breast-cancer-mlflow

You are an expert terminal agent. You MUST use the provided tools to complete tasks. Use the terminal tool to run shell commands, read_file to read files, write_file to write files, search_files to search, and patch to edit files. Do NOT write out solutions as text - execute them using the tools. Always start by exploring the environment with terminal commands.

## Goal

Build a complete machine learning classification pipeline for breast cancer diagnosis with MLflow experiment tracking, model registry, and FastAPI deployment. Train multiple classification models, track experiments with MLflow, register the best model, and serve predictions through a REST API.

## Background

You will work with pre-split Breast Cancer Wisconsin data containing diagnostic information for classifying tumors as malignant (0) or benign (1). The dataset includes 30 numerical features computed from digitized images of fine needle aspirate (FNA) of breast masses. The training data includes the target column, but the test data does NOT include the target column - you must predict it.

Data files are located at:
- /app/data/train.csv - Training data with 30 features + target column (227 samples, 31 columns)
- /app/data/test.csv - Test data with 30 features only, NO target column (342 samples, 30 columns)

You have access to scikit-learn (version 1.7.2), MLflow (version 2.19.0), FastAPI (version 0.115.6), uvicorn (version 0.34.0), pandas (version 2.2.3), and requests (version 2.32.3).

## Steps

### Step 1. Load and Explore the Data

- Load training data from /app/data/train.csv
  - Contains 30 feature columns + "target" column (31 columns total)
  - Target: 0=malignant, 1=benign
- Load test data from /app/data/test.csv
  - Contains 30 feature columns only (NO target column, 30 columns total)
  - You must predict the target values for these samples
- Verify the data structure and check for any missing values
- For training, split train.csv into train/validation sets:
  - test_size=0.2
  - random_state=42
  - stratify=y (maintain class distribution)
- Save the split information to /app/output/split_info.json:
  ```json
  {
    "random_state": 42,
    "test_size": 0.2,
    "train_size": <int>,
    "val_size": <int>,
    "train_indices": [<list of original indices used for training>],
    "val_indices": [<list of original indices used for validation>]
  }
  ```

### Step 2. Connect to MLflow Tracking Server

An MLflow tracking server is already running at http://127.0.0.1:5000.
- Set MLflow tracking URI to http://127.0.0.1:5000 in your training script
- The server uses /app/mlflow directory for storage

### Step 3. Train Multiple Classification Models with MLflow Tracking

Train at least THREE different classification models with MLflow experiment tracking:

**Model 1 - Logistic Regression** (LogisticRegression from sklearn.linear_model)
- Allowed parameters (use only these): C, max_iter, solver, penalty, random_state
- Create an MLflow experiment named "breast-cancer-classification"
- Log parameters: model_type="logistic_regression" and all hyperparameters used
- Log metrics: train_accuracy, val_accuracy, precision, recall, f1_score
- Log the trained model using mlflow.sklearn.log_model()

**Model 2 - Support Vector Machine** (SVC from sklearn.svm)
- Allowed parameters (use only these): kernel, C, gamma, degree, random_state
- Use the same experiment "breast-cancer-classification"
- Log parameters: model_type="svm" and all hyperparameters used
- Log metrics: train_accuracy, val_accuracy, precision, recall, f1_score
- Log the trained model using mlflow.sklearn.log_model()

**Model 3 - Random Forest Classifier** (RandomForestClassifier from sklearn.ensemble)
- Allowed parameters (use only these): n_estimators, max_depth, min_samples_split, min_samples_leaf, random_state
- Use the same experiment "breast-cancer-classification"
- Log parameters: model_type="random_forest" and all hyperparameters used
- Log metrics: train_accuracy, val_accuracy, precision, recall, f1_score
- Log the trained model using mlflow.sklearn.log_model()

Choose appropriate hyperparameters for each model to achieve good classification performance.

For EACH model:
- Start an MLflow run using mlflow.start_run()
- Train on the training data (X_train, y_train from train.csv split)
- Calculate training accuracy
- Calculate validation set metrics: 
… (8213 more chars)

I'll build this complete ML pipeline step by step. Let me start by exploring the environment and data.

read_file({"path": "/app/data/train.csv", "limit": 10})

read_file({"path": "/app/data/test.csv", "limit": 10})

terminal({"command": "ls -la /app/ && echo \"---\" && ls -la /app/data/ 2>/dev/null && echo \"---\" && ls -la /app/mlflow/ 2>/dev/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|", "total_lines": 228, "file_size": 48762, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 228 lines)", "is_binary": false, "is_image": false}

{"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|", "total_lines": 343, "file_size": 72424, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 343 lines)", "is_binary": false, "is_image": false}

{"output": "total 28\ndrwxr-xr-x 1 root root 4096 Mar  4 09:20 .\ndrwxr-xr-x 1 root root 4096 Apr 24 20:09 ..\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\n---\ntotal 132\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\n---\ntotal 12\ndrwxr-xr-x 3 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 artifacts", "exit_code": 0, "error": null}

Good, I can see the data. Let me check the MLflow server status and create the training script.

terminal({"command": "curl -s http://127.0.0.1:5000/ 2>&1 | head -5"})

terminal({"command": "python3 -c \"import sklearn; print(sklearn.__version__); import mlflow; print(mlflow.__version__); import fastapi; print(fastapi.__version__)\""})

{"output": "bash: line 3: curl: command not found", "exit_code": 0, "error": null}

{"output": "1.7.2\n2.19.0\n0.115.6", "exit_code": 0, "error": null}

Good, MLflow server is running and all packages are available. Let me create the training script first.

write_file({"path": "/app/train.py", "content": "import pandas as pd\nimport numpy as np\nimport json\nimport mlflow\nimport mlflow.sklearn\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_sco…)

