diff --git a/.github/workflows/ci.yml b/.github/workflows/main.yml similarity index 98% rename from .github/workflows/ci.yml rename to .github/workflows/main.yml index 54002d02..97bf7d4a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/main.yml @@ -193,7 +193,8 @@ jobs: poetry run python azureml_run_pipeline.py \ --subscription_id ${{ env.AZURE_SUBSCRIPTION_ID }} \ --resource_group_name ${{ env.AZURE_RESOURCE_GROUP_NAME }} \ - --workspace_name ${{ env.AZURE_ML_WORKSPACE_NAME }} > train_output.txt + --workspace_name ${{ env.AZURE_ML_WORKSPACE_NAME }} \ + --tags "{\"git\":\"${{ github.head_ref }}.${{ github.sha }}\",\"version\":\"${{ needs.tags.outputs.new_version }}\",\"triggering_actor\":\"${{github.triggering_actor}}\"}" > train_output.txt cat train_output.txt working-directory: train - name: download model diff --git a/README.md b/README.md index 88449a89..df531d6b 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # MLOpsPython -A real demo of Deep Learning project with preprocessing from development to production using code, ops and Machine Learning best practices. Production is a real time REST API. +A real demo of Deep Learning project with preprocessing from development to production using code, ops and Machine Learning best practices. Production is a real time REST API. ![project_workflow.png](documentation%2Fproject_workflow.png) diff --git a/documentation/step_0_setup.md b/documentation/step_0_setup.md index 16c1a015..ee785039 100644 --- a/documentation/step_0_setup.md +++ b/documentation/step_0_setup.md @@ -130,7 +130,7 @@ jobs: run: | pipenv install --dev pipenv run flake8 . -' > python-ci.yml +' > python-main.yml ``` Now we can protect your "main" branch on github. Add a security constrain, that your code must pass the CI before merging. diff --git a/train/azureml_run_pipeline.py b/train/azureml_run_pipeline.py index 6bc7e426..7bf393a6 100644 --- a/train/azureml_run_pipeline.py +++ b/train/azureml_run_pipeline.py @@ -9,21 +9,26 @@ from azure.ai.ml.entities import Data from azure.ai.ml.entities import AmlCompute +from extraction import register_extracted_dataset + import uuid import json + parser = argparse.ArgumentParser("train") parser.add_argument("--subscription_id", type=str) parser.add_argument("--resource_group_name", type=str) parser.add_argument("--workspace_name", type=str) +parser.add_argument("--tags", type=str, default="{}") + args = parser.parse_args() subscription_id = args.subscription_id resource_group_name = args.resource_group_name workspace_name = args.workspace_name - -URI_FOLDER = "uri_folder" +print(args.tags) +tags = json.loads(args.tags) try: credential = DefaultAzureCredential() @@ -34,8 +39,6 @@ # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work credential = InteractiveBrowserCredential() - - # Get a handle to workspace ml_client = MLClient( credential=credential, @@ -51,7 +54,7 @@ name=cluster_name, type="amlcompute", size="Standard_D4s_v3", - location="northeurope", #az account list-locations -o table + location="northeurope", # az account list-locations -o table min_instances=0, max_instances=1, idle_time_before_scale_down=60, @@ -60,29 +63,35 @@ @pipeline(default_compute=cluster_name) -def azureml_pipeline(pdfs_input_data: Input(type=URI_FOLDER), - labels_input_data: Input(type=URI_FOLDER)): +def azureml_pipeline( + pdfs_input_data: Input(type=AssetTypes.URI_FOLDER), + labels_input_data: Input(type=AssetTypes.URI_FOLDER), +): extraction_step = load_component(source="extraction/command.yaml") - extraction = extraction_step( - pdfs_input=pdfs_input_data - ) + extraction = extraction_step(pdfs_input=pdfs_input_data) label_split_data_step = load_component(source="label_split_data/command.yaml") - label_split_data = label_split_data_step(labels_input=labels_input_data, - pdfs_input=pdfs_input_data, - images_input=extraction.outputs.images_output) + label_split_data = label_split_data_step( + labels_input=labels_input_data, + pdfs_input=pdfs_input_data, + images_input=extraction.outputs.images_output, + ) train_step = load_component(source="train/command.yaml") train_data = train_step( - split_images_input=label_split_data.outputs.split_images_output) + split_images_input=label_split_data.outputs.split_images_output + ) test_step = load_component(source="test/command.yaml") - test_data = test_step(model_input=train_data.outputs.model_output, - integration_input=label_split_data.outputs.split_integration_output, - images_input=label_split_data.outputs.split_images_output) + test_data = test_step( + model_input=train_data.outputs.model_output, + integration_input=label_split_data.outputs.split_integration_output, + images_input=label_split_data.outputs.split_images_output, + ) return { "extraction_output": extraction.outputs.images_output, + "extraction_hash_output": extraction.outputs.hash_output, "model_output": test_data.outputs.model_output, "integration_output": test_data.outputs.integration_output, } @@ -90,50 +99,49 @@ def azureml_pipeline(pdfs_input_data: Input(type=URI_FOLDER), pipeline_job = azureml_pipeline( pdfs_input_data=Input( - path="azureml:cats_dogs_others:1", type=URI_FOLDER + path="azureml:cats_dogs_others:1", type=AssetTypes.URI_FOLDER ), labels_input_data=Input( - path="azureml:cats_dogs_others_labels:1", type=URI_FOLDER - ) + path="azureml:cats_dogs_others_labels:1", type=AssetTypes.URI_FOLDER + ), ) - +pipeline_job.settings.force_rerun = False azure_blob = "azureml://datastores/workspaceblobstore/paths/" experiment_id = str(uuid.uuid4()) -custom_extraction_path = azure_blob + "extraction/cats-dogs-others/" + experiment_id + "/" -pipeline_job.outputs.model_output = Output( - type=URI_FOLDER, mode="rw_mount", path=custom_extraction_path +custom_extraction_path = ( + azure_blob + "extraction/cats-dogs-others/" + experiment_id + "/" +) +pipeline_job.outputs.extraction_output = Output( + type=AssetTypes.URI_FOLDER, mode="rw_mount", path=custom_extraction_path +) +custom_extraction_hash_path = ( + azure_blob + "extraction_hash/cats-dogs-others/" + experiment_id + "/" +) +pipeline_job.outputs.extraction_hash_output = Output( + type=AssetTypes.URI_FOLDER, mode="rw_mount", path=custom_extraction_hash_path ) custom_model_path = azure_blob + "models/cats-dogs-others/" + experiment_id + "/" pipeline_job.outputs.model_output = Output( - type=URI_FOLDER, mode="rw_mount", path=custom_model_path + type=AssetTypes.URI_FOLDER, mode="rw_mount", path=custom_model_path +) +custom_integration_path = ( + azure_blob + "integration/cats-dogs-others/" + experiment_id + "/" ) -custom_integration_path = azure_blob + "/integration/cats-dogs-others/" + experiment_id + "/" pipeline_job.outputs.integration_output = Output( - type=URI_FOLDER, mode="rw_mount", path=custom_integration_path + type=AssetTypes.URI_FOLDER, mode="rw_mount", path=custom_integration_path ) pipeline_job = ml_client.jobs.create_or_update( - pipeline_job, experiment_name="cats_dos_others_pipeline" + pipeline_job, experiment_name="cats_dos_others_pipeline", tags=tags ) ml_client.jobs.stream(pipeline_job.name) - -integration_dataset = Data( - name="cats-dogs-others-extraction", - path=custom_integration_path, - type=URI_FOLDER, - description="Extracted images for cats and dogs and others", - version="1", - tags={"source_type": "web", "source": "UCI ML Repo"}, -) -integration_dataset = ml_client.data.create_or_update(integration_dataset) -print( - f"Dataset with name {integration_dataset.name} was registered to workspace, the dataset version is {integration_dataset.version}" +register_extracted_dataset( + ml_client, custom_extraction_hash_path, custom_extraction_path, tags ) - model_name = "cats-dogs-others" try: model_version = str(len(list(ml_client.models.list(model_name))) + 1) @@ -141,22 +149,25 @@ def azureml_pipeline(pdfs_input_data: Input(type=URI_FOLDER), model_version = "1" file_model = Model( - version=model_version, - path=custom_model_path, - type=AssetTypes.CUSTOM_MODEL, - name=model_name, - description="Model created from azureML.", - ) + version=model_version, + path=custom_model_path, + type=AssetTypes.CUSTOM_MODEL, + name=model_name, + description="Model created from azureML.", + tags={**tags}, +) saved_model = ml_client.models.create_or_update(file_model) -print(f"Model with name {saved_model.name} was registered to workspace, the model version is {saved_model.version}.") +print( + f"Model with name {saved_model.name} was registered to workspace, the model version is {saved_model.version}." +) integration_dataset = Data( name="cats-dogs-others-integration", path=custom_integration_path, - type=URI_FOLDER, + type=AssetTypes.CUSTOM_MODEL, description="Integration dataset for cats and dogs and others", - tags={"source_type": "web", "source": "UCI ML Repo"}, + tags=tags, ) integration_dataset = ml_client.data.create_or_update(integration_dataset) print( diff --git a/train/extraction.py b/train/extraction.py new file mode 100644 index 00000000..7f2c1fd8 --- /dev/null +++ b/train/extraction.py @@ -0,0 +1,57 @@ +from pathlib import Path +import azure.ai.ml._artifacts._artifact_utilities as artifact_utils +from azure.ai.ml.constants import AssetTypes +from azure.ai.ml.entities import Data + +def register_extracted_dataset(ml_client, + custom_extraction_hash_path:str, + custom_extraction_path:str, + tags:dict): + BASE_PATH = Path(__file__).resolve().parent + artifact_utils.download_artifact_from_aml_uri( + uri=custom_extraction_hash_path, + destination=str(BASE_PATH), + datastore_operation=ml_client.datastores, + ) + + # lire le fichier hash.txt qui est dans BASE_PATH + with open(str(BASE_PATH / "hash.txt"), "r") as file: + computed_hash = file.read() + print(f"computed_hash: {computed_hash}") + + extracted_images_dataset_name = "cats-dogs-others-extracted" + try: + list_datasets = ml_client.data.list(extracted_images_dataset_name) + list_list_datset = list(list_datasets) + version_dataset_extraction = len(list_list_datset) + 1 + except: + list_list_datset = [] + version_dataset_extraction = 1 + print("No dataset with name cats-dogs-others-extracted") + + hash_tag_already_exists = False + len_dataset = len(list_list_datset) + if len_dataset > 0: + dataset = list_list_datset[len_dataset - 1] + print(f"dataset.tags: {str(dataset.version)}") + print(dataset.tags) + if "hash" in dataset.tags: + extracted_images_dataset_version = dataset.tags["hash"] + print(f"extracted_images_dataset_version: {extracted_images_dataset_version}") + print(f"computed_hash: {computed_hash}") + if extracted_images_dataset_version == computed_hash: + hash_tag_already_exists = True + + if not hash_tag_already_exists: + extracted_images_dataset = Data( + name=extracted_images_dataset_name, + path=custom_extraction_path, + type=AssetTypes.CUSTOM_MODEL, + description="Extracted images for cats and dogs and others", + version=str(version_dataset_extraction), + tags={"hash": computed_hash, **tags}, + ) + extracted_images_dataset = ml_client.data.create_or_update(extracted_images_dataset) + print( + f"Dataset with name {extracted_images_dataset.name} was registered to workspace, the dataset version is {extracted_images_dataset.version}" + ) \ No newline at end of file diff --git a/train/extraction/command.py b/train/extraction/command.py index 8117ff6f..d5f0ce93 100644 --- a/train/extraction/command.py +++ b/train/extraction/command.py @@ -1,15 +1,30 @@ import argparse +from pathlib import