diff --git a/.github/workflows/test-all.yaml b/.github/workflows/test-all.yaml index 162804863..d8eb6c6ed 100644 --- a/.github/workflows/test-all.yaml +++ b/.github/workflows/test-all.yaml @@ -116,6 +116,10 @@ jobs: permissions: contents: write steps: + - name: Get the current branch name + shell: bash + run: echo "branch=${GITHUB_REF#refs/heads/}" >> $GITHUB_OUTPUT + id: branch - uses: actions/checkout@v4 with: fetch-depth: 0 @@ -128,7 +132,9 @@ jobs: pip install --upgrade pip pip install -r requirements.txt - name: Regenerate README tables - run: python -m cli.cli update-readme --asset functions --asset modules + env: + CHANNEL: ${{ steps.branch.outputs.branch }} + run: python -m cli.cli update-readme -c $CHANNEL --asset functions --asset modules - name: Commit & push (if changed) env: USERNAME: ${{ secrets.USERNAME }} diff --git a/cli/README.md b/cli/README.md index 4a3cd3bfc..69749536b 100644 --- a/cli/README.md +++ b/cli/README.md @@ -60,7 +60,7 @@ Example: Regenerate the `README.md` files in each of the asset directories (functions/modules). Usage: - `python -m cli.cli update-readme --asset TYPE` + `python -m cli.cli update-readme -c CHANNEL --asset TYPE` Example: - `python -m cli.cli update-readme --asset functions --asset modules` \ No newline at end of file + `python -m cli.cli update-readme -c master --asset functions --asset modules` diff --git a/cli/common/update_readme.py b/cli/common/update_readme.py index 6bcab8d33..89b6aa094 100644 --- a/cli/common/update_readme.py +++ b/cli/common/update_readme.py @@ -25,6 +25,7 @@ COLUMNS = ("Name", "Description", "Kind", "Categories") @click.command("update-readme") +@click.option("-c", "--channel", default="master", help="Name of build channel") @click.option( "--asset", multiple=True, @@ -34,7 +35,7 @@ ) @click.option("--check", is_flag=True, help="Do not write; exit non‑zero if README(s) would change.") -def update_readme(asset: Iterable[str], +def update_readme(channel: str, asset: Iterable[str], check: bool) -> None: """ Regenerate the README tables for asset types from their item.yaml files. @@ -50,7 +51,7 @@ def update_readme(asset: Iterable[str], root = Path(".").resolve() asset_dir = root / t readme = asset_dir / "README.md" - rows = _rows_for_asset_type(asset_dir) + rows = _rows_for_asset_type(channel, asset_dir) table_md = _build_table_md(rows) old = readme.read_text() if readme.exists() else f"# {t.title()}\n\n" new = _replace_block(old, table_md) @@ -58,7 +59,7 @@ def update_readme(asset: Iterable[str], changed_any = True touched.append(str(readme)) else: - if _update_one(t): + if _update_one(channel, t): changed_any = True touched.append(str((Path(t) / "README.md").as_posix())) @@ -78,7 +79,7 @@ def update_readme(asset: Iterable[str], click.echo("No README changes.") -def _rows_for_asset_type(asset_dir: Path) -> List[Tuple[str, str, str, str]]: +def _rows_for_asset_type(channel: str, asset_dir: Path) -> List[Tuple[str, str, str, str]]: """Scan /src/*/item.yaml and return table rows.""" src = asset_dir / "src" if not src.exists(): @@ -97,7 +98,9 @@ def _rows_for_asset_type(asset_dir: Path) -> List[Tuple[str, str, str, str]]: cats = data.get("categories") or [] cats_str = ", ".join(c.strip() for c in cats) if isinstance(cats, list) else str(cats).strip() # Link the name to its source directory - link = f"[{asset_name}]({(asset_dir / 'src' / asset_name).as_posix()})" + # Construct the relative path from the repo root for the asset + rel_path = asset_dir.relative_to(Path(".").resolve()) + link = f"[{asset_name}](https://github.com/mlrun/functions/tree/{channel}/{rel_path}/src/{asset_name})" rows.append((link, desc, kind, cats_str)) rows.sort(key=lambda r: r[0].lower()) @@ -140,13 +143,13 @@ def _replace_block(readme_text: str, new_block: str) -> str: return readme_text[:start_close] + "\n" + new_block + "\n" + readme_text[ei:] -def _update_one(asset_type: str) -> bool: +def _update_one(channel: str, asset_type: str) -> bool: """Generate/replace the table in /README.md. Return True if changed.""" root = Path(".").resolve() asset_dir = root / asset_type readme = asset_dir / "README.md" - rows = _rows_for_asset_type(asset_dir) + rows = _rows_for_asset_type(channel, asset_dir) table_md = _build_table_md(rows) old = readme.read_text() if readme.exists() else f"# {asset_type.title()}\n\n" new = _replace_block(old, table_md) diff --git a/cli/marketplace/conf.template b/cli/marketplace/conf.template index 93c83c9d3..e26f065aa 100644 --- a/cli/marketplace/conf.template +++ b/cli/marketplace/conf.template @@ -15,8 +15,14 @@ import re import sys import os -sys.path.insert(0, "{{sphinx_docs_target}}") -sys.path.insert(0, os.path.abspath(os.path.join("{{sphinx_docs_target}}", "../functions"))) +import pathlib + +DOCS_DIR = pathlib.Path(__file__).resolve().parent +REPO_ROOT = DOCS_DIR.parent + +# Add both source trees +sys.path.insert(0, str(REPO_ROOT / "functions")) +sys.path.insert(0, str(REPO_ROOT / "modules")) # -- Project information ----------------------------------------------------- diff --git a/functions/README.md b/functions/README.md index 08b1c7ad9..3618833a5 100644 --- a/functions/README.md +++ b/functions/README.md @@ -9,40 +9,40 @@ it is expected that contributors follow certain guidelines/protocols (please chi | Name | Description | Kind | Categories | | --- | --- | --- | --- | -| [aggregate](/home/runner/work/functions/functions/functions/src/aggregate) | Rolling aggregation over Metrics and Lables according to specifications | job | data-preparation | -| [arc_to_parquet](/home/runner/work/functions/functions/functions/src/arc_to_parquet) | retrieve remote archive, open and save as parquet | job | utils | -| [auto_trainer](/home/runner/work/functions/functions/functions/src/auto_trainer) | Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM. | job | machine-learning, model-training | -| [azureml_serving](/home/runner/work/functions/functions/functions/src/azureml_serving) | AzureML serving function | serving | machine-learning, model-serving | -| [azureml_utils](/home/runner/work/functions/functions/functions/src/azureml_utils) | Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom. | job | model-serving, utils | -| [batch_inference](/home/runner/work/functions/functions/functions/src/batch_inference) | Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving | -| [batch_inference_v2](/home/runner/work/functions/functions/functions/src/batch_inference_v2) | Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving | -| [describe](/home/runner/work/functions/functions/functions/src/describe) | describe and visualizes dataset stats | job | data-analysis | -| [describe_dask](/home/runner/work/functions/functions/functions/src/describe_dask) | describe and visualizes dataset stats | job | data-analysis | -| [describe_spark](/home/runner/work/functions/functions/functions/src/describe_spark) | | job | data-analysis | -| [feature_selection](/home/runner/work/functions/functions/functions/src/feature_selection) | Select features through multiple Statistical and Model filters | job | data-preparation, machine-learning | -| [gen_class_data](/home/runner/work/functions/functions/functions/src/gen_class_data) | Create a binary classification sample dataset and save. | job | data-generation | -| [github_utils](/home/runner/work/functions/functions/functions/src/github_utils) | add comments to github pull request | job | utils | -| [hugging_face_serving](/home/runner/work/functions/functions/functions/src/hugging_face_serving) | Generic Hugging Face model server. | serving | genai, model-serving | -| [load_dataset](/home/runner/work/functions/functions/functions/src/load_dataset) | load a toy dataset from scikit-learn | job | data-preparation | -| [mlflow_utils](/home/runner/work/functions/functions/functions/src/mlflow_utils) | Mlflow model server, and additional utils. | serving | model-serving, utils | -| [model_server](/home/runner/work/functions/functions/functions/src/model_server) | generic sklearn model server | nuclio:serving | model-serving, machine-learning | -| [model_server_tester](/home/runner/work/functions/functions/functions/src/model_server_tester) | test model servers | job | monitoring, model-serving | -| [noise_reduction](/home/runner/work/functions/functions/functions/src/noise_reduction) | Reduce noise from audio files | job | data-preparation, audio | -| [onnx_utils](/home/runner/work/functions/functions/functions/src/onnx_utils) | ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun. | job | utils, deep-learning | -| [open_archive](/home/runner/work/functions/functions/functions/src/open_archive) | Open a file/object archive into a target directory | job | utils | -| [pii_recognizer](/home/runner/work/functions/functions/functions/src/pii_recognizer) | This function is used to recognize PII in a directory of text files | job | data-preparation, NLP | -| [pyannote_audio](/home/runner/work/functions/functions/functions/src/pyannote_audio) | pyannote's speech diarization of audio files | job | deep-learning, audio | -| [question_answering](/home/runner/work/functions/functions/functions/src/question_answering) | GenAI approach of question answering on a given data | job | genai | -| [send_email](/home/runner/work/functions/functions/functions/src/send_email) | Send Email messages through SMTP server | job | utils | -| [silero_vad](/home/runner/work/functions/functions/functions/src/silero_vad) | Silero VAD (Voice Activity Detection) functions. | job | deep-learning, audio | -| [sklearn_classifier](/home/runner/work/functions/functions/functions/src/sklearn_classifier) | train any classifier using scikit-learn's API | job | machine-learning, model-training | -| [sklearn_classifier_dask](/home/runner/work/functions/functions/functions/src/sklearn_classifier_dask) | train any classifier using scikit-learn's API over Dask | job | machine-learning, model-training | -| [structured_data_generator](/home/runner/work/functions/functions/functions/src/structured_data_generator) | GenAI approach of generating structured data according to a given schema | job | data-generation, genai | -| [test_classifier](/home/runner/work/functions/functions/functions/src/test_classifier) | test a classifier using held-out or new data | job | machine-learning, model-testing | -| [text_to_audio_generator](/home/runner/work/functions/functions/functions/src/text_to_audio_generator) | Generate audio file from text using different speakers | job | data-generation, audio | -| [tf2_serving](/home/runner/work/functions/functions/functions/src/tf2_serving) | tf2 image classification server | nuclio:serving | model-serving, machine-learning | -| [transcribe](/home/runner/work/functions/functions/functions/src/transcribe) | Transcribe audio files into text files | job | audio, genai | -| [translate](/home/runner/work/functions/functions/functions/src/translate) | Translate text files from one language to another | job | genai, NLP | -| [v2_model_server](/home/runner/work/functions/functions/functions/src/v2_model_server) | generic sklearn model server | serving | model-serving, machine-learning | -| [v2_model_tester](/home/runner/work/functions/functions/functions/src/v2_model_tester) | test v2 model servers | job | model-testing, machine-learning | +| [aggregate](https://github.com/mlrun/functions/tree/development/functions/src/aggregate) | Rolling aggregation over Metrics and Lables according to specifications | job | data-preparation | +| [arc_to_parquet](https://github.com/mlrun/functions/tree/development/functions/src/arc_to_parquet) | retrieve remote archive, open and save as parquet | job | utils | +| [auto_trainer](https://github.com/mlrun/functions/tree/development/functions/src/auto_trainer) | Automatic train, evaluate and predict functions for the ML frameworks - Scikit-Learn, XGBoost and LightGBM. | job | machine-learning, model-training | +| [azureml_serving](https://github.com/mlrun/functions/tree/development/functions/src/azureml_serving) | AzureML serving function | serving | machine-learning, model-serving | +| [azureml_utils](https://github.com/mlrun/functions/tree/development/functions/src/azureml_utils) | Azure AutoML integration in MLRun, including utils functions for training models on Azure AutoML platfrom. | job | model-serving, utils | +| [batch_inference](https://github.com/mlrun/functions/tree/development/functions/src/batch_inference) | Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving | +| [batch_inference_v2](https://github.com/mlrun/functions/tree/development/functions/src/batch_inference_v2) | Batch inference (also knows as prediction) for the common ML frameworks (SciKit-Learn, XGBoost and LightGBM) while performing data drift analysis. | job | model-serving | +| [describe](https://github.com/mlrun/functions/tree/development/functions/src/describe) | describe and visualizes dataset stats | job | data-analysis | +| [describe_dask](https://github.com/mlrun/functions/tree/development/functions/src/describe_dask) | describe and visualizes dataset stats | job | data-analysis | +| [describe_spark](https://github.com/mlrun/functions/tree/development/functions/src/describe_spark) | | job | data-analysis | +| [feature_selection](https://github.com/mlrun/functions/tree/development/functions/src/feature_selection) | Select features through multiple Statistical and Model filters | job | data-preparation, machine-learning | +| [gen_class_data](https://github.com/mlrun/functions/tree/development/functions/src/gen_class_data) | Create a binary classification sample dataset and save. | job | data-generation | +| [github_utils](https://github.com/mlrun/functions/tree/development/functions/src/github_utils) | add comments to github pull request | job | utils | +| [hugging_face_serving](https://github.com/mlrun/functions/tree/development/functions/src/hugging_face_serving) | Generic Hugging Face model server. | serving | genai, model-serving | +| [load_dataset](https://github.com/mlrun/functions/tree/development/functions/src/load_dataset) | load a toy dataset from scikit-learn | job | data-preparation | +| [mlflow_utils](https://github.com/mlrun/functions/tree/development/functions/src/mlflow_utils) | Mlflow model server, and additional utils. | serving | model-serving, utils | +| [model_server](https://github.com/mlrun/functions/tree/development/functions/src/model_server) | generic sklearn model server | nuclio:serving | model-serving, machine-learning | +| [model_server_tester](https://github.com/mlrun/functions/tree/development/functions/src/model_server_tester) | test model servers | job | monitoring, model-serving | +| [noise_reduction](https://github.com/mlrun/functions/tree/development/functions/src/noise_reduction) | Reduce noise from audio files | job | data-preparation, audio | +| [onnx_utils](https://github.com/mlrun/functions/tree/development/functions/src/onnx_utils) | ONNX intigration in MLRun, some utils functions for the ONNX framework, optimizing and converting models from different framework to ONNX using MLRun. | job | utils, deep-learning | +| [open_archive](https://github.com/mlrun/functions/tree/development/functions/src/open_archive) | Open a file/object archive into a target directory | job | utils | +| [pii_recognizer](https://github.com/mlrun/functions/tree/development/functions/src/pii_recognizer) | This function is used to recognize PII in a directory of text files | job | data-preparation, NLP | +| [pyannote_audio](https://github.com/mlrun/functions/tree/development/functions/src/pyannote_audio) | pyannote's speech diarization of audio files | job | deep-learning, audio | +| [question_answering](https://github.com/mlrun/functions/tree/development/functions/src/question_answering) | GenAI approach of question answering on a given data | job | genai | +| [send_email](https://github.com/mlrun/functions/tree/development/functions/src/send_email) | Send Email messages through SMTP server | job | utils | +| [silero_vad](https://github.com/mlrun/functions/tree/development/functions/src/silero_vad) | Silero VAD (Voice Activity Detection) functions. | job | deep-learning, audio | +| [sklearn_classifier](https://github.com/mlrun/functions/tree/development/functions/src/sklearn_classifier) | train any classifier using scikit-learn's API | job | machine-learning, model-training | +| [sklearn_classifier_dask](https://github.com/mlrun/functions/tree/development/functions/src/sklearn_classifier_dask) | train any classifier using scikit-learn's API over Dask | job | machine-learning, model-training | +| [structured_data_generator](https://github.com/mlrun/functions/tree/development/functions/src/structured_data_generator) | GenAI approach of generating structured data according to a given schema | job | data-generation, genai | +| [test_classifier](https://github.com/mlrun/functions/tree/development/functions/src/test_classifier) | test a classifier using held-out or new data | job | machine-learning, model-testing | +| [text_to_audio_generator](https://github.com/mlrun/functions/tree/development/functions/src/text_to_audio_generator) | Generate audio file from text using different speakers | job | data-generation, audio | +| [tf2_serving](https://github.com/mlrun/functions/tree/development/functions/src/tf2_serving) | tf2 image classification server | nuclio:serving | model-serving, machine-learning | +| [transcribe](https://github.com/mlrun/functions/tree/development/functions/src/transcribe) | Transcribe audio files into text files | job | audio, genai | +| [translate](https://github.com/mlrun/functions/tree/development/functions/src/translate) | Translate text files from one language to another | job | genai, NLP | +| [v2_model_server](https://github.com/mlrun/functions/tree/development/functions/src/v2_model_server) | generic sklearn model server | serving | model-serving, machine-learning | +| [v2_model_tester](https://github.com/mlrun/functions/tree/development/functions/src/v2_model_tester) | test v2 model servers | job | model-testing, machine-learning | diff --git a/functions/src/translate/function.yaml b/functions/src/translate/function.yaml index 9595b77a3..eb1ffd345 100644 --- a/functions/src/translate/function.yaml +++ b/functions/src/translate/function.yaml @@ -1,4 +1,8 @@ +verbose: false spec: + description: Translate text files from one language to another + filename: /Users/Daniel_Perez/PycharmProjects/functions/functions/src/translate/translate.py + command: '' entry_points: open_mpi_handler: lineno: 56 @@ -8,24 +12,24 @@ spec: - name: root_worker_inputs type: Dict[str, Any] default: null - name: open_mpi_handler - has_kwargs: false doc: '' + has_kwargs: false has_varargs: false + name: open_mpi_handler decorator: lineno: 68 parameters: - name: handler - name: decorator - has_kwargs: false doc: '' + has_kwargs: false has_varargs: false + name: decorator wrapper: lineno: 73 - name: wrapper - has_kwargs: true doc: '' + has_kwargs: true has_varargs: false + name: wrapper translate: outputs: - doc: 'A tuple of:' @@ -75,8 +79,6 @@ spec: type: bool doc: 'Whether to present logs of a progress bar and errors. Default: True.' default: false - name: translate - has_kwargs: false doc: 'Translate text files using a transformer model from Huggingface''s hub according to the source and target languages @@ -89,27 +91,26 @@ spec: * text_file - The text file path. * translation_file - The translation text file name in the output directory.' + has_kwargs: false has_varargs: false + name: translate + disable_auto_mount: false + image: '' + default_handler: translate build: + functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import operator
import pathlib
from functools import reduce, wraps
from typing import Any, Dict, List, Tuple, Union

