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1. MLflow is installed from pip inside the container, unless MLFLOW_DEV environment variable is set. This applies to both Azure ML and SageMaker containers.
2. The SageMaker init script is translated to Python.
3. Refactored mlflow sagemaker code.
- Python api moved to sagemaker/__init__. This is to provide python api (cli
targets can not be easily called from python) usable e.g. from notebooks.
4. Translated some of sh script to build and push image to ECR to Python.
5. Added test for mlflow sagemaker run-local.- Loading branch information
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| Original file line number | Diff line number | Diff line change |
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| from __future__ import print_function | ||
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| import os | ||
| import shutil | ||
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| import mlflow | ||
| from mlflow import pyfunc | ||
| from mlflow.models import Model | ||
| from mlflow.tracking import _get_model_log_dir | ||
| from mlflow.utils.logging_utils import eprint | ||
| from mlflow.utils.file_utils import TempDir | ||
| from mlflow.version import VERSION as mlflow_version | ||
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| def deploy(app_name, model_path, run_id, mlflow_home): | ||
| """ | ||
| Deploy MLflow model to Azure ML. | ||
| NOTE: This command is to be called from correctly initialized Azure ML environment. | ||
| At the moment this means it has to be run from console launched from Azure ML Workbench. | ||
| Caller is reponsible for setting up Azure ML environment and accounts. | ||
| NOTE: Azure ML can not handle any Conda environment. In particular python version seems to be | ||
| fixed. If the model contains Conda environment and it has been trained outside of Azure | ||
| ML, the Conda environment might need to be edited to work with Azure ML. | ||
| :param mlflow_home: | ||
| :param app_name: Name of the deployed application | ||
| :param model_path: Local or MLflow-run-relative path to the model to be exported | ||
| :param run_id: If provided, run_id is used to retrieve the model logged with MLflow. | ||
| """ | ||
| if run_id: | ||
| model_path = _get_model_log_dir(model_path, run_id) | ||
| model_path = os.path.abspath(model_path) | ||
| with TempDir(chdr=True, remove_on_exit=True): | ||
| exec_str = _export(app_name, model_path, mlflow_home=mlflow_home) | ||
| eprint("executing", '"{}"'.format(exec_str)) | ||
| # Use os.system instead of subprocess due to the fact that currently all azureml commands | ||
| # have to be called within the same shell (launched from azureml workbench app by the user). | ||
| # We can change this once there is a python api (or general cli) available. | ||
| os.system(exec_str) | ||
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| def export(output, model_path, run_id, mlflow_home): | ||
| """ | ||
| Export MLflow model as Azure ML compatible model ready to be deployed. | ||
| Export MLflow model out with everything needed to deploy on Azure ML. | ||
| Output includes sh script with command to deploy the generated model to Azure ML. | ||
| NOTE: This command does not need Azure ML environment to run. | ||
| NOTE: Azure ML can not handle any Conda environment. If the model contains Conda environment | ||
| and it has been trained outside of Azure ML, the Conda environment might need to be edited. | ||
| :param output: Output folder where the model is going to be exported to. | ||
| :param model_path: Local or MLflow-run-relative path to the model to be exported | ||
| :param run_id: If provided, run_id is used to retrieve model logged with MLflow. | ||
| """ | ||
| output = os.path.abspath(output) | ||
| if os.path.exists(output): | ||
| raise Exception("output folder {} already exists".format(output)) | ||
| os.mkdir(output) | ||
| if run_id: | ||
| model_path = _get_model_log_dir(model_path, run_id) | ||
| model_path = os.path.abspath(model_path) | ||
| curr_dir = os.path.abspath(os.getcwd()) | ||
| os.chdir(output) | ||
| try: | ||
| exec_str = _export("$1", model_path, mlflow_home=mlflow_home) | ||
| with open("create_service.sh", "w") as f: | ||
| f.write("\n".join(["#! /bin/sh", "cd {}".format(output), exec_str, ""])) | ||
| finally: | ||
| os.chdir(curr_dir) | ||
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| def _export(app_name, model_path, mlflow_home): | ||
| conf = _load_conf(model_path) | ||
| score_py = "score.py" # NOTE: azure ml requires the main module to be in the current directory | ||
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| with open(score_py, "w") as f: | ||
| f.write(SCORE_SRC) | ||
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| deps = "" | ||
| mlflow_dep = "mlflow=={}".format(mlflow_version) | ||
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| if mlflow_home: | ||
| eprint("MLFLOW_HOME =", mlflow_home) | ||
| # copy current version of mlflow | ||
| mlflow_dir = mlflow.utils.file_utils._copy_project(src_path=mlflow_home, dst_path="./") | ||
| deps = "-d {}".format(mlflow_dir) | ||
| mlflow_dep = "-e /var/azureml-app/{}".format(mlflow_dir) | ||
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| with open("requirements.txt", "w") as f: | ||
| f.write(mlflow_dep + "\n") | ||
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| shutil.copytree(src=model_path, dst="model") | ||
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| env = "-c {}".format(os.path.join("model", conf[pyfunc.ENV])) \ | ||
| if pyfunc.ENV in conf else "" | ||
| cmd = "az ml service create realtime -n {name} " + \ | ||
| "--model-file model -f score.py {conda_env} {deps} -r python -p requirements.txt" | ||
| return cmd.format(name=app_name, conda_env=env, deps=deps) | ||
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| def _load_conf(path): | ||
| path = os.path.abspath(path) | ||
| model = Model.load(os.path.join(path, "MLmodel")) | ||
| if pyfunc.FLAVOR_NAME not in model.flavors: | ||
| raise Exception("Currently only supports pyfunc format.") | ||
| return model.flavors[pyfunc.FLAVOR_NAME] | ||
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| SCORE_SRC = """ | ||
| import pandas as pd | ||
| from mlflow.pyfunc import load_pyfunc | ||
| from mlflow.utils import get_jsonable_obj | ||
| def init(): | ||
| global model | ||
| model = load_pyfunc("model") | ||
| def run(s): | ||
| input_df = pd.read_json(s, orient="records") | ||
| return get_jsonable_obj(model.predict(input_df)) | ||
| """ |
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