breast-cancer-mlflow

{"processes": []}

{"output": "total 28\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root 4096 Apr 22 18:07 ..\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 api\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 data\ndrwxr-xr-x 3 root root 4096 Mar  4 09:20 mlflow\ndrwxr-xr-x 2 root root 4096 Mar  4 09:20 output\n-rwxr-xr-x 1 root root  212 Mar  4 09:20 start.sh", "exit_code": 0, "error": null}

{"output": "total 136\ndrwxr-xr-x 1 root root  4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root  4096 Mar  4 09:20 ..\ndrwxr-xr-x 2 root root  4096 Mar  4 09:20 submission\n-rw-rw-r-- 1 root root 72424 Mar  4 09:05 test.csv\n-rw-rw-r-- 1 root root 48762 Mar  4 09:05 train.csv", "exit_code": 0, "error": null}

{"content": "     1|mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension,target\n     2|12.99,14.23,84.08,514.3,0.09462,0.09965,0.03738,0.02098,0.1652,0.07238,0.1814,0.6412,0.9219,14.41,0.005231,0.02305,0.03113,0.007315,0.01639,0.005701,13.72,16.91,87.38,576.0,0.1142,0.1975,0.145,0.0585,0.2432,0.1009,1\n     3|16.16,21.54,106.2,809.8,0.1008,0.1284,0.1043,0.05613,0.216,0.05891,0.4332,1.265,2.844,43.68,0.004877,0.01952,0.02219,0.009231,0.01535,0.002373,19.47,31.68,129.7,1175.0,0.1395,0.3055,0.2992,0.1312,0.348,0.07619,0\n     4|9.876,19.4,63.95,298.3,0.1005,0.09697,0.06154,0.03029,0.1945,0.06322,0.1803,1.222,1.528,11.77,0.009058,0.02196,0.03029,0.01112,0.01609,0.00357,10.76,26.83,72.22,361.2,0.1559,0.2302,0.2644,0.09749,0.2622,0.0849,1\n     5|11.62,18.18,76.38,408.8,0.1175,0.1483,0.102,0.05564,0.1957,0.07255,0.4101,1.74,3.027,27.85,0.01459,0.03206,0.04961,0.01841,0.01807,0.005217,13.36,25.4,88.14,528.1,0.178,0.2878,0.3186,0.1416,0.266,0.0927,1\n     6|13.0,25.13,82.61,520.2,0.08369,0.05073,0.01206,0.01762,0.1667,0.05449,0.2621,1.232,1.657,21.19,0.006054,0.008974,0.005681,0.006336,0.01215,0.001514,14.34,31.88,91.06,628.5,0.1218,0.1093,0.04462,0.05921,0.2306,0.06291,1\n     7|13.74,17.91,88.12,585.0,0.07944,0.06376,0.02881,0.01329,0.1473,0.0558,0.25,0.7574,1.573,21.47,0.002838,0.01592,0.0178,0.005828,0.01329,0.001976,15.34,22.46,97.19,725.9,0.09711,0.1824,0.1564,0.06019,0.235,0.07014,1\n     8|7.691,25.44,48.34,170.4,0.08668,0.1199,0.09252,0.01364,0.2037,0.07751,0.2196,1.479,1.445,11.73,0.01547,0.06457,0.09252,0.01364,0.02105,0.007551,8.678,31.89,54.49,223.6,0.1596,0.3064,0.3393,0.05,0.279,0.1066,1\n     9|14.29,16.82,90.3,632.6,0.06429,0.02675,0.00725,0.00625,0.1508,0.05376,0.1302,0.7198,0.8439,10.77,0.003492,0.00371,0.004826,0.003608,0.01536,0.001381,14.91,20.65,94.44,684.6,0.08567,0.05036,0.03866,0.03333,0.2458,0.0612,1\n    10|12.07,13.44,77.83,445.2,0.11,0.09009,0.03781,0.02798,0.1657,0.06608,0.2513,0.504,1.714,18.54,0.007327,0.01153,0.01798,0.007986,0.01962,0.002234,13.45,15.77,86.92,549.9,0.1521,0.1632,0.1622,0.07393,0.2781,0.08052,1\n    11|11.43,17.31,73.66,398.0,0.1092,0.09486,0.02031,0.01861,0.1645,0.06562,0.2843,1.908,1.937,21.38,0.006664,0.01735,0.01158,0.00952,0.02282,0.003526,12.78,26.76,82.66,503.0,0.1413,0.1792,0.07708,0.06402,0.2584,0.08096,1\n    12|8.219,20.7,53.27,203.9,0.09405,0.1305,0.1321,0.02168,0.2222,0.08261,0.1935,1.962,1.243,10.21,0.01243,0.05416,0.07753,0.01022,0.02309,0.01178,9.092,29.72,58.08,249.8,0.163,0.431,0.5381,0.07879,0.3322,0.1486,1\n    13|12.46,24.04,83.97,475.9,0.1186,0.2396,0.2273,0.08543,0.203,0.08243,0.2976,1.599,2.039,23.94,0.007149,0.07217,0.07743,0.01432,0.01789,0.01008,15.09,40.68,97.65,711.4,0.1853,1.058,1.105,0.221,0.4366,0.2075,0\n    14|12.18,14.08,77.25,461.4,0.07734,0.03212,0.01123,0.005051,0.1673,0.05649,0.2113,0.5996,1.438,15.82,0.005343,0.005767,0.01123,0.005051,0.01977,0.0009502,12.85,16.47,81.6,513.1,0.1001,0.05332,0.04116,0.01852,0.2293,0.06037,1\n    15|14.59,22.68,96.39,657.1,0.08473,0.133,0.1029,0.03736,0.1454,0.06147,0.2254,1.108,2.224,19.54,0.004242,0.04639,0.06578,0.01606,0.01638,0.004406,15.48,27.27,105.9,733.5,0.1026,0.3171,0.3662,0.1105,0.2258,0.08004,1\n    16|8.888,14.64,58.79,244.0,0.09783,0.1531,0.08606,0.02872,0.1902,0.0898,0.5262,0.8522,3.168,25.44,0.01721,0.09368,0.05671,0.01766,0.02541,0.02193,9.733,15.67,62.56,284.4,0.1207,0.2436,0.1434,0.04786,0.2254,0.1084,1\n    17|14.06,17.18,89.75,609.1,0.08045,0.05361,0.02681,0.03251,0.1641,0.05764,0.1504,1.685,1.237,12.67,0.005371,0.01273,0.01132,0.009155,0.01719,0.001444,14.92,25.34,96.

