general improvements as per sap model

2026-06-08 11:17:25 +00:00 · 2025-11-05 10:43:41 +00:00 · 2025-11-05 10:43:41 +00:00 · f092af0180
commit f092af0180
parent 8d44c6874a
18 changed files with 351 additions and 93 deletions
--- a/.github/workflows/MLPipelinePullRequest.yml
+++ b/.github/workflows/MLPipelinePullRequest.yml
@ -31,6 +31,80 @@ jobs:
  #     run: |
  #       echo "Please choose one of these tags: 'major', 'major', 'patch'"
  #       exit(1)
+  
+  Verify-Lambda:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Install packages to retrieve artifacts
+      env:
+        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
+        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
+      run: |
+        pip install --upgrade pip
+        pip install -r modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt
+    - name: Retrieve artifacts (dvc.lock)
+      env:
+        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
+        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
+      run: |
+        cd modules/ml-pipeline/src/pipeline
+        dvc pull -r experiments
+    - name: Set timestamp
+      id: set_timestamp
+      run: |
+        echo "timestamp=$(date +%Y%m%d)" >> $GITHUB_ENV
+        echo "Generated timestamp: ${timestamp}"
+    - name: Upload sample row dataset to S3
+      env:
+        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
+        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
+      run: |
+        cd modules/ml-pipeline/src/pipeline/data/prepared_data/
+        aws s3 cp sample_test.parquet s3://retrofit-data-dev/sap_change_model/sample_data_for_cicd/${timestamp}/sample_test.parquet
+    - name: Build Lambda docker Image
+      run: |
+        docker build . --file ./deployment/Dockerfile.prediction.lambda --tag lambda_test
+    - name: Run lambda docker container
+      env:
+        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
+        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
+      run: |
+        docker run -d -p 9000:8080 \
+          -e AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID} \
+          -e AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY} \
+          -e RUNTIME_ENVIRONMENT=dev \
+          -e PREDICTIONS_BUCKET=retrofit-sap-predictions-dev lambda_test
+    - name: Test Lambda endpoint
+      run: |
+        sleep 2
+        curl -X POST "http://localhost:9000/2015-03-31/functions/function/invocations" \
+          -H "Content-Type: application/json" \
+          -d "{\"body\": \"{\\\"file_location\\\": \\\"s3://retrofit-data-dev/sap_change_model/sample_data_for_cicd/${timestamp}/sample_test.parquet\\\", \\\"property_id\\\": 1, \\\"portfolio_id\\\": 4, \\\"created_at\\\": \\\"now\\\", \\\"warm\\\": true}\"}"
+    - name: Get Lambda logs
+      run: |
+        docker logs $(docker ps -al -q)
+    - name: Test Lambda endpoint again
+      run: |
+        sleep 2
+        curl -X POST "http://localhost:9000/2015-03-31/functions/function/invocations" \
+          -H "Content-Type: application/json" \
+          -d "{\"body\": \"{\\\"file_location\\\": \\\"s3://retrofit-data-dev/sap_change_model/sample_data_for_cicd/${timestamp}/sample_test.parquet\\\", \\\"property_id\\\": 1, \\\"portfolio_id\\\": 4, \\\"created_at\\\": \\\"now\\\", \\\"testing\\\": true}\"}"
+    - name: Get Lambda logs
+      run: |
+        docker logs $(docker ps -al -q)
+    - name: Stop Lambda container
+      run: |
+        docker stop lambda_test || echo "Container already stopped"
+    - name: Remove uploaded sample row dataset from S3
+      if: always()
+      env:
+        AWS_ACCESS_KEY_ID: ${{ secrets.ROBOT_AWS_ACCESS_KEY_ID }}
+        AWS_SECRET_ACCESS_KEY: ${{ secrets.ROBOT_AWS_SECRET_ACCESS_KEY }}
+      run: |
+        aws s3 rm --recursive s3://retrofit-data-dev/sap_change_model/sample_data_for_cicd/${timestamp}/

  Verify-Model:

--- a/README.md
+++ b/README.md
@ -83,3 +83,13 @@ curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d
 ```

 This will send a POST request to the running Lambda function and pass in the required data as JSON.