import pandas as pd
import transformers
from tqdm import tqdm

# Get the global logger:
_LOGGER = logging.getLogger()


def _check_mlrun_and_open_mpi() -> Tuple["mlrun.MLClientCtx", "mpi4py.MPI.Intracomm"]:
    is_mpi = False
    try:
        import mlrun

        context = mlrun.get_or_create_ctx(name="mlrun")
        is_mpi = context.labels.get("kind", "job") == "mpijob"

        if is_mpi:
            try:
                from mpi4py import MPI

                return context, MPI.COMM_WORLD
            except ModuleNotFoundError as mpi4py_not_found:
                context.logger.error(
                    "To distribute the function using MLRun's 'mpijob' you need to have `mpi4py` package in your "
                    "interpreter. Please run `pip install mpi4py` and make sure you have open-mpi."
                )
                raise mpi4py_not_found
        else:
            return context, None
    except ModuleNotFoundError as module_not_found:
        if is_mpi:
            raise module_not_found
    return None, None


def open_mpi_handler(
    worker_inputs: List[str], root_worker_inputs: Dict[str, Any] = None
):
    global _LOGGER

    # Check for MLRun and OpenMPI availability:
    context, comm = _check_mlrun_and_open_mpi()

    # Check if MLRun is available, set the global logger to MLRun's:
    if context:
        _LOGGER = context.logger

    def decorator(handler):
        if comm is None or comm.Get_size() == 1:
            return handler

        @wraps(handler)
        def wrapper(**kwargs):
            # Get the open mpi environment properties:
            size = comm.Get_size()
            rank = comm.Get_rank()

            # Give the correct chunk of the workers inputs:
            for worker_input in worker_inputs:
                input_argument = kwargs[worker_input]
                if input_argument is None:
                    continue
                if isinstance(input_argument, (str, pathlib.Path)):
                    input_argument = _get_text_files(
                        data_path=pathlib.Path(input_argument).absolute()
                    )
                if len(input_argument) < size:
                    raise ValueError(
                        f"Cannot split the input '{worker_input}' of length {len(input_argument)} to {size} workers. "
                        f"Please reduce the amount of workers for this input."
                    )
                even_chunk_size = len(input_argument) // size
                chunk_start = rank * even_chunk_size
                chunk_end = (
                    (rank + 1) * even_chunk_size
                    if rank + 1 < size
                    else len(input_argument)
                )
                context.logger.info(
                    f"Rank #{rank}: Processing input chunk of '{worker_input}' "
                    f"from index {chunk_start} to {chunk_end}."
                )
                if isinstance(input_argument, list):
                    input_argument = input_argument[chunk_start:chunk_end]
                elif isinstance(input_argument, pd.DataFrame):
                    input_argument = input_argument.iloc[chunk_start:chunk_end:, :]
                kwargs[worker_input] = input_argument

            # Set the root worker only arguments:
            if rank == 0 and root_worker_inputs:
                kwargs.update(root_worker_inputs)

            # Run the worker:
            output = handler(**kwargs)

            # Send the output to the root rank (rank #0):
            output = comm.gather(output, root=0)
            if rank == 0:
                # Join the outputs:
                context.logger.info("Collecting data from workers to root worker.")
                output_directory = output[0][0]
                dataframe = pd.concat(objs=[df for _, df, _ in output], axis=0)
                errors_dictionary = reduce(
                    operator.ior, [err for _, _, err in output], {}
                )
                return output_directory, dataframe, errors_dictionary
            return None

        return wrapper

    return decorator


@open_mpi_handler(worker_inputs=["data_path"], root_worker_inputs={"verbose": True})
def translate(
    data_path: Union[str, List[str], pathlib.Path],
    output_directory: str,
    model_name: str = None,
    source_language: str = None,
    target_language: str = None,
    device: str = None,
    model_kwargs: dict = None,
    batch_size: int = 1,
    translation_kwargs: dict = None,
    verbose: bool = False,
) -> Tuple[str, pd.DataFrame, dict]:
    """
    Translate text files using a transformer model from Huggingface's hub according to the source and target languages
    given (or using the directly provided model name). The end result is a directory of translated text files and a
    dataframe containing the following columns:

    * text_file - The text file path.
    * translation_file - The translation text file name in the output directory.

    :param data_path:          A directory of text files or a single file or a list of files to translate.
    :param output_directory:   Directory where the translated files will be saved.
    :param model_name:         The name of a model to load. If None, the model name is constructed using the source and
                               target languages parameters.
    :param source_language:    The source language code (e.g., 'en' for English).
    :param target_language:    The target language code (e.g., 'en' for English).
    :param model_kwargs:       Keyword arguments to pass regarding the loading of the model in HuggingFace's `pipeline`
                               function.
    :param device:             The device index for transformers. Default will prefer cuda if available.
    :param batch_size:         The number of batches to use in translation. The files are translated one by one, but the
                               sentences can be batched.
    :param translation_kwargs: Additional keyword arguments to pass to a `transformers.TranslationPipeline` when doing
                               the translation inference. Notice the batch size here is being added automatically.
    :param verbose:            Whether to present logs of a progress bar and errors. Default: True.