{"content": "     1|mean radius,mean texture,mean perimeter,mean area,mean smoothness,mean compactness,mean concavity,mean concave points,mean symmetry,mean fractal dimension,radius error,texture error,perimeter error,area error,smoothness error,compactness error,concavity error,concave points error,symmetry error,fractal dimension error,worst radius,worst texture,worst perimeter,worst area,worst smoothness,worst compactness,worst concavity,worst concave points,worst symmetry,worst fractal dimension\n     2|12.31,16.52,79.19,470.9,0.09172,0.06829,0.03372,0.02272,0.172,0.05914,0.2505,1.025,1.74,19.68,0.004854,0.01819,0.01826,0.007965,0.01386,0.002304,14.11,23.21,89.71,611.1,0.1176,0.1843,0.1703,0.0866,0.2618,0.07609\n     3|10.16,19.59,64.73,311.7,0.1003,0.07504,0.005025,0.01116,0.1791,0.06331,0.2441,2.09,1.648,16.8,0.01291,0.02222,0.004174,0.007082,0.02572,0.002278,10.65,22.88,67.88,347.3,0.1265,0.12,0.01005,0.02232,0.2262,0.06742\n     4|15.46,23.95,103.8,731.3,0.1183,0.187,0.203,0.0852,0.1807,0.07083,0.3331,1.961,2.937,32.52,0.009538,0.0494,0.06019,0.02041,0.02105,0.006,17.11,36.33,117.7,909.4,0.1732,0.4967,0.5911,0.2163,0.3013,0.1067\n     5|9.742,15.67,61.5,289.9,0.09037,0.04689,0.01103,0.01407,0.2081,0.06312,0.2684,1.409,1.75,16.39,0.0138,0.01067,0.008347,0.009472,0.01798,0.004261,10.75,20.88,68.09,355.2,0.1467,0.0937,0.04043,0.05159,0.2841,0.08175\n     6|12.05,14.63,78.04,449.3,0.1031,0.09092,0.06592,0.02749,0.1675,0.06043,0.2636,0.7294,1.848,19.87,0.005488,0.01427,0.02322,0.00566,0.01428,0.002422,13.76,20.7,89.88,582.6,0.1494,0.2156,0.305,0.06548,0.2747,0.08301\n     7|14.58,13.66,94.29,658.8,0.09832,0.08918,0.08222,0.04349,0.1739,0.0564,0.4165,0.6237,2.561,37.11,0.004953,0.01812,0.03035,0.008648,0.01539,0.002281,16.76,17.24,108.5,862.0,0.1223,0.1928,0.2492,0.09186,0.2626,0.07048\n     8|14.87,16.67,98.64,682.5,0.1162,0.1649,0.169,0.08923,0.2157,0.06768,0.4266,0.9489,2.989,41.18,0.006985,0.02563,0.03011,0.01271,0.01602,0.003884,18.81,27.37,127.1,1095.0,0.1878,0.448,0.4704,0.2027,0.3585,0.1065\n     9|12.81,13.06,81.29,508.8,0.08739,0.03774,0.009193,0.0133,0.1466,0.06133,0.2889,0.9899,1.778,21.79,0.008534,0.006364,0.00618,0.007408,0.01065,0.003351,13.63,16.15,86.7,570.7,0.1162,0.05445,0.02758,0.0399,0.1783,0.07319\n    10|9.742,19.12,61.93,289.7,0.1075,0.08333,0.008934,0.01967,0.2538,0.07029,0.6965,1.747,4.607,43.52,0.01307,0.01885,0.006021,0.01052,0.031,0.004225,11.21,23.17,71.79,380.9,0.1398,0.1352,0.02085,0.04589,0.3196,0.08009\n    11|7.729,25.49,47.98,178.8,0.08098,0.04878,0.0,0.0,0.187,0.07285,0.3777,1.462,2.492,19.14,0.01266,0.009692,0.0,0.0,0.02882,0.006872,9.077,30.92,57.17,248.0,0.1256,0.0834,0.0,0.0,0.3058,0.09938\n    12|14.6,23.29,93.97,664.7,0.08682,0.06636,0.0839,0.05271,0.1627,0.05416,0.4157,1.627,2.914,33.01,0.008312,0.01742,0.03389,0.01576,0.0174,0.002871,15.79,31.71,102.2,758.2,0.1312,0.1581,0.2675,0.1359,0.2477,0.06836\n    13|10.05,17.53,64.41,310.8,0.1007,0.07326,0.02511,0.01775,0.189,0.06331,0.2619,2.015,1.778,16.85,0.007803,0.01449,0.0169,0.008043,0.021,0.002778,11.16,26.84,71.98,384.0,0.1402,0.1402,0.1055,0.06499,0.2894,0.07664\n    14|12.86,13.32,82.82,504.8,0.1134,0.08834,0.038,0.034,0.1543,0.06476,0.2212,1.042,1.614,16.57,0.00591,0.02016,0.01902,0.01011,0.01202,0.003107,14.04,21.08,92.8,599.5,0.1547,0.2231,0.1791,0.1155,0.2382,0.08553\n    15|11.43,15.39,73.06,399.8,0.09639,0.06889,0.03503,0.02875,0.1734,0.05865,0.1759,0.9938,1.143,12.67,0.005133,0.01521,0.01434,0.008602,0.01501,0.001588,12.32,22.02,79.93,462.0,0.119,0.1648,0.1399,0.08476,0.2676,0.06765\n    16|8.95,15.76,58.74,245.2,0.09462,0.1243,0.09263,0.02308,0.1305,0.07163,0.3132,0.9789,3.28,16.94,0.01835,0.0676,0.09263,0.02308,0.02384,0.005601,9.414,17.07,63.34,270.0,0.1179,0.1879,0.1544,0.03846,0.1652,0.07722\n    17|14.03,21.25,89.79,603.4,0.0907,0.06945,0.01462,0.01896,0.1517,0.05835,0.2589,1.503,1.667,22.07,0.007389,0.01383,0.007302,0.01004,0.01263,0.002925,15.33,30.28,98.27,715.5,0.1287,0.1513,0.06231,0.07963,0.2226,0.07617\n    1

