From 29ea01f6a4cd24e10545e007e6e646b9e1f4339d Mon Sep 17 00:00:00 2001 From: deusebio Date: Thu, 12 Dec 2024 14:23:00 +0100 Subject: [PATCH] [KF-5958] Upgrade to Python 3.10+ using Poetry (#137) --- README.md | 2 +- poetry.lock | 1820 +++++++++++++++++ pyproject.toml | 39 +- requirements-fmt.in | 2 - requirements-fmt.txt | 24 - requirements-lint.in | 8 - requirements-lint.txt | 51 - requirements.in | 6 - requirements.txt | 206 -- .../e2e-wine/e2e-wine-kfp-mlflow-kserve.ipynb | 186 +- .../cpu/katib/katib-integration.ipynb | 76 +- .../cpu/kfp_v1/kfp-v1-integration.ipynb | 16 +- .../cpu/kfp_v2/kfp-v2-integration.ipynb | 39 +- .../cpu/kserve/kserve-integration.ipynb | 11 +- .../cpu/mlflow-kserve/mlflow-kserve.ipynb | 49 +- .../mlflow-minio-integration.ipynb | 30 +- .../cpu/mlflow/mlflow-integration.ipynb | 33 +- .../cpu/training/training-integration.ipynb | 34 +- .../kfp-tensorflow-integration.ipynb | 38 +- tox.ini | 81 +- 20 files changed, 2179 insertions(+), 572 deletions(-) create mode 100644 poetry.lock delete mode 100644 requirements-fmt.in delete mode 100644 requirements-fmt.txt delete mode 100644 requirements-lint.in delete mode 100644 requirements-lint.txt delete mode 100644 requirements.in delete mode 100644 requirements.txt diff --git a/README.md b/README.md index aacfae2..c45f502 100644 --- a/README.md +++ b/README.md @@ -213,7 +213,7 @@ juju config knative-serving http-proxy=http://10.0.13.50:3128/ https-proxy=http: #### Running using Notebook -##### Prerequistes +##### Prerequisites Edit the [PodDefault](tests/proxy-poddefault.yaml.j2) to replace the placeholders for: diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..16f562a --- /dev/null +++ b/poetry.lock @@ -0,0 +1,1820 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + +[[package]] +name = "anyio" +version = "4.7.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false 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+[tool.poetry.group.fmt] +optional = true + +[tool.poetry.group.fmt.dependencies] +black = {extras=["jupyter"], version="*"} +isort = "*" + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +black = {extras=["jupyter"], version="*"} +codespell = "*" +# Flake is transitive dependencies of other packages +# flake8 = ">=6.0" +flake8-builtins = "*" +flake8-copyright = "*" +isort = "*" +pep8-naming = "*" +pyproject-flake8 = ">=6.0.0" + # Linting tools configuration [tool.flake8] max-line-length = 99 diff --git a/requirements-fmt.in b/requirements-fmt.in deleted file mode 100644 index 7559a40..0000000 --- a/requirements-fmt.in +++ /dev/null @@ -1,2 +0,0 @@ -black -isort diff --git a/requirements-fmt.txt b/requirements-fmt.txt deleted file mode 100644 index a35b0f5..0000000 --- a/requirements-fmt.txt +++ /dev/null @@ -1,24 +0,0 @@ -# -# This file is autogenerated by pip-compile with Python 3.8 -# by the following command: -# -# pip-compile requirements-fmt.in -# -black==23.9.1 - # via -r requirements-fmt.in -click==8.1.7 - # via black -isort==5.12.0 - # via -r requirements-fmt.in -mypy-extensions==1.0.0 - # via black -packaging==23.1 - # via black -pathspec==0.11.2 - # via black -platformdirs==3.10.0 - # via black -tomli==2.0.1 - # via black -typing-extensions==4.8.0 - # via black diff --git a/requirements-lint.in b/requirements-lint.in deleted file mode 100644 index 07a4a51..0000000 --- a/requirements-lint.in +++ /dev/null @@ -1,8 +0,0 @@ -black -codespell -flake8 -flake8-builtins -flake8-copyright -isort -pep8-naming -pyproject-flake8 diff --git a/requirements-lint.txt b/requirements-lint.txt deleted file mode 100644 index 4330e9c..0000000 --- a/requirements-lint.txt +++ /dev/null @@ -1,51 +0,0 @@ -# -# This file is autogenerated by pip-compile with Python 3.8 -# by the following command: -# -# pip-compile requirements-lint.in -# -black==23.9.1 - # via -r requirements-lint.in -click==8.1.7 - # via black -codespell==2.2.5 - # via -r requirements-lint.in -flake8==6.0.0 - # via - # -r requirements-lint.in - # flake8-builtins - # pep8-naming - # pyproject-flake8 -flake8-builtins==2.1.0 - # via -r requirements-lint.in -flake8-copyright==0.2.4 - # via -r requirements-lint.in -isort==5.12.0 - # via -r requirements-lint.in -mccabe==0.7.0 - # via flake8 -mypy-extensions==1.0.0 - # via black -packaging==23.1 - # via black -pathspec==0.11.2 - # via black -pep8-naming==0.13.3 - # via -r requirements-lint.in -platformdirs==3.10.0 - # via black -pycodestyle==2.10.0 - # via flake8 -pyflakes==3.0.1 - # via flake8 -pyproject-flake8==6.0.0.post1 - # via -r requirements-lint.in -tomli==2.0.1 - # via - # black - # pyproject-flake8 -typing-extensions==4.8.0 - # via black - -# The following packages are considered to be unsafe in a requirements file: -# setuptools diff --git a/requirements.in b/requirements.in deleted file mode 100644 index f78ad17..0000000 --- a/requirements.in +++ /dev/null @@ -1,6 +0,0 @@ -juju<4.0 -lightkube -pytest -pytest-operator -pytest-dependency -tenacity diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 495ad49..0000000 --- a/requirements.txt +++ /dev/null @@ -1,206 +0,0 @@ -# -# This file is autogenerated by pip-compile with Python 3.8 -# by the following command: -# -# pip-compile requirements.in -# -anyio==4.0.0 - # via httpx -asttokens==2.4.1 - # via stack-data -backcall==0.2.0 - # via ipython -bcrypt==4.0.1 - # via paramiko -cachetools==5.3.2 - # via google-auth -certifi==2023.7.22 - # via - # httpcore - # httpx - # kubernetes - # requests -cffi==1.16.0 - # via - # cryptography - # pynacl -charset-normalizer==3.3.2 - # via requests -cryptography==41.0.5 - # via paramiko -decorator==5.1.1 - # via - # ipdb - # ipython -exceptiongroup==1.1.3 - # via - # anyio - # pytest -executing==2.0.1 - # via stack-data -google-auth==2.23.4 - # via kubernetes -h11==0.14.0 - # via httpcore -httpcore==1.0.2 - # via httpx -httpx==0.25.1 - # via lightkube -hvac==2.0.0 - # via juju -idna==3.4 - # via - # anyio - # httpx - # requests -iniconfig==2.0.0 - # via pytest -ipdb==0.13.13 - # via pytest-operator -ipython==8.12.3 - # via ipdb -jedi==0.19.1 - # via ipython -jinja2==3.1.2 - # via pytest-operator -juju==3.2.3.0 - # via - # -r requirements.in - # pytest-operator -kubernetes==28.1.0 - # via juju -lightkube==0.15.0 - # via -r requirements.in -lightkube-models==1.28.1.4 - # via lightkube -macaroonbakery==1.3.1 - # via juju -markupsafe==2.1.3 - # via jinja2 -matplotlib-inline==0.1.6 - # via ipython -mypy-extensions==1.0.0 - # via typing-inspect -oauthlib==3.2.2 - # via - # kubernetes - # requests-oauthlib -packaging==23.2 - # via pytest -paramiko==2.12.0 - # via juju -parso==0.8.3 - # via jedi -pexpect==4.8.0 - # via ipython -pickleshare==0.7.5 - # via ipython -pluggy==1.3.0 - # via pytest -prompt-toolkit==3.0.41 - # via ipython -protobuf==3.20.3 - # via macaroonbakery -ptyprocess==0.7.0 - # via pexpect -pure-eval==0.2.2 - # via stack-data -pyasn1==0.5.0 - # via - # juju - # pyasn1-modules - # rsa -pyasn1-modules==0.3.0 - # via google-auth -pycparser==2.21 - # via cffi -pygments==2.16.1 - # via ipython -pymacaroons==0.13.0 - # via macaroonbakery -pynacl==1.5.0 - # via - # macaroonbakery - # paramiko - # pymacaroons -pyrfc3339==1.1 - # via - # juju - # macaroonbakery -pytest==7.4.3 - # via - # -r requirements.in - # pytest-asyncio - # pytest-dependency - # pytest-operator -pytest-asyncio==0.21.1 - # via pytest-operator -pytest-dependency==0.6.0 - # via -r requirements.in -pytest-operator==0.31.0 - # via -r requirements.in -python-dateutil==2.8.2 - # via kubernetes -pytz==2023.3.post1 - # via pyrfc3339 -pyyaml==6.0.1 - # via - # juju - # kubernetes - # lightkube - # pytest-operator -requests==2.31.0 - # via - # hvac - # kubernetes - # macaroonbakery - # requests-oauthlib -requests-oauthlib==1.3.1 - # via kubernetes -rsa==4.9 - # via google-auth -six==1.16.0 - # via - # asttokens - # kubernetes - # macaroonbakery - # paramiko - # pymacaroons - # python-dateutil -sniffio==1.3.0 - # via - # anyio - # httpx -stack-data==0.6.3 - # via ipython -tenacity==8.2.3 - # via -r requirements.in -tomli==2.0.1 - # via - # ipdb - # pytest -toposort==1.10 - # via juju -traitlets==5.13.0 - # via - # ipython - # matplotlib-inline -typing-extensions==4.8.0 - # via - # ipython - # typing-inspect -typing-inspect==0.9.0 - # via juju -urllib3==1.26.18 - # via - # kubernetes - # requests -wcwidth==0.2.10 - # via prompt-toolkit -websocket-client==1.6.4 - # via kubernetes -websockets==8.1 - # via juju - -# The following packages are considered to be unsafe in a requirements file: -# setuptools diff --git a/tests/notebooks/cpu/e2e-wine/e2e-wine-kfp-mlflow-kserve.ipynb b/tests/notebooks/cpu/e2e-wine/e2e-wine-kfp-mlflow-kserve.ipynb index dd75f1a..9840d88 100644 --- a/tests/notebooks/cpu/e2e-wine/e2e-wine-kfp-mlflow-kserve.ipynb +++ b/tests/notebooks/cpu/e2e-wine/e2e-wine-kfp-mlflow-kserve.ipynb @@ -56,23 +56,25 @@ "source": [ "HTTP_PROXY = HTTPS_PROXY = NO_PROXY = None\n", "\n", - "if os.environ.get('HTTP_PROXY') and os.environ.get('HTTPS_PROXY') and os.environ.get('NO_PROXY'):\n", - " HTTP_PROXY = os.environ['HTTP_PROXY']\n", - " HTTPS_PROXY = os.environ['HTTPS_PROXY']\n", + "if os.environ.get(\"HTTP_PROXY\") and os.environ.get(\"HTTPS_PROXY\") and os.environ.get(\"NO_PROXY\"):\n", + " HTTP_PROXY = os.environ[\"HTTP_PROXY\"]\n", + " HTTPS_PROXY = os.environ[\"HTTPS_PROXY\"]\n", " # add `.kubeflow` to NO_PROXY needed for pipelines\n", - " NO_PROXY = os.environ['NO_PROXY']\n", + " NO_PROXY = os.environ[\"NO_PROXY\"]\n", + "\n", "\n", "def add_proxy(obj, http_proxy=HTTP_PROXY, https_proxy=HTTPS_PROXY, no_proxy=NO_PROXY):\n", " \"\"\"Adds the proxy env vars to the PipelineTask object.\"\"\"\n", " return (\n", - " obj.set_env_variable(name='http_proxy', value=http_proxy)\n", - " .set_env_variable(name='https_proxy', value=https_proxy)\n", - " .set_env_variable(name='HTTP_PROXY', value=http_proxy)\n", - " .set_env_variable(name='HTTPS_PROXY', value=https_proxy)\n", - " .set_env_variable(name='no_proxy', value=no_proxy)\n", - " .set_env_variable(name='NO_PROXY', value=no_proxy)\n", + " obj.set_env_variable(name=\"http_proxy\", value=http_proxy)\n", + " .set_env_variable(name=\"https_proxy\", value=https_proxy)\n", + " .set_env_variable(name=\"HTTP_PROXY\", value=http_proxy)\n", + " .set_env_variable(name=\"HTTPS_PROXY\", value=https_proxy)\n", + " .set_env_variable(name=\"no_proxy\", value=no_proxy)\n", + " .set_env_variable(name=\"NO_PROXY\", value=no_proxy)\n", " )\n", "\n", + "\n", "def proxy_envs_set() -> bool:\n", " if HTTP_PROXY and HTTPS_PROXY and NO_PROXY:\n", " return True\n", @@ -91,11 +93,12 @@ "MLFLOW_RUN_NAME = \"elastic_net_models\"\n", "MLFLOW_MODEL_NAME = \"wine-elasticnet\"\n", "\n", + "\n", "@component(\n", " base_image=\"python:3.11\", # Use Python 3.11 base image\n", - " packages_to_install=[\"requests==2.32.3\", \"pandas==2.2.2\"]\n", + " packages_to_install=[\"requests==2.32.3\", \"pandas==2.2.2\"],\n", ")\n", - "def download_dataset(url: str, dataset_path: OutputPath('Dataset')) -> None:\n", + "def download_dataset(url: str, dataset_path: OutputPath(\"Dataset\")) -> None:\n", " import requests\n", " import pandas as pd\n", "\n", @@ -105,32 +108,41 @@ "\n", " # Convert the response content to a Pandas DataFrame\n", " from io import StringIO\n", + "\n", " dataset = pd.read_csv(StringIO(response.text), header=0, sep=\";\")\n", "\n", " # Save the DataFrame to a CSV file at the specified output path\n", " dataset.to_csv(dataset_path, index=False)\n", "\n", + "\n", "@component(\n", " base_image=\"python:3.11\", # Use Python 3.11 base image\n", - " packages_to_install=[\"pandas==2.2.2\", \"pyarrow==15.0.2\"]\n", + " packages_to_install=[\"pandas==2.2.2\", \"pyarrow==15.0.2\"],\n", ")\n", - "def preprocess_dataset(dataset: InputPath('Dataset'), output_file: OutputPath('Dataset')) -> None:\n", + "def preprocess_dataset(dataset: InputPath(\"Dataset\"), output_file: OutputPath(\"Dataset\")) -> None:\n", " import pandas as pd\n", - " \n", + "\n", " # Read the CSV file into a DataFrame\n", " df = pd.read_csv(dataset, header=0)\n", - " \n", + "\n", " # Preprocess the DataFrame by standardizing column names\n", " df.columns = [c.lower().replace(\" \", \"_\") for c in df.columns]\n", - " \n", + "\n", " # Save the preprocessed DataFrame as a Parquet file\n", " df.to_parquet(output_file)\n", "\n", + "\n", "@component(\n", " base_image=\"python:3.11\", # Use Python 3.11 base image\n", - " packages_to_install=[\"pandas==2.2.2\", \"scikit-learn==1.5.1\", \"mlflow==2.15.1\", \"pyarrow==15.0.2\", \"boto3==1.34.162\"]\n", + " packages_to_install=[\n", + " \"pandas==2.2.2\",\n", + " \"scikit-learn==1.5.1\",\n", + " \"mlflow==2.15.1\",\n", + " \"pyarrow==15.0.2\",\n", + " \"boto3==1.34.162\",\n", + " ],\n", ")\n", - "def train_model(dataset: InputPath('Dataset'), run_name: str, model_name: str) -> str:\n", + "def train_model(dataset: InputPath(\"Dataset\"), run_name: str, model_name: str) -> str:\n", " import os\n", " import mlflow\n", " import pandas as pd\n", @@ -139,35 +151,38 @@ "\n", " # Load the preprocessed dataset\n", " df = pd.read_parquet(dataset)\n", - " \n", + "\n", " # Define the target column for prediction\n", " target_column = \"quality\"\n", "\n", " # Split the data into training and testing sets\n", " train_x, test_x, train_y, test_y = train_test_split(\n", " df.drop(columns=[target_column]),\n", - " df[target_column], test_size=0.25,\n", - " random_state=42, stratify=df[target_column]\n", + " df[target_column],\n", + " test_size=0.25,\n", + " random_state=42,\n", + " stratify=df[target_column],\n", " )\n", "\n", " # Enable MLflow auto logging for scikit-learn models\n", " mlflow.sklearn.autolog()\n", - " \n", + "\n", " # Start an MLflow run and train the model\n", " with mlflow.start_run(run_name=run_name) as run:\n", " mlflow.set_tag(\"author\", \"kf-testing\")\n", " lr = ElasticNet(alpha=0.5, l1_ratio=0.5, random_state=42)\n", " lr.fit(train_x, train_y)\n", " mlflow.sklearn.log_model(lr, \"model\", registered_model_name=model_name)\n", - " \n", + "\n", " # Return the model artifact URI as a string\n", " model_uri = f\"{run.info.artifact_uri}/model\"\n", " print(model_uri)\n", " return model_uri\n", "\n", + "\n", "@component(\n", " base_image=\"python:3.11\", # Use Python 3.11 base image\n", - " packages_to_install=[\"kserve==0.13.1\", \"kubernetes==26.1.0\", \"tenacity==9.0.0\"]\n", + " packages_to_install=[\"kserve==0.13.1\", \"kubernetes==26.1.0\", \"tenacity==9.0.0\"],\n", ")\n", "def