+
+For the testing of warm or testing of the lambda, use:
+
+```json
+curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{"body": "{\"file_location\": \"s3://retrofit-data-dev/sap_change_model/one_sample_test_dataset.parquet\", \"property_id\": 1, \"portfolio_id\": 4, \"created_at\": \"now\", \"testing\": \"true\"}"}'
+```
+or
+```json
+curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{"body": "{\"file_location\": \"s3://retrofit-data-dev/sap_change_model/one_sample_test_dataset.parquet\", \"property_id\": 1, \"portfolio_id\": 4, \"created_at\": \"now\", \"warm\": \"true\"}"}'
+```
--- a/deployment/Dockerfile.prediction.lambda
+++ b/deployment/Dockerfile.prediction.lambda
@ -1,19 +1,24 @@
-FROM public.ecr.aws/lambda/python:3.10
+FROM public.ecr.aws/lambda/python:3.12

 # Set the working directory
 WORKDIR ${LAMBDA_TASK_ROOT}
-ENV PYTHONPATH "${PYTHONPATH}:${LAMBDA_TASK_ROOT}"
+ENV PYTHONPATH="${PYTHONPATH}:${LAMBDA_TASK_ROOT}"
+ENV MPLCONFIGDIR="/tmp/matplotlib"

 # Environment variables
 ARG RUNTIME_ENVIRONMENT
 ENV RUNTIME_ENVIRONMENT=${RUNTIME_ENVIRONMENT}

 # Install necessary build tools - required to test locally
-RUN yum install -y gcc python3-devel gcc-c++
+RUN dnf install -y gcc python3-devel gcc-c++

 # Install python packages
 COPY modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt ./requirements.txt
-RUN pip install --no-cache-dir -r ./requirements.txt
+
+RUN pip install uv
+
+RUN uv pip install -r requirements.txt --system
+# RUN pip install --no-cache-dir -r ./requirements.txt

 # Copy the project code
 COPY modules/ml-pipeline/src/pipeline ./pipeline
@ -22,4 +27,4 @@ COPY deployment/handlers/prediction_app.py ./pipeline/prediction_app.py
 WORKDIR ${LAMBDA_TASK_ROOT}/pipeline


-CMD [ "prediction_app.handler" ]
+CMD [ "prediction_app.handler" ]
--- a/deployment/handlers/prediction_app.py
+++ b/deployment/handlers/prediction_app.py
@ -47,6 +47,30 @@ def upload_dataframe_to_s3(df, bucket, s3_file_name):
        return False


+def warming_up_invocation(
+    model,
+    model_filepath: str,
+):
+    """
+    Function to handle warm up invocations
+    """
+    import pandas as pd
+    import numpy as np
+
+    model.load_model(model_filepath)
+
+    warmup_df = pd.DataFrame(
+        np.zeros((1, len(model.model.original_features))),
+        columns=model.model.original_features,
+    )
+
+    # model_names = model.model.model_names()
+    # if "NeuralNetFastAI" in model_names:
+    #     model.model.predict(warmup_df, model="NeuralNetFastAI")
+    # else:
+    model.predict(data=warmup_df)
+
+
 def handler(event, context):
    """
    Take in event and trigger the prediction pipeline
@ -66,9 +90,6 @@ def handler(event, context):
        created_at = body["created_at"]

        # TODO: Implement the loading of the model and prediction
-
-        storage_filepath = f"s3://{PREDICTIONS_BUCKET}/{portfolio_id}/{property_id}/{created_at}.parquet"
-
        logger.info(f"--- Initiate MLModel ---")

        build_model_params = settings.build_model
@ -78,6 +99,32 @@ def handler(event, context):

        model = model_factory(build_model_params["model_type"])

+        model_filepath = build_model_params["model_save_filepath"]
+
+        if "warm" in body:
+            logger.info("Warm up invocation - synthetic prediction")
+
+            warming_up_invocation(model=model, model_filepath=model_filepath)
+
+            return {
+                "statusCode": 200,
+                "body": json.dumps(
+                    {
+                        "message": "Successfully warmed up invocation",
+                    }
+                ),
+            }
+
+        if "testing" in body:
+            logger.info(