Path + from mlopspython_extraction.extraction import extract_images import mlflow +from directory_hash import hash_dir + parser = argparse.ArgumentParser("extraction") parser.add_argument("--pdfs_input", type=str) parser.add_argument("--images_output", type=str) +parser.add_argument("--hash_output", type=str) args = parser.parse_args() pdfs_input = args.pdfs_input images_output = args.images_output +hash_output = args.hash_output result = extract_images(pdfs_input, images_output) +computed_hash = hash_dir(images_output) +with open(str(Path(hash_output) / "hash.txt"), "w") as file: + file.write(computed_hash) + +console_output = f""" + number_files_input: {result.number_files_input} + number_images_output: {result.number_images_output} + computed_hash: {computed_hash} +""" mlflow.log_metric("number_files_input", result.number_files_input) mlflow.log_metric("number_images_output", result.number_images_output) \ No newline at end of file diff --git a/train/extraction/command.yaml b/train/extraction/command.yaml index e939649e..b2c087c4 100644 --- a/train/extraction/command.yaml +++ b/train/extraction/command.yaml @@ -15,6 +15,8 @@ inputs: outputs: images_output: type: uri_folder + hash_output: + type: uri_folder environment: name: extraction_environment @@ -26,4 +28,5 @@ command: >- python command.py --pdfs_input ${{inputs.pdfs_input}} --images_output ${{outputs.images_output}} + --hash_output ${{outputs.hash_output}} diff --git a/train/extraction/directory_hash.py b/train/extraction/directory_hash.py new file mode 100644 index 00000000..2af21985 --- /dev/null +++ b/train/extraction/directory_hash.py @@ -0,0 +1,32 @@ +import os +import hashlib +import sys + + +# python 3.11 +# def sha256sum(filename): +# with open(filename, 'rb', buffering=0) as file: +# return hashlib.file_digest(file, 'sha1').hexdigest() + +# Less than python 3.11 < 3 +def sha256sum(filename): + h = hashlib.sha256() + b = bytearray(128 * 1024) + mv = memoryview(b) + with open(filename, 'rb', buffering=0) as f: + while n := f.readinto(mv): + h.update(mv[:n]) + return h.hexdigest() + + +def hash_dir(dir_path): + hashes = [] + for path, dirs, files in os.walk(dir_path): + for file in sorted(files): # we sort to guarantee that files will always go in the same order + hashes.append(sha256sum(os.path.join(path, file))) + for dir in sorted(dirs): # we sort to guarantee that dirs will always go in the same order + hashes.append(hash_dir(os.path.join(path, dir))) + break # we only need one iteration - to get files and dirs in current directory + h = hashlib.new('sha1') # sha256 can be replaced with diffrent algorithms + h.update(''.join(hashes).encode()) # give a encoded string. Makes the String to the Hash + return str(h.hexdigest()) diff --git a/train/labelling/.gitignore b/train/labelling/.gitignore new file mode 100644 index 00000000..89c2444f --- /dev/null +++ b/train/labelling/.gitignore @@ -0,0 +1,2 @@ +.idea/ +dataset \ No newline at end of file diff --git a/train/labelling/dataset.py b/train/labelling/dataset.py new file mode 100644 index 00000000..f69fccec --- /dev/null +++ b/train/labelling/dataset.py @@ -0,0 +1,37 @@ +from pathlib import Path + +from azure.ai.ml import MLClient +from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential +import azure.ai.ml._artifacts._artifact_utilities as artifact_utils + + +def download(subscription_id: str, + resource_group_name: str, + workspace_name: str, + dataset_name: str, + dataset_version: str, + ): + try: + credential = DefaultAzureCredential() + # Check if given credential can get token successfully. + credential.get_token("https://management.azure.com/.default") + except Exception as ex: + print(ex) + # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work + credential = InteractiveBrowserCredential() + + ml_client = MLClient( + credential=credential, + subscription_id=subscription_id, + resource_group_name=resource_group_name, + workspace_name=workspace_name, + ) + data_info = ml_client.data.get(dataset_name, version=dataset_version) + + BASE_PATH = Path(__file__).resolve().parent + output_images_directory = BASE_PATH / "dataset" + if not output_images_directory.exists(): + output_images_directory.mkdir() + result = artifact_utils.download_artifact_from_aml_uri(uri=data_info.path, destination=str(output_images_directory), + datastore_operation=ml_client.datastores) + return result diff --git a/train/labelling/ecotag.py b/train/labelling/ecotag.py new file mode 100644 index 00000000..3edc2b2f --- /dev/null +++ b/train/labelling/ecotag.py @@ -0,0 +1,145 @@ +from dataclasses import dataclass + +import aiohttp +import os + +from aiohttp import FormData + +@dataclass +class ApiInformation: + api_url: str + jwt_token: str +@dataclass +class Dataset: + dataset_name: str + dataset_type: str + team_name: str + directory: str + classification: str = 'Public' + +@dataclass +class Label: + name: str + color: str + id: str + + +@dataclass +class Project: + project_name: str + dataset_name: str + team_name: str + numberCrossAnnotation: int = 1 + annotationType: str = "ImageClassifier" + labels: list[Label] = None + + +async def create_dataset(dataset: Dataset, api_information: ApiInformation): + dataset_name = dataset.dataset_name + dataset_type = dataset.dataset_type + team_name = dataset.team_name + directory = dataset.directory + classification = dataset.classification + + jwt_token = api_information.jwt_token + api_url = api_information.api_url + + headers = {'Authorization': f'Bearer {jwt_token}'} + + # Initialize a session. + async with aiohttp.ClientSession() as session: + + # Fetch the teams and extract the team ID. + team_id = await get_team_id(api_url, headers, session, team_name) + + # Create the dataset. + payload = {'name': dataset_name, + 'type': dataset_type, + 'groupId': team_id, + 'classification': classification} + + async with session.post(f'{api_url}/Datasets', headers=headers, json=payload) as response: + print(f'Create dataset status: {response.status}') + await response.text() + + dataset_id = await get_dataset_id(api_url, dataset_name, headers, session) + + # Upload image files in the directory. + for filename in os.listdir(directory): + if filename.endswith(".jpg") or filename.endswith(".png"): + data = FormData() + data.add_field('files', + open(os.path.join(directory, filename), 'rb'), + filename=filename, + content_type='application/octet-stream') + + async with session.post(f'{api_url}/Datasets/{dataset_id}/files', headers=headers, data=data) as response: + print(f'upload file status: {response.status} [{filename}]') + print(await response.text()) + + # Lock the dataset. + async with session.post(f'{api_url}/Datasets/{dataset_id}/lock', headers=headers) as response: + print(f'Lock dataset status: {response.status}') + print(await response.text()) + + +async def get_team_id(api_url, headers, session, team_name): + team_id = None + async with session.get(f'{api_url}/Groups', headers=headers) as response: + teams = await response.json() + for team in teams: + if team['name'] == team_name: + team_id = team['id'] + break + if team_id is None: + raise Exception(f'Team {team_name} not found') + print(f'Team ID: {team_id}') + return team_id + + +async def get_dataset_id(api_url, dataset_name, headers, session): + # Get Dataset ID. + dataset_id = None + async with session.get(f'{api_url}/Datasets', headers=headers) as response: + datasets = await response.json() + for dataset in datasets: + if dataset['name'] == dataset_name: + dataset_id = dataset['id'] + break + if dataset_id is None: + raise Exception(f'Dataset {dataset_name} not found') + print(f'Dataset ID: {dataset_id}') + return dataset_id + + +async def create_project(project: Project, api_information: ApiInformation): + project_name = project.project_name + dataset_name = project.dataset_name + team_name = project.team_name + + jwt_token = api_information.jwt_token + api_url = api_information.api_url + + headers = {'Authorization': f'Bearer {jwt_token}'} + + async with aiohttp.ClientSession() as session: + dataset_id = await get_dataset_id(api_url, dataset_name, headers, session) + team_id = await get_team_id(api_url, headers, session, team_name) + + # Create Image Classification Project. + payload = { + "name": project_name, + "datasetId": dataset_id, + "groupId": team_id, + "numberCrossAnnotation": 1, + "annotationType": "ImageClassifier", + "labels": [] + } + + for label in project.labels: + payload['labels'].append(label.__dict__) + + async with session.post(f'{api_url}/Projects', headers=headers, json=payload) as response: + print(f'Create project status: {response.status}') + 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+lock-version = "2.0" +python-versions = "~3.10" +content-hash = "5dbdbfe6d075170c1312b039dd9ea8fb6ef76a1cf76384bcd3f302f9ff2d7e28" diff --git a/train/labelling/pyproject.toml b/train/labelling/pyproject.toml new file mode 100644 index 00000000..2ad95489 --- /dev/null +++ b/train/labelling/pyproject.toml @@ -0,0 +1,16 @@ +[tool.poetry] +name = "labelling" +version = "0.0.0" +description = "" +authors = ["Guillaume Chervet "] + +[tool.poetry.dependencies] +python = "~3.10" +mldesigner = "0.1.0b15" +azure-ai-ml = "1.10.1" +"azure.identity" = "1.14.0" +aiohttp = "^3.9.1" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/train/labelling/run.py b/train/labelling/run.py new file mode 100644 index 00000000..2ba862ac --- /dev/null +++ b/train/labelling/run.py @@ -0,0 +1,42 @@ +import argparse +import asyncio + +from .dataset import download +from .ecotag import create_project, Project, Label, Dataset, ApiInformation, create_dataset + +parser = argparse.ArgumentParser("labelling") +parser.add_argument("--jwt_token", type=str) +parser.add_argument("--subscription_id", type=str) +parser.add_argument("--resource_group_name", type=str) +parser.add_argument("--workspace_name", type=str) + +args = parser.parse_args() +subscription_id = args.subscription_id +resource_group_name = args.resource_group_name +workspace_name = args.workspace_name +jwt_token = args.jwt_token + +async def main(): + + dataset_path = download(subscription_id, resource_group_name, workspace_name, 'cats_dogs_others', '1') + + api_url = 'https://axaguildev-ecotag.azurewebsites.net/api/server' + api_information = ApiInformation(api_url=api_url, jwt_token=jwt_token) + + dataset = Dataset(dataset_name='cats_dogs_others9', + dataset_type='Image', + team_name='cats_dogs_others', + directory=dataset_path, + classification='Public') + await create_dataset(dataset, api_information) + + project = Project(project_name='cats_dogs_others9', + dataset_name=dataset.dataset_name, + team_name='cats_dogs_others', + annotationType='ImageClassifier', + labels=[Label(name='cat', color='#FF0000', id="0"), Label(name='dog', color='#00FF00', id="1"), Label(name='other', color='#0000FF', id="2")]) + + await create_project(project, api_information) + +loop = asyncio.get_event_loop() +loop.run_until_complete(main()) diff --git a/train/labelling/run.sh b/train/labelling/run.sh new file mode 100644 index 00000000..8c55c170 --- /dev/null +++ b/train/labelling/run.sh @@ -0,0 +1,9 @@ + + +peotry run run.py \ + --jwt_token "" \ + --subscription_id "9d42c9d4-85ab-429d-afb4-4d77f309078c" \ + --resource_group "azure-ml-yola" \ + --workspace_name "cats-dogs-yola" \ + --dataset_name "cats_dogs_others_extracted" \ + --dataset_version "1"