deploy_model_with_kserve(model_uri: str, isvc_name: str) -> str:\n", " from kubernetes.client import V1ObjectMeta\n", @@ -192,13 +207,11 @@ " spec=V1beta1InferenceServiceSpec(\n", " predictor=V1beta1PredictorSpec(\n", " service_account_name=\"kserve-controller-s3\",\n", - " sklearn=V1beta1SKLearnSpec(\n", - " storage_uri=model_uri\n", - " )\n", + " sklearn=V1beta1SKLearnSpec(storage_uri=model_uri),\n", " )\n", - " )\n", + " ),\n", " )\n", - " \n", + "\n", " # Deploy the Inference Service using KServe\n", " client = KServeClient()\n", " client.create(isvc)\n", @@ -215,70 +228,78 @@ " # Wait until the service is ready and get the service URL\n", " assert_isvc_created(client, isvc_name)\n", " isvc_resp = client.get(isvc_name)\n", - " isvc_url = isvc_resp['status']['address']['url']\n", + " isvc_url = isvc_resp[\"status\"][\"address\"][\"url\"]\n", " print(\"Inference URL:\", isvc_url)\n", - " \n", + "\n", " return isvc_url\n", "\n", + "\n", "# Fetch environment variables for MLflow tracking and AWS credentials\n", "# These are guaranteed to be present because of the mlflow's poddefault please refer to [this guide](https://documentation.ubuntu.com/charmed-mlflow/en/latest/tutorial/mlflow-kubeflow/\n", "\n", - "mlflow_tracking_uri = os.getenv('MLFLOW_TRACKING_URI')\n", - "mlflow_s3_endpoint_url = os.getenv('MLFLOW_S3_ENDPOINT_URL')\n", - "aws_access_key_id = os.getenv('AWS_ACCESS_KEY_ID')\n", - "aws_secret_access_key = os.getenv('AWS_SECRET_ACCESS_KEY')\n", + "mlflow_tracking_uri = os.getenv(\"MLFLOW_TRACKING_URI\")\n", + "mlflow_s3_endpoint_url = os.getenv(\"MLFLOW_S3_ENDPOINT_URL\")\n", + "aws_access_key_id = os.getenv(\"AWS_ACCESS_KEY_ID\")\n", + "aws_secret_access_key = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n", + "\n", "\n", - "@pipeline(name='download-preprocess-train-deploy-pipeline')\n", + "@pipeline(name=\"download-preprocess-train-deploy-pipeline\")\n", "def download_preprocess_train_deploy_pipeline(url: str):\n", " # Step 1: Download the dataset from the URL\n", " download_task = download_dataset(url=url)\n", - " \n", + "\n", " # Step 2: Preprocess the downloaded dataset\n", - " preprocess_task = preprocess_dataset(\n", - " dataset=download_task.outputs['dataset_path']\n", - " )\n", - " \n", + " preprocess_task = preprocess_dataset(dataset=download_task.outputs[\"dataset_path\"])\n", + "\n", " # Step 3: Train the model on the preprocessed dataset\n", - " train_task = train_model(\n", - " dataset=preprocess_task.outputs['output_file'], run_name=MLFLOW_RUN_NAME, model_name=MLFLOW_MODEL_NAME\n", - " ).set_env_variable(name='MLFLOW_TRACKING_URI', value=mlflow_tracking_uri)\\\n", - " .set_env_variable(name='MLFLOW_S3_ENDPOINT_URL', value=mlflow_s3_endpoint_url)\\\n", - " .set_env_variable(name='AWS_ACCESS_KEY_ID', value=aws_access_key_id)\\\n", - " .set_env_variable(name='AWS_SECRET_ACCESS_KEY', value=aws_secret_access_key)\n", - " \n", + " train_task = (\n", + " train_model(\n", + " dataset=preprocess_task.outputs[\"output_file\"],\n", + " run_name=MLFLOW_RUN_NAME,\n", + " model_name=MLFLOW_MODEL_NAME,\n", + " )\n", + " .set_env_variable(name=\"MLFLOW_TRACKING_URI\", value=mlflow_tracking_uri)\n", + " .set_env_variable(name=\"MLFLOW_S3_ENDPOINT_URL\", value=mlflow_s3_endpoint_url)\n", + " .set_env_variable(name=\"AWS_ACCESS_KEY_ID\", value=aws_access_key_id)\n", + " .set_env_variable(name=\"AWS_SECRET_ACCESS_KEY\", value=aws_secret_access_key)\n", + " )\n", + "\n", " # Step 4: Deploy the trained model with KServe\n", " deploy_task = deploy_model_with_kserve(\n", " model_uri=train_task.output, isvc_name=ISVC_NAME\n", - " ).set_env_variable(name='AWS_SECRET_ACCESS_KEY', value=aws_secret_access_key)\n", + " ).set_env_variable(name=\"AWS_SECRET_ACCESS_KEY\", value=aws_secret_access_key)\n", + "\n", "\n", "# This pipeline definition is identical to the one above with the only difference being\n", "# that environment variables are added to each step of the pipeline, in order to enable them\n", - "# to run behind a proxy. Which pipeline is used is defined in the next cell according to if \n", + "# to run behind a proxy. Which pipeline is used is defined in the next cell according to if\n", "# such environment variables are set.\n", - "@pipeline(name='download-preprocess-train-deploy-pipeline')\n", + "@pipeline(name=\"download-preprocess-train-deploy-pipeline\")\n", "def download_preprocess_train_deploy_pipeline_proxy(url: str):\n", " # Step 1: Download the dataset from the URL\n", " download_task = add_proxy(download_dataset(url=url))\n", - " \n", + "\n", " # Step 2: Preprocess the downloaded dataset\n", - " preprocess_task = add_proxy(preprocess_dataset(\n", - " dataset=download_task.outputs['dataset_path']\n", - " )\n", - " )\n", - " \n", + " preprocess_task = add_proxy(preprocess_dataset(dataset=download_task.outputs[\"dataset_path\"]))\n", + "\n", " # Step 3: Train the model on the preprocessed dataset\n", - " train_task = add_proxy(train_model(\n", - " dataset=preprocess_task.outputs['output_file'], run_name=MLFLOW_RUN_NAME, model_name=MLFLOW_MODEL_NAME\n", - " ).set_env_variable(name='MLFLOW_TRACKING_URI', value=mlflow_tracking_uri)\\\n", - " .set_env_variable(name='MLFLOW_S3_ENDPOINT_URL', value=mlflow_s3_endpoint_url)\\\n", - " .set_env_variable(name='AWS_ACCESS_KEY_ID', value=aws_access_key_id)\\\n", - " .set_env_variable(name='AWS_SECRET_ACCESS_KEY', value=aws_secret_access_key)\n", + " train_task = add_proxy(\n", + " train_model(\n", + " dataset=preprocess_task.outputs[\"output_file\"],\n", + " run_name=MLFLOW_RUN_NAME,\n", + " model_name=MLFLOW_MODEL_NAME,\n", + " )\n", + " .set_env_variable(name=\"MLFLOW_TRACKING_URI\", value=mlflow_tracking_uri)\n", + " .set_env_variable(name=\"MLFLOW_S3_ENDPOINT_URL\", value=mlflow_s3_endpoint_url)\n", + " .set_env_variable(name=\"AWS_ACCESS_KEY_ID\", value=aws_access_key_id)\n", + " .set_env_variable(name=\"AWS_SECRET_ACCESS_KEY\", value=aws_secret_access_key)\n", " )\n", - " \n", + "\n", " # Step 4: Deploy the trained model with KServe\n", - " deploy_task = add_proxy(deploy_model_with_kserve(\n", - " model_uri=train_task.output, isvc_name=ISVC_NAME\n", - " ).set_env_variable(name='AWS_SECRET_ACCESS_KEY', value=aws_secret_access_key)\n", + " deploy_task = add_proxy(\n", + " deploy_model_with_kserve(\n", + " model_uri=train_task.output, isvc_name=ISVC_NAME\n", + " ).set_env_variable(name=\"AWS_SECRET_ACCESS_KEY\", value=aws_secret_access_key)\n", " )" ] }, @@ -295,27 +316,38 @@ "\n", "# Define the URL for the dataset\n", "# This URL points to the dataset that will be downloaded and processed in the pipeline.\n", - "url = 'https://raw.githubusercontent.com/canonical/kubeflow-examples/main/e2e-wine-kfp-mlflow/winequality-red.csv'\n", + "url = \"https://raw.githubusercontent.com/canonical/kubeflow-examples/main/e2e-wine-kfp-mlflow/winequality-red.csv\"\n", "\n", "# If proxy environment variables are set, use the `_proxy` pipeline definition.\n", "if proxy_envs_set():\n", " # Compile the pipeline to a YAML file\n", - " # This step translates the Python-based pipeline definition into a YAML file \n", + " # This step translates the Python-based pipeline definition into a YAML file\n", " # that can be used to run the pipeline in Kubeflow Pipelines.