{"output": "", "exit_code": 0, "error": null}

{"bytes_written": 6551, "dirs_created": true}

{"output": "Loaded training data: (227, 31)\nLoaded test data: (342, 30)\nTraceback (most recent call last):\n  File \"/app/train_model.py\", line 195, in <module>\n    main()\n    ~~~~^^\n  File \"/app/train_model.py\", line 106, in main\n    X_train, X_val, y_train, y_val = split_data(train_df)\n                                     ~~~~~~~~~~^^^^^^^^^^\n  File \"/app/train_model.py\", line 33, in split_data\n    X_train, X_val, y_train, y_val, train_indices, val_indices = train_test_split(\n                                                                 ~~~~~~~~~~~~~~~~^\n        X, y, test_size=test_size, random_state=random_state, stratify=y, return_indices=True\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/usr/local/lib/python3.13/site-packages/sklearn/utils/_param_validation.py\", line 196, in wrapper\n    params = func_sig.bind(*args, **kwargs)\n  File \"/usr/local/lib/python3.13/inspect.py\", line 3264, in bind\n    return self._bind(args, kwargs)\n           ~~~~~~~~~~^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/inspect.py\", line 3253, in _bind\n    raise TypeError(\n        'got an unexpected keyword argument {arg!r}'.format(\n            arg=next(iter(kwargs))))\nTypeError: got an unexpected keyword argument 'return_indices'", "exit_code": 1, "error": null}

{"success": true, "diff": "--- a//app/train_model.py\n+++ b//app/train_model.py\n@@ -30,9 +30,16 @@\n     X = train_df.drop(columns=[\"target\"])\n     y = train_df[\"target\"]\n     \n-    X_train, X_val, y_train, y_val, train_indices, val_indices = train_test_split(\n-        X, y, test_size=test_size, random_state=random_state, stratify=y, return_indices=True\n-    )\n+    # Get original indices before splitting\n+    original_indices = train_df.index.tolist()\n+    \n+    X_train, X_val, y_train, y_val = train_test_split(\n+        X, y, test_size=test_size, random_state=random_state, stratify=y\n+    )\n+    \n+    # Get the indices used for training and validation\n+    train_indices = X_train.index.tolist()\n+    val_indices = X_val.index.tolist()\n     \n     # Save split information\n     split_info = {\n@@ -40,8 +47,8 @@\n         \"test_size\": test_size,\n         \"train_size\": len(X_train),\n         \"val_size\": len(X_val),\n-        \"train_indices\": train_indices.tolist(),\n-        \"val_indices\": val_indices.tolist()\n+        \"train_indices\": train_indices,\n+        \"val_indices\": val_indices\n     }\n     \n     with open(\"/app/output/split_info.json\", \"w\") as f:\n", "files_modified": ["/app/train_model.py"], "lint": {"status": "ok", "output": ""}}