{"bytes_written": 7395, "dirs_created": true}

{"output": "============================================================\nStep 1: Loading and exploring data\n============================================================\nTraining data shape: (227, 31)\nTest data shape: (342, 30)\nTraining columns: ['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']\nTest columns: ['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']\nTraining missing values: 0\nTest missing values: 0\nTarget distribution:\ntarget\n1    142\n0     85\nName: count, dtype: int64\nTraceback (most recent call last):\n  File \"/app/train.py\", line 41, in <module>\n    X_train, X_val, y_train, y_val, train_idx, val_idx = train_test_split(\n    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: not enough values to unpack (expected 6, got 4)", "exit_code": 1, "error": null}

I need to fix the train_test_split to also return indices. Let me update the script.

patch({"mode": "replace", "path": "/app/train.py", "old_string": "X_train, X_val, y_train, y_val, train_idx, val_idx = train_test_split(\n    X, y, test_size=0.2, random_state=42, stratify=y\n)", "new_string": "indices = np.arange(len(X))\nX_train, X_val, y_train, y_val, train_idx, val_idx = train_test_split(\n    X, y, indices, test_size=0.2, random_state=42, stratify=y\n)"})

{"success": true, "diff": "--- a//app/train.py\n+++ b//app/train.py\n@@ -38,8 +38,9 @@\n X = train_df[feature_cols].values\n y = train_df['target'].values\n \n+indices = np.arange(len(X))\n X_train, X_val, y_train, y_val, train_idx, val_idx = train_test_split(\n-    X, y, test_size=0.2, random_state=42, stratify=y\n+    X, y, indices, test_size=0.2, random_state=42, stratify=y\n )\n \n print(f\"\\nTrain size: {len(X_train)}, Val size: {len(X_val)}\")\n", "files_modified": ["/app/train.py"], "lint": {"status": "ok", "output": ""}}

{"output": "2026/04/24 20:13:42 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\n2026/04/24 20:13:45 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpe7th8dam/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. \n2026/04/24 20:13:45 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.\n2026/04/24 20:13:47 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpzmjyz0t1/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. \n2026/04/24 20:13:47 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.\n2026/04/24 20:13:50 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpwrv4k3ld/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. \n2026/04/24 20:13:50 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.\n============================================================\nStep 1: Loading and exploring data\n============================================================\nTraining data shape: (227, 31)\nTest data shape: (342, 30)\nTraining columns: ['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']\nTest columns: ['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']\nTraining missing values: 0\nTest missing values: 0\nTarget distribution:\ntarget\n1    142\n0     85\nName: count, dtype: int64\n\nTrain size: 181, Va
… (3750 more chars)

The model registry needs the registered model to be created first. Let me fix the registration approach.

patch({"mode": "replace", "path": "/app/train.py", "old_string": "# Register model\nmv = client.create_model_version(\n    name=model_name,\n    source=model_uri,\n    run_id=best_run_id,\n)\nprint(f\"Model registered: {model_name} version {mv.version}\")", "new_string": "# Register model - first create the registered model if it doesn't exist\ntry:\n    client.get_registered_model(model_name)\nexcept E…)

{"success": true, "diff": "--- a//app/train.py\n+++ b//app/train.py\n@@ -173,7 +173,12 @@\n model_name = \"breast-cancer-classifier\"\n model_uri = f\"runs:/{best_run_id}/model\"\n \n-# Register model\n+# Register model - first create the registered model if it doesn't exist\n+try:\n+    client.get_registered_model(model_name)\n+except Exception:\n+    client.create_registered_model(model_name)\n+\n mv = client.create_model_version(\n     name=model_name,\n     source=model_uri,\n", "files_modified": ["/app/train.py"], "lint": {"status": "ok", "output": ""}}

{"output": "2026/04/24 20:14:21 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\n2026/04/24 20:14:24 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmp4vfgbsk3/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. \n2026/04/24 20:14:24 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.\n2026/04/24 20:14:26 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpw3_kz9ft/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. \n2026/04/24 20:14:26 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.\n2026/04/24 20:14:29 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpbnd6dnx1/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. \n2026/04/24 20:14:29 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.\n2026/04/24 20:14:29 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\n============================================================\nStep 1: Loading and exploring data\n============================================================\nTraining data shape: (227, 31)\nTest data shape: (342, 30)\nTraining columns: ['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']\nTest columns: ['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 symmetr
… (2007 more chars)

Training completed successfully. The logistic regression model is the best with val_accuracy of 0.9348. Now let me verify the output files and create the FastAPI application.

terminal({"command": "cat /app/output/split_info.json | python3 -m json.tool | head -10 && echo \"---\" && cat /app/output/experiments.json | python3 -m json.tool | head -20 && echo \"---\" && cat /app/output/best_model_info.json | python3 -m json.tool"})

{"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---\n{\n    \"experiment_name\": \"breast-cancer-classification\",\n    \"runs\": [\n        {\n            \"run_id\": \"e1df19cff276471ea6a446f6734306cb\",\n            \"model_type\": \"logistic_regression\",\n            \"hyperparameters\": {\n                \"C\": 1.0,\n                \"max_iter\": 1000,\n                \"solver\": \"lbfgs\",\n                \"penalty\": \"l2\",\n                \"random_state\": 42\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---\n{\n    \"model_name\": \"breast-cancer-classifier\",\n    \"model_version\": 1,\n    \"model_stage\": \"Production\",\n    \"run_id\": \"e1df19cff276471ea6a446f6734306cb\",\n    \"model_type\": \"logistic_regression\",\n    \"hyperparameters\": {\n        \"C\": 1.0,\n        \"max_iter\": 1000,\n        \"solver\": \"lbfgs\",\n        \"penalty\": \"l2\",\n        \"random_state\": 42\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}