\n", - " kfp.compiler.Compiler().compile(download_preprocess_train_deploy_pipeline_proxy, 'download_preprocess_train_deploy_pipeline_proxy.yaml')\n", + " kfp.compiler.Compiler().compile(\n", + " download_preprocess_train_deploy_pipeline_proxy,\n", + " \"download_preprocess_train_deploy_pipeline_proxy.yaml\",\n", + " )\n", " # Run the pipeline\n", " # This command starts a new run of the compiled pipeline, passing in the dataset URL as an argument.\n", " # Setting enable_caching to False to overcome https://github.com/canonical/bundle-kubeflow/issues/1067\n", - " run = client.create_run_from_pipeline_func(download_preprocess_train_deploy_pipeline_proxy, arguments={'url': url}, enable_caching=False)\n", + " run = client.create_run_from_pipeline_func(\n", + " download_preprocess_train_deploy_pipeline_proxy,\n", + " arguments={\"url\": url},\n", + " enable_caching=False,\n", + " )\n", "else:\n", " # Compile the pipeline to a YAML file\n", - " # This step translates the Python-based pipeline definition into a YAML file \n", + " # This step translates the Python-based pipeline definition into a YAML file\n", " # that can be used to run the pipeline in Kubeflow Pipelines.\n", - " kfp.compiler.Compiler().compile(download_preprocess_train_deploy_pipeline, 'download_preprocess_train_deploy_pipeline.yaml')\n", + " kfp.compiler.Compiler().compile(\n", + " download_preprocess_train_deploy_pipeline, \"download_preprocess_train_deploy_pipeline.yaml\"\n", + " )\n", " # Run the pipeline\n", " # This command starts a new run of the compiled pipeline, passing in the dataset URL as an argument.\n", " # Setting enable_caching to False to overcome https://github.com/canonical/bundle-kubeflow/issues/1067\n", - " run = client.create_run_from_pipeline_func(download_preprocess_train_deploy_pipeline, arguments={'url': url}, enable_caching=False)" + " run = client.create_run_from_pipeline_func(\n", + " download_preprocess_train_deploy_pipeline, arguments={\"url\": url}, enable_caching=False\n", + " )" ] }, { @@ -361,7 +393,7 @@ "# Retrieve the Inference Service details\n", "# Fetches the Inference Service by name and extracts the URL for making predictions.\n", "isvc_resp = kserve_client.get(ISVC_NAME)\n", - "inference_service_url = isvc_resp['status']['address']['url']\n", + "inference_service_url = isvc_resp[\"status\"][\"address\"][\"url\"]\n", "print(\"Inference URL:\", inference_service_url)\n", "\n", "# Define the input data for prediction\n", @@ -369,7 +401,7 @@ "input_data = {\n", " \"instances\": [\n", " [7.4, 0.7, 0.0, 1.9, 0.076, 11.0, 34.0, 0.9978, 3.51, 0.56, 9.4],\n", - " [7.8, 0.88, 0.0, 2.6, 0.098, 25.0, 67.0, 0.9968, 3.2, 0.68, 9.8]\n", + " [7.8, 0.88, 0.0, 2.6, 0.098, 25.0, 67.0, 0.9968, 3.2, 0.68, 9.8],\n", " ]\n", "}\n", "\n", diff --git a/tests/notebooks/cpu/katib/katib-integration.ipynb b/tests/notebooks/cpu/katib/katib-integration.ipynb index beb6467..3be875a 100644 --- a/tests/notebooks/cpu/katib/katib-integration.ipynb +++ b/tests/notebooks/cpu/katib/katib-integration.ipynb @@ -117,7 +117,11 @@ "outputs": [], "source": [ "def proxy_envs_set():\n", - " if os.environ.get('HTTP_PROXY') and os.environ.get('HTTPS_PROXY') and os.environ.get('NO_PROXY'):\n", + " if (\n", + " os.environ.get(\"HTTP_PROXY\")\n", + " and os.environ.get(\"HTTPS_PROXY\")\n", + " and os.environ.get(\"NO_PROXY\")\n", + " ):\n", " return True\n", " return False" ] @@ -134,49 +138,37 @@ " name=EXPERIMENT_NAME,\n", ")\n", "\n", - "algorithm_spec=V1beta1AlgorithmSpec(\n", - " algorithm_name=\"cmaes\"\n", - ")\n", + "algorithm_spec = V1beta1AlgorithmSpec(algorithm_name=\"cmaes\")\n", "\n", - "objective_spec=V1beta1ObjectiveSpec(\n", + "objective_spec = V1beta1ObjectiveSpec(\n", " type=\"minimize\",\n", - " goal= 0.001,\n", + " goal=0.001,\n", " objective_metric_name=\"loss\",\n", - " additional_metric_names=[\"Train-accuracy\"]\n", + " additional_metric_names=[\"Train-accuracy\"],\n", ")\n", "\n", "# experiment search space\n", "# in this example we tune learning rate, number of layer, and optimizer\n", - "parameters=[\n", + "parameters = [\n", " V1beta1ParameterSpec(\n", " name=\"lr\",\n", " parameter_type=\"double\",\n", - " feasible_space=V1beta1FeasibleSpace(\n", - " min=\"0.01\",\n", - " max=\"0.06\"\n", - " ),\n", + " feasible_space=V1beta1FeasibleSpace(min=\"0.01\", max=\"0.06\"),\n", " ),\n", " V1beta1ParameterSpec(\n", " name=\"momentum\",\n", " parameter_type=\"double\",\n", - " feasible_space=V1beta1FeasibleSpace(\n", - " min=\"0.5\",\n", - " max=\"0.9\"\n", - " ),\n", + " feasible_space=V1beta1FeasibleSpace(min=\"0.5\", max=\"0.9\"),\n", " ),\n", "]\n", "\n", "# JSON template specification for the Trial's Worker Kubernetes Job\n", - "trial_spec={\n", + "trial_spec = {\n", " \"apiVersion\": \"batch/v1\",\n", " \"kind\": \"Job\",\n", " \"spec\": {\n", " \"template\": {\n", - " \"metadata\": {\n", - " \"annotations\": {\n", - " \"sidecar.istio.io/inject\": \"false\"\n", - " }\n", - " },\n", + " \"metadata\": {\"annotations\": {\"sidecar.istio.io/inject\": \"false\"}},\n", " \"spec\": {\n", " \"containers\": [\n", " {\n", @@ -189,33 +181,29 @@ " \"--batch-size=16384\",\n", " \"--lr=${trialParameters.learningRate}\",\n", " \"--momentum=${trialParameters.momentum}\",\n", - " ]\n", + " ],\n", " }\n", " ],\n", - " \"restartPolicy\": \"Never\"\n", - " }\n", + " \"restartPolicy\": \"Never\",\n", + " },\n", " }\n", - " }\n", + " },\n", "}\n", "\n", "if proxy_envs_set():\n", - " trial_spec['spec']['template']['metadata']['labels']={\"notebook-proxy\": \"true\"}\n", + " trial_spec[\"spec\"][\"template\"][\"metadata\"][\"labels\"] = {\"notebook-proxy\": \"true\"}\n", "\n", - "trial_template=V1beta1TrialTemplate(\n", + "trial_template = V1beta1TrialTemplate(\n", " primary_container_name=\"training-container\",\n", " trial_parameters=[\n", " V1beta1TrialParameterSpec(\n", - " name=\"learningRate\",\n", - " description=\"Learning rate for the training model\",\n", - " reference=\"lr\"\n", + " name=\"learningRate\", description=\"Learning rate for the training model\", reference=\"lr\"\n", " ),\n", " V1beta1TrialParameterSpec(\n", - " name=\"momentum\",\n", - " description=\"Momentum for the training model\",\n", - " reference=\"momentum\"\n", + " name=\"momentum\", description=\"Momentum for the training model\", reference=\"momentum\"\n", " ),\n", " ],\n", - " trial_spec=trial_spec\n", + " trial_spec=trial_spec,\n", ")\n", "\n", "experiment = V1beta1Experiment(\n", @@ -230,7 +218,7 @@ " objective=objective_spec,\n", " parameters=parameters,\n", " trial_template=trial_template,\n", - " )\n", + " ),\n", ")" ] }, @@ -533,17 +521,25 @@ " # fetch the existing Experiment names\n", " # verify that the Experiment was deleted successfully\n", " experiments = {exp.metadata.name for exp in client.list_experiments()}\n", - " assert experiment_name not in experiments, f\"Failed to delete Katib Experiment {experiment_name}!\"\n", + " assert (\n", + " experiment_name not in experiments\n", + " ), f\"Failed to delete Katib Experiment {experiment_name}!