{"output": "2026/04/22 18:13:25 INFO mlflow.tracking.fluent: Experiment with name 'breast-cancer-classification' does not exist. Creating a new experiment.\n2026/04/22 18:13:25 WARNING mlflow.utils.git_utils: Failed to import Git (the Git executable is probably not on your PATH), so Git SHA is not available. Error: Failed to initialize: Bad git executable.\nThe git executable must be specified in one of the following ways:\n    - be included in your $PATH\n    - be set via $GIT_PYTHON_GIT_EXECUTABLE\n    - explicitly set via git.refresh(<full-path-to-git-executable>)\n\nAll git commands will error until this is rectified.\n\nThis initial message can be silenced or aggravated in the future by setting the\n$GIT_PYTHON_REFRESH environment variable. Use one of the following values:\n    - quiet|q|silence|s|silent|none|n|0: for no message or exception\n    - warn|w|warning|log|l|1: for a warning message (logging level CRITICAL, displayed by default)\n    - error|e|exception|raise|r|2: for a raised exception\n\nExample:\n    export GIT_PYTHON_REFRESH=quiet\n\n/usr/local/lib/python3.13/site-packages/sklearn/linear_model/_logistic.py:473: ConvergenceWarning: lbfgs failed to converge after 1000 iteration(s) (status=1):\nSTOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n\nIncrease the number of iterations to improve the convergence (max_iter=1000).\nYou might also want to scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n2026/04/22 18:13:29 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpatyndljb/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 18:13:29 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n2026/04/22 18:13:31 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmp2s7i7ttx/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 18:13:31 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n2026/04/22 18:13:32 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmp2s8_y5xy/model/model.pkl, flavor: sklearn). Fall back to return ['scikit-learn==1.7.2', 'cloudpickle==3.1.2']. Set logging level to DEBUG to see the full traceback. \n2026/04/22 18:13:32 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\nSuccessfully registered model 'breast-cancer-classifier'.\n2026/04/22 18:13:32 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: breast-cancer-classifier, version 1\nCreated version '1' of model 'breast-cancer-classifier'.\n/app/train_model.py:165: FutureWarning: ``mlflow.tracking.client.MlflowClient.transition_model_version_stage`` is deprecated since 2.9.0. Model registry stages will be removed in a future major release. To learn more about the deprecation of model registry stages, see our migration guide here: https://mlflow.org/docs/latest/model-registry.html#migrating-from-stages\n  client.transition_model_version_stage(\nLoaded training data: (227, 31)\nLoaded test data: (342, 30)\nTrain size: 181, Val size: 46\n\nTraining Logistic Re