All validation metrics meet the thresholds. Now let me create the FastAPI application.

write_file({"content": "import mlflow\nimport mlflow.sklearn\nfrom mlflow.tracking import MlflowClient\nfrom fastapi import FastAPI, HTTPException\nfrom pydantic import BaseModel\nfrom typing import List\nimport numpy as np\nimport json\n\n# Set MLflow tracking URI\nmlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n\napp = FastAPI(title=\"Breast Cancer Classifier API\")\n\n# Global variables for model\nmod…)

{"bytes_written": 3742, "dirs_created": true}

Now let me start the FastAPI server in the background.

terminal({"command": "cd /app && python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1", "background": true, "notify_on_complete": false, "watch_patterns": ["UP", "ERROR", "error"]})

{"output": "Background process started", "session_id": "proc_f847ed4b6c59", "pid": null, "exit_code": 0, "error": null, "watch_patterns": ["UP", "ERROR", "error"]}

{"session_id": "proc_f847ed4b6c59", "command": "cd /app && python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000 2>&1", "status": "exited", "pid": null, "uptime_seconds": 5, "output_preview": "", "exit_code": -1}

{"session_id": "proc_f847ed4b6c59", "status": "exited", "output": "", "total_lines": 0, "showing": "0 lines"}

The server exited immediately. Let me check what happened and try a different approach.

terminal({"command": "cd /app && python3 -c \"from api.app import app; print('Import OK')\" 2>&1"})

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

{"output": "", "exit_code": -1, "error": "Foreground command uses shell-level background wrappers (nohup/disown/setsid). Use terminal(background=true) so Hermes can track the process, then run readiness checks and tests in separate commands.", "status": "error"}

{"output": "Background process started", "session_id": "proc_f0fe9ed12b6b", "pid": null, "exit_code": 0, "error": null}

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

{"session_id": "proc_f0fe9ed12b6b", "command": "cd /app && python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000", "status": "exited", "pid": null, "uptime_seconds": 15, "output_preview": "", "exit_code": -1}

{"output": "/usr/local/lib/python3.13/site-packages/mlflow/store/artifact/utils/models.py:31: FutureWarning: ``mlflow.tracking.client.MlflowClient.get_latest_versions`` 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  latest = client.get_latest_versions(name, None if stage is None else [stage])\nModel loaded successfully\n[1]", "exit_code": 0, "error": null}

The model loads fine. The issue is with uvicorn backgrounding. Let me try a different approach.

terminal({"command": "cd /app && python3 -c \"\nimport uvicorn\nfrom api.app import app\nuvicorn.run(app, host='0.0.0.0', port=8000)\n\" &\nsleep 5\npython3 -c \"\nimport urllib.request\ntry:\n    r = urllib.request.urlopen('http://localhost:8000/health')\n    print(r.read().decode())\nexcept Exception as e:\n    print(f'Error: {e}')\n\"", "timeout": 15})

{"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"}

{"bytes_written": 138, "dirs_created": true}

{"output": "Background process started", "session_id": "proc_8f918e96ec11", "pid": null, "exit_code": 0, "error": null}

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

{"session_id": "proc_8f918e96ec11", "command": "cd /app && python3 api/run_server.py", "status": "exited", "pid": null, "uptime_seconds": 18, "output_preview": "", "exit_code": -1}

The server keeps exiting. Let me check what's happening with the startup event.

terminal({"command": "cd /app && python3 -c \"\nimport mlflow\nmlflow.set_tracking_uri('http://127.0.0.1:5000')\nfrom mlflow.tracking import MlflowClient\nclient = MlflowClient()\nversions = client.search_model_versions(\\\"name='breast-cancer-classifier'\\\", order_by=['version_number DESC'])\nprint(f'Found {len(versions)} versions')\nif versions:\n    mv = versions[0]\n    print(f'Version: {mv.version}, …)

{"output": "Found 1 versions\nVersion: 1, Stage: Production\nModel type: logistic_regression", "exit_code": 0, "error": null}

The model registry works fine. The issue might be that the background process is not capturing output properly. Let me try using pty mode.

terminal({"background": true, "command": "cd /app && python3 -c \"\nimport sys\nsys.stdout.flush()\nprint('Starting server...', flush=True)\nimport uvicorn\nfrom api.app import app\nprint('App imported, starting uvicorn...', flush=True)\nuvicorn.run(app, host='0.0.0.0', port=8000, log_level='info')\n\"", "pty": true})

{"output": "Background process started", "session_id": "proc_057d82b1c058", "pid": null, "exit_code": 0, "error": null}

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

{"session_id": "proc_057d82b1c058", "command": "cd /app && python3 -c \"\nimport sys\nsys.stdout.flush()\nprint('Starting server...', flush=True)\nimport uvicorn\nfrom api.app import app\nprint('App imported, starting uvicorn...', flush=True)\nuvicorn.run(app, host='0.0.0.0', port=8000, log_level='info')\n\"", "status": "exited", "pid": null, "uptime_seconds": 25, "output_preview": "", "exit_code": -1}

{"output": "App loaded OK\n/usr/local/lib/python3.13/site-packages/mlflow/store/artifact/utils/models.py:31: FutureWarning: ``mlflow.tracking.client.MlflowClient.get_latest_versions`` 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  latest = client.get_latest_versions(name, None if stage is None else [stage])\nModel loaded: LogisticRegression(max_iter=1000, random_state=42)\nRun ID: e1df19cff276471ea6a446f6734306cb\nModel type: logistic_regression", "exit_code": 0, "error": null}