    :returns: A tuple of:

              * Path to the output directory.
              * A dataframe dataset of the translated file names.
              * A dictionary of errored files that were not translated.
    """
    global _LOGGER

    # Get the input text files to translate:
    if verbose:
        _LOGGER.info("Collecting text files.")
    if isinstance(data_path, str):
        data_path = pathlib.Path(data_path).absolute()
        text_files = _get_text_files(data_path=data_path)
    else:
        text_files = data_path
    if verbose:
        _LOGGER.info(f"Collected {len(text_files)} text files.")

    # Get the translation pipeline:
    if verbose:
        _LOGGER.info(f"Loading model - using device '{device}'.")
    translation_pipeline, model_name = _get_translation_pipeline(
        model_name=model_name,
        source_language=source_language,
        target_language=target_language,
        device=device,
        model_kwargs=model_kwargs,
        batch_size=batch_size if batch_size != 1 else None,
    )
    if verbose:
        _LOGGER.info(f"Model '{model_name}' was loaded successfully.")

    # Prepare the successes dataframe and errors dictionary to be returned:
    successes = []
    errors = {}

    # Create the output directory:
    output_directory = pathlib.Path(output_directory)
    output_directory.mkdir(parents=True, exist_ok=True)

    # Prepare the translation keyword arguments:
    translation_kwargs = translation_kwargs or {}

    # Go over the audio files and transcribe:
    for text_file in tqdm(
        text_files, desc="Translating", unit="file", disable=not verbose
    ):
        try:
            # Translate:
            translation = _translate(
                text_file=text_file,
                translation_pipeline=translation_pipeline,
                translation_kwargs=translation_kwargs,
            )
            # Write the transcription to file:
            translation_file = _save_to_file(
                translation=translation,
                file_name=text_file.stem,
                output_directory=output_directory,
            )
            # Note as a success in the list:
            successes.append(
                [
                    text_file.name,
                    translation_file.name,
                ]
            )
        except Exception as exception:
            # Note the exception as error in the dictionary:
            if verbose:
                _LOGGER.warning(f"Error in file: '{text_file.name}'")
            errors[str(text_file.name)] = str(exception)
            continue

    # Construct the translations dataframe:
    columns = [
        "text_file",
        "translation_file",
    ]
    successes = pd.DataFrame(
        successes,
        columns=columns,
    )

    # Print the head of the produced dataframe and return:
    if verbose:
        _LOGGER.info(
            f"Done ({successes.shape[0]}/{len(text_files)})\n"
            f"Translations summary:\n"
            f"{successes.head()}"
        )
    return str(output_directory), successes, errors


def _get_text_files(
    data_path: pathlib.Path,
) -> List[pathlib.Path]:
    # Check if the path is of a directory or a file:
    if data_path.is_dir():
        # Get all files inside the directory:
        text_files = list(data_path.glob("*.*"))
    elif data_path.is_file():
        text_files = [data_path]
    else:
        raise ValueError(
            f"Unrecognized data path. The parameter `data_path` must be either a directory path or a file path. "
            f"Given: {str(data_path)} "
        )

    return text_files


def _get_translation_pipeline(
    model_name: str = None,
    source_language: str = None,
    target_language: str = None,
    device: str = None,
    model_kwargs: dict = None,
    batch_size: int = None,
) -> Tuple[transformers.Pipeline, str]:
    # Construct the model name - if model name is provided (not None) then we take it, otherwise we check both source
    # and target were provided to construct the model name:
    if model_name is None and (source_language is None or target_language is None):
        raise ValueError(
            "No model name were given and missing source and / or target languages. In order to translate you must "
            "pass a `model_name` or both `source_language` and `target_language`."
        )
    elif model_name is None:
        model_name = f"Helsinki-NLP/opus-mt-{source_language}-{target_language}"

    # Initialize the translation pipeline:
    try:
        translation_pipeline = transformers.pipeline(
            task="translation",
            model=model_name,
            tokenizer=model_name,
            device=device,
            model_kwargs=model_kwargs,
            batch_size=batch_size,
        )
    except OSError as load_exception:
        if (
            "is not a valid model identifier listed on 'https://huggingface.co/models'"
            in str(load_exception)
            and source_language
        ):
            raise ValueError(
                f"The model '{model_name}' is not a valid model identifier. "
                f"The parameters `source_language` and `target_language` are used to construct a Helsinki model for "
                f"text to text generation, but the model created from the given languages does not exist. "
                f"You may check language identifiers at "
                f"https://developers.google.com/admin-sdk/directory/v1/languages, and if the error was not fixed, one "
                f"or more language code might be with 3 letters and needs to be found online. "
                f"Remember, you can always choose a model directly from the Huggingface hub by using the `model_name` "
                f"parameter."
            ) from load_exception
        raise load_exception

    return translation_pipeline, model_name


def _translate(
    text_file: pathlib.Path,
    translation_pipeline: transformers.Pipeline,
    translation_kwargs: dict,
) -> str:
    # Read the text from file:
    with open(text_file, "r") as fp:
        text = fp.read()

    # Split to paragraphs and each paragraph to sentences:
    paragraphs = [paragraph.split(".") for paragraph in text.split("\n")]

    # Discover the newline indexes to restore the file to its structure post translation:
    newlines_indexes = []
    for paragraph in paragraphs[:-1]:
        if len(newlines_indexes) == 0:
            newlines_indexes.append(len(paragraph) - 1)
        else:
            newlines_indexes.append(newlines_indexes[-1] + len(paragraph))

    # Prepare the batches (each sentence from the paragraphs). Notice we add a dot not only to restore the sentence
    # structure but to ignore empty strings as it will ruin the translation:
    sentences = [f"{line}." for paragraph in paragraphs for line in paragraph]

    # Translate the sentences:
    translations = translation_pipeline(sentences, **translation_kwargs)

    # Restructure the full text from the sentences:
    translated_text = []
    newline_index = newlines_indexes.pop(0) if newlines_indexes else None
    for i, translation in enumerate(translations):
        # Get the translation:
        text = translation["translation_text"]
        # Validate if it was an empty sentence before:
        if text == ".":
            text = ""
        # Check if needed to insert a newline:
        if newline_index and newline_index == i:
            text += "\n"
            newline_index = newlines_indexes.pop(0) if newlines_indexes else None
        # Collect it:
        translated_text.append(text)
    translated_text = "".join(translated_text)

    return translated_text


def _save_to_file(
    translation: str, file_name: str, output_directory: pathlib.Path
) -> pathlib.Path:
    # Prepare the file full path (checking for no duplications):
    translation_file = output_directory / f"{file_name}.txt"
    i = 1
    while translation_file.exists():
        i += 1
        translation_file = output_directory / f"{file_name}_{i}.txt"

    # Make sure all directories are created:
    translation_file.parent.mkdir(exist_ok=True, parents=True)

    # Write to file:
    with open(translation_file, "w") as fp:
        fp.write(translation)

    return translation_file
 + origin_filename: '' + base_image: mlrun/mlrun requirements: - transformers - sentencepiece - - torch + - torch>=2.6 - tqdm code_origin: '' - functionSourceCode: # Copyright 2023 Iguazio
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import operator
import pathlib
from functools import reduce, wraps
from typing import Any, Dict, List, Tuple, Union

import pandas as pd
import transformers
from tqdm import tqdm

# Get the global logger:
_LOGGER = logging.getLogger()


def _check_mlrun_and_open_mpi() -> Tuple["mlrun.MLClientCtx", "mpi4py.MPI.Intracomm"]:
    is_mpi = False
    try:
        import mlrun

        context = mlrun.get_or_create_ctx(name="mlrun")
        is_mpi = context.labels.get("kind", "job") == "mpijob"

        if is_mpi:
            try:
                from mpi4py import MPI

                return context, MPI.COMM_WORLD
            except ModuleNotFoundError as mpi4py_not_found:
                context.logger.error(
                    "To distribute the function using MLRun's 'mpijob' you need to have `mpi4py` package in your "
                    "interpreter. Please run `pip install mpi4py` and make sure you have open-mpi."
                )
                raise mpi4py_not_found
        else:
            return context, None
    except ModuleNotFoundError as module_not_found:
        if is_mpi:
            raise module_not_found
    return None, None


def open_mpi_handler(
    worker_inputs: List[str], root_worker_inputs: Dict[str, Any] = None
):
    global _LOGGER