\"\n", "\n", " # fetch the existing Trials and retrieve the names of the Experiments these belong to\n", " # verify that the Trials were removed successfully\n", - " trials = {tr.metadata.labels.get(\"katib.kubeflow.org/experiment\") for tr in client.list_trials()}\n", - " assert experiment_name not in trials, f\"Katib Trials of Experiment {experiment_name} were not removed!\"\n", + " trials = {\n", + " tr.metadata.labels.get(\"katib.kubeflow.org/experiment\") for tr in client.list_trials()\n", + " }\n", + " assert (\n", + " experiment_name not in trials\n", + " ), f\"Katib Trials of Experiment {experiment_name} were not removed!\"\n", "\n", " # fetch the existing Suggestion names\n", " # verify that the Suggestion was removed successfully\n", " suggestions = {sugg.metadata.name for sugg in client.list_suggestions()}\n", - " assert experiment_name not in suggestions, f\"Katib Suggestion {experiment_name} was not removed!\"" + " assert (\n", + " experiment_name not in suggestions\n", + " ), f\"Katib Suggestion {experiment_name} was not removed!\"" ] }, { diff --git a/tests/notebooks/cpu/kfp_v1/kfp-v1-integration.ipynb b/tests/notebooks/cpu/kfp_v1/kfp-v1-integration.ipynb index a36ccd0..d0c98fc 100644 --- a/tests/notebooks/cpu/kfp_v1/kfp-v1-integration.ipynb +++ b/tests/notebooks/cpu/kfp_v1/kfp-v1-integration.ipynb @@ -57,7 +57,7 @@ "metadata": {}, "outputs": [], "source": [ - "EXPERIMENT_NAME = 'Simple notebook pipeline' " + "EXPERIMENT_NAME = \"Simple notebook pipeline\"" ] }, { @@ -68,8 +68,8 @@ "outputs": [], "source": [ "def add(a: float, b: float) -> float:\n", - " '''Calculates sum of two arguments'''\n", - " print(a, '+', b, '=', a + b)\n", + " \"\"\"Calculates sum of two arguments\"\"\"\n", + " print(a, \"+\", b, \"=\", a + b)\n", " return a + b" ] }, @@ -82,7 +82,7 @@ "source": [ "add_op = kfp.components.func_to_container_op(\n", " func=add,\n", - " base_image='python:3.7',\n", + " base_image=\"python:3.7\",\n", ")" ] }, @@ -94,11 +94,11 @@ "outputs": [], "source": [ "@dsl.pipeline(\n", - " name='Calculation pipeline',\n", - " description='A toy pipeline that performs arithmetic calculations.'\n", + " name=\"Calculation pipeline\",\n", + " description=\"A toy pipeline that performs arithmetic calculations.\",\n", ")\n", "def calc_pipeline(a: float = 0, b: float = 7):\n", - " add_task = add_op(a, 4) \n", + " add_task = add_op(a, 4)\n", " add_2_task = add_op(a, b)\n", " add_3_task = add_op(add_task.output, add_2_task.output)" ] @@ -135,7 +135,7 @@ } ], "source": [ - "arguments = {'a': '7', 'b': '8'}\n", + "arguments = {\"a\": \"7\", \"b\": \"8\"}\n", "run = client.create_run_from_pipeline_func(\n", " calc_pipeline,\n", " arguments=arguments,\n", diff --git a/tests/notebooks/cpu/kfp_v2/kfp-v2-integration.ipynb b/tests/notebooks/cpu/kfp_v2/kfp-v2-integration.ipynb index 44cf524..8e4efc3 100644 --- a/tests/notebooks/cpu/kfp_v2/kfp-v2-integration.ipynb +++ b/tests/notebooks/cpu/kfp_v2/kfp-v2-integration.ipynb @@ -66,7 +66,7 @@ }, "outputs": [], "source": [ - "EXPERIMENT_NAME = 'Flip a coin and output tails/heads pipeline' " + "EXPERIMENT_NAME = \"Flip a coin and output tails/heads pipeline\"" ] }, { @@ -78,10 +78,10 @@ "source": [ "HTTP_PROXY = HTTPS_PROXY = NO_PROXY = None\n", "\n", - "if os.environ.get('HTTP_PROXY') and os.environ.get('HTTPS_PROXY') and os.environ.get('NO_PROXY'):\n", - " HTTP_PROXY = os.environ['HTTP_PROXY']\n", - " HTTPS_PROXY = os.environ['HTTPS_PROXY']\n", - " NO_PROXY = os.environ['NO_PROXY']" + "if os.environ.get(\"HTTP_PROXY\") and os.environ.get(\"HTTPS_PROXY\") and os.environ.get(\"NO_PROXY\"):\n", + " HTTP_PROXY = os.environ[\"HTTP_PROXY\"]\n", + " HTTPS_PROXY = os.environ[\"HTTPS_PROXY\"]\n", + " NO_PROXY = os.environ[\"NO_PROXY\"]" ] }, { @@ -94,12 +94,12 @@ "def add_proxy(obj, http_proxy=HTTP_PROXY, https_proxy=HTTPS_PROXY, no_proxy=NO_PROXY):\n", " \"\"\"Adds the proxy env vars to the PipelineTask object.\"\"\"\n", " return (\n", - " obj.set_env_variable(name='http_proxy', value=http_proxy)\n", - " .set_env_variable(name='https_proxy', value=https_proxy)\n", - " .set_env_variable(name='HTTP_PROXY', value=http_proxy)\n", - " .set_env_variable(name='HTTPS_PROXY', value=https_proxy)\n", - " .set_env_variable(name='no_proxy', value=no_proxy)\n", - " .set_env_variable(name='NO_PROXY', value=no_proxy)\n", + " obj.set_env_variable(name=\"http_proxy\", value=http_proxy)\n", + " .set_env_variable(name=\"https_proxy\", value=https_proxy)\n", + " .set_env_variable(name=\"HTTP_PROXY\", value=http_proxy)\n", + " .set_env_variable(name=\"HTTPS_PROXY\", value=https_proxy)\n", + " .set_env_variable(name=\"no_proxy\", value=no_proxy)\n", + " .set_env_variable(name=\"NO_PROXY\", value=no_proxy)\n", " )" ] }, @@ -126,12 +126,13 @@ "outputs": [], "source": [ "@dsl.component()\n", - "def flip_coin(force_flip_result: str = '') -> str:\n", + "def flip_coin(force_flip_result: str = \"\") -> str:\n", " \"\"\"Flip a coin and output heads or tails randomly.\"\"\"\n", " if force_flip_result:\n", " return force_flip_result\n", " import random\n", - " result = 'heads' if random.randint(0, 1) == 0 else 'tails'\n", + "\n", + " result = \"heads\" if random.randint(0, 1) == 0 else \"tails\"\n", " return result" ] }, @@ -159,12 +160,12 @@ }, "outputs": [], "source": [ - "@dsl.pipeline(name='condition-v2')\n", - "def condition_pipeline(text: str = 'condition test', force_flip_result: str = ''):\n", + "@dsl.pipeline(name=\"condition-v2\")\n", + "def condition_pipeline(text: str = \"condition test\", force_flip_result: str = \"\"):\n", " flip1 = flip_coin(force_flip_result=force_flip_result)\n", " print_msg(msg=flip1.output)\n", "\n", - " with dsl.Condition(flip1.output == 'heads'):\n", + " with dsl.Condition(flip1.output == \"heads\"):\n", " flip2 = flip_coin()\n", " print_msg(msg=flip2.output)\n", " print_msg(msg=text)" @@ -177,12 +178,12 @@ "metadata": {}, "outputs": [], "source": [ - "@dsl.pipeline(name='condition-v2')\n", - "def condition_pipeline_proxy(text: str = 'condition test', force_flip_result: str = ''):\n", + "@dsl.pipeline(name=\"condition-v2\")\n", + "def condition_pipeline_proxy(text: str = \"condition test\", force_flip_result: str = \"\"):\n", " flip1 = add_proxy(flip_coin(force_flip_result=force_flip_result))\n", " add_proxy(print_msg(msg=flip1.output))\n", "\n", - " with dsl.Condition(flip1.output == 'heads'):\n", + " with dsl.Condition(flip1.output == \"heads\"):\n", " flip2 = add_proxy(flip_coin())\n", " add_proxy(print_msg(msg=flip2.output))\n", " add_proxy(print_msg(msg=text))" diff --git a/tests/notebooks/cpu/kserve/kserve-integration.ipynb b/tests/notebooks/cpu/kserve/kserve-integration.ipynb index c4eaecc..517d113 100644 --- a/tests/notebooks/cpu/kserve/kserve-integration.ipynb +++ b/tests/notebooks/cpu/kserve/kserve-integration.ipynb @@ -107,7 +107,7 @@ " storage_uri=\"gs://kfserving-examples/models/sklearn/1.0/model\"\n", " )\n", " )\n", - " )\n", + " ),\n", ")" ] }, @@ -206,7 +206,7 @@ "outputs": [], "source": [ "isvc_resp = client.get(ISVC_NAME)\n", - "isvc_url = isvc_resp['status']['address']['url']\n", + "isvc_url = isvc_resp[\"status\"][\"address\"][\"url\"]\n", "print(\"Inference URL:\", isvc_url)" ] }, @@ -226,12 +226,7 @@ "metadata": {}, "outputs": [], "source": [ - "inference_input = {\n", - " \"instances\": [\n", - " [6.8, 2.8, 4.8, 1.4],\n", - " [6.0, 3.4, 4.5, 1.6]\n", - " ]\n", - "}\n", + "inference_input = {\"instances\": [[6.8, 2.8, 4.8, 1.4], [6.0, 3.4, 4.5, 1.6]]}\n", "response = requests.post(f\"{isvc_url}/v1/models/sklearn-iris:predict\", json=inference_input)\n", "print(response.text)" ] diff --git a/tests/notebooks/cpu/mlflow-kserve/mlflow-kserve.ipynb b/tests/notebooks/cpu/mlflow-kserve/mlflow-kserve.ipynb index f99c06d..e1787a0 100644 --- a/tests/notebooks/cpu/mlflow-kserve/mlflow-kserve.ipynb +++ b/tests/notebooks/cpu/mlflow-kserve/mlflow-kserve.ipynb @@ -230,7 +230,10 @@ } ], "source": [ - "data = pd.read_csv(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\", sep=\";\")\n", + "data = pd.read_csv(\n", + " \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\",\n", + " sep=\";\",\n", + ")\n", "data.head()" ] }, @@ -311,7 +314,9 @@ "outputs": [], "source": [ "# check that the experiment was created successfully\n", - "assert mlflow.get_experiment(experiment_id).name == wine_experiment_name, f\"Failed to create experiment {wine_experiment_name}!