+                "Testing invocation for CI/CD - save file to same location in S3"
+            )
+            storage_filepath = body["file_location"].replace(
+                ".parquet", "_output.parquet"
+            )
+        else:
+            storage_filepath = f"s3://{PREDICTIONS_BUCKET}/{portfolio_id}/{property_id}/{created_at}.parquet"
+
        logger.info(f"--- Initiate Input DataClient ---")
        input_dataclient = dataclient_factory(
            dataclient_type="aws-s3",
@ -95,7 +142,7 @@ def handler(event, context):
            output_dataclient=output_dataclient,
            model=model,
            target=feature_process_params["feature_processor_config"]["target"],
-            model_filepath=build_model_params["model_save_filepath"],
+            model_filepath=model_filepath,
            test_data_filepath=body["file_location"],
            predictions_output_filepath=storage_filepath,
            predictions_column_name=generate_predictions_params[
--- a/deployment/serverless.yml
+++ b/deployment/serverless.yml
@ -51,3 +51,4 @@ functions:
          path: /predict
          method: POST
    timeout: 120 # Set max run time to 2 minutes - we shouldn't need this much time so this can be reviewed
+    memorySize: 3008
--- a/modules/ml-pipeline/Makefile
+++ b/modules/ml-pipeline/Makefile
@ -1,7 +1,8 @@
 export PYENV_ROOT=$(HOME)/.pyenv
 export PATH := $(PYENV_ROOT)/bin:$(PATH)
-PYTHON_VERSION ?= 3.10.12
+PYTHON_VERSION ?= 3.12.12
 CONDA_ENV=dev_env_pipeline
+CONDA_ACTIVATE=source $$(conda info --base)/etc/profile.d/conda.sh ; conda deactivate ; conda activate

 .PHONY: init
 init: dev-conda
@ -12,11 +13,15 @@ dev-conda:
 	# conda remove --name ${CONDA_ENV} --all -y || echo "No environment created previously"
 	conda create --name ${CONDA_ENV} python=$(PYTHON_VERSION) -y
 	conda init bash
-	conda run -v -n ${CONDA_ENV} pip install --upgrade pip
-	conda run -v -n ${CONDA_ENV} pip install -r src/pipeline/requirements/training/requirements-dev.txt
-	conda run -v -n ${CONDA_ENV} pip install -r src/pipeline/requirements/version_control/requirements.txt
-	conda run -v -n ${CONDA_ENV} pre-commit install
-	conda run -v -n ${CONDA_ENV} pip install ipykernel
+	${CONDA_ACTIVATE} ${CONDA_ENV} && \
+		which pip && \
+		pip install --upgrade pip && \
+		pip install uv && \
+		uv pip install -r src/pipeline/requirements/training/requirements-dev.txt && \
+		uv pip install -r src/pipeline/requirements/version_control/requirements.txt && \
+		pre-commit install && \
+		uv pip install ipykernel && \
+		conda install llvm-openmp -y
 	echo "TO ACTIVATE ENVIRONMENT, USE THE FOLLOWING COMMAND"
 	echo "conda activate ${CONDA_ENV}"

@ -33,4 +38,4 @@ dev-pyenv:

 .PHONY: dvc-init
 dvc-init:
-	. .dev_env_pipeline/bin/activate && dvc init --subdir
+	. .dev_env_pipeline/bin/activate && dvc init --subdir
--- a/modules/ml-pipeline/src/Prediction.Dockerfile
+++ b/modules/ml-pipeline/src/Prediction.Dockerfile
@ -1,16 +1,21 @@
 # Dockerfile that can be used to test loading a model to generate a prediction (part of CI/CD flow)
-FROM python:3.10.12-slim
+FROM python:3.12.12-slim

 RUN apt-get update && apt-get install -y libgomp1 gcc python3-dev

 COPY pipeline/requirements/predictions/requirements.txt requirements.txt

 RUN pip install --upgrade pip
-RUN pip install -r requirements.txt
+
+RUN pip install uv
+
+RUN uv pip install -r requirements.txt --system
+
+# RUN pip install -r requirements.txt

 # Assuming in the CI/CD step, there will be a dvc pull step to get data and model, so will just need to run a single script
 COPY pipeline/ /home/pipeline/

 WORKDIR /home/pipeline/

-CMD [ "python", "3_generate_predictions.py"]
+CMD [ "python", "3_generate_predictions.py"]
--- a/modules/ml-pipeline/src/pipeline/1_prepare_data.py