The model loads fine. The issue is with the background process handling. Let me try a different approach using a shell wrapper script.

write_file({"content": "#!/bin/bash\ncd /app\nexec python3 -m uvicorn api.app:app --host 0.0.0.0 --port 8000 --log-level info\n", "path": "/app/start_server.sh"})

{"bytes_written": 100, "dirs_created": true}

{"output": "Background process started", "session_id": "proc_b0bda483b1ff", "pid": null, "exit_code": 0, "error": null}

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

{"session_id": "proc_b0bda483b1ff", "command": "bash /app/start_server.sh", "status": "exited", "pid": null, "uptime_seconds": 18, "output_preview": "", "exit_code": -1}

The background processes keep showing as exited. This might be a process tracking issue. Let me try running the server differently - using a Python script that handles the server startup more directly.

terminal({"command": "ps aux | grep -E \"uvicorn|python\" | grep -v grep"})

{"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"}

Let me try a different approach - I'll write a comprehensive script that handles everything including server startup and API testing.

write_file({"content": "#!/usr/bin/env python3\n\"\"\"\nComplete pipeline: Start FastAPI server, verify, generate predictions, save results.\n\"\"\"\nimport subprocess\nimport time\nimport sys\nimport os\nimport json\nimport pandas as pd\nimport numpy as np\nimport urllib.request\nimport urllib.error\n\ndef make_request(url, data=None, method=\"GET\"):\n    \"\"\"Make HTTP request and return response.\"\"\"\…)

{"bytes_written": 5564, "dirs_created": true}

{"output": "============================================================\nStarting FastAPI server...\n============================================================\nServer process started with PID: 1449\nWaiting for server to be ready...\nERROR: Server failed to start!", "exit_code": 1, "error": null}

Let me debug the server startup issue more carefully.

terminal({"command": "cd /app && python3 -c \"\nimport subprocess\nimport sys\nproc = subprocess.Popen(\n    [sys.executable, '-m', 'uvicorn', 'api.app:app', '--host', '0.0.0.0', '--port', '8000'],\n    cwd='/app',\n    stdout=subprocess.PIPE,\n    stderr=subprocess.PIPE,\n)\nimport time\ntime.sleep(5)\nstdout, stderr = proc.communicate(timeout=5)\nprint('STDOUT:', stdout.decode())\nprint('STDERR:', stderr…)

{"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"}

{"bytes_written": 425, "dirs_created": true}

{"output": "Traceback (most recent call last):\n  File \"/app/debug_server.py\", line 15, in <module>\n    stdout, stderr = proc.communicate(timeout=5)\n                     ~~~~~~~~~~~~~~~~^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/subprocess.py\", line 1219, in communicate\n    stdout, stderr = self._communicate(input, endtime, timeout)\n                     ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/subprocess.py\", line 2123, in _communicate\n    self._check_timeout(endtime, orig_timeout, stdout, stderr)\n    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.13/subprocess.py\", line 1263, in _check_timeout\n    raise TimeoutExpired(\n    ...<2 lines>...\n            stderr=b''.join(stderr_seq) if stderr_seq else None)\nsubprocess.TimeoutExpired: Command '['/usr/local/bin/python3', '-m', 'uvicorn', 'api.app:app', '--host', '0.0.0.0', '--port', '8000']' timed out after 5 seconds", "exit_code": 1, "error": null}

The server is actually running (it didn't exit in 5 seconds). The issue was with my wait logic. Let me fix the pipeline script.

write_file({"content": "#!/usr/bin/env python3\n\"\"\"\nComplete pipeline: Start FastAPI server, verify, generate predictions, save results.\n\"\"\"\nimport subprocess\nimport time\nimport sys\nimport os\nimport json\nimport pandas as pd\nimport numpy as np\nimport urllib.request\nimport urllib.error\nimport signal\n\ndef make_request(url, data=None, method=\"GET\"):\n    \"\"\"Make HTTP request and return r…)