\"" + "assert (\n", + " mlflow.get_experiment(experiment_id).name == wine_experiment_name\n", + "), f\"Failed to create experiment {wine_experiment_name}!\"" ] }, { @@ -331,20 +336,22 @@ "source": [ "def experiment(alpha, l1_ratio):\n", " mlflow.sklearn.autolog()\n", - " with mlflow.start_run(run_name='wine_models', experiment_id=experiment_id) as run:\n", - " mlflow.set_tag(\"author\", \"kf-testing\")\n", - " lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)\n", - " lr.fit(train_x, train_y)\n", + " with mlflow.start_run(run_name=\"wine_models\", experiment_id=experiment_id) as run:\n", + " mlflow.set_tag(\"author\", \"kf-testing\")\n", + " lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)\n", + " lr.fit(train_x, train_y)\n", + "\n", + " pred_y = lr.predict(test_x)\n", + " mlflow.log_metric(\"rmse\", np.sqrt(mean_squared_error(test_y, pred_y)))\n", + " mlflow.log_metric(\"r2\", r2_score(test_y, pred_y))\n", + " mlflow.log_metric(\"mae\", mean_absolute_error(test_y, pred_y))\n", "\n", - " pred_y = lr.predict(test_x)\n", - " mlflow.log_metric(\"rmse\", np.sqrt(mean_squared_error(test_y, pred_y)))\n", - " mlflow.log_metric(\"r2\", r2_score(test_y, pred_y))\n", - " mlflow.log_metric(\"mae\", mean_absolute_error(test_y, pred_y))\n", + " signature = infer_signature(test_x, pred_y)\n", + " result = mlflow.sklearn.log_model(\n", + " lr, \"model\", registered_model_name=\"wine-elasticnet\", signature=signature\n", + " )\n", + " model_uri = f\"{mlflow.get_artifact_uri()}/{result.artifact_path}\"\n", "\n", - " signature = infer_signature(test_x, pred_y)\n", - " result = mlflow.sklearn.log_model(lr, \"model\", registered_model_name=\"wine-elasticnet\", signature=signature)\n", - " model_uri = f\"{mlflow.get_artifact_uri()}/{result.artifact_path}\"\n", - " \n", " return run, model_uri" ] }, @@ -436,11 +443,9 @@ " spec=V1beta1InferenceServiceSpec(\n", " predictor=V1beta1PredictorSpec(\n", " service_account_name=\"kserve-controller-s3\",\n", - " sklearn=V1beta1SKLearnSpec(\n", - " storage_uri=model_uri\n", - " )\n", + " sklearn=V1beta1SKLearnSpec(storage_uri=model_uri),\n", " )\n", - " )\n", + " ),\n", ")" ] }, @@ -454,6 +459,7 @@ "client = KServeClient()\n", "client.create(isvc)\n", "\n", + "\n", "@retry(\n", " wait=wait_exponential(multiplier=2, min=1, max=10),\n", " stop=stop_after_attempt(30),\n", @@ -463,6 +469,7 @@ " \"\"\"Wait for the Inference Service to be created successfully.\"\"\"\n", " assert client.is_isvc_ready(ISVC_NAME), f\"Failed to create Inference Service {isvc_name}.\"\n", "\n", + "\n", "assert_isvc_created(client, ISVC_NAME)" ] }, @@ -490,7 +497,7 @@ ], "source": [ "isvc_resp = client.get(ISVC_NAME)\n", - "isvc_url = isvc_resp['status']['address']['url']\n", + "isvc_url = isvc_resp[\"status\"][\"address\"][\"url\"]\n", "print(\"Inference URL:\", isvc_url)" ] }, @@ -512,9 +519,7 @@ ], "source": [ "inference_input = {\n", - " \"instances\": [\n", - " [10.1, 0.37, 0.34, 2.4, 0.085, 5.0, 17.0, 0.99683, 3.17, 0.65, 10.6]\n", - " ]\n", + " \"instances\": [[10.1, 0.37, 0.34, 2.4, 0.085, 5.0, 17.0, 0.99683, 3.17, 0.65, 10.6]]\n", "}\n", "response = requests.post(f\"{isvc_url}/v1/models/{ISVC_NAME}:predict\", json=inference_input)\n", "print(response.text)" diff --git a/tests/notebooks/cpu/mlflow-minio/mlflow-minio-integration.ipynb b/tests/notebooks/cpu/mlflow-minio/mlflow-minio-integration.ipynb index 6c602b3..5522fde 100644 --- a/tests/notebooks/cpu/mlflow-minio/mlflow-minio-integration.ipynb +++ b/tests/notebooks/cpu/mlflow-minio/mlflow-minio-integration.ipynb @@ -221,11 +221,15 @@ "# check that the bucket only contains the uploaded object\n", "objects = [obj for obj in mc.list_objects(BUCKET)]\n", "assert len(objects) == 1, f\"Expected only 1 object in bucket {BUCKET}!\"\n", - "assert objects[0].object_name == UPLOADED_OBJECT, \"The uploaded and local object names do not match!\"\n", + "assert (\n", + " objects[0].object_name == UPLOADED_OBJECT\n", + "), \"The uploaded and local object names do not match!\"\n", "\n", "# check that the size is the same\n", "file_stat = os.stat(LOCAL_OBJECT)\n", - "assert objects[0].size == file_stat.st_size, \"The uploaded and local objects are not of the same size!\"" + "assert (\n", + " objects[0].size == file_stat.st_size\n", + "), \"The uploaded and local objects are not of the same size!\"" ] }, { @@ -272,7 +276,9 @@ "assert os.path.exists(DOWNLOADED_OBJECT), f\"Failed to download object {UPLOADED_OBJECT}!\"\n", "\n", "# check that its content matches that of the original file\n", - "assert filecmp.cmp(LOCAL_OBJECT, DOWNLOADED_OBJECT, shallow=False), f\"Downloaded object {DOWNLOADED_OBJECT} does not match the original!\"" + "assert filecmp.cmp(\n", + " LOCAL_OBJECT, DOWNLOADED_OBJECT, shallow=False\n", + "), f\"Downloaded object {DOWNLOADED_OBJECT} does not match the original!\"" ] }, { @@ -315,13 +321,15 @@ "outputs": [], "source": [ "local = pd.read_csv(LOCAL_CSV, delimiter=\";\")\n", - "uploaded = pd.read_csv(f\"s3://{BUCKET}/{UPLOADED_CSV}\", delimiter=\";\",storage_options={\n", - " \"key\": os.environ[\"AWS_ACCESS_KEY_ID\"],\n", - " \"secret\": os.environ[\"AWS_SECRET_ACCESS_KEY\"],\n", - " \"client_kwargs\":{\n", - " \"endpoint_url\": os.environ[\"MINIO_ENDPOINT_URL\"]\n", - " }\n", - "})" + "uploaded = pd.read_csv(\n", + " f\"s3://{BUCKET}/{UPLOADED_CSV}\",\n", + " delimiter=\";\",\n", + " storage_options={\n", + " \"key\": os.environ[\"AWS_ACCESS_KEY_ID\"],\n", + " \"secret\": os.environ[\"AWS_SECRET_ACCESS_KEY\"],\n", + " \"client_kwargs\": {\"endpoint_url\": os.environ[\"MINIO_ENDPOINT_URL\"]},\n", + " },\n", + ")" ] }, { @@ -462,7 +470,7 @@ "except Exception as e:\n", " if not isinstance(e, NoSuchKey):\n", " raise\n", - " \n", + "\n", "assert not res, f\"Failed to delete {UPLOADED_OBJECT}!\"" ] }, diff --git a/tests/notebooks/cpu/mlflow/mlflow-integration.ipynb b/tests/notebooks/cpu/mlflow/mlflow-integration.ipynb index cb28e58..985542a 100644 --- a/tests/notebooks/cpu/mlflow/mlflow-integration.ipynb +++ b/tests/notebooks/cpu/mlflow/mlflow-integration.ipynb @@ -204,7 +204,10 @@ } ], "source": [ - "data = pd.read_csv(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\", sep=\";\")\n", + "data = pd.read_csv(\n", + " \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\",\n", + " sep=\";\",\n", + ")\n", "data.head()" ] }, @@ -313,7 +316,9 @@ "outputs": [], "source": [ "# check that the experiment was created successfully\n", - "assert mlflow.get_experiment(experiment_id).name == wine_experiment_name, f\"Failed to create experiment {wine_experiment_name}!