    # Check for MLRun and OpenMPI availability:
    context, comm = _check_mlrun_and_open_mpi()

    # Check if MLRun is available, set the global logger to MLRun's:
    if context:
        _LOGGER = context.logger

    def decorator(handler):
        if comm is None or comm.Get_size() == 1:
            return handler

        @wraps(handler)
        def wrapper(**kwargs):
            # Get the open mpi environment properties:
            size = comm.Get_size()
            rank = comm.Get_rank()

            # Give the correct chunk of the workers inputs:
            for worker_input in worker_inputs:
                input_argument = kwargs[worker_input]
                if input_argument is None:
                    continue
                if isinstance(input_argument, (str, pathlib.Path)):
                    input_argument = _get_text_files(
                        data_path=pathlib.Path(input_argument).absolute()
                    )
                if len(input_argument) < size:
                    raise ValueError(
                        f"Cannot split the input '{worker_input}' of length {len(input_argument)} to {size} workers. "
                        f"Please reduce the amount of workers for this input."
                    )
                even_chunk_size = len(input_argument) // size
                chunk_start = rank * even_chunk_size
                chunk_end = (
                    (rank + 1) * even_chunk_size
                    if rank + 1 < size
                    else len(input_argument)
                )
                context.logger.info(
                    f"Rank #{rank}: Processing input chunk of '{worker_input}' "
                    f"from index {chunk_start} to {chunk_end}."
                )
                if isinstance(input_argument, list):
                    input_argument = input_argument[chunk_start:chunk_end]
                elif isinstance(input_argument, pd.DataFrame):
                    input_argument = input_argument.iloc[chunk_start:chunk_end:, :]
                kwargs[worker_input] = input_argument

            # Set the root worker only arguments:
            if rank == 0 and root_worker_inputs:
                kwargs.update(root_worker_inputs)

            # Run the worker:
            output = handler(**kwargs)

            # Send the output to the root rank (rank #0):
            output = comm.gather(output, root=0)
            if rank == 0:
                # Join the outputs:
                context.logger.info("Collecting data from workers to root worker.")
                output_directory = output[0][0]
                dataframe = pd.concat(objs=[df for _, df, _ in output], axis=0)
                errors_dictionary = reduce(
                    operator.ior, [err for _, _, err in output], {}
                )
                return output_directory, dataframe, errors_dictionary
            return None

        return wrapper

    return decorator


@open_mpi_handler(worker_inputs=["data_path"], root_worker_inputs={"verbose": True})
def translate(
    data_path: Union[str, List[str], pathlib.Path],
    output_directory: str,
    model_name: str = None,
    source_language: str = None,
    target_language: str = None,
    device: str = None,
    model_kwargs: dict = None,
    batch_size: int = 1,
    translation_kwargs: dict = None,
    verbose: bool = False,
) -> Tuple[str, pd.DataFrame, dict]:
    """
    Translate text files using a transformer model from Huggingface's hub according to the source and target languages
    given (or using the directly provided model name). The end result is a directory of translated text files and a
    dataframe containing the following columns:

    * text_file - The text file path.
    * translation_file - The translation text file name in the output directory.

    :param data_path:          A directory of text files or a single file or a list of files to translate.
    :param output_directory:   Directory where the translated files will be saved.
    :param model_name:         The name of a model to load. If None, the model name is constructed using the source and
                               target languages parameters.
    :param source_language:    The source language code (e.g., 'en' for English).
    :param target_language:    The target language code (e.g., 'en' for English).
    :param model_kwargs:       Keyword arguments to pass regarding the loading of the model in HuggingFace's `pipeline`
                               function.
    :param device:             The device index for transformers. Default will prefer cuda if available.
    :param batch_size:         The number of batches to use in translation. The files are translated one by one, but the
                               sentences can be batched.
    :param translation_kwargs: Additional keyword arguments to pass to a `transformers.TranslationPipeline` when doing
                               the translation inference. Notice the batch size here is being added automatically.
    :param verbose:            Whether to present logs of a progress bar and errors. Default: True.

    :returns: A tuple of:

              * Path to the output directory.
              * A dataframe dataset of the translated file names.
              * A dictionary of errored files that were not translated.
    """
    global _LOGGER

    # Get the input text files to translate:
    if verbose:
        _LOGGER.info("Collecting text files.")
    if isinstance(data_path, str):
        data_path = pathlib.Path(data_path).absolute()
        text_files = _get_text_files(data_path=data_path)
    else:
        text_files = data_path
    if verbose:
        _LOGGER.info(f"Collected {len(text_files)} text files.")

    # Get the translation pipeline:
    if verbose:
        _LOGGER.info(f"Loading model - using device '{device}'.")
    translation_pipeline, model_name = _get_translation_pipeline(
        model_name=model_name,
        source_language=source_language,
        target_language=target_language,
        device=device,
        model_kwargs=model_kwargs,
        batch_size=batch_size if batch_size != 1 else None,
    )
    if verbose:
        _LOGGER.info(f"Model '{model_name}' was loaded successfully.")

    # Prepare the successes dataframe and errors dictionary to be returned:
    successes = []
    errors = {}

    # Create the output directory:
    output_directory = pathlib.Path(output_directory)
    output_directory.mkdir(parents=True, exist_ok=True)

    # Prepare the translation keyword arguments:
    translation_kwargs = translation_kwargs or {}

    # Go over the audio files and transcribe:
    for text_file in tqdm(
        text_files, desc="Translating", unit="file", disable=not verbose
    ):
        try:
            # Translate:
            translation = _translate(
                text_file=text_file,
                translation_pipeline=translation_pipeline,
                translation_kwargs=translation_kwargs,
            )
            # Write the transcription to file:
            translation_file = _save_to_file(
                translation=translation,
                file_name=text_file.stem,
                output_directory=output_directory,
            )
            # Note as a success in the list:
            successes.append(
                [
                    text_file.name,
                    translation_file.name,
                ]
            )
        except Exception as exception:
            # Note the exception as error in the dictionary:
            if verbose:
                _LOGGER.warning(f"Error in file: '{text_file.name}'")
            errors[str(text_file.name)] = str(exception)
            continue

    # Construct the translations dataframe:
    columns = [
        "text_file",
        "translation_file",
    ]
    successes = pd.DataFrame(
        successes,
        columns=columns,
    )

    # Print the head of the produced dataframe and return:
    if verbose:
        _LOGGER.info(
            f"Done ({successes.shape[0]}/{len(text_files)})\n"
            f"Translations summary:\n"
            f"{successes.head()}"
        )
    return str(output_directory), successes, errors


def _get_text_files(
    data_path: pathlib.Path,
) -> List[pathlib.Path]:
    # Check if the path is of a directory or a file:
    if data_path.is_dir():
        # Get all files inside the directory:
        text_files = list(data_path.glob("*.*"))
    elif data_path.is_file():
        text_files = [data_path]
    else:
        raise ValueError(
            f"Unrecognized data path. The parameter `data_path` must be either a directory path or a file path. "
            f"Given: {str(data_path)} "
        )

    return text_files


def _get_translation_pipeline(
    model_name: str = None,
    source_language: str = None,
    target_language: str = None,
    device: str = None,
    model_kwargs: dict = None,
    batch_size: int = None,
) -> Tuple[transformers.Pipeline, str]:
    # Construct the model name - if model name is provided (not None) then we take it, otherwise we check both source
    # and target were provided to construct the model name:
    if model_name is None and (source_language is None or target_language is None):
        raise ValueError(
            "No model name were given and missing source and / or target languages. In order to translate you must "
            "pass a `model_name` or both `source_language` and `target_language`."
        )
    elif model_name is None:
        model_name = f"Helsinki-NLP/opus-mt-{source_language}-{target_language}"