+++ b/modules/ml-pipeline/src/pipeline/1_prepare_data.py
@ -29,6 +29,7 @@ data_filepath = prepare_data_params["data_filepath"]
 train_proportion = prepare_data_params["train_proportion"]
 output_train_filepath = prepare_data_params["output_train_filepath"]
 output_test_filepath = prepare_data_params["output_test_filepath"]
+sample_test_filepath = prepare_data_params["sample_test_filepath"]
 feature_processor_config = feature_process_params["feature_processor_config"]

 logger.info(f"--- Initiate DataClient ---")
@ -99,6 +100,10 @@ def prepare_data(

    logger.info("--- Outputting data ---")

+    output_dataclient.save_data(
+        obj=data.sample(1), location=sample_test_filepath, save_config=None
+    )
+
    output_dataclient.save_data(
        obj=train, location=output_train_filepath, save_config=None
    )
--- a/modules/ml-pipeline/src/pipeline/5_generate_scenarios.py
+++ b/modules/ml-pipeline/src/pipeline/5_generate_scenarios.py
@ -99,6 +99,12 @@ def generate_scenario_predictions(
            ]
        )

+    # TEMPORARY FIX: ADD is_post_sap10_starting and is_post_sap10_ending if not present
+    if "is_post_sap10_starting" not in scenario_data.columns:
+        scenario_data["is_post_sap10_starting"] = False
+    if "is_post_sap10_ending" not in scenario_data.columns:
+        scenario_data["is_post_sap10_ending"] = False
+
    logger.info("--- Loading Model ---")

    model.load_model(model_filepath)
--- a/modules/ml-pipeline/src/pipeline/configs/build_model.yaml
+++ b/modules/ml-pipeline/src/pipeline/configs/build_model.yaml
@ -14,9 +14,23 @@ default:
      output_filepath: ./data/model/allmodels/
      problem_type: regression
      eval_metric: mean_squared_error #mean_absolute_error
-      time_limit: 1800
+      time_limit: 3600
      presets: medium_quality
-      excluded_model_types: ['RF', 'CAT', 'NN_TORCH', 'KNN', 'XT']
-      infer_limit: 0.05
+      excluded_model_types: ['RF', 'CAT', 'NN_TORCH', 'KNN', 'XT', 'FASTAI']
+      infer_limit: 1
      infer_limit_batch_size: 10000
+      fit_strategy: "parallel"
      ag_args_ensemble: {'num_folds_parallel': 2}
+      num_gpus: 0
+      hyperparameters:
+        {
+        'NN_TORCH': [{}],
+        'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 3, 'ag_args': {'name_suffix': 'Large', 'priority': 0,}}],
+        # 'GBM': [{}],
+        'CAT': [{}],
+        'XGB': [{}],
+        'FASTAI': [{}],
+        'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
+        'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
+        'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],
+        }
--- a/modules/ml-pipeline/src/pipeline/configs/settings.yaml
+++ b/modules/ml-pipeline/src/pipeline/configs/settings.yaml
@ -28,6 +28,7 @@ default:
    train_proportion: 0.9
    output_train_filepath: ./data/prepared_data/train.parquet
    output_test_filepath: ./data/prepared_data/test.parquet
+    sample_test_filepath: ./data/prepared_data/sample_test.parquet

  feature_processor:
    feature_processor_type: dataframe
--- a/modules/ml-pipeline/src/pipeline/core/MLModels.py
+++ b/modules/ml-pipeline/src/pipeline/core/MLModels.py
@ -1,4 +1,4 @@
-""""
+""" "
 Implementations of MLModels, all of which will have four methods to:
 - Load model
 - Save Model
@ -11,9 +11,6 @@ import joblib
 import pandas as pd
 from pathlib import Path
 from typing import Union, List
-from sklearn import linear_model
-from sklearn.svm import SVR
-from autogluon.tabular import TabularDataset, TabularPredictor
 from core.interface.InterfaceModels import MLModel
 from core.Logger import logger

@ -69,6 +66,8 @@ class SKLearnLinearRegression:
        """
        Method to train a model
        """