\"" + "assert (\n", + " mlflow.get_experiment(experiment_id).name == wine_experiment_name\n", + "), f\"Failed to create experiment {wine_experiment_name}!\"" ] }, { @@ -325,19 +330,21 @@ "source": [ "def experiment(alpha, l1_ratio):\n", " mlflow.sklearn.autolog()\n", - " with mlflow.start_run(run_name='wine_models', experiment_id=experiment_id) as run:\n", - " mlflow.set_tag(\"author\", \"kf-testing\")\n", - " lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)\n", - " lr.fit(train_x, train_y)\n", + " with mlflow.start_run(run_name=\"wine_models\", experiment_id=experiment_id) as run:\n", + " mlflow.set_tag(\"author\", \"kf-testing\")\n", + " lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)\n", + " lr.fit(train_x, train_y)\n", + "\n", + " pred_y = lr.predict(test_x)\n", + " mlflow.log_metric(\"rmse\", np.sqrt(mean_squared_error(test_y, pred_y)))\n", + " mlflow.log_metric(\"r2\", r2_score(test_y, pred_y))\n", + " mlflow.log_metric(\"mae\", mean_absolute_error(test_y, pred_y))\n", "\n", - " pred_y = lr.predict(test_x)\n", - " mlflow.log_metric(\"rmse\", np.sqrt(mean_squared_error(test_y, pred_y)))\n", - " mlflow.log_metric(\"r2\", r2_score(test_y, pred_y))\n", - " mlflow.log_metric(\"mae\", mean_absolute_error(test_y, pred_y))\n", + " signature = infer_signature(test_x, pred_y)\n", + " mlflow.sklearn.log_model(\n", + " lr, \"model\", registered_model_name=\"wine-elasticnet\", signature=signature\n", + " )\n", "\n", - " signature = infer_signature(test_x, pred_y)\n", - " mlflow.sklearn.log_model(lr, \"model\", registered_model_name=\"wine-elasticnet\", signature=signature)\n", - " \n", " return run" ] }, diff --git a/tests/notebooks/cpu/training/training-integration.ipynb b/tests/notebooks/cpu/training/training-integration.ipynb index 9cc0b56..ccc8ff0 100644 --- a/tests/notebooks/cpu/training/training-integration.ipynb +++ b/tests/notebooks/cpu/training/training-integration.ipynb @@ -153,7 +153,7 @@ "outputs": [], "source": [ "training_labels = {}\n", - "if os.environ.get('HTTP_PROXY') and os.environ.get('HTTPS_PROXY') and os.environ.get('NO_PROXY'):\n", + "if os.environ.get(\"HTTP_PROXY\") and os.environ.get(\"HTTPS_PROXY\") and os.environ.get(\"NO_PROXY\"):\n", " training_labels = {\"notebook-proxy\": \"true\"}" ] }, @@ -194,16 +194,18 @@ " name=TFJOB_CONTAINER,\n", " image=TFJOB_IMAGE,\n", " command=[\n", - " \"sh\", \"-c\",\n", + " \"sh\",\n", + " \"-c\",\n", " # Download MNIST dataset using curl to overcome proxy problems https://github.com/canonical/training-operator/issues/182\n", " # Mnist data loads from /tmp/tensorflow/mnist/input_data [see reference in mnist_with_summaries.py](https://github.com/kubeflow/training-operator/blob/master/examples/tensorflow/mnist_with_summaries/mnist_with_summaries.py#L213)\n", - " \"mkdir -p /tmp/tensorflow/mnist/input_data/ && \" +\n", - " \"curl -L -o /tmp/tensorflow/mnist/input_data/train-images-idx3-ubyte.gz https://github.com/golbin/TensorFlow-MNIST/raw/master/mnist/data/train-images-idx3-ubyte.gz && \" +\n", - " \"curl -L -o /tmp/tensorflow/mnist/input_data/train-labels-idx1-ubyte.gz https://github.com/golbin/TensorFlow-MNIST/raw/master/mnist/data/train-labels-idx1-ubyte.gz && \" +\n", - " \"curl -L -o /tmp/tensorflow/mnist/input_data/t10k-images-idx3-ubyte.gz https://github.com/golbin/TensorFlow-MNIST/raw/master/mnist/data/t10k-images-idx3-ubyte.gz && \" +\n", - " \"curl -L -o /tmp/tensorflow/mnist/input_data/t10k-labels-idx1-ubyte.gz https://github.com/golbin/TensorFlow-MNIST/raw/master/mnist/data/t10k-labels-idx1-ubyte.gz && \" +\n", + " \"mkdir -p /tmp/tensorflow/mnist/input_data/ && \"\n", + " + \"curl -L -o /tmp/tensorflow/mnist/input_data/train-images-idx3-ubyte.gz https://github.com/golbin/TensorFlow-MNIST/raw/master/mnist/data/train-images-idx3-ubyte.gz && \"\n", + " + \"curl -L -o /tmp/tensorflow/mnist/input_data/train-labels-idx1-ubyte.gz https://github.com/golbin/TensorFlow-MNIST/raw/master/mnist/data/train-labels-idx1-ubyte.gz && \"\n", + " + \"curl -L -o /tmp/tensorflow/mnist/input_data/t10k-images-idx3-ubyte.gz https://github.com/golbin/TensorFlow-MNIST/raw/master/mnist/data/t10k-images-idx3-ubyte.gz && \"\n", + " + \"curl -L -o /tmp/tensorflow/mnist/input_data/t10k-labels-idx1-ubyte.gz https://github.com/golbin/TensorFlow-MNIST/raw/master/mnist/data/t10k-labels-idx1-ubyte.gz && \"\n", + " +\n", " # Run the TensorFlow script after downloading the dataset\n", - " \"python /var/tf_mnist/mnist_with_summaries.py --log_dir=/train/logs --learning_rate=0.01 --batch_size=150\"\n", + " \"python /var/tf_mnist/mnist_with_summaries.py --log_dir=/train/logs --learning_rate=0.01 --batch_size=150\",\n", " ],\n", ")\n", "\n", @@ -211,7 +213,9 @@ " replicas=2,\n", " restart_policy=\"Never\",\n", " template=V1PodTemplateSpec(\n", - " metadata=V1ObjectMeta(annotations={\"sidecar.istio.io/inject\": \"false\"}, labels=training_labels),\n", + " metadata=V1ObjectMeta(\n", + " annotations={\"sidecar.istio.io/inject\": \"false\"}, labels=training_labels\n", + " ),\n", " spec=V1PodSpec(containers=[container]),\n", " ),\n", ")\n", @@ -220,7 +224,9 @@ " replicas=1,\n", " restart_policy=\"Never\",\n", " template=V1PodTemplateSpec(\n", - " metadata=V1ObjectMeta(annotations={\"sidecar.istio.io/inject\": \"false\"}, labels=training_labels),\n", + " metadata=V1ObjectMeta(\n", + " annotations={\"sidecar.istio.io/inject\": \"false\"}, labels=training_labels\n", + " ),\n", " spec=V1PodSpec(containers=[container]),\n", " ),\n", ")\n", @@ -229,7 +235,9 @@ " replicas=1,\n", " restart_policy=\"Never\",\n", " template=V1PodTemplateSpec(\n", - " metadata=V1ObjectMeta(annotations={\"sidecar.istio.io/inject\": \"false\"}, labels=training_labels),\n", + " metadata=V1ObjectMeta(\n", + " annotations={\"sidecar.istio.io/inject\": \"false\"}, labels=training_labels\n", + " ),\n", " spec=V1PodSpec(containers=[container]),\n", " ),\n", ")\n", @@ -468,7 +476,9 @@ " replicas=1,\n", " restart_policy=\"OnFailure\",\n", " template=V1PodTemplateSpec(\n", - " metadata=V1ObjectMeta(annotations={\"sidecar.istio.io/inject\": \"false\"}, labels=training_labels),\n", + " metadata=V1ObjectMeta(\n", + " annotations={\"sidecar.istio.io/inject\": \"false\"}, labels=training_labels\n", + " ),\n", " spec=V1PodSpec(containers=[container]),\n", " ),\n", ")\n", diff --git a/tests/notebooks/gpu/kfp-tensorflow/kfp-tensorflow-integration.ipynb b/tests/notebooks/gpu/kfp-tensorflow/kfp-tensorflow-integration.ipynb index 081db64..03317e4 100644 --- a/tests/notebooks/gpu/kfp-tensorflow/kfp-tensorflow-integration.ipynb +++ b/tests/notebooks/gpu/kfp-tensorflow/kfp-tensorflow-integration.ipynb @@ -51,7 +51,7 @@ "metadata": {}, "outputs": [], "source": [ - "EXPERIMENT_NAME = 'Check access to GPU'" + "EXPERIMENT_NAME = \"Check access to GPU\"" ] }, { @@ -62,22 +62,26 @@ "source": [ "HTTP_PROXY = HTTPS_PROXY = NO_PROXY = None\n", "\n", - "if os.environ.get('HTTP_PROXY') and os.environ.get('HTTPS_PROXY') and os.environ.get('NO_PROXY'):\n", - " HTTP_PROXY = os.environ['HTTP_PROXY']\n", - " HTTPS_PROXY = os.environ['HTTPS_PROXY']\n", - " NO_PROXY = os.environ['NO_PROXY']\n", + "if os.environ.get(\"HTTP_PROXY\") and os.environ.get(\"HTTPS_PROXY\") and os.environ.get(\"NO_PROXY\"):\n", + " HTTP_PROXY = os.environ[\"HTTP_PROXY\"]\n", + " HTTPS_PROXY = os.environ[\"HTTPS_PROXY\"]\n", + " NO_PROXY = os.environ[\"NO_PROXY\"]\n", "\n", - "def add_proxy(task: dsl.PipelineTask, http_proxy=HTTP_PROXY, https_proxy=HTTPS_PROXY, no_proxy=NO_PROXY) -> dsl.PipelineTask:\n", + "\n", + "def add_proxy(\n", + " task: dsl.PipelineTask, http_proxy=HTTP_PROXY, https_proxy=HTTPS_PROXY, no_proxy=NO_PROXY\n", + ") -> dsl.PipelineTask:\n", " \"\"\"Adds the proxy env vars to the PipelineTask object.