{"bytes_written": 5481, "dirs_created": true}

{"output": "OpenBLAS blas_thread_init: pthread_create failed for thread 22 of 32: Resource temporarily unavailable\nOpenBLAS blas_thread_init: ensure that your address space and process count limits are big enough (ulimit -a)\nOpenBLAS blas_thread_init: or set a smaller OPENBLAS_NUM_THREADS to fit into what you have available\nOpenBLAS blas_thread_init: RLIMIT_NPROC -1 current, -1 max\nOpenBLAS blas_thread_init: pthread_create failed for thread 23 of 32: Resource temporarily unavailable\nOpenBLAS blas_thread_init: ensure that your address space and process count limits are big enough (ulimit -a)\nOpenBLAS blas_thread_init: or set a smaller OPENBLAS_NUM_THREADS to fit into what you have available\nOpenBLAS blas_thread_init: RLIMIT_NPROC -1 current, -1 max\nOpenBLAS blas_thread_init: pthread_create failed for thread 24 of 32: Resource temporarily unavailable\nOpenBLAS blas_thread_init: ensure that your address space and process count limits are big enough (ulimit -a)\nOpenBLAS blas_thread_init: or set a smaller OPENBLAS_NUM_THREADS to fit into what you have available\nOpenBLAS blas_thread_init: RLIMIT_NPROC -1 current, -1 max\nOpenBLAS blas_thread_init: pthread_create failed for thread 25 of 32: Resource temporarily unavailable\nOpenBLAS blas_thread_init: ensure that your address space and process count limits are big enough (ulimit -a)\nOpenBLAS blas_thread_init: or set a smaller OPENBLAS_NUM_THREADS to fit into what you have available\nOpenBLAS blas_thread_init: RLIMIT_NPROC -1 current, -1 max\nOpenBLAS blas_thread_init: pthread_create failed for thread 26 of 32: Resource temporarily unavailable\nOpenBLAS blas_thread_init: ensure that your address space and process count limits are big enough (ulimit -a)\nOpenBLAS blas_thread_init: or set a smaller OPENBLAS_NUM_THREADS to fit into what you have available\nOpenBLAS blas_thread_init: RLIMIT_NPROC -1 current, -1 max\nOpenBLAS blas_thread_init: pthread_create failed for thread 27 of 32: Resource temporarily unavailable\nOpenBLAS blas_thread_init: ensure that your address space and process count limits are big enough (ulimit -a)\nOpenBLAS blas_thread_init: or set a smaller OPENBLAS_NUM_THREADS to fit into what you have available\nOpenBLAS blas_thread_init: RLIMIT_NPROC -1 current, -1 max\nOpenBLAS blas_thread_init: pthread_create failed for thread 28 of 32: Resource temporarily unavailable\nOpenBLAS blas_thread_init: ensure that your address space and process count limits are big enough (ulimit -a)\nOpenBLAS blas_thread_init: or set a smaller OPENBLAS_NUM_THREADS to fit into what you have available\nOpenBLAS blas_thread_init: RLIMIT_NPROC -1 current, -1 max\nOpenBLAS blas_thread_init: pthread_create failed for thread 29 of 32: Resource temporarily unavailable\nOpenBLAS blas_thread_init: ensure that your address space and process count limits are big enough (ulimit -a)\nOpenBLAS blas_thread_init: or set a smaller OPENBLAS_NUM_THREADS to fit into what you have available\nOpenBLAS blas_thread_init: RLIMIT_NPROC -1 current, -1 max\nOpenBLAS blas_thread_init: pthread_create failed for thread 30 of 32: Resource temporarily unavailable\nOpenBLAS blas_thread_init: ensure that your address space and process count limits are big enough (ulimit -a)\nOpenBLAS blas_thread_init: or set a smaller OPENBLAS_NUM_THREADS to fit into what you have available\nOpenBLAS blas_thread_init: RLIMIT_NPROC -1 current, -1 max\nOpenBLAS blas_thread_init: pthread_create failed for thread 31 of 32: Resource temporarily unavailable\nOpenBLAS blas_thread_init: ensure that your address space and process count limits are big enough (ulimit -a)\nOpenBLAS blas_thread_init: or set a smaller OPENBLAS_NUM_THREADS to fit into what you have available\nOpenBLAS blas_thread_init: RLIMIT_NPROC -1 current, -1 max\nTraceback (most recent call last):\n  File \"/app/run_pipeline.py\", line 10, in <module>\n    import pandas as pd\n  File \"/usr/local/lib/python3.13/site-packages/pandas/__init__.py\", line 19, in <module>\n    raise
… (528 more chars)

There's a numpy import issue. Let me check the environment and fix this.

terminal({"command": "export OPENBLAS_NUM_THREADS=4 && export OMP_NUM_THREADS=4 && export MKL_NUM_THREADS=4 && cd /app && python3 -c \"import numpy; print(numpy.__version__); import pandas; print(pandas.__version__)\""})

{"output": "2.2.1\n2.2.3", "exit_code": 0, "error": null}

The issue is with thread limits. Let me update the pipeline script with environment variables.

patch({"mode": "replace", "path": "/app/run_pipeline.py", "old_string": "    # Start server\n    env = os.environ.copy()\n    env['PYTHONUNBUFFERED'] = '1'", "new_string": "    # Start server\n    env = os.environ.copy()\n    env['PYTHONUNBUFFERED'] = '1'\n    env['OPENBLAS_NUM_THREADS'] = '4'\n    env['OMP_NUM_THREADS'] = '4'\n    env['MKL_NUM_THREADS'] = '4'"})

{"success": true, "diff": "--- a//app/run_pipeline.py\n+++ b//app/run_pipeline.py\n@@ -50,6 +50,9 @@\n     # Start server\n     env = os.environ.copy()\n     env['PYTHONUNBUFFERED'] = '1'\n+    env['OPENBLAS_NUM_THREADS'] = '4'\n+    env['OMP_NUM_THREADS'] = '4'\n+    env['MKL_NUM_THREADS'] = '4'\n     \n     proc = subprocess.Popen(\n         [sys.executable, '-m', 'uvicorn', 'api.app:app', '--host', '0.0.0.0', '--port', '8000'],\n", "files_modified": ["/app/run_pipeline.py"], "lint": {"status": "ok", "output": ""}}

{"success": true, "diff": "--- a//app/run_pipeline.py\n+++ b//app/run_pipeline.py\n@@ -12,6 +12,11 @@\n import urllib.request\n import urllib.error\n import signal\n+\n+# Set thread limits to avoid resource issues\n+os.environ['OPENBLAS_NUM_THREADS'] = '4'\n+os.environ['OMP_NUM_THREADS'] = '4'\n+os.environ['MKL_NUM_THREADS'] = '4'\n \n def make_request(url, data=None, method=\"GET\"):\n     \"\"\"Make HTTP request and return response.\"\"\"\n", "files_modified": ["/app/run_pipeline.py"], "lint": {"status": "ok", "output": ""}}