{"output": "{\n  \"random_state\": 42,\n  \"test_size\": 0.2,\n  \"train_size\": 181,\n  \"val_size\": 46,\n  \"train_indices\": [\n    60,\n    172,\n    141,\n    90,\n    180,\n    80,\n    150,\n    133,\n    169,\n    154,\n    98,\n    81,\n    136,\n    166,\n    35,\n    184,\n    29,\n    41,\n    48,\n    45,\n    140,\n    177,\n    76,\n    196,\n    126,\n    144,\n    75,\n    44,\n    74,\n    130,\n    92,\n    115,\n    215,\n    21,\n    43,\n    167,\n    181,\n    139,\n    121,\n    120,\n    189,\n    137,\n    102,\n    207,\n    124,\n    62,\n    182,\n    34,\n    104,\n    200,\n    63,\n    105,\n    78,\n    165,\n    221,\n    148,\n    99,\n    176,\n    53,\n    173,\n    153,\n    185,\n    23,\n    86,\n    70,\n    127,\n    201,\n    143,\n    108,\n    26,\n    51,\n    163,\n    211,\n    170,\n    112,\n    195,\n    32,\n    206,\n    52,\n    174,\n    224,\n    114,\n    204,\n    125,\n    69,\n    91,\n    4,\n    220,\n    107,\n    191,\n    17,\n    188,\n    24,\n    155,\n    25,\n    73,\n    113,\n    156,\n    123,\n    10,\n    95,\n    217,\n    116,\n    138,\n    216,\n    57,\n    30,\n    213,\n    19,\n    72,\n    175,\n    203,\n    135,\n    22,\n    66,\n    103,\n    122,\n    97,\n    190,\n    85,\n    146,\n    158,\n    82,\n    218,\n    101,\n    100,\n    36,\n    65,\n    6,\n    39,\n    40,\n    61,\n    210,\n    56,\n    164,\n    33,\n    219,\n    27,\n    0,\n    223,\n    187,\n    64,\n    55,\n    202,\n    8,\n    226,\n    118,\n    12,\n    212,\n    161,\n    15,\n    18,\n    142,\n    222,\n    179,\n    128,\n    1,\n    178,\n    89,\n    109,\n    50,\n    83,\n    38,\n    84,\n    42,\n    159,\n    152,\n    111,\n    77,\n    2,\n    28,\n    183,\n    96,\n    3,\n    129,\n    157,\n    160,\n    49,\n    214,\n    194,\n    14\n  ],\n  \"val_indices\": [\n    145,\n    59,\n    79,\n    117,\n    151,\n    171,\n    37,\n    208,\n    106,\n    9,\n    5,\n    87,\n    88,\n    11,\n    67,\n    199,\n    197,\n    132,\n    192,\n    93,\n    134,\n    147,\n    198,\n    209,\n    68,\n    71,\n    119,\n    58,\n    47,\n    54,\n    162,\n    186,\n    46,\n    149,\n    193,\n    16,\n    13,\n    110,\n    205,\n    7,\n    225,\n    20,\n    131,\n    31,\n    168,\n    94\n  ]\n}", "exit_code": 0, "error": null}

