From 77675bbeb88dc9a925612e5009900273d9c8d084 Mon Sep 17 00:00:00 2001 From: Avik Basu <3485425+ab93@users.noreply.github.com> Date: Mon, 22 May 2023 17:14:05 -0700 Subject: [PATCH] feat!: use numalogic v0.4a0 for using redis model store (#133) Signed-off-by: Avik Basu --- .github/workflows/ci.yml | 2 +- .github/workflows/coverage.yml | 5 +- .github/workflows/lint.yml | 28 +- .github/workflows/pypi.yml | 2 +- Dockerfile | 20 - numaprom/clients/sentinel.py | 56 +- numaprom/tools.py | 56 +- numaprom/udf/inference.py | 44 +- numaprom/udf/postprocess.py | 19 +- numaprom/udf/preprocess.py | 38 +- numaprom/udf/threshold.py | 29 +- numaprom/udf/window.py | 16 +- numaprom/udsink/train.py | 123 +- numaprom/udsink/train_rollout.py | 158 +- poetry.lock | 2058 ++++++++++----------------- pyproject.toml | 14 +- requirements/requirements-torch.txt | 4 +- tests/__init__.py | 52 +- tests/test_tools.py | 6 +- tests/test_trainer.py | 10 - tests/test_watcher.py | 2 +- tests/tools.py | 79 +- tests/udf/test_inference.py | 33 +- tests/udf/test_postprocess.py | 11 +- tests/udf/test_preprocess.py | 24 +- tests/udf/test_threshold.py | 21 +- tests/udf/test_window.py | 7 +- 27 files changed, 1207 insertions(+), 1710 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 2b72968..bf98fb4 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -17,7 +17,7 @@ jobs: - uses: actions/checkout@v3 - name: Install poetry - run: pipx install poetry + run: pipx install poetry==1.4.2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml index 8d669d3..f1aa42c 100644 --- a/.github/workflows/coverage.yml +++ b/.github/workflows/coverage.yml @@ -19,7 +19,7 @@ jobs: - uses: actions/checkout@v3 - name: Install poetry - run: pipx install poetry + run: pipx install poetry==1.4.2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 @@ -30,7 +30,8 @@ jobs: - name: Install dependencies run: | poetry env use ${{ matrix.python-version }} - poetry install --all-extras --with dev,torch + poetry install --all-extras --with dev + poetry run pip install --no-cache -r requirements/requirements-torch.txt - name: Run Coverage run: | diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index affd6e8..ac10830 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -10,30 +10,12 @@ jobs: black: name: Black format runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - python-version: ["3.10"] - steps: - - uses: actions/checkout@v3 - - - name: Install poetry - run: pipx install poetry - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - cache: 'poetry' - - - name: Install dependencies - run: | - poetry env use ${{ matrix.python-version }} - poetry install --with dev - - - name: Black format check - run: poetry run black --check . + - uses: actions/checkout@v3 + - uses: psf/black@stable + with: + options: "--check --verbose" + version: "~= 23.3" flake8: name: flake8 check diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 745d8f5..2305520 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -19,7 +19,7 @@ jobs: - uses: actions/checkout@v3 - name: Install poetry - run: pipx install poetry + run: pipx install poetry==1.4.2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 diff --git a/Dockerfile b/Dockerfile index b680985..5b65e35 100644 --- a/Dockerfile +++ b/Dockerfile @@ -33,26 +33,6 @@ RUN apt-get update \ && chmod +x /dumb-init \ && curl -sSL https://install.python-poetry.org | python3 - -#################################################################################################### -# mlflow: used for running the mlflow server -#################################################################################################### -FROM builder AS mlflow - -WORKDIR $PYSETUP_PATH -COPY ./pyproject.toml ./poetry.lock ./ -RUN poetry install --only mlflowserver --no-cache --no-root && \ - rm -rf ~/.cache/pypoetry/ - -ADD . /app -WORKDIR /app - -RUN chmod +x entry.sh - -ENTRYPOINT ["/dumb-init", "--"] -CMD ["/app/entry.sh"] - -EXPOSE 5000 - #################################################################################################### # udf: used for running the udf vertices #################################################################################################### diff --git a/numaprom/clients/sentinel.py b/numaprom/clients/sentinel.py index a095c56..3e07b90 100644 --- a/numaprom/clients/sentinel.py +++ b/numaprom/clients/sentinel.py @@ -1,22 +1,41 @@ +import os from typing import Optional +from numalogic.tools.types import redis_client_t from redis.backoff import ExponentialBackoff -from redis.client import Redis from redis.exceptions import RedisClusterException, RedisError from redis.retry import Retry from redis.sentinel import Sentinel, MasterNotFoundError from numaprom import get_logger +from numaprom._config import RedisConf +from numaprom.watcher import ConfigManager _LOGGER = get_logger(__name__) -SENTINEL_MASTER_CLIENT: Optional[Redis] = None +SENTINEL_MASTER_CLIENT: Optional[redis_client_t] = None def get_redis_client( - host: str, port: int, password: str, mastername: str, recreate: bool = False -) -> Redis: + host: str, + port: int, + password: str, + mastername: str, + decode_responses: bool = False, + recreate: bool = False, +) -> redis_client_t: """ Return a master redis client for sentinel connections, with retry. + + Args: + host: Redis host + port: Redis port + password: Redis password + mastername: Redis sentinel master name + decode_responses: Whether to decode responses + recreate: Whether to flush and recreate the client + + Returns: + Redis client instance """ global SENTINEL_MASTER_CLIENT @@ -34,7 +53,11 @@ def get_redis_client( MasterNotFoundError, ), ) - sentinel_args = {"sentinels": [(host, port)], "socket_timeout": 0.1, "decode_responses": True} + sentinel_args = { + "sentinels": [(host, port)], + "socket_timeout": 0.1, + "decode_responses": decode_responses, + } _LOGGER.info("Sentinel redis params: %s", sentinel_args) @@ -43,3 +66,26 @@ def get_redis_client( ) SENTINEL_MASTER_CLIENT = sentinel.master_for(mastername) return SENTINEL_MASTER_CLIENT + + +def get_redis_client_from_conf(redis_conf: RedisConf = None, **kwargs) -> redis_client_t: + """ + Return a master redis client from config for sentinel connections, with retry. + + Args: + redis_conf: RedisConf object with host, port, master_name, etc. + **kwargs: Additional arguments to pass to get_redis_client. + + Returns: + Redis client instance + """ + if not redis_conf: + redis_conf = ConfigManager.get_redis_config() + + return get_redis_client( + redis_conf.host, + redis_conf.port, + password=os.getenv("REDIS_AUTH"), + mastername=redis_conf.master_name, + **kwargs + ) diff --git a/numaprom/tools.py b/numaprom/tools.py index aca35cc..a6c8183 100644 --- a/numaprom/tools.py +++ b/numaprom/tools.py @@ -4,23 +4,18 @@ from datetime import timedelta, datetime from functools import wraps from json import JSONDecodeError -from typing import Optional, Sequence, List +from typing import List -import boto3 import numpy as np import pandas as pd import pytz -from botocore.session import get_session -from mlflow.entities.model_registry import ModelVersion -from mlflow.exceptions import RestException from numalogic.config import PostprocessFactory from numalogic.models.threshold import SigmoidThreshold -from numalogic.registry import MLflowRegistry, ArtifactData from pynumaflow.function import Messages, Message from numaprom import get_logger, MetricConf -from numaprom.entities import TrainerPayload, StreamPayload from numaprom.clients.prometheus import Prometheus +from numaprom.entities import TrainerPayload, StreamPayload from numaprom.watcher import ConfigManager _LOGGER = get_logger(__name__) @@ -121,37 +116,6 @@ def is_host_reachable(hostname: str, port=None, max_retries=5, sleep_sec=5) -> b return False -def load_model( - skeys: Sequence[str], dkeys: Sequence[str], artifact_type: str = "pytorch" -) -> Optional[ArtifactData]: - set_aws_session() - try: - registry_conf = ConfigManager.get_registry_config() - ml_registry = MLflowRegistry( - tracking_uri=registry_conf.tracking_uri, artifact_type=artifact_type - ) - return ml_registry.load(skeys=skeys, dkeys=dkeys) - except RestException as warn: - if warn.error_code == 404: - return None - _LOGGER.warning("Non 404 error from mlflow: %r", warn) - except Exception as ex: - _LOGGER.error("Unexpected error while loading model from MLflow database: %r", ex) - return None - - -def save_model( - skeys: Sequence[str], dkeys: Sequence[str], model, artifact_type="pytorch", **metadata -) -> Optional[ModelVersion]: - set_aws_session() - registry_conf = ConfigManager.get_registry_config() - ml_registry = MLflowRegistry( - tracking_uri=registry_conf.tracking_uri, artifact_type=artifact_type - ) - version = ml_registry.save(skeys=skeys, dkeys=dkeys, artifact=model, **metadata) - return version - - def fetch_data( payload: TrainerPayload, metric_config: MetricConf, @@ -183,22 +147,6 @@ def fetch_data( return df -def set_aws_session() -> None: - """ - Setup default aws session by refreshing credentials. - """ - session = get_session() - credentials = session.get_credentials() - if not credentials: - _LOGGER.debug("No AWS credentials object returned") - return - boto3.setup_default_session( - aws_access_key_id=credentials.access_key, - aws_secret_access_key=credentials.secret_key, - aws_session_token=credentials.token, - ) - - def calculate_static_thresh(payload: StreamPayload, upper_limit: float): """ Calculates anomaly scores using static thresholding. diff --git a/numaprom/udf/inference.py b/numaprom/udf/inference.py index 2d2fa72..a0cf34d 100644 --- a/numaprom/udf/inference.py +++ b/numaprom/udf/inference.py @@ -1,21 +1,23 @@ import time -from datetime import datetime, timedelta from numalogic.config import NumalogicConf from numalogic.models.autoencoder import AutoencoderTrainer -from numalogic.registry import ArtifactData +from numalogic.registry import ArtifactData, RedisRegistry from numalogic.tools.data import StreamingDataset +from numalogic.tools.exceptions import RedisRegistryError from orjson import orjson from pynumaflow.function import Datum from torch.utils.data import DataLoader -from numaprom import get_logger, MetricConf +from numaprom