})
-    bundle.other[model_name] = m
-    return bundle
-
-
-@op("Model inference")
-@ops.slow
-def model_inference(
-    bundle: core.Bundle,
-    *,
-    model_name: str = "model",
-    input_mapping: ModelInferenceInputMapping,
-    output_mapping: ModelOutputMapping,
-):
-    """Executes a trained model."""
-    if input_mapping is None or output_mapping is None:
-        return ops.Result(bundle, error="Mapping is unset.")
-    m = bundle.other[model_name]
-    assert m.trained, "The model is not trained."
-    inputs = pytorch_core.to_tensors(bundle, input_mapping)
-    outputs = m.inference(inputs)
-    bundle = bundle.copy()
-    copied = set()
-    for k, v in output_mapping.map.items():
-        if not v.df or not v.column:
-            continue
-        if v.df not in copied:
-            bundle.dfs[v.df] = bundle.dfs[v.df].copy()
-            copied.add(v.df)
-        bundle.dfs[v.df][v.column] = outputs[k].detach().numpy().tolist()
-    return bundle
-
-
-@op("Train/test split")
-def train_test_split(bundle: core.Bundle, *, table_name: str, test_ratio: float = 0.1):
-    """Splits a dataframe in the bundle into separate "_train" and "_test" dataframes."""
-    df = bundle.dfs[table_name]
-    test = df.sample(frac=test_ratio)
-    train = df.drop(test.index)
-    bundle = bundle.copy()
-    bundle.dfs[f"{table_name}_train"] = train
-    bundle.dfs[f"{table_name}_test"] = test
-    return bundle
-
-
-@op("View loss", view="visualization")
-def view_loss(
-    bundle: core.Bundle,
-):
-    loss = bundle.dfs["training"].training_loss.tolist()
-    v = {
-        "title": {"text": "Training loss"},
-        "xAxis": {"type": "category"},
-        "yAxis": {"type": "value"},
-        "series": [{"data": loss, "type": "line"}],
-    }
-    return v

lynxkite-graph-analytics/src/lynxkite_graph_analytics/ml_ops.py

@@ -0,0 +1,192 @@
+"""Operations for machine learning."""
+
+import numpy as np
+from . import core
+from lynxkite.core import workspace
+from .pytorch import pytorch_core
+from lynxkite.core import ops
+from tqdm import tqdm
+import cuml
+import joblib
+import pandas as pd
+import pathlib
+
+
+mem = joblib.Memory(".joblib-cache")
+op = ops.op_registration(core.ENV)
+
+
+def load_ws(model_workspace: str):
+    cwd = pathlib.Path()
+    path = cwd / model_workspace
+    assert path.is_relative_to(cwd)
+    assert path.exists(), f"Workspace {path} does not exist"
+    ws = workspace.load(path)
+    return ws
+
+
+@op("Define model")
+def define_model(
+    bundle: core.Bundle,
+    *,
+    model_workspace: str,
+    save_as: str = "model",
+):
+    """Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
+    assert model_workspace, "Model workspace is unset."
+    ws = load_ws(model_workspace)
+    # Build the model without inputs, to get its interface.
+    m = pytorch_core.build_model(ws)
+    m.source_workspace = model_workspace
+    bundle = bundle.copy()
+    bundle.other[save_as] = m
+    return bundle
+
+
+# These contain the same mapping, but they get different UIs.
+# For inputs, you select existing columns. For outputs, you can create new columns.
+class ModelInferenceInputMapping(pytorch_core.ModelMapping):
+    pass
+
+
+class ModelTrainingInputMapping(pytorch_core.ModelMapping):
+    pass
+
+
+class ModelOutputMapping(pytorch_core.ModelMapping):
+    pass
+
+
+@op("Train model")
+@ops.slow
+def train_model(
+    bundle: core.Bundle,
+    *,
+    model_name: str = "model",
+    input_mapping: ModelTrainingInputMapping,
+    epochs: int = 1,
+):
+    """Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
+    m = bundle.other[model_name].copy()
+    inputs = pytorch_core.to_tensors(bundle, input_mapping)
+    t = tqdm(range(epochs), desc="Training model")
+    losses = []
+    for _ in t:
+        loss = m.train(inputs)
+        t.set_postfix({"loss": loss})
+        losses.append(loss)
+    m.trained = True
+    bundle = bundle.copy()
+    bundle.dfs["training"] = pd.DataFrame({"training_loss": losses})
+    bundle.other[model_name] = m
+    return bundle
+
+
+@op("Model inference")
+@ops.slow
+def model_inference(
+    bundle: core.Bundle,
+    *,
+    model_name: str = "model",
+    input_mapping: ModelInferenceInputMapping,
+    output_mapping: ModelOutputMapping,
+):
+    """Executes a trained model."""
+    if input_mapping is None or output_mapping is None:
+        return ops.Result(bundle, error="Mapping is unset.")
+    m = bundle.other[model_name]
+    assert m.trained, "The model is not trained."
+    inputs = pytorch_core.to_tensors(bundle, input_mapping)
+    outputs = m.inference(inputs)
+    bundle = bundle.copy()
+    copied = set()
+    for k, v in output_mapping.map.items():
+        if not v.df or not v.column:
+            continue
+        if v.df not in copied:
+            bundle.dfs[v.df] = bundle.dfs[v.df].copy()
+            copied.add(v.df)
+        bundle.dfs[v.df][v.column] = outputs[k].detach().numpy().tolist()
+    return bundle
+
+
+@op("Train/test split")
+def train_test_split(bundle: core.Bundle, *, table_name: str, test_ratio: float = 0.1):
+    """Splits a dataframe in the bundle into separate "_train" and "_test" dataframes."""
+    df = bundle.dfs[table_name]
+    test = df.sample(frac=test_ratio).reset_index()
+    train = df.drop(test.index).reset_index()
+    bundle = bundle.copy()
+    bundle.dfs[f"{table_name}_train"] = train
+    bundle.dfs[f"{table_name}_test"] = test
+    return bundle
+
+
+@op("View loss", view="visualization")
+def view_loss(bundle: core.Bundle):
+    loss = bundle.dfs["training"].training_loss.tolist()
+    v = {
+        "title": {"text": "Training loss"},
+        "xAxis": {"type": "category"},
+        "yAxis": {"type": "value"},
+        "series": [{"data": loss, "type": "line"}],
+    }
+    return v
+
+
+@op("Reduce dimensions")
+def reduce_dimensions(bundle: core.Bundle):
+    return
+
+
+VIRIDIS = [
+    "#440154",
+    "#482777",
+    "#3E4989",
+    "#31688E",
+    "#26828E",
+    "#1F9E89",
+    "#35B779",
+    "#6CCE59",
+    "#B4DE2C",
+    "#FDE725",
+]
+
+
+@op("View vectors", view="visualization")
+def view_vectors(
+    bundle: core.Bundle,
+    *,
+    table_name: str = "nodes",
+    vector_column: str = "",
+    label_column: str = "",
+):
+    vec = np.stack(bundle.dfs[table_name][vector_column].to_numpy())
+    proj = cuml.manifold.umap.UMAP(n_components=2).fit_transform(vec)
+    color = cuml.manifold.umap.UMAP(n_components=1).fit_transform(vec)
+    data = [[*p.tolist(), "", c.item()] for p, c in zip(proj, color)]
+    if label_column:
+        for i, row in enumerate(bundle.dfs[table_name][label_column]):
+            data[i][2] = row
+    size = 50 / len(data) ** 0.5
+    v = {
+        "title": {
+            "text": f"UMAP projection of {vector_column}",
+        },
+        "visualMap": {
+            "min": color[:, 0].min().item(),
+            "max": color[:, 0].max().item(),
+            "right": 10,
+            "top": "center",
+            "calculable": True,
+            "dimension": 3,
+            "inRange": {"color": VIRIDIS},
+        },
+        "tooltip": {"trigger": "item", "formatter": "GET_THIRD_VALUE"}
+        if label_column
+        else {"show": False},
+        "xAxis": [{"type": "value"}],
+        "yAxis": [{"type": "value"}],
+        "series": [{"type": "scatter", "symbolSize": size, "data": data}],
+    }
+    return v