{"output": "{\n  \"experiment_name\": \"breast-cancer-classification\",\n  \"runs\": [\n    {\n      \"run_id\": \"a9d5eb49afcf473ebdbf8b30efb8a3f5\",\n      \"model_type\": \"logistic_regression\",\n      \"hyperparameters\": {\n        \"C\": 1.0,\n        \"class_weight\": null,\n        \"dual\": false,\n        \"fit_intercept\": true,\n        \"intercept_scaling\": 1,\n        \"l1_ratio\": null,\n        \"max_iter\": 1000,\n        \"multi_class\": \"deprecated\",\n        \"n_jobs\": null,\n        \"penalty\": \"l2\",\n        \"random_state\": 42,\n        \"solver\": \"lbfgs\",\n        \"tol\": 0.0001,\n        \"verbose\": 0,\n        \"warm_start\": false\n      },\n      \"train_accuracy\": 0.9613259668508287,\n      \"val_accuracy\": 0.9347826086956522,\n      \"precision\": 0.90625,\n      \"recall\": 1.0,\n      \"f1_score\": 0.9508196721311475\n    },\n    {\n      \"run_id\": \"411d20eba8774bc387b5e24b86bf54c6\",\n      \"model_type\": \"svm\",\n      \"hyperparameters\": {\n        \"C\": 1.0,\n        \"break_ties\": false,\n        \"cache_size\": 200,\n        \"class_weight\": null,\n        \"coef0\": 0.0,\n        \"decision_function_shape\": \"ovr\",\n        \"degree\": 3,\n        \"gamma\": \"scale\",\n        \"kernel\": \"rbf\",\n        \"max_iter\": -1,\n        \"probability\": false,\n        \"random_state\": 42,\n        \"shrinking\": true,\n        \"tol\": 0.001,\n        \"verbose\": false\n      },\n      \"train_accuracy\": 0.9226519337016574,\n      \"val_accuracy\": 0.9130434782608695,\n      \"precision\": 0.8787878787878788,\n      \"recall\": 1.0,\n      \"f1_score\": 0.9354838709677419\n    },\n    {\n      \"run_id\": \"db6b4da164d34d318186cb33cc041e22\",\n      \"model_type\": \"random_forest\",\n      \"hyperparameters\": {\n        \"bootstrap\": true,\n        \"ccp_alpha\": 0.0,\n        \"class_weight\": null,\n        \"criterion\": \"gini\",\n        \"max_depth\": 10,\n        \"max_features\": \"sqrt\",\n        \"max_leaf_nodes\": null,\n        \"max_samples\": null,\n        \"min_impurity_decrease\": 0.0,\n        \"min_samples_leaf\": 2,\n        \"min_samples_split\": 5,\n        \"min_weight_fraction_leaf\": 0.0,\n        \"monotonic_cst\": null,\n        \"n_estimators\": 100,\n        \"n_jobs\": null,\n        \"oob_score\": false,\n        \"random_state\": 42,\n        \"verbose\": 0,\n        \"warm_start\": false\n      },\n      \"train_accuracy\": 0.994475138121547,\n      \"val_accuracy\": 0.9130434782608695,\n      \"precision\": 0.9032258064516129,\n      \"recall\": 0.9655172413793104,\n      \"f1_score\": 0.9333333333333333\n    }\n  ],\n  \"best_run_id\": \"a9d5eb49afcf473ebdbf8b30efb8a3f5\",\n  \"best_model_type\": \"logistic_regression\"\n}", "exit_code": 0, "error": null}