import get_logger +from numaprom.clients.sentinel import get_redis_client_from_conf from numaprom.entities import PayloadFactory from numaprom.entities import Status, StreamPayload, Header -from numaprom.tools import load_model, msg_forward +from numaprom.tools import msg_forward from numaprom.watcher import ConfigManager _LOGGER = get_logger(__name__) +REDIS_CLIENT = get_redis_client_from_conf() def _run_inference( @@ -40,17 +42,6 @@ def _run_inference( return payload -def _is_model_stale( - payload: StreamPayload, artifact_data: ArtifactData, metric_config: MetricConf -) -> bool: - date_updated = artifact_data.extras["last_updated_timestamp"] / 1000 - stale_date = (datetime.now() - timedelta(hours=int(metric_config.retrain_freq_hr))).timestamp() - if date_updated < stale_date: - _LOGGER.info("%s - Model found is stale for %s", payload.uuid, payload.composite_keys) - return True - return False - - @msg_forward def inference(_: str, datum: Datum) -> bytes: _start_time = time.perf_counter() @@ -74,10 +65,23 @@ def inference(_: str, datum: Datum) -> bytes: numalogic_conf = metric_config.numalogic_conf # Load inference model - artifact_data = load_model( - skeys=[payload.composite_keys["namespace"], payload.composite_keys["name"]], - dkeys=[numalogic_conf.model.name], - ) + model_registry = RedisRegistry(client=REDIS_CLIENT) + try: + artifact_data = model_registry.load( + skeys=[payload.composite_keys["namespace"], payload.composite_keys["name"]], + dkeys=[numalogic_conf.model.name], + ) + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while fetching inference artifact, keys: %s, err: %r", + payload.uuid, + payload.composite_keys, + err, + ) + payload.set_header(Header.STATIC_INFERENCE) + payload.set_status(Status.RUNTIME_ERROR) + return orjson.dumps(payload, option=orjson.OPT_SERIALIZE_NUMPY) + if not artifact_data: _LOGGER.info( "%s - Inference artifact not found, forwarding for static thresholding. Keys: %s", @@ -89,7 +93,7 @@ def inference(_: str, datum: Datum) -> bytes: return orjson.dumps(payload, option=orjson.OPT_SERIALIZE_NUMPY) # Check if current model is stale - if _is_model_stale(payload, artifact_data, metric_config): + if RedisRegistry.is_artifact_stale(artifact_data, int(metric_config.retrain_freq_hr)): payload.set_header(Header.MODEL_STALE) # Generate predictions diff --git a/numaprom/udf/postprocess.py b/numaprom/udf/postprocess.py index d01d1ce..93f2dea 100644 --- a/numaprom/udf/postprocess.py +++ b/numaprom/udf/postprocess.py @@ -1,16 +1,16 @@ import os import time -import numpy as np from typing import List -from orjson import orjson -from redis.exceptions import RedisError, RedisClusterException +import numpy as np +from orjson import orjson from pynumaflow.function import Datum +from redis.exceptions import RedisError, RedisClusterException from redis.sentinel import MasterNotFoundError from numaprom import get_logger, UnifiedConf +from numaprom.clients.sentinel import get_redis_client_from_conf from numaprom.entities import Status, PrometheusPayload, StreamPayload, Header -from numaprom.clients.sentinel import get_redis_client from numaprom.tools import msgs_forward, WindowScorer from numaprom.watcher import ConfigManager @@ -22,14 +22,7 @@ def __save_to_redis( payload: StreamPayload, final_score: float, recreate: bool, unified_config: UnifiedConf ): - redis_conf = ConfigManager.get_redis_config() - r = get_redis_client( - redis_conf.host, - redis_conf.port, - password=AUTH, - mastername=redis_conf.master_name, - recreate=recreate, - ) + r = get_redis_client_from_conf(recreate=recreate) metric_name = payload.composite_keys["name"] @@ -50,7 +43,7 @@ def __save_to_redis( anomalies = [] for m in unified_config.unified_metrics: if r.hexists(name=r_key, key=m): - anomalies.append(float(r.hget(name=r_key, key=m))) + anomalies.append(float(r.hget(name=r_key, key=m).decode())) else: _LOGGER.debug( "%s - Unable to generate unified anomaly, missing metric: %s, redis_key: %s", diff --git a/numaprom/udf/preprocess.py b/numaprom/udf/preprocess.py index a55fd8d..08ec613 100644 --- a/numaprom/udf/preprocess.py +++ b/numaprom/udf/preprocess.py @@ -1,15 +1,29 @@ +import os import time import orjson +from numalogic.registry import RedisRegistry +from numalogic.tools.exceptions import RedisRegistryError from pynumaflow.function import Datum from numaprom import get_logger +from numaprom.clients.sentinel import get_redis_client from numaprom.entities import Status, StreamPayload, Header -from numaprom.tools import msg_forward, load_model +from numaprom.tools import msg_forward from numaprom.watcher import ConfigManager _LOGGER = get_logger(__name__) +AUTH = os.getenv("REDIS_AUTH") +REDIS_CONF = ConfigManager.get_redis_config() +REDIS_CLIENT = get_redis_client( + REDIS_CONF.host, + REDIS_CONF.port, + password=AUTH, + mastername=REDIS_CONF.master_name, + recreate=False, +) + @msg_forward def preprocess(_: str, datum: Datum) -> bytes: @@ -24,11 +38,23 @@ def preprocess(_: str, datum: Datum) -> bytes: preprocess_cfgs = metric_config.numalogic_conf.preprocess # Load preprocess artifact - preproc_artifact = load_model( - skeys=[payload.composite_keys["namespace"], payload.composite_keys["name"]], - dkeys=[_conf.name for _conf in preprocess_cfgs], - artifact_type="sklearn", - ) + model_registry = RedisRegistry(client=REDIS_CLIENT) + try: + preproc_artifact = model_registry.load( + skeys=[payload.composite_keys["namespace"], payload.composite_keys["name"]], + dkeys=[_conf.name for _conf in preprocess_cfgs], + ) + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while fetching preproc artifact, keys: %s, err: %r", + payload.uuid, + payload.composite_keys, + err, + ) + payload.set_header(Header.STATIC_INFERENCE) + payload.set_status(Status.RUNTIME_ERROR) + return orjson.dumps(payload, option=orjson.OPT_SERIALIZE_NUMPY) + if not preproc_artifact: _LOGGER.info( "%s - Preprocess artifact not found, forwarding for static thresholding. Keys: %s", diff --git a/numaprom/udf/threshold.py b/numaprom/udf/threshold.py index 4218700..0257a5b 100644 --- a/numaprom/udf/threshold.py +++ b/numaprom/udf/threshold.py @@ -1,13 +1,16 @@ import time from collections import OrderedDict +from numalogic.registry import RedisRegistry +from numalogic.tools.exceptions import RedisRegistryError from orjson import orjson from pynumaflow.function import Datum from numaprom import get_logger from numaprom._constants import TRAIN_VTX_KEY, POSTPROC_VTX_KEY +from numaprom.clients.sentinel import get_redis_client_from_conf from numaprom.entities import Status, TrainerPayload, PayloadFactory, Header -from numaprom.tools import conditional_forward, calculate_static_thresh, load_model +from numaprom.tools import conditional_forward, calculate_static_thresh from numaprom.watcher import ConfigManager _LOGGER = get_logger(__name__) @@ -56,11 +59,25 @@ def threshold(_: str, datum: Datum) -> list[tuple[str, bytes]]: ] # load threshold artifact - thresh_artifact = load_model( - skeys=[payload.composite_keys["namespace"], payload.composite_keys["name"]], - dkeys=[thresh_cfg.name], - artifact_type="sklearn", - ) + model_registry = RedisRegistry(client=get_redis_client_from_conf()) + try: + thresh_artifact = model_registry.load( + skeys=[payload.composite_keys["namespace"], payload.composite_keys["name"]], + dkeys=[thresh_cfg.name], + ) + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while fetching threshold artifact, keys: %s, err: %r", + payload.uuid, + payload.composite_keys, + err, + ) + payload.set_header(Header.STATIC_INFERENCE) + payload.set_status(Status.RUNTIME_ERROR) + return [ + (TRAIN_VTX_KEY, orjson.dumps(train_payload)), + (POSTPROC_VTX_KEY, _get_static_thresh_payload(payload, metric_config)), + ] if not thresh_artifact: _LOGGER.info( "%s - Threshold artifact not found, performing static thresholding. Keys: %s", diff --git a/numaprom/udf/window.py b/numaprom/udf/window.py index fee1b3f..9771c7d 100644 --- a/numaprom/udf/window.py +++ b/numaprom/udf/window.py @@ -10,15 +10,13 @@ from redis.exceptions import RedisError, RedisClusterException from numaprom import get_logger +from numaprom.clients.sentinel import get_redis_client_from_conf from numaprom.entities import StreamPayload, Status, Header -from numaprom.clients.sentinel import get_redis_client from numaprom.tools import msg_forward, create_composite_keys from numaprom.watcher import ConfigManager _LOGGER = get_logger(__name__) -AUTH = os.getenv("REDIS_AUTH") - # TODO get the replacement value from config def _clean_arr( @@ -44,15 +42,7 @@ def __aggregate_window( Returns an empty list if adding the element does not create a new entry to the set. """ - redis_conf = ConfigManager.get_redis_config() - redis_client = get_redis_client( - redis_conf.host, - redis_conf.port, - password=AUTH, - mastername=redis_conf.master_name, - recreate=recreate, - ) - + redis_client = get_redis_client_from_conf(recreate=recreate) with redis_client.pipeline() as pl: pl.zadd(key, {f"{value}::{ts}": ts}) pl.zremrangebyrank(key, -(buff_size + 10), -buff_size) @@ -61,7 +51,7 @@ def __aggregate_window( _is_new, _, _window = out if not _is_new: return [] - _window = list(map(lambda x: (float(x[0].split("::")[0]), x[1]), _window)) + _window = list(map(lambda x: (float(x[0].decode().split("::")[0]), x[1]), _window)) return _window diff --git a/numaprom/udsink/train.py b/numaprom/udsink/train.py index 7e9cf46..ecd7e85 100644 --- a/numaprom/udsink/train.py +++ b/numaprom/udsink/train.py @@ -1,26 +1,30 @@ import os import time -import numpy as np -import pandas as pd from typing import List -from orjson import orjson -from sklearn.pipeline import make_pipeline -from torch.utils.data import DataLoader +import numpy as np +import pandas as pd from numalogic.config import PreprocessFactory, ModelInfo, ThresholdFactory, ModelFactory from numalogic.models.autoencoder import AutoencoderTrainer +from numalogic.registry import RedisRegistry from numalogic.tools.data import StreamingDataset +from numalogic.tools.exceptions import RedisRegistryError +from numalogic.tools.types import redis_client_t +from orjson import orjson from pynumaflow.sink import Datum, Responses, Response +from sklearn.pipeline import make_pipeline +from