\"\"\"\n", " return (\n", - " task.set_env_variable(name='http_proxy', value=http_proxy)\n", - " .set_env_variable(name='https_proxy', value=https_proxy)\n", - " .set_env_variable(name='HTTP_PROXY', value=http_proxy)\n", - " .set_env_variable(name='HTTPS_PROXY', value=https_proxy)\n", - " .set_env_variable(name='no_proxy', value=no_proxy)\n", - " .set_env_variable(name='NO_PROXY', value=no_proxy)\n", + " task.set_env_variable(name=\"http_proxy\", value=http_proxy)\n", + " .set_env_variable(name=\"https_proxy\", value=https_proxy)\n", + " .set_env_variable(name=\"HTTP_PROXY\", value=http_proxy)\n", + " .set_env_variable(name=\"HTTPS_PROXY\", value=https_proxy)\n", + " .set_env_variable(name=\"no_proxy\", value=no_proxy)\n", + " .set_env_variable(name=\"NO_PROXY\", value=no_proxy)\n", " )\n", "\n", + "\n", "def proxy_envs_set():\n", " \"\"\"Check if the proxy env vars are set\"\"\"\n", " if HTTP_PROXY and HTTPS_PROXY and NO_PROXY:\n", @@ -96,15 +100,18 @@ " \"\"\"Check access to a GPU.\"\"\"\n", " import tensorflow as tf\n", "\n", - " gpus = tf.config.list_physical_devices('GPU')\n", + " gpus = tf.config.list_physical_devices(\"GPU\")\n", " print(\"GPU list:\", gpus)\n", " if not gpus:\n", " raise RuntimeError(\"No GPU has been detected.\")\n", - " return str(len(gpus)>0)\n", + " return str(len(gpus) > 0)\n", + "\n", "\n", "def add_gpu_request(task: dsl.PipelineTask) -> dsl.PipelineTask:\n", " \"\"\"Add a request field for a GPU to the container created by the PipelineTask object.\"\"\"\n", - " return ( task.add_node_selector_constraint(accelerator = \"nvidia.com/gpu\").set_accelerator_limit(limit = 1) )" + " return task.add_node_selector_constraint(accelerator=\"nvidia.com/gpu\").set_accelerator_limit(\n", + " limit=1\n", + " )" ] }, { @@ -119,6 +126,7 @@ " gpu_check1 = add_gpu_request(gpu_check())\n", " return gpu_check1.output\n", "\n", + "\n", "@dsl.pipeline\n", "def gpu_check_pipeline_proxy() -> str:\n", " \"\"\"Create a pipeline that runs code to check access to a GPU and sets the appropriate proxy ENV variables.\"\"\"\n", diff --git a/tox.ini b/tox.ini index 3c05a26..c518558 100644 --- a/tox.ini +++ b/tox.ini @@ -12,66 +12,59 @@ driver_path = {toxinidir}/driver/ tst_path = {toxinidir}/tests/ [testenv] -passenv = +allowlist_externals = + /bin/bash + poetry + echo + +pass_env = PYTHONPATH CHARM_BUILD_DIR MODEL_SETTINGS KUBECONFIG -setenv = +set_env = PYTHONPATH = {toxinidir} PYTHONBREAKPOINT=ipdb.set_trace PY_COLORS=1 + local: LOCAL = True + remote: LOCAL = False +deps = + poetry [testenv:update-requirements] -allowlist_externals = - bash - find - pip-compile - xargs +description = Update dependencies commands = - ; we must preserve the order of compilation, since each *.in file depends on some *.txt file. - ; For example, requirements-unit.in depends on requirements.txt and we must compile first - ; requirements.txt to ensure that requirements-unit.txt get the same dependency as the requirements.txt - bash -c 'for pattern in "requirements.in" "requirements-fmt.in" "requirements*.in"; do find . -type f -name "$pattern" -exec bash -c "cd \$(dirname "{}") && pip-compile --resolver=backtracking \$(basename "{}")" \;; done' -deps = - pip-tools -description = Update requirements files by executing pip-compile on all requirements*.in files, including those in subdirs. + poetry update [testenv:fmt] -commands = - isort {[vars]all_path} - black {[vars]all_path} -deps = - -r requirements-fmt.txt description = Apply coding style standards to code +commands = + poetry install --no-root --only fmt + poetry run isort {[vars]all_path} + poetry run black {[vars]all_path} [testenv:lint] +description = Check code against coding style standards commands = + poetry install --no-root --only lint # uncomment the following line if this charm owns a lib # codespell {[vars]lib_path} - codespell {toxinidir}/. --skip {toxinidir}/./.git --skip {toxinidir}/./.tox \ + poetry run codespell {toxinidir}/. --skip {toxinidir}/./.git --skip {toxinidir}/./.tox \ --skip {toxinidir}/./build --skip {toxinidir}/./lib --skip {toxinidir}/./venv \ - --skip {toxinidir}/./.mypy_cache \ + --skip {toxinidir}/./.mypy_cache --skip {toxinidir}/./poetry.lock \ --skip {toxinidir}/./icon.svg --skip *.json.tmpl \ --ignore-regex=".*codespell-ignore." # pflake8 wrapper supports config from pyproject.toml - pflake8 {[vars]all_path} - isort --check-only --diff {[vars]all_path} - black --check --diff {[vars]all_path} -deps = - -r requirements-lint.txt -description = Check code against coding style standards + poetry run pflake8 {[vars]all_path} + poetry run isort --check-only --diff {[vars]all_path} + poetry run black --check --diff {[vars]all_path} [testenv:kubeflow-{local,remote}] +description = Run UATs for Kubeflow commands = # run all tests apart from the ones that use MLFlow - pytest -vv --tb native {[vars]driver_path} -s --filter "not mlflow" --model kubeflow {posargs} -setenv = - local: LOCAL = True - remote: LOCAL = False -deps = - -r requirements.txt -description = Run UATs for Kubeflow + poetry install --no-root + poetry run pytest -vv --tb native {[vars]driver_path} -s --filter "not mlflow" --model kubeflow {posargs} [testenv:uats-{local,remote}] # provide a filter when calling tox to (de)select test cases based on their names, e.g. @@ -81,22 +74,14 @@ description = Run UATs for Kubeflow # $ tox -e uats -- --filter "not kserve" # this simulates the behaviour of running 'pytest -k ""' directly on the test suite: # https://docs.pytest.org/en/7.4.x/reference/reference.html#command-line-flags -commands = - pytest -vv --tb native {[vars]driver_path} -s --model kubeflow {posargs} -setenv = - local: LOCAL = True - remote: LOCAL = False -deps = - -r requirements.txt description = Run UATs for Kubeflow and Integrations +commands = + poetry install --no-root + poetry run pytest -vv --tb native {[vars]driver_path} -s --model kubeflow {posargs} [testenv:mlflow-{local,remote}] +description = Run UATs for MLflow commands = # run all MLFlow UATs - pytest -vv --tb native {[vars]driver_path} -s --filter "mlflow" --model kubeflow {posargs} -setenv = - local: LOCAL = True - remote: LOCAL = False -deps = - -r requirements.txt -description = Run UATs for MLflow + poetry install --no-root + poetry run pytest -vv --tb native {[vars]driver_path} -s --filter "mlflow" --model kubeflow {posargs}