+        from sklearn import linear_model
+
        self.model = linear_model.LinearRegression()

        x_train = data.iloc[:, data.columns != target]
@ -117,6 +116,7 @@ class SKLearnSVMRegression:
        """
        Method to train a model
        """
+        from sklearn.svm import SVR

        validate_dict_keys(
            list(model_hyperparameters.keys()),
@ -152,12 +152,17 @@ class AutogluonAutoML:
        "infer_limit",
        "infer_limit_batch_size",
        "ag_args_ensemble",
+        "fit_strategy",
+        "num_gpus",
+        "hyperparameters",
    ]

    def load_model(self, path: Union[Path, str]) -> None:
        """
        Method to load a model
        """
+        from autogluon.tabular import TabularPredictor
+
        filepath = str(path)
        self.model = TabularPredictor.load(path=filepath)

@ -183,6 +188,10 @@ class AutogluonAutoML:
        """
        Method to train a model
        """
+        from autogluon.tabular import TabularDataset, TabularPredictor
+
+        # Force Parallel Model fitting
+        os.environ["AG_FORCE_PARALLEL"] = "True"

        validate_dict_keys(
            keys_1=list(model_hyperparameters.keys()),
@ -209,6 +218,9 @@ class AutogluonAutoML:
            infer_limit=model_hyperparameters["infer_limit"],
            infer_limit_batch_size=model_hyperparameters["infer_limit_batch_size"],
            ag_args_ensemble=model_hyperparameters["ag_args_ensemble"],
+            fit_strategy=model_hyperparameters["fit_strategy"],
+            num_gpus=model_hyperparameters["num_gpus"],
+            hyperparameters=model_hyperparameters["hyperparameters"].to_dict(),
        )

    def predict(
--- a/modules/ml-pipeline/src/pipeline/dvc.lock
+++ b/modules/ml-pipeline/src/pipeline/dvc.lock
@ -16,8 +16,8 @@ stages:
    deps:
    - path: 1_prepare_data.py
      hash: md5
-      md5: 11a3b8bfdfe199ab7ecc39ccc5652649
-      size: 4298
+      md5: a5ce162e1c402c0f811a80ef78cf4dd5
+      size: 4481
    params:
      configs/settings.yaml:
        default.feature_processor.feature_processor_config.drop_columns:
@ -76,15 +76,17 @@ stages:
          s3://retrofit-data-dev/energy_consumption/2024-07-25/energy_consumption_dataset.parquet
        default.prepare_data.input_dataclient_type: aws-s3
        default.prepare_data.output_dataclient_type: local
-        default.prepare_data.output_test_filepath: ./data/prepared_data/test.parquet
-        default.prepare_data.output_train_filepath: ./data/prepared_data/train.parquet
+        default.prepare_data.output_test_filepath: 
+          ./data/prepared_data/test.parquet
+        default.prepare_data.output_train_filepath: 
+          ./data/prepared_data/train.parquet
        default.prepare_data.train_proportion: 0.9
    outs:
    - path: data/prepared_data/
      hash: md5
-      md5: a6241dcbb3fe1d3b39d1a300ea64dfc9.dir
-      size: 9606500
-      nfiles: 2
+      md5: 93be1608a3ac26ad1cc61e03e9eda405.dir
+      size: 9623159
+      nfiles: 3
  build_model:
    cmd: python 2_build_model.py
    deps:
@ -94,9 +96,9 @@ stages:
      size: 4820
    - path: data/prepared_data
      hash: md5
-      md5: a6241dcbb3fe1d3b39d1a300ea64dfc9.dir
-      size: 9606500
-      nfiles: 2
+      md5: 93be1608a3ac26ad1cc61e03e9eda405.dir
+      size: 9623159
+      nfiles: 3
    params:
      configs/build_model.yaml:
        default:
@ -112,7 +114,7 @@ stages:
              output_filepath: ./data/model/allmodels/
              problem_type: regression
              eval_metric: mean_squared_error
-              time_limit: 1800
+              time_limit: 3600
              presets: medium_quality
              excluded_model_types:
              - RF
@ -120,25 +122,94 @@ stages:
              - NN_TORCH
              - KNN
              - XT
-              infer_limit: 0.05
+              - FASTAI
+              infer_limit: 1
              infer_limit_batch_size: 10000
+              fit_strategy: parallel
              ag_args_ensemble:
                num_folds_parallel: 2
+              num_gpus: 0
+              hyperparameters:
+                NN_TORCH:
+                - {}
+                GBM:
+                - extra_trees: true
+                  ag_args:
+                    name_suffix: XT
+                - {}
+                - learning_rate: 0.03
+                  num_leaves: 128
+                  feature_fraction: 0.9
+                  min_data_in_leaf: 3
+                  ag_args:
+                    name_suffix: Large
+                    priority: 0
+                CAT:
+                - {}
+                XGB:
+                - {}
+                FASTAI:
+                - {}
+                RF:
+                - criterion: gini
+                  ag_args:
+                    name_suffix: Gini
+                    problem_types:
+                    - binary
+                    - multiclass
+                - criterion: entropy
+                  ag_args:
+                    name_suffix: Entr
+                    problem_types:
+                    - binary
+                    - multiclass
+                - criterion: squared_error
+                  ag_args:
+                    name_suffix: MSE
+                    problem_types:
+                    - regression
+                    - quantile
+                XT:
+                - criterion: gini
+                  ag_args:
+                    name_suffix: Gini
+                    problem_types:
+                    - binary
+                    - multiclass
+                - criterion: entropy
+                  ag_args:
+                    name_suffix: Entr
+                    problem_types:
+                    - binary
+                    - multiclass
+                - criterion: squared_error
+                  ag_args:
+                    name_suffix: MSE
+                    problem_types:
+                    - regression
+                    - quantile
+                KNN:
+                - weights: uniform
+                  ag_args:
+                    name_suffix: Unif
+                - weights: distance
+                  ag_args:
+                    name_suffix: Dist
    outs:
    - path: data/fit_predictions/
      hash: md5
-      md5: 5e07647b4dd0145a6d52d6ef729a3bde.dir
-      size: 1545562
+      md5: 19815d7a7ce972b3fedd470f2166f748.dir
+      size: 1545546
      nfiles: 1
    - path: data/model/
      hash: md5
-      md5: ce14e6f1e69c5513a04403eb00e0db0a.dir
-      size: 99464470
-      nfiles: 35
+      md5: af81d84cb9ec873c8a613c74cbd43259.dir
+      size: 210837341
+      nfiles: 31
    - path: metrics/fit_metrics.json
      hash: md5
-      md5: 425c0c6c13742d2d21051bf7ceb90127
-      size: 218
+      md5: 4ab313ac1c4194dd411aae48a0133805
+      size: 211
  generate_predictions:
    cmd: python 3_generate_predictions.py
    deps:
@ -148,26 +219,28 @@ stages:
      size: 2464
    - path: data/model
      hash: md5
-      md5: ce14e6f1e69c5513a04403eb00e0db0a.dir
-      size: 99464470
-      nfiles: 35
+      md5: af81d84cb9ec873c8a613c74cbd43259.dir
+      size: 210837341
+      nfiles: 31
    - path: data/prepared_data
      hash: md5
-      md5: a6241dcbb3fe1d3b39d1a300ea64dfc9.dir
-      size: 9606500
-      nfiles: 2
+      md5: 93be1608a3ac26ad1cc61e03e9eda405.dir
+      size: 9623159
+      nfiles: 3
    params:
      configs/settings.yaml:
        default.generate_predictions.input_dataclient_type: local