torch.utils.data import DataLoader from numaprom import get_logger +from numaprom.clients.sentinel import get_redis_client_from_conf from numaprom.entities import TrainerPayload -from numaprom.clients.sentinel import get_redis_client -from numaprom.tools import save_model, fetch_data +from numaprom.tools import fetch_data from numaprom.watcher import ConfigManager _LOGGER = get_logger(__name__) -AUTH = os.getenv("REDIS_AUTH") + +REQUEST_EXPIRY = int(os.getenv("REQUEST_EXPIRY", 300)) def clean_data(df: pd.DataFrame, limit=12) -> pd.DataFrame: @@ -71,16 +75,7 @@ def _find_threshold(x_reconerr, thresh_cfg: ModelInfo): return thresh_clf -def _is_new_request(payload: TrainerPayload) -> bool: - redis_conf = ConfigManager.get_redis_config() - redis_client = get_redis_client( - redis_conf.host, - redis_conf.port, - password=AUTH, - mastername=redis_conf.master_name, - recreate=False, - ) - +def _is_new_request(redis_client: redis_client_t, payload: TrainerPayload) -> bool: _ckeys = ":".join([payload.composite_keys["namespace"], payload.composite_keys["name"]]) r_key = f"train::{_ckeys}" @@ -88,19 +83,20 @@ def _is_new_request(payload: TrainerPayload) -> bool: if value: return False - redis_client.setex(r_key, time=redis_conf.expiry, value=1) + redis_client.setex(r_key, time=REQUEST_EXPIRY, value=1) return True def train(datums: List[Datum]) -> Responses: responses = Responses() + redis_client = get_redis_client_from_conf() for _datum in datums: payload = TrainerPayload(**orjson.loads(_datum.value)) _LOGGER.debug("%s - Starting Training for keys: %s", payload.uuid, payload.composite_keys) - is_new = _is_new_request(payload) + is_new = _is_new_request(redis_client, payload) if not is_new: _LOGGER.debug( "%s - Skipping train request with keys: %s", payload.uuid, payload.composite_keys @@ -129,8 +125,8 @@ def train(datums: List[Datum]) -> Responses: responses.append(Response.as_success(_datum.id)) continue - preproc_cfg = metric_config.numalogic_conf.preprocess - x_train, preproc_clf = _preprocess(train_df.to_numpy(), preproc_cfg) + preproc_cfgs = metric_config.numalogic_conf.preprocess + x_train, preproc_clf = _preprocess(train_df.to_numpy(), preproc_cfgs) trainer_cfg = metric_config.numalogic_conf.trainer x_reconerr, anomaly_model = _train_model(payload.uuid, x_train, model_cfg, trainer_cfg) @@ -141,27 +137,68 @@ def train(datums: List[Datum]) -> Responses: # TODO change this to just use **composite_keys skeys = [payload.composite_keys["namespace"], payload.composite_keys["name"]] - version = save_model( - skeys=skeys, dkeys=["preproc"], model=preproc_clf, artifact_type="sklearn" - ) - _LOGGER.info( - "%s - Preproc model saved with skeys: %s with version: %s", payload.uuid, skeys, version - ) - - version = save_model(skeys=skeys, dkeys=[model_cfg.name], model=anomaly_model) - _LOGGER.info( - "%s - Model saved with skeys: %s with version: %s", payload.uuid, skeys, version - ) - - version = save_model( - skeys=skeys, dkeys=["thresh"], model=thresh_clf, artifact_type="sklearn" - ) - _LOGGER.info( - "%s - Threshold model saved with skeys: %s with version: %s", - payload.uuid, - skeys, - version, - ) + # Save main model + model_registry = RedisRegistry(client=redis_client) + try: + version = model_registry.save( + skeys=skeys, + dkeys=[model_cfg.name], + artifact=anomaly_model, + uuid=payload.uuid, + train_size=train_df.shape[0], + ) + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while saving Model with skeys: %s, err: %r", payload.uuid, skeys, err + ) + else: + _LOGGER.info( + "%s - Model saved with skeys: %s with version: %s", payload.uuid, skeys, version + ) + # Save preproc model + try: + version = model_registry.save( + skeys=skeys, + dkeys=[_conf.name for _conf in preproc_cfgs], + artifact=preproc_clf, + uuid=payload.uuid, + ) + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while saving Preproc model with skeys: %s, err: %r", + payload.uuid, + skeys, + err, + ) + else: + _LOGGER.info( + "%s - Preproc model saved with skeys: %s with version: %s", + payload.uuid, + skeys, + version, + ) + # Save threshold model + try: + version = model_registry.save( + skeys=skeys, + dkeys=[thresh_cfg.name], + artifact=thresh_clf, + uuid=payload.uuid, + ) + except RedisRegistryError as err: + _LOGGER.error( + "%s - Error while saving Threshold model with skeys: %s, err: %r", + payload.uuid, + skeys, + err, + ) + else: + _LOGGER.info( + "%s - Threshold model saved with skeys: %s with version: %s", + payload.uuid, + skeys, + version, + ) responses.append(Response.as_success(_datum.id)) diff --git a/numaprom/udsink/train_rollout.py b/numaprom/udsink/train_rollout.py index 9c003fd..f8badca 100644 --- a/numaprom/udsink/train_rollout.py +++ b/numaprom/udsink/train_rollout.py @@ -6,21 +6,24 @@ import pandas as pd from numalogic.config import PreprocessFactory, ModelInfo, ThresholdFactory, ModelFactory from numalogic.models.autoencoder import AutoencoderTrainer +from numalogic.registry import RedisRegistry from numalogic.tools.data import StreamingDataset +from numalogic.tools.exceptions import RedisRegistryError +from numalogic.tools.types