    # Initialize the translation pipeline:
    try:
        translation_pipeline = transformers.pipeline(
            task="translation",
            model=model_name,
            tokenizer=model_name,
            device=device,
            model_kwargs=model_kwargs,
            batch_size=batch_size,
        )
    except OSError as load_exception:
        if (
            "is not a valid model identifier listed on 'https://huggingface.co/models'"
            in str(load_exception)
            and source_language
        ):
            raise ValueError(
                f"The model '{model_name}' is not a valid model identifier. "
                f"The parameters `source_language` and `target_language` are used to construct a Helsinki model for "
                f"text to text generation, but the model created from the given languages does not exist. "
                f"You may check language identifiers at "
                f"https://developers.google.com/admin-sdk/directory/v1/languages, and if the error was not fixed, one "
                f"or more language code might be with 3 letters and needs to be found online. "
                f"Remember, you can always choose a model directly from the Huggingface hub by using the `model_name` "
                f"parameter."
            ) from load_exception
        raise load_exception

    return translation_pipeline, model_name


def _translate(
    text_file: pathlib.Path,
    translation_pipeline: transformers.Pipeline,
    translation_kwargs: dict,
) -> str:
    # Read the text from file:
    with open(text_file, "r") as fp:
        text = fp.read()

    # Split to paragraphs and each paragraph to sentences:
    paragraphs = [paragraph.split(".") for paragraph in text.split("\n")]

    # Discover the newline indexes to restore the file to its structure post translation:
    newlines_indexes = []
    for paragraph in paragraphs[:-1]:
        if len(newlines_indexes) == 0:
            newlines_indexes.append(len(paragraph) - 1)
        else:
            newlines_indexes.append(newlines_indexes[-1] + len(paragraph))

    # Prepare the batches (each sentence from the paragraphs). Notice we add a dot not only to restore the sentence
    # structure but to ignore empty strings as it will ruin the translation:
    sentences = [f"{line}." for paragraph in paragraphs for line in paragraph]

    # Translate the sentences:
    translations = translation_pipeline(sentences, **translation_kwargs)

    # Restructure the full text from the sentences:
    translated_text = []
    newline_index = newlines_indexes.pop(0) if newlines_indexes else None
    for i, translation in enumerate(translations):
        # Get the translation:
        text = translation["translation_text"]
        # Validate if it was an empty sentence before:
        if text == ".":
            text = ""
        # Check if needed to insert a newline:
        if newline_index and newline_index == i:
            text += "\n"
            newline_index = newlines_indexes.pop(0) if newlines_indexes else None
        # Collect it:
        translated_text.append(text)
    translated_text = "".join(translated_text)

    return translated_text


def _save_to_file(
    translation: str, file_name: str, output_directory: pathlib.Path
) -> pathlib.Path:
    # Prepare the file full path (checking for no duplications):
    translation_file = output_directory / f"{file_name}.txt"
    i = 1
    while translation_file.exists():
        i += 1
        translation_file = output_directory / f"{file_name}_{i}.txt"