{"output": "============================================================\nStarting FastAPI server...\n============================================================\nServer process started with PID: 1795\nWaiting for server to be ready...\nServer is ready!\n\n============================================================\nStep 6: Verifying server health\n============================================================\nHealth check: {\n  \"status\": \"healthy\",\n  \"model_loaded\": true,\n  \"mlflow_tracking_uri\": \"http://127.0.0.1:5000\"\n}\n\n============================================================\nStep: Checking model info\n============================================================\nModel info: {\n  \"model_name\": \"breast-cancer-classifier\",\n  \"model_version\": 1,\n  \"model_stage\": \"Production\",\n  \"model_type\": \"logistic_regression\"\n}\n\n============================================================\nStep: Testing single prediction\n============================================================\nSingle prediction: {\n  \"prediction\": 1,\n  \"prediction_label\": \"benign\",\n  \"model_name\": \"breast-cancer-classifier\",\n  \"model_version\": 1\n}\n\n============================================================\nStep 7: Generating test predictions\n============================================================\nTest data shape: (342, 30)\n  Processed samples 0-49 (50/342)\n  Processed samples 50-99 (100/342)\n  Processed samples 100-149 (150/342)\n  Processed samples 150-199 (200/342)\n  Processed samples 200-249 (250/342)\n  Processed samples 250-299 (300/342)\n  Processed samples 300-341 (342/342)\n\nPredictions saved to /app/data/submission/prediction.csv\nTotal predictions: 342\n\nPrediction distribution:\nprediction_label\nbenign       213\nmalignant    129\nName: count, dtype: int64\n\nVerifying first 3 predictions via /predict endpoint:\n  Sample 0: {'prediction': 1, 'prediction_label': 'benign', 'model_name': 'breast-cancer-classifier', 'model_version': 1}\n  Sample 1: {'prediction': 1, 'prediction_label': 'benign', 'model_name': 'breast-cancer-classifier', 'model_version': 1}\n  Sample 2: {'prediction': 0, 'prediction_label': 'malignant', 'model_name': 'breast-cancer-classifier', 'model_version': 1}\n\n============================================================\nAll predictions generated successfully!\nServer is still running on port 8000\n============================================================", "exit_code": 0, "error": null}

Everything is working. Now let me verify all the outputs and make sure the server is still running. Let me also verify the prediction CSV and check all the requirements.

read_file({"path": "/app/data/submission/prediction.csv", "limit": 15})

terminal({"command": "cd /app && OPENBLAS_NUM_THREADS=4 python3 -c \"\nimport pandas as pd\ndf = pd.read_csv('/app/data/submission/prediction.csv')\nprint(f'Shape: {df.shape}')\nprint(f'Columns: {df.columns.tolist()}')\nprint(f'Index: {df.index[0]} to {df.index[-1]}')\nprint(f'Predictions distribution:')\nprint(df['y_pred'].value_counts())\nprint(f'Labels distribution:')\nprint(df['prediction_label'].value…)

{"content": "     1|sample_index,y_pred,prediction_label\n     2|0,1,benign\n     3|1,1,benign\n     4|2,0,malignant\n     5|3,1,benign\n     6|4,1,benign\n     7|5,0,malignant\n     8|6,0,malignant\n     9|7,1,benign\n    10|8,1,benign\n    11|9,1,benign\n    12|10,1,benign\n    13|11,1,benign\n    14|12,1,benign\n    15|13,1,benign\n    16|", "total_lines": 343, "file_size": 4760, "truncated": true, "hint": "Use offset=16 to continue reading (showing 1-15 of 343 lines)", "is_binary": false, "is_image": false}

{"output": "Shape: (342, 3)\nColumns: ['sample_index', 'y_pred', 'prediction_label']\nIndex: 0 to 341\nPredictions distribution:\ny_pred\n1    213\n0    129\nName: count, dtype: int64\nLabels distribution:\nprediction_label\nbenign       213\nmalignant    129\nName: count, dtype: int64\nMin sample_index: 0, Max: 341", "exit_code": 0, "error": null}

Good. Let me verify the server is still running and do a final health check.

terminal({"command": "cd /app && OPENBLAS_NUM_THREADS=4 python3 -c \"\nimport urllib.request\nimport json\n\n# Health check\nwith urllib.request.urlopen('http://localhost:8000/health') as r:\n    print('Health:', json.loads(r.read().decode()))\n\n# Model info\nwith urllib.request.urlopen('http://localhost:8000/model-info') as r:\n    print('Model info:', json.loads(r.read().decode()))\n\" 2>&1"})

{"output": "Health: {'status': 'healthy', 'model_loaded': True, 'mlflow_tracking_uri': 'http://127.0.0.1:5000'}\nModel info: {'model_name': 'breast-cancer-classifier', 'model_version': 1, 'model_stage': 'Production', 'model_type': 'logistic_regression'}", "exit_code": 0, "error": null}

Now let me verify all the JSON files are valid and check all the output requirements.

terminal({"command": "cd /app && OPENBLAS_NUM_THREADS=4 python3 -c \"\nimport json\n\n# Verify all JSON files\nfiles = [\n    '/app/output/split_info.json',\n    '/app/output/experiments.json',\n    '/app/output/best_model_info.json'\n]\n\nfor f in files:\n    with open(f) as fp:\n        data = json.load(fp)\n    print(f'{f}: OK')\n    print(json.dumps(data, indent=2)[:500])\n    print('---')\n\" 2>&1"})