import redis_client_t from orjson import orjson from pynumaflow.sink import Datum, Responses, Response from sklearn.pipeline import make_pipeline from torch.utils.data import DataLoader -from numaprom import get_logger +from numaprom import get_logger, MetricConf +from numaprom.clients.sentinel import get_redis_client_from_conf from numaprom.entities import TrainerPayload -from numaprom.clients.sentinel import get_redis_client -from numaprom.tools import save_model, fetch_data +from numaprom.tools import fetch_data from numaprom.watcher import ConfigManager _LOGGER = get_logger(__name__) -AUTH = os.getenv("REDIS_AUTH") +REQUEST_EXPIRY = int(os.getenv("REQUEST_EXPIRY", 300)) # TODO: extract all good hashes, including when there are 2 hashes at a time @@ -84,35 +87,27 @@ def _find_threshold(x_reconerr, thresh_cfg: ModelInfo): return thresh_clf -def _is_new_request(payload: TrainerPayload) -> bool: - redis_conf = ConfigManager.get_redis_config() - redis_client = get_redis_client( - redis_conf.host, - redis_conf.port, - password=AUTH, - mastername=redis_conf.master_name, - recreate=False, - ) - +def _is_new_request(redis_client: redis_client_t, payload: TrainerPayload) -> bool: _ckeys = ":".join([payload.composite_keys["namespace"], payload.composite_keys["name"]]) r_key = f"trainrollout::{_ckeys}" value = redis_client.get(r_key) if value: return False - redis_client.setex(r_key, time=redis_conf.expiry, value=1) + redis_client.setex(r_key, time=REQUEST_EXPIRY, value=1) return True def train_rollout(datums: Iterator[Datum]) -> Responses: responses = Responses() + redis_client = get_redis_client_from_conf() for _datum in datums: payload = TrainerPayload(**orjson.loads(_datum.value)) _LOGGER.debug("%s - Starting Training for keys: %s", payload.uuid, payload.composite_keys) - is_new = _is_new_request(payload) + is_new = _is_new_request(redis_client, payload) if not is_new: _LOGGER.debug( "%s - Skipping rollouts train request with keys: %s", @@ -123,9 +118,8 @@ def train_rollout(datums: Iterator[Datum]) -> Responses: continue metric_config = ConfigManager.get_metric_config(payload.composite_keys) - model_cfg = metric_config.numalogic_conf.model - # ToDo: standardize the label name + # TODO: standardize the label name if "rollouts_pod_template_hash" in payload.composite_keys: hash_label = "rollouts_pod_template_hash" else: @@ -157,78 +151,92 @@ def train_rollout(datums: Iterator[Datum]) -> Responses: responses.append(Response.as_success(_datum.id)) continue - preproc_cfgs = metric_config.numalogic_conf.preprocess - x_train, preproc_clf = _preprocess(train_df.to_numpy(), preproc_cfgs) + _train_and_save(metric_config, payload, redis_client, train_df) - trainer_cfg = metric_config.numalogic_conf.trainer - x_reconerr, anomaly_model, trainer = _train_model( - payload.uuid, x_train, model_cfg, trainer_cfg - ) + responses.append(Response.as_success(_datum.id)) + + return responses + + +def _train_and_save( + metric_config: MetricConf, + payload: TrainerPayload, + redis_client: redis_client_t, + train_df: pd.DataFrame, +) -> None: + model_cfg = metric_config.numalogic_conf.model + preproc_cfgs = metric_config.numalogic_conf.preprocess + + x_train, preproc_clf = _preprocess(train_df.to_numpy(), preproc_cfgs) - thresh_cfg = metric_config.numalogic_conf.threshold - thresh_clf = _find_threshold(x_reconerr, thresh_cfg) + trainer_cfg = metric_config.numalogic_conf.trainer + x_reconerr, anomaly_model, trainer = _train_model(payload.uuid, x_train, model_cfg, trainer_cfg) - skeys = [payload.composite_keys["namespace"], payload.composite_keys["name"]] + thresh_cfg = metric_config.numalogic_conf.threshold + thresh_clf = _find_threshold(x_reconerr, thresh_cfg) - # TODO 1. catch mlflow exception - # TODO 2. if one of the models fail to save, - # delete the previously saved models and transition stage + skeys = [payload.composite_keys["namespace"], payload.composite_keys["name"]] - # Save main model - version = save_model( + # TODO if one of the models fail to save, delete the previously saved models and transition stage + # Save main model + model_registry = RedisRegistry(client=redis_client) + try: + version = model_registry.save( skeys=skeys, dkeys=[model_cfg.name], - model=anomaly_model, + artifact=anomaly_model, uuid=payload.uuid, train_size=train_df.shape[0], ) - if version: - _LOGGER.info( - "%s - Model saved with skeys: %s with version: %s", payload.uuid, skeys, version - ) - else: - _LOGGER.error("%s - Error while saving Model with skeys: %s", payload.uuid, skeys) - - # Save preproc model - version = save_model( + except RedisRegistryError as err: + _LOGGER.exception( + "%s - Error while saving Model with skeys: %s, err: %r", payload.uuid, skeys, err + ) + else: + _LOGGER.info( + "%s - Model saved with skeys: %s with version: %s", payload.uuid, skeys, version + ) + # Save preproc model + try: + version = model_registry.save( skeys=skeys, dkeys=[_conf.name for _conf in preproc_cfgs], - model=preproc_clf, - artifact_type="sklearn", + artifact=preproc_clf, uuid=payload.uuid, ) - if version: - _LOGGER.info( - "%s - Preproc model saved