    # Make sure all directories are created:
    translation_file.parent.mkdir(exist_ok=True, parents=True)

    # Write to file:
    with open(translation_file, "w") as fp:
        fp.write(translation)

    return translation_file
 - base_image: mlrun/mlrun - origin_filename: '' - image: '' - default_handler: translate - disable_auto_mount: false - command: '' - description: Translate text files from one language to another -verbose: false +kind: job metadata: + tag: '' categories: - genai - NLP - tag: '' name: translate -kind: job diff --git a/functions/src/translate/item.yaml b/functions/src/translate/item.yaml index eb0e821e4..68f176ac2 100644 --- a/functions/src/translate/item.yaml +++ b/functions/src/translate/item.yaml @@ -12,7 +12,7 @@ labels: author: Iguazio maintainers: [] marketplaceType: '' -mlrunVersion: 1.7.0 +mlrunVersion: 1.10.0-rc41 name: translate platformVersion: 3.5.3 spec: @@ -23,8 +23,8 @@ spec: requirements: - transformers - sentencepiece - - torch + - torch>=2.6 - tqdm url: '' -version: 0.2.0 +version: 0.3.0 test_valid: True diff --git a/functions/src/translate/requirements.txt b/functions/src/translate/requirements.txt index 94e548463..746da576c 100644 --- a/functions/src/translate/requirements.txt +++ b/functions/src/translate/requirements.txt @@ -1,4 +1,4 @@ transformers tqdm -torch +torch>=2.6 sentencepiece \ No newline at end of file diff --git a/modules/README.md b/modules/README.md index 05e7cfefd..c3ed1c597 100644 --- a/modules/README.md +++ b/modules/README.md @@ -6,6 +6,19 @@ | Name | Description | Kind | Categories | | --- | --- | --- | --- | -| [count_events](/home/runner/work/functions/functions/modules/src/count_events) | Count events in each time window | monitoring_application | model-serving | -| [histogram_data_drift](/home/runner/work/functions/functions/modules/src/histogram_data_drift) | Model-monitoring application for detecting and visualizing data drift | monitoring_application | model-serving, structured-ML | +| [count_events](https://github.com/mlrun/functions/tree/development/modules/src/count_events) | Count events in each time window | monitoring_application | model-serving | +| [evidently_iris](https://github.com/mlrun/functions/tree/development/modules/src/evidently_iris) | Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset | monitoring_application | model-serving, structured-ML | +| [histogram_data_drift](https://github.com/mlrun/functions/tree/development/modules/src/histogram_data_drift) | Model-monitoring application for detecting and visualizing data drift | monitoring_application | model-serving, structured-ML | +| [openai_proxy_app](https://github.com/mlrun/functions/tree/development/modules/src/openai_proxy_app) | OpenAI application runtime based on fastapi | generic | genai | + + +## Catalog + + +| Name | Description | Kind | Categories | +| --- | --- | --- | --- | +| [count_events](https://github.com/mlrun/functions/tree/development/modules/src/count_events) | Count events in each time window | monitoring_application | model-serving | +| [evidently_iris](https://github.com/mlrun/functions/tree/development/modules/src/evidently_iris) | Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset | monitoring_application | model-serving, structured-ML | +| [histogram_data_drift](https://github.com/mlrun/functions/tree/development/modules/src/histogram_data_drift) | Model-monitoring application for detecting and visualizing data drift | monitoring_application | model-serving, structured-ML | +| [openai_proxy_app](https://github.com/mlrun/functions/tree/development/modules/src/openai_proxy_app) | OpenAI application runtime based on fastapi | generic | genai | diff --git a/modules/src/evidently_iris/evidently_iris.ipynb b/modules/src/evidently_iris/evidently_iris.ipynb new file mode 100644 index 000000000..c3299f82f --- /dev/null +++ b/modules/src/evidently_iris/evidently_iris.ipynb @@ -0,0 +1,1295 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8f92a6bb-e4b4-4b5d-91c7-2e99c97798c6", + "metadata": {}, + "source": [ + "# Evidently Iris Demo\n", + "\n", + "In this notebook, we’ll import the hub’s Evidently demo app, which monitors data quality and drift on Scikit-Learn’s Iris dataset. We’ll run it using the `evaluate()` method with a slightly modified dataset as the monitored data.\n", + "\n", + "The Evidently Iris module demonstrates a simple example of integrating MLRun with Evidently for data monitoring, which you can adapt to fit your own project needs or use as a reference implementation." + ] + }, + { + "cell_type": "markdown", + "id": "a6775277-5f4f-4261-9a06-5c6d87cb85c7", + "metadata": {}, + "source": [ + "## Set up an MLRun project and prepare the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d7a8c256-035f-4261-b494-f3f3cbd8c77c", + "metadata": {}, + "outputs": [], + "source": [ + "import mlrun\n", + "project = mlrun.get_or_create_project(\"evidently-demo\",'./evidently-demo')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1e89667f-f84e-492a-a886-61104bc5ce49", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_iris\n", + "import pandas as pd\n", + "from mlrun.feature_store.api import norm_column_name\n", + "\n", + "iris = load_iris()\n", + "columns = [norm_column_name(col) for col in iris.feature_names]\n", + "current_df = pd.DataFrame(iris.data, columns=columns)\n", + "current_df[\"sepal_length_cm\"] += 0.3 # simulate drift" + ] + }, + { + "cell_type": "markdown", + "id": "af6e56af-c99d-481e-a32e-f7e5eac4ae3a", + "metadata": {}, + "source": [ + "## Get the module from the hub and edit its defaults" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "35a4bb6b-d15e-4bfd-8d04-2fa188cb36cc", + "metadata": {}, + "outputs": [], + "source": [ + "hub_mod = mlrun.get_hub_module(\"hub://evidently_iris\", download_files=True)\n", + "src_file_path = hub_mod.get_module_file_path()" + ] + }, + { + "cell_type": "markdown", + "id": "ba0c043b-7356-44da-b6d2-84eb02718482", + "metadata": {}, + "source": [ + "We need to modify the class defaults to include the Evidently workspace path and project ID parameters. This can be done in one of two ways: either by editing the downloaded source file directly and then evaluating with the standard class, or - as we’ll do now - by adding an inheriting class to the same file and evaluating using that new class.\n", + "\n", + "(Note: this is only needed when runnning the app using `evaluate()`. When setting it as a real-time function we can simply pass the parameters)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4e9253a9-58bd-4732-8eb1-80a7d15b2e7a", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import uuid\n", + "\n", + "ws = Path(\"./evidently_workspace\")\n", + "ws.mkdir(parents=True, exist_ok=True) # will create if missing\n", + "evidently_project_id = str(uuid.uuid4())\n", + "\n", + "wrapper_code = f\"\"\"\n", + "class EvidentlyIrisMonitoringAppWithWorkspaceSet(EvidentlyIrisMonitoringApp):\n", + " def __init__(self) -> None:\n", + " super().__init__(evidently_workspace_path=\"{ws}\", evidently_project_id=\"{evidently_project_id}\")\n", + " \"\"\"\n", + "\n", + "with open(src_file_path, \"a\") as f:\n", + " f.write(wrapper_code)" + ] + }, + { + "cell_type": "markdown", + "id": "5776541f-2d6f-4c10-9246-75fe14e1bbea", + "metadata": {}, + "source": [ + "Now we can actually import it as a module, using the `module()` method" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3742576d-6da2-423d-8c1c-2861712a698f", + "metadata": {}, + "outputs": [], + "source": [ + "app_module = hub_mod.module()\n", + "evidently_app = app_module.EvidentlyIrisMonitoringAppWithWorkspaceSet" + ] + }, + { + "cell_type": "markdown", + "id": "57a81ea8-f203-4152-9492-a0f7b916d02b", + "metadata": {}, + "source": [ + "## Run the app\n", + "We are ready to call `evaluate()` (notice that the run is linked to the current (active) project that we created at the beggining of the notebook)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d8103577-8523-4b64-bd67-e93bbde8dd06", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2025-11-17 09:14:43,241 [info] Changing function name - adding `\"-batch\"` suffix: {\"func_name\":\"evidentlyirismonitoringappwithworkspaceset-batch\"}\n", + "> 2025-11-17 09:14:43,580 [info] Storing function: {\"db\":\"http://mlrun-api:8080\",\"name\":\"evidentlyirismonitoringappwithworkspaceset-batch--handler\",\"uid\":\"9ecf72a1bd82498c92d5897809b6a438\"}\n", + "> 2025-11-17 09:14:43,856 [info] downloading v3io:///projects/evidently-demo/artifacts/evidentlyirismonitoringappwithworkspaceset-batch_sample_data.parquet to local temp file\n", + "> 2025-11-17 09:14:43,890 [info] Running evidently app\n", + "> 2025-11-17 09:14:46,214 [info] Logged evidently object\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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evidently-demo0Nov 17 09:14:43NaTcompletedrunevidentlyirismonitoringappwithworkspaceset-batch--handler
v3io_user=iguazio
kind=local
owner=iguazio
host=jupyter-97c64f97b-8qtcv
sample_data
write_output=False
existing_data_handling=fail_on_overlap
stream_profile=None
return={result_name: 'data_drift_test', result_value: 0.5, result_kind: 0, result_status: 1, result_extra_data: '{}'}
evidently_report=store://artifacts/evidently-demo/evidentlyirismonitoringappwithworkspaceset-batch--handler_evidently_report#0@9ecf72a1bd82498c92d5897809b6a438^2f82c069b396f23b4daae81540ffa386b44f165c