{"output": "/app/output/split_info.json: OK\n{\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\n---\n/app/output/experiments.json: OK\n{\n  \"experiment_name\": \"breast-cancer-classification\",\n  \"runs\": [\n    {\n      \"run_id\": \"e1df19cff276471ea6a446f6734306cb\",\n      \"model_type\": \"logistic_regression\",\n      \"hyperparameters\": {\n        \"C\": 1.0,\n        \"max_iter\": 1000,\n        \"solver\": \"lbfgs\",\n        \"penalty\": \"l2\",\n        \"random_state\": 42\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/app/output/best_model_info.json: OK\n{\n  \"model_name\": \"breast-cancer-classifier\",\n  \"model_version\": 1,\n  \"model_stage\": \"Production\",\n  \"run_id\": \"e1df19cff276471ea6a446f6734306cb\",\n  \"model_type\": \"logistic_regression\",\n  \"hyperparameters\": {\n    \"C\": 1.0,\n    \"max_iter\": 1000,\n    \"solver\": \"lbfgs\",\n    \"penalty\": \"l2\",\n    \"random_state\": 42\n  },\n  \"val_accuracy\": 0.9347826086956522,\n  \"precision\": 0.90625,\n  \"recall\": 1.0,\n  \"f1_score\": 0.9508196721311475\n}\n---", "exit_code": 0, "error": null}

All JSON files are valid. Now let me verify the MLflow server has all the expected data and check the model registry.

terminal({"command": "cd /app && OPENBLAS_NUM_THREADS=4 python3 -c \"\nimport mlflow\nfrom mlflow.tracking import MlflowClient\n\nmlflow.set_tracking_uri('http://127.0.0.1:5000')\nclient = MlflowClient()\n\n# Check experiment\nexp = mlflow.get_experiment_by_name('breast-cancer-classification')\nprint(f'Experiment: {exp.name} (ID: {exp.experiment_id})')\n\n# Check runs\nruns = mlflow.search_runs(experiment_…)

{"output": "Experiment: breast-cancer-classification (ID: 1)\nNumber of runs: 6\n  Run ad371d574ed54e8ba1e352041bd37371: model_type=random_forest, val_acc=0.9130\n  Run 8cbcf7ce6e6048c981fa2123c6d0b31c: model_type=svm, val_acc=0.9130\n  Run e1df19cff276471ea6a446f6734306cb: model_type=logistic_regression, val_acc=0.9348\n  Run 815b67720ef34fb2a95e6ea4b6d20ffe: model_type=random_forest, val_acc=0.9130\n  Run b763d249ed3a4867a37310c6a5c04838: model_type=svm, val_acc=0.9130\n  Run a7acea9d8c234265bc195f51cde2d945: model_type=logistic_regression, val_acc=0.9348\n\nRegistered model: breast-cancer-classifier\n  Version 1: stage=Production, run_id=e1df19cff276471ea6a446f6734306cb", "exit_code": 0, "error": null}

Everything looks good. There are 6 runs (3 from the first training attempt, 3 from the second). The registered model is correctly pointing to the best run. Let me now verify the FastAPI app file and do a final comprehensive check.

read_file({"path": "/app/api/app.py"})

{"content": "     1|import mlflow\n     2|import mlflow.sklearn\n     3|from mlflow.tracking import MlflowClient\n     4|from fastapi import FastAPI, HTTPException\n     5|from pydantic import BaseModel\n     6|from typing import List\n     7|import numpy as np\n     8|import json\n     9|\n    10|# Set MLflow tracking URI\n    11|mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n    12|\n    13|app = FastAPI(title=\"Breast Cancer Classifier API\")\n    14|\n    15|# Global variables for model\n    16|model = None\n    17|model_name = \"breast-cancer-classifier\"\n    18|model_version = None\n    19|model_stage = None\n    20|model_type = None\n    21|\n    22|class PredictionRequest(BaseModel):\n    23|    features: List[float]\n    24|\n    25|class BatchPredictionRequest(BaseModel):\n    26|    samples: List[List[float]]\n    27|\n    28|@app.on_event(\"startup\")\n    29|async def startup_event():\n    30|    global model, model_version, model_stage, model_type\n    31|    \n    32|    print(\"Loading model from MLflow Model Registry...\")\n    33|    \n    34|    # Load the registered model from MLflow Model Registry\n    35|    model_uri = f\"models:/{model_name}/Production\"\n    36|    model = mlflow.sklearn.load_model(model_uri)\n    37|    \n    38|    # Get model version info\n    39|    client = MlflowClient()\n    40|    versions = client.search_model_versions(f\"name='{model_name}'\", order_by=[\"version_number DESC\"])\n    41|    if versions:\n    42|        mv = versions[0]\n    43|        model_version = int(mv.version)\n    44|        model_stage = mv.current_stage\n    45|        \n    46|        # Get run info to determine model type\n    47|        run = client.get_run(mv.run_id)\n    48|        model_type = run.data.params.get(\"model_type\", \"unknown\")\n    49|    \n    50|    print(f\"Model loaded: {model_name} v{model_version} ({model_stage}) - type: {model_type}\")\n    51|\n    52|@app.get(\"/health\")\n    53|async def health():\n    54|    return {\n    55|        \"status\": \"healthy\",\n    56|        \"model_loaded\": model is not None,\n    57|        \"mlflow_tracking_uri\": \"http://127.0.0.1:5000\"\n    58|    }\n    59|\n    60|@app.get(\"/model-info\")\n    61|async def model_info():\n    62|    if model is None:\n    63|        raise HTTPException(status_code=503, detail=\"Model not loaded\")\n    64|    return {\n    65|        \"model_name\": model_name,\n    66|        \"model_version\": model_version,\n    67|        \"model_stage\": model_stage,\n    68|        \"model_type\": model_type\n    69|    }\n    70|\n    71|@app.post(\"/predict\")\n    72|async def predict(request: PredictionRequest):\n    73|    if model is None:\n    74|        raise HTTPException(status_code=503, detail=\"Model not loaded\")\n    75|    \n    76|    features = np.array(request.features).reshape(1, -1)\n    77|    \n    78|    if features.shape[1] != 30:\n    79|        raise HTTPException(status_code=400, detail=f\"Expected 30 features, got {features.shape[1]}\")\n    80|    \n    81|    prediction = int(model.predict(features)[0])\n    82|    prediction_label = \"malignant\" if prediction == 0 else \"benign\"\n    83|    \n    84|    return {\n    85|        \"prediction\": prediction,\n    86|        \"prediction_label\": prediction_label,\n    87|        \"model_name\": model_name,\n    88|        \"model_version\": model_version\n    89|    }\n    90|\n    91|@app.post(\"/predict-batch\")\n    92|async def predict_batch(request: BatchPredictionRequest):\n    93|    if model is None:\n    94|        raise HTTPException(status_code=503, detail=\"Model not loaded\")\n    95|    \n    96|    samples = request.samples\n    97|    \n    98|    if len(samples) == 0:\n    99|        raise HTTPException(status_code=422, detail=\"Samples list is empty\")\n   100|    \n   101|    for i, sample in enumerate(samples):\n   102|        if len(sample) != 30:\n   103|            raise HTTPException(status_code
… (962 more chars)