        default.generate_predictions.output_dataclient_type: local
        default.generate_predictions.predictions_column_name: predictions
-        default.generate_predictions.predictions_output_filepath: ./data/predictions/predictions.parquet
-        default.generate_predictions.test_data_filepath: ./data/prepared_data/test.parquet
+        default.generate_predictions.predictions_output_filepath: 
+          ./data/predictions/predictions.parquet
+        default.generate_predictions.test_data_filepath: 
+          ./data/prepared_data/test.parquet
    outs:
    - path: data/predictions/
      hash: md5
-      md5: ddaa04115c5dd4299974048080d762f5.dir
-      size: 163540
+      md5: 216bd74b3a26af7201e7cb60822bec30.dir
+      size: 163454
      nfiles: 1
  generate_metrics:
    cmd: python 4_generate_metrics.py
@ -178,14 +251,14 @@ stages:
      size: 3484
    - path: data/predictions
      hash: md5
-      md5: ddaa04115c5dd4299974048080d762f5.dir
-      size: 163540
+      md5: 216bd74b3a26af7201e7cb60822bec30.dir
+      size: 163454
      nfiles: 1
    - path: data/prepared_data
      hash: md5
-      md5: a6241dcbb3fe1d3b39d1a300ea64dfc9.dir
-      size: 9606500
-      nfiles: 2
+      md5: 93be1608a3ac26ad1cc61e03e9eda405.dir
+      size: 9623159
+      nfiles: 3
    params:
      configs/settings.yaml:
        default.generate_metrics.dataclient_type: local
@ -194,15 +267,15 @@ stages:
    outs:
    - path: metrics/metrics.json
      hash: md5
-      md5: 0beb72a28af4af37a619181b14c2e311
-      size: 218
+      md5: f60c70d023919a9d1e5c0da1b0ec2af5
+      size: 209
  generate_scenerio_metrics:
    cmd: python 5_generate_scenarios.py
    deps:
    - path: 5_generate_scenarios.py
      hash: md5
-      md5: 40506749fefd926d47c60ff5b16db307
-      size: 5337
+      md5: 872b0c762ce1c8933fcbc5f54d5d4b5d
+      size: 5658
    params:
      configs/scenarios.yaml:
        default.scenarios:
--- a/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements-dev.txt
+++ b/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements-dev.txt
@ -1,7 +1,7 @@
-joblib==1.3.2
-boto3==1.28.17
-pandas==2.1.4
-autogluon.tabular[all]==1.0.0
-dynaconf==3.2.1
-pyarrow==13.0.0
-pre-commit==3.3.3
+joblib==1.5.2
+boto3==1.40.61
+pandas==2.3.3
+autogluon.tabular[all]==1.4.0
+dynaconf==3.2.12
+pyarrow==20.0.0
+pre-commit==4.3.0
--- a/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt
+++ b/modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt
@ -1,7 +1,7 @@
-joblib==1.3.2
-boto3==1.28.17
-pandas==2.1.4
-autogluon.tabular[all]==1.0.0
-dynaconf==3.2.1
-pyarrow==13.0.0
-PyYAML==6.0.1
+joblib==1.5.2
+boto3==1.40.61
+pandas==2.3.3
+autogluon.tabular[all]==1.4.0
+dynaconf==3.2.12
+pyarrow==20.0.0
+PyYAML==6.0.3
--- a/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt
+++ b/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt
@ -1,10 +1,10 @@
-joblib==1.3.2
-boto3==1.28.17
-pandas==2.1.4
-autogluon.tabular[all]==1.0.0
-ray==2.6.3
-dynaconf==3.2.1
-alibi==0.9.5
-shap==0.42.1
-pyarrow==13.0.0
-pre-commit==3.3.3
+joblib==1.5.2
+boto3==1.40.61
+pandas==2.3.3
+autogluon.tabular[all]==1.4.0
+ray==2.44.1
+dynaconf==3.2.12
+# alibi
+shap==0.49.1
+pyarrow==20.0.0
+pre-commit==4.3.0
--- a/modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt
+++ b/modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt
@ -1,4 +1,4 @@
-boto3==1.28.41
-pandas==2.1.4
-autogluon.tabular[all]==1.0.0
-dynaconf==3.2.1
+boto3==1.40.61
+pandas==2.3.3
+autogluon.tabular[all]==1.4.0
+dynaconf==3.2.12
--- a/modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt
+++ b/modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt
@ -1,4 +1,4 @@
 dvc==3.51.0
 dvc-s3==3.2.0
-gto==1.7.1
+gto==1.9.0
 pyOpenSSL==23.3.0