{"output": "{\n  \"model_name\": \"breast-cancer-classifier\",\n  \"model_version\": 1,\n  \"model_stage\": \"Production\",\n  \"run_id\": \"a9d5eb49afcf473ebdbf8b30efb8a3f5\",\n  \"model_type\": \"logistic_regression\",\n  \"hyperparameters\": {\n    \"C\": 1.0,\n    \"class_weight\": null,\n    \"dual\": false,\n    \"fit_intercept\": true,\n    \"intercept_scaling\": 1,\n    \"l1_ratio\": null,\n    \"max_iter\": 1000,\n    \"multi_class\": \"deprecated\",\n    \"n_jobs\": null,\n    \"penalty\": \"l2\",\n    \"random_state\": 42,\n    \"solver\": \"lbfgs\",\n    \"tol\": 0.0001,\n    \"verbose\": 0,\n    \"warm_start\": false\n  },\n  \"val_accuracy\": 0.9347826086956522,\n  \"precision\": 0.90625,\n  \"recall\": 1.0,\n  \"f1_score\": 0.9508196721311475\n}", "exit_code": 0, "error": null}

{"bytes_written": 4809, "dirs_created": true}

{"output": "Background process started", "session_id": "proc_d46ef9bbb608", "pid": null, "exit_code": 0, "error": null}

{"status": "exited", "exit_code": -1, "output": ""}

{"processes": [{"session_id": "proc_d46ef9bbb608", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:17:41", "uptime_seconds": 30, "status": "exited", "output_preview": "", "exit_code": -1}]}

{"session_id": "proc_d46ef9bbb608", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

{"output": "Background process started", "session_id": "proc_69773652f10f", "pid": null, "exit_code": 0, "error": null}

{"status": "exited", "exit_code": -1, "output": ""}

{"output": "No log file", "exit_code": 0, "error": null}

{"output": "", "exit_code": -1, "error": "Foreground command uses '&' backgrounding. Use terminal(background=true) for long-lived processes, then run health checks and tests in follow-up terminal calls.", "status": "error"}

{"output": "Background process started", "session_id": "proc_ad4eed019e47", "pid": null, "exit_code": 0, "error": null, "notify_on_complete": true}