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b/modules/src/evidently_iris/evidently_iris.py @@ -0,0 +1,117 @@ +# Copyright 2025 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional + +import pandas as pd +from sklearn.datasets import load_iris + +import mlrun.model_monitoring.applications.context as mm_context +from mlrun.common.schemas.model_monitoring.constants import ( + ResultKindApp, + ResultStatusApp, +) +from mlrun.feature_store.api import norm_column_name +from mlrun.model_monitoring.applications import ModelMonitoringApplicationResult +from mlrun.model_monitoring.applications.evidently import EvidentlyModelMonitoringApplicationBase + +from evidently.core.report import Report, Snapshot +from evidently.metrics import DatasetMissingValueCount, ValueDrift +from evidently.presets import DataDriftPreset, DataSummaryPreset +from evidently.ui.workspace import ( + STR_UUID, + OrgID, +) + +_PROJECT_NAME = "Iris Monitoring" +_PROJECT_DESCRIPTION = "Test project using iris dataset" + + +class EvidentlyIrisMonitoringApp(EvidentlyModelMonitoringApplicationBase): + """ + This model monitoring application is a simple example of integrating MLRun with Evidently for data monitoring, + which you can adapt to fit your own project needs or use as a reference implementation. + """ + NAME = "Evidently-App-Test" + + def __init__( + self, + evidently_project_id: Optional["STR_UUID"] = None, + evidently_workspace_path: Optional[str] = None, + cloud_workspace: bool = False, + evidently_organization_id: Optional["OrgID"] = None, + ) -> None: + self.org_id = evidently_organization_id + self._init_iris_data() + super().__init__( + evidently_project_id=evidently_project_id, + evidently_workspace_path=evidently_workspace_path, + cloud_workspace=cloud_workspace, + ) + + def _init_iris_data(self) -> None: + iris = load_iris() + self.columns = [norm_column_name(col) for col in iris.feature_names] + self.train_set = pd.DataFrame(iris.data, columns=self.columns) + + def do_tracking( + self, monitoring_context: mm_context.MonitoringApplicationContext + ) -> ModelMonitoringApplicationResult: + monitoring_context.logger.info("Running evidently app") + + sample_df = monitoring_context.sample_df[self.columns] + + data_drift_report_run = self.create_report_run( + sample_df, monitoring_context.end_infer_time + ) + self.evidently_workspace.add_run( + self.evidently_project_id, data_drift_report_run + ) + + self.log_evidently_object( + monitoring_context, data_drift_report_run, "evidently_report" + ) + monitoring_context.logger.info("Logged evidently object") + + return ModelMonitoringApplicationResult( + name="data_drift_test", + value=0.5, + kind=ResultKindApp.data_drift, + status=ResultStatusApp.potential_detection, + ) + + def create_report_run( + self, sample_df: pd.DataFrame, schedule_time: pd.Timestamp + ) -> "Snapshot": + metrics = [ + DataDriftPreset(), + DatasetMissingValueCount(), + DataSummaryPreset(), + ] + metrics.extend( + [ + ValueDrift(column=col_name, method="wasserstein") + for col_name in self.columns + ] + ) + + data_drift_report = Report( + metrics=metrics, + metadata={"timestamp": str(schedule_time)}, + include_tests=True, + ) + + return data_drift_report.run( + current_data=sample_df, reference_data=self.train_set + ) diff --git a/modules/src/evidently_iris/item.yaml b/modules/src/evidently_iris/item.yaml new file mode 100644 index 000000000..262b7e1b7 --- /dev/null +++ b/modules/src/evidently_iris/item.yaml @@ -0,0 +1,21 @@ +apiVersion: v1 +categories: +- model-serving +- structured-ML +description: Demonstrates Evidently integration in MLRun for data quality and drift monitoring using the Iris dataset +example: evidently_iris.ipynb +generationDate: 2025-11-09 +hidden: false +labels: + author: Iguazio +mlrunVersion: 1.10.0-rc41 +name: evidently_iris +spec: + filename: evidently_iris.py + image: mlrun/mlrun + kind: monitoring_application + requirements: + - scikit-learn~=1.5.2 + - evidently~=0.7.5 + - pandas +version: 1.0.0 \ No newline at end of file diff --git a/modules/src/evidently_iris/requirements.txt b/modules/src/evidently_iris/requirements.txt new file mode 100644 index 000000000..6bd12d901 --- /dev/null +++ b/modules/src/evidently_iris/requirements.txt @@ -0,0 +1,3 @@ +scikit-learn~=1.5.2 +evidently~=0.7.5 +pandas \ No newline at end of file diff --git a/modules/src/evidently_iris/test_evidently_iris.py b/modules/src/evidently_iris/test_evidently_iris.py new file mode 100644 index 000000000..6488768fd --- /dev/null +++ b/modules/src/evidently_iris/test_evidently_iris.py @@ -0,0 +1,72 @@ +# Copyright 2023 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import warnings +from contextlib import AbstractContextManager +from contextlib import nullcontext as does_not_raise +from pathlib import Path +from uuid import uuid4 + +import pytest +import semver + +from mlrun.errors import MLRunIncompatibleVersionError +from mlrun.model_monitoring.applications.evidently.base import ( + _check_evidently_version, +) + +from evidently_iris import EvidentlyIrisMonitoringApp + + +@pytest.mark.parametrize( + ("cur", "ref", "expectation"), + [ + ("0.4.11", "0.4.11", does_not_raise()), + ("0.4.12", "0.4.11", does_not_raise()), + ("1.23.0", "1.1.32", does_not_raise()), + ("0.4.11", "0.4.12", pytest.raises(MLRunIncompatibleVersionError)), + ("0.4.11", "0.4.12", pytest.raises(MLRunIncompatibleVersionError)), + ("1.0.3", "0.9.9", pytest.raises(MLRunIncompatibleVersionError)), + ("0.6.0", "0.3.0", pytest.warns(UserWarning)), + pytest.param("0.6.0", "0.3.0", does_not_raise(), marks=pytest.mark.xfail), + ], +) +def test_version_check( + cur: str, + ref: str, + expectation: AbstractContextManager, +) -> None: + with warnings.catch_warnings(): + warnings.simplefilter("error") + with expectation: + _check_evidently_version( + cur=semver.Version.parse(cur), ref=semver.Version.parse(ref) + ) + + +def test_demo_evidently_app(tmpdir: Path) -> None: + """Test that the workspace and the project's dashboards are created""" + evidently_app = EvidentlyIrisMonitoringApp( + evidently_project_id=uuid4(), evidently_workspace_path=str(tmpdir) + ) + run = evidently_app.create_report_run( + sample_df=evidently_app.train_set, schedule_time=None + ) + added_run_uid = evidently_app.evidently_workspace.add_run( + project_id=evidently_app.evidently_project_id, + run=run, + ).id + assert evidently_app.evidently_workspace.list_runs( + project_id=evidently_app.evidently_project_id + ) == [added_run_uid], "Different project runs than expected" diff --git a/modules/src/openai_proxy_app/item.yaml b/modules/src/openai_proxy_app/item.yaml new file mode 100644 index 000000000..bf295cf2a --- /dev/null +++ b/modules/src/openai_proxy_app/item.yaml @@ -0,0 +1,19 @@ +apiVersion: v1 +categories: +- genai +description: OpenAI application runtime based on fastapi +example: openai_proxy_app.ipynb +generationDate: 2025-11-11:12-25 +hidden: false +labels: + author: Iguazio +mlrunVersion: 1.10.0 +name: openai_proxy_app +spec: + filename: openai_proxy_app.py + image: mlrun/mlrun + requirements: + - fastapi>=0.110,<1.0 + - requests>=2.31,<3.0 + kind: generic +version: 1.0.0 diff --git a/modules/src/openai_proxy_app/openai_proxy_app.ipynb b/modules/src/openai_proxy_app/openai_proxy_app.ipynb new file mode 100644 index 000000000..123934fbd --- /dev/null +++ b/modules/src/openai_proxy_app/openai_proxy_app.ipynb @@ -0,0 +1,72 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "220629c8-17aa-45f6-ac81-0ca31e165412", + "metadata": {}, + "source": [ + "# OpenAI Module Demo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "967b4d5d-7250-40bf-8149-de11e1e3244c", + "metadata": {}, + "outputs": [], + "source": [ + "import mlrun\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "17d208f4-a00a-42ef-a849-0fa79bed10cb", + "metadata": {}, + "outputs": [], + "source": [ + "project = mlrun.get_or_create_project(\"fastapi-openai\",user_project=True,context=\"./src\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67c93a0d-8240-48b8-808e-9cd0af418309", + "metadata": {}, + "outputs": [], + "source": [ + "app = mlrun.import_module(\"hub://openai\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93e67d6a-5f53-4bda-b0b5-4e2977088139", + "metadata": {}, + "outputs": [], + "source": "app.OpenAIModule.deploy()" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/modules/src/openai_proxy_app/openai_proxy_app.py b/modules/src/openai_proxy_app/openai_proxy_app.py new file mode 100644 index 000000000..a0e9df7ac --- /dev/null +++ b/modules/src/openai_proxy_app/openai_proxy_app.py @@ -0,0 +1,56 @@ +# Copyright 2025 Iguazio +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +#This module acts as a lightweight gateway to OpenAI-compatible APIs. +#You can send chat prompts, create embeddings, or get model responses without worrying about authentication or endpoint differences. +#It simplifies access so you can test, analyze, or integrate AI features directly into your projects or notebooks with minimal setup. + + +BASE64 = "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+CMD = r''' +set -e +python - <<'PY' +import os, base64, pathlib +code = os.environ["BASE64"] +pathlib.Path("/opt/app").mkdir(parents=True, exist_ok=True) +with open("/opt/app/openai_proxy_app.py","wb") as f: + f.write(base64.b64decode(code)) +print("Wrote /opt/app/openai_proxy_app.py") +PY + +exec gunicorn openai:app \ + --chdir /opt/app \ + --bind 0.0.0.0:8000 \ + --worker-class uvicorn.workers.UvicornWorker \ + --log-level info +'''.strip() +class OpenAIModule: + def __init__(self,project): + self.project = project + self.fastapi_app = self.project.set_function(name="openai",kind="application",image="python:3.11") + self.fastapi_app.with_requirements([ + "fastapi>=0.110,<1.0", + "uvicorn[standard]>=0.29,<1.0", + "gunicorn>=21.2,<22.0", + "requests>=2.31,<3.0", + ]) + self.fastapi_app.set_env("BASE64",BASE64) + self.fastapi_app.set_internal_application_port(8000) + self.fastapi_app.spec.command = "/bin/sh" + self.fastapi_app.spec.args = ["-c", CMD] + + + + +