with skeys: %s with version: %s", - payload.uuid, - skeys, - version, - ) - else: - _LOGGER.error( - "%s - Error while saving Preproc model with skeys: %s", payload.uuid, skeys - ) - - # Save threshold model - version = save_model( + except RedisRegistryError as err: + _LOGGER.exception( + "%s - 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b/pyproject.toml index 7b4d8b3..dd0f517 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "numalogic-prometheus" -version = "0.3.2" +version = "0.4a1" description = "ML inference on numaflow using numalogic on Prometheus metrics" authors = ["Numalogic developers"] packages = [{ include = "numaprom" }] @@ -19,10 +19,9 @@ repository = "https://github.com/numaproj/numalogic-prometheus" [tool.poetry.dependencies] python = "~3.10" -redis = "^4.3.1" +redis = {extras = ["hiredis"], version = "^4.5" } pynumaflow = "~0.3" -numalogic = {version = "~0.3.7", extras = ["mlflow"]} -boto3 = "^1.25.2" +numalogic = {version = "0.4a0", extras = ["redis"], allow-prereleases = true} orjson = "^3.8.4" omegaconf = "^2.3.0" watchdog = "^3.0.0" @@ -31,16 +30,15 @@ watchdog = "^3.0.0" optional = true [tool.poetry.group.mlflowserver.dependencies] -mlflow = "^1.30.0" +mlflow = "^2.2" [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -pytorch-lightning = "^1.8.6" coverage = "^6.3" -black = "^22.1" -fakeredis = "^1.8" +black = "^23.1" +fakeredis = "^2.11" flake8 = "^5.0" pytest = "^7.1" pytest-cov = "^4.0" diff --git a/requirements/requirements-torch.txt b/requirements/requirements-torch.txt index cb33403..b2598b1 100644 --- a/requirements/requirements-torch.txt +++ b/requirements/requirements-torch.txt @@ -1,3 +1,3 @@ # torch related requirements file -torch >= 1.3, <2.0; --index-url https://download.pytorch.org/whl/cpu -pytorch-lightning >=1.8, < 2.0 \ No newline at end of file +torch >= 2.0, <3.0; -i https://download.pytorch.org/whl/cpu +pytorch-lightning >=2.0, < 3.0 \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py index 99c23b9..9229c6c 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1,14 +1,58 @@ +import os from unittest.mock import patch import fakeredis +from numalogic.config import NumalogicConf +from omegaconf import OmegaConf + +from numaprom import DataConf +from numaprom._config import PipelineConf +from numaprom._constants import TESTS_RESOURCES, DEFAULT_CONFIG_DIR +from numaprom.watcher import ConfigManager server = fakeredis.FakeServer() -redis_client = fakeredis.FakeStrictRedis(server=server, decode_responses=True) +redis_client = fakeredis.FakeStrictRedis(server=server, decode_responses=False) + + +def mock_configs(): + schema: DataConf = OmegaConf.structured(DataConf) + + conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "config.yaml")) + app_configs = OmegaConf.merge(schema, conf).configs + + conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "default-config.yaml")) + default_configs = OmegaConf.merge(schema, conf).configs + + conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "numalogic_config.yaml")) + schema: NumalogicConf = OmegaConf.structured(NumalogicConf) + default_numalogic = OmegaConf.merge(schema, conf) + + conf = OmegaConf.load(os.path.join(DEFAULT_CONFIG_DIR, "pipeline_config.yaml")) + schema: PipelineConf = OmegaConf.structured(PipelineConf) + pipeline_config = OmegaConf.merge(schema, conf) + + return app_configs, default_configs, default_numalogic, pipeline_config + with patch("numaprom.clients.sentinel.get_redis_client") as mock_get_redis_client: mock_get_redis_client.return_value = redis_client - from numaprom.udf import window - from numaprom.udsink import train, train_rollout + with patch( + "numaprom.clients.sentinel.get_redis_client_from_conf" + ) as mock_get_redis_client_from_conf: + mock_get_redis_client_from_conf.return_value = redis_client + with patch.object(ConfigManager, "load_configs") as mock_confs: + mock_confs.return_value = mock_configs() + from numaprom.udf import window, preprocess, inference, threshold + from numaprom.udsink import train, train_rollout -__all__ = ["redis_client", "window", "train", "train_rollout"] +__all__ = [ + "redis_client", + "window", + "train", + "train_rollout", + "preprocess", + "inference", + "threshold", + "mock_configs", +] diff --git a/tests/test_tools.py b/tests/test_tools.py index eb48e91..0d9e06d 100644 --- a/tests/test_tools.py +++ b/tests/test_tools.py @@ -2,7 +2,7 @@ import socket import unittest from collections import OrderedDict -from unittest.mock import patch, Mock +from unittest.mock import patch import numpy as np @@ -10,7 +10,6 @@ from numaprom.entities import StreamPayload from numaprom.tools import is_host_reachable, WindowScorer from numaprom.watcher import ConfigManager -from tests.tools import mock_configs DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") REQ_2xx = os.path.join(DATA_DIR, "2xx.txt") @@ -32,7 +31,6 @@ def test_is_host_reachable_err(self): self.assertFalse(is_host_reachable("google.com", max_retries=2, sleep_sec=1)) -@patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) class TestWindowScorer(unittest.TestCase): def test_get_winscore(self): metric_conf = ConfigManager().get_metric_config( @@ -47,7 +45,7 @@ def test_get_winscore(self): composite_keys=OrderedDict({"namespace": "sandbox_numalogic_demo2"}), win_raw_arr=stream, win_arr=stream.copy(), - win_ts_arr=list(range(10)), + win_ts_arr=list(map(str, range(10))), ) winscorer = WindowScorer(metric_conf) final_score = winscorer.get_final_winscore(payload) diff --git a/tests/test_trainer.py b/tests/test_trainer.py index 45da48f..05e62c9 100644 --- a/tests/test_trainer.py +++ b/tests/test_trainer.py @@ -5,17 +5,14 @@ from typing import Union from unittest.mock import patch, Mock -from numalogic.registry import MLflowRegistry from pynumaflow.sink import Datum from numaprom._constants import TESTS_DIR from numaprom.clients.prometheus import Prometheus -from numaprom.watcher import ConfigManager from tests.tools import ( mock_argocd_query_metric, mock_rollout_query_metric, mock_rollout_query_metric2, - mock_configs, ) from tests import train, redis_client, train_rollout @@ -34,8 +31,6 @@ def as_datum(data: Union[str, bytes, dict], msg_id="1") -> Datum: ) -@patch("numaprom.tools.set_aws_session", Mock(return_value=None)) -@patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) class TestTrainer(unittest.TestCase): train_payload = { "uuid": "123124543", @@ -59,7 +54,6 @@ class TestTrainer(unittest.TestCase): def setUp(self) -> None: redis_client.flushall() - @patch.object(MLflowRegistry, "save", Mock(return_value=1)) @patch.object(Prometheus, "query_metric", Mock(return_value=mock_argocd_query_metric())) def test_argocd_trainer_01(self): datums = [as_datum(self.train_payload)] @@ -68,7 +62,6 @@ def test_argocd_trainer_01(self): self.assertEqual("1", _out.items()[0].id) self.assertEqual(1, len(_out.items())) - @patch.object(MLflowRegistry, "save", Mock(return_value=1)) @patch.object(Prometheus, "query_metric", Mock(return_value=mock_argocd_query_metric())) def test_argocd_trainer_02(self): datums = [as_datum(self.train_payload), as_datum(self.train_payload, msg_id="2")] @@ -80,14 +73,12 @@ def test_argocd_trainer_02(self): self.assertEqual("2", _out.items()[1].id) self.assertEqual(2, len(_out.items())) - @patch.object(MLflowRegistry, "save", Mock(return_value=1)) @patch.object(Prometheus, "query_metric", Mock(return_value=mock_rollout_query_metric())) def test_argo_rollout_trainer_01(self): _out = train_rollout([as_datum(self.train_payload)]) self.assertTrue(_out.items()[0].success) self.assertEqual("1", _out.items()[0].id) - @patch.object(MLflowRegistry, "save", Mock(return_value=1)) @patch.object(Prometheus, "query_metric", Mock(return_value=mock_rollout_query_metric())) def test_argo_rollout_trainer_02(self): datums = [as_datum(self.train_payload), as_datum(self.train_payload, msg_id="2")] @@ -99,7 +90,6 @@ def test_argo_rollout_trainer_02(self): self.assertEqual("2", _out.items()[1].id) self.assertEqual(2, len(_out.items())) - @patch.object(MLflowRegistry, "save", Mock(return_value=1)) @patch.object(Prometheus, "query_metric", Mock(return_value=mock_rollout_query_metric2())) def test_argo_rollout_trainer_03(self): _out = train_rollout([as_datum(self.train_payload2)]) diff --git a/tests/test_watcher.py b/tests/test_watcher.py index 95bd497..9edaae7 100644 --- a/tests/test_watcher.py +++ b/tests/test_watcher.py @@ -3,7 +3,7 @@ from unittest.mock import patch, Mock from numaprom.watcher import ConfigManager -from tests.tools import mock_configs +from tests import mock_configs @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) diff --git a/tests/tools.py b/tests/tools.py index 4f33c51..2606833 100644 --- a/tests/tools.py +++ b/tests/tools.py @@ -2,27 +2,23 @@ import json import os import sys +from typing import Union from unittest import mock from unittest.mock import MagicMock, patch, Mock +import fakeredis import numpy as np import pandas as pd -import torch -from mlflow.entities.model_registry import ModelVersion -from numalogic.config import NumalogicConf from numalogic.models.autoencoder.variants import VanillaAE, LSTMAE from numalogic.models.threshold import StdDevThreshold -from numalogic.registry import ArtifactData, MLflowRegistry -from omegaconf import OmegaConf +from numalogic.registry import ArtifactData, RedisRegistry from pynumaflow.function import Datum, Messages from pynumaflow.function._dtypes import DROP from sklearn.preprocessing import MinMaxScaler -from numaprom._config import PipelineConf -from numaprom._constants import TESTS_DIR, POSTPROC_VTX_KEY, TESTS_RESOURCES, DEFAULT_CONFIG_DIR +from numaprom._constants import TESTS_DIR, POSTPROC_VTX_KEY from numaprom.factory import HandlerFactory -from numaprom import DataConf - +from tests import window, preprocess sys.modules["numaprom.mlflow"] = MagicMock() MODEL_DIR = os.path.join(TESTS_DIR, "resources", "models") @@ -44,18 +40,23 @@ def get_datum(data: str or bytes) -> Datum: ) -def get_stream_data(data_path: str): +def get_stream_data(data_path: str) -> dict[str, Union[dict, str, list]]: with open(data_path) as fp: data = json.load(fp) return data +def get_mock_redis_client(): + server = fakeredis.FakeServer() + redis_client