lynxkite-graph-analytics/uv.lock

@@ -104,6 +104,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815 },
 ]
 
+[[package]]
+name = "cachetools"
+version = "5.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/81/3747dad6b14fa2cf53fcf10548cf5aea6913e96fab41a3c198676f8948a5/cachetools-5.5.2.tar.gz", hash = "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4", size = 28380 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/72/76/20fa66124dbe6be5cafeb312ece67de6b61dd91a0247d1ea13db4ebb33c2/cachetools-5.5.2-py3-none-any.whl", hash = "sha256:d26a22bcc62eb95c3beabd9f1ee5e820d3d2704fe2967cbe350e20c8ffcd3f0a", size = 10080 },
+]
+
 [[package]]
 name = "certifi"
 version = "2025.1.31"
@@ -161,6 +170,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0e/f6/65ecc6878a89bb1c23a086ea335ad4bf21a588990c3f535a227b9eea9108/charset_normalizer-3.4.1-py3-none-any.whl", hash = "sha256:d98b1668f06378c6dbefec3b92299716b931cd4e6061f3c875a71ced1780ab85", size = 49767 },
 ]
 
+[[package]]
+name = "click"
+version = "8.1.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "platform_system == 'Windows'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188 },
+]
+
+[[package]]
+name = "cloudpickle"
+version = "3.1.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/52/39/069100b84d7418bc358d81669d5748efb14b9cceacd2f9c75f550424132f/cloudpickle-3.1.1.tar.gz", hash = "sha256:b216fa8ae4019d5482a8ac3c95d8f6346115d8835911fd4aefd1a445e4242c64", size = 22113 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7e/e8/64c37fadfc2816a7701fa8a6ed8d87327c7d54eacfbfb6edab14a2f2be75/cloudpickle-3.1.1-py3-none-any.whl", hash = "sha256:c8c5a44295039331ee9dad40ba100a9c7297b6f988e50e87ccdf3765a668350e", size = 20992 },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -221,30 +251,111 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c1/31/1ae946f11dfbd229222e6d6ad8e7bd1891d3d48bde5fbf7a0beb9491f8e3/contourpy-1.3.1-cp313-cp313t-win_amd64.whl", hash = "sha256:287ccc248c9e0d0566934e7d606201abd74761b5703d804ff3df8935f523d546", size = 236668 },
 ]
 
+[[package]]
+name = "cuda-bindings"
+version = "12.8.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pywin32", marker = "sys_platform == 'win32'" },
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