Now let me do a comprehensive final validation of all requirements:

terminal({"command": "cd /app && OPENBLAS_NUM_THREADS=4 python3 -c \"\nimport json\nimport pandas as pd\nimport numpy as np\nimport urllib.request\n\nprint('=' * 60)\nprint('FINAL VALIDATION')\nprint('=' * 60)\n\n# 1. Check split_info.json\nprint('\\n1. Split Info:')\nwith open('/app/output/split_info.json') as f:\n    split = json.load(f)\nprint(f'   random_state: {split[\\\"random_state\\\"]}')\nprint(f'…)

{"output": "============================================================\nFINAL VALIDATION\n============================================================\n\n1. Split Info:\n   random_state: 42\n   test_size: 0.2\n   train_size: 181\n   val_size: 46\n   train_indices count: 181\n   val_indices count: 46\n   PASS\n\n2. Experiments:\n   experiment_name: breast-cancer-classification\n   Number of runs: 3\n   best_run_id: e1df19cff276471ea6a446f6734306cb\n   best_model_type: logistic_regression\n   PASS\n\n3. Best Model Info:\n   model_name: breast-cancer-classifier\n   model_version: 1\n   model_stage: Production\n   run_id: e1df19cff276471ea6a446f6734306cb\n   model_type: logistic_regression\n   val_accuracy: 0.9348\n   precision: 0.9062\n   recall: 1.0000\n   f1_score: 0.9508\n   PASS\n\n4. Predictions:\n   Shape: (342, 3)\n   Columns: ['sample_index', 'y_pred', 'prediction_label']\n   sample_index range: 0 to 341\n   y_pred distribution: {1: np.int64(213), 0: np.int64(129)}\n   prediction_label distribution: {'benign': np.int64(213), 'malignant': np.int64(129)}\n   Class 0: 129 (37.7%)\n   Class 1: 213 (62.3%)\n   PASS\n\n5. API Endpoints:\n   /health: {'status': 'healthy', 'model_loaded': True, 'mlflow_tracking_uri': 'http://127.0.0.1:5000'}\n   /model-info: {'model_name': 'breast-cancer-classifier', 'model_version': 1, 'model_stage': 'Production', 'model_type': 'logistic_regression'}\n   /predict: {'prediction': 1, 'prediction_label': 'benign', 'model_name': 'breast-cancer-classifier', 'model_version': 1}\n   /predict-batch: count=2, predictions=2\n   PASS\n\n============================================================\nALL VALIDATIONS PASSED!\n============================================================", "exit_code": 0, "error": null}

All validations passed. Let me do one final check to make sure the server is still running and accessible:

terminal({"command": "cd /app && OPENBLAS_NUM_THREADS=4 python3 -c \"\nimport urllib.request\nimport json\n\n# Final health check\nwith urllib.request.urlopen('http://localhost:8000/health') as r:\n    print(json.loads(r.read().decode()))\n\" 2>&1"})

{"output": "{'status': 'healthy', 'model_loaded': True, 'mlflow_tracking_uri': 'http://127.0.0.1:5000'}", "exit_code": 0, "error": null}

The complete ML classification pipeline is built and verified. Here's a summary of everything that was accomplished:

## Summary

### Step 1: Data Loading & Splitting
- Loaded training data (227 samples, 31 columns) and test data (342 samples, 30 columns, no target)
- Split training data 80/20 with stratification and random_state=42 (181 train, 46 validation)
- Saved split info to `/app/output/split_info.json`

### Step 2: MLflow Tracking
- Connected to MLflow tracking server at `http://127.0.0.1:5000`
- Created experiment "breast-cancer-classification"

### Step 3: Model Training (3 models)
- **Logistic Regression**: val_accuracy=0.9348, precision=0.9062, recall=1.0, f1=0.9508
- **SVM (RBF)**: val_accuracy=0.9130, precision=0.8788, recall=1.0, f1=0.9355
- **Random Forest**: val_accuracy=0.9130, precision=0.9032, recall=0.9655, f1=0.9333

### Step 4: Best Model Registration
- Best model: **Logistic Regression** (val_accuracy=0.9348)
- Registered as "breast-cancer-classifier" v1 in Production stage
- Saved results to `/app/output/experiments.json` and `/app/output/best_model_info.json`

### Step 5-6: FastAPI Server
- Created `/app/api/app.py` with 4 endpoints: `/health`, `/model-info`, `/predict`, `/predict-batch`
- Server running at `http://localhost:8000`, loading model from MLflow Model Registry

### Step 7: Test Predictions
- Generated predictions for all 342 test samples via the API
- Saved to `/app/data/submission/prediction.csv` (213 benign, 129 malignant)
- All performance thresholds met (val accuracy ≥0.9, all metrics ≥0.9)