{"status": "exited", "exit_code": -1, "output": ""}

{"output": "", "exit_code": -1, "error": "This foreground command appears to start a long-lived server/watch process. Run it with background=true, verify readiness (health endpoint/log signal), then execute tests in a separate command.", "status": "error"}

{"output": "Background process started", "session_id": "proc_3af93042b950", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_3af93042b950", "command": "cd /app && python -m uvicorn api.app:app --host 0.0.0.0 --port 8000", "status": "exited", "pid": null, "uptime_seconds": 20, "output_preview": "", "exit_code": -1}

{"output": "Import successful", "exit_code": 0, "error": null}

{"output": "", "exit_code": -1, "error": "Foreground command uses '&' backgrounding. Use terminal(background=true) for long-lived processes, then run health checks and tests in follow-up terminal calls.", "status": "error"}

{"processes": [{"session_id": "proc_d46ef9bbb608", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:17:41", "uptime_seconds": 324, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_69773652f10f", "command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 > /app/api/server.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:19:03", "uptime_seconds": 242, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_ad4eed019e47", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:20:44", "uptime_seconds": 141, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_3af93042b950", "command": "cd /app && python -m uvicorn api.app:app --host 0.0.0.0 --port 8000", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:21:51", "uptime_seconds": 74, "status": "exited", "output_preview": "", "exit_code": -1}]}

{"status": "already_exited", "exit_code": -1}

{"status": "already_exited", "exit_code": -1}

{"status": "already_exited", "exit_code": -1}

{"status": "already_exited", "exit_code": -1}

{"output": "Background process started", "session_id": "proc_bca4f7f5c5ce", "pid": null, "exit_code": 0, "error": null}

{"status": "exited", "exit_code": -1, "output": ""}

{"session_id": "proc_bca4f7f5c5ce", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

{"output": "---\ntotal 24\ndrwxr-xr-x 1 root root 4096 Apr 22 18:22 .\ndrwxr-xr-x 1 root root 4096 Apr 22 18:13 ..\ndrwxr-xr-x 2 root root 4096 Apr 22 18:22 __pycache__\n-rw-r--r-- 1 root root 4809 Apr 22 18:17 app.py", "exit_code": 0, "error": null}

{"output": "Background process started", "session_id": "proc_8680b4e6cfa5", "pid": null, "exit_code": 0, "error": null}

{"session_id": "proc_8680b4e6cfa5", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "status": "exited", "pid": null, "uptime_seconds": 19, "output_preview": "", "exit_code": -1}

{"session_id": "proc_8680b4e6cfa5", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

{"processes": [{"session_id": "proc_d46ef9bbb608", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:17:41", "uptime_seconds": 551, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_69773652f10f", "command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 > /app/api/server.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:19:03", "uptime_seconds": 469, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_ad4eed019e47", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:20:44", "uptime_seconds": 368, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_3af93042b950", "command": "cd /app && python -m uvicorn api.app:app --host 0.0.0.0 --port 8000", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:21:51", "uptime_seconds": 301, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_bca4f7f5c5ce", "command": "cd /app && nohup uvicorn api.app:app --host 0.0.0.0 --port 8000 > /app/api/server.log 2>&1 &", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:24:34", "uptime_seconds": 138, "status": "exited", "output_preview": "", "exit_code": -1}, {"session_id": "proc_8680b4e6cfa5", "command": "cd /app && uvicorn api.app:app --host 0.0.0.0 --port 8000 --timeout-keep-alive 120", "cwd": "/app", "pid": null, "started_at": "2026-04-22T13:25:55", "uptime_seconds": 57, "status": "exited", "output_preview": "", "exit_code": -1}]}

{"status": "already_exited", "exit_code": -1}

The processes are exiting immediately. Let me check if there's an issue with the app or if it's a different problem.

→ process({"action": "kill", "session_id": "proc_d46ef9bbb608"})

{"status": "already_exited", "exit_code": -1}