autogen/test/pipeline_tuning_example/data_prep/env.yaml

name: data-prep-env
channels:
  - conda-forge
dependencies:
  - python=3.8
  - numpy=1.21.2
  - pip=21.2.4
  - scikit-learn=0.24.2
  - scipy=1.7.1
  - pandas>=1.1,<1.2
  - pip:
    # - inference-schema[numpy-support]==1.3.0
    # - xlrd==2.0.1
    - mlflow==1.26.1
    - azureml-mlflow==1.42.0
Add pipeline tuner component and dependencies. (#671) * add pipeline tuner component and dependencies. * clean code. * do not need force rerun. * replace the resources. * update metrics retrieving. * Update test/pipeline_tuning_example/requirements.txt * Update test/pipeline_tuning_example/train/env.yaml * Update test/pipeline_tuning_example/tuner/env.yaml * Update test/pipeline_tuning_example/tuner/tuner_func.py * Update test/pipeline_tuning_example/data_prep/env.yaml * fix issues found by lint with flake8. * add documentation * add data. * do not need AML resource for local run. * AML -> AzureML * clean code. * Update website/docs/Examples/Tune-AzureML pipeline.md * rename and add pip install. * update figure name. * align docs with code. * remove extra line. 2022-08-10 20:20:21 -07:00			`name: data-prep-env`
			`channels:`
			`- conda-forge`
			`dependencies:`
			`- python=3.8`
			`- numpy=1.21.2`
			`- pip=21.2.4`
			`- scikit-learn=0.24.2`
			`- scipy=1.7.1`
			`- pandas>=1.1,<1.2`
			`- pip:`
			`# - inference-schema[numpy-support]==1.3.0`
			`# - xlrd==2.0.1`
			`- mlflow==1.26.1`
			`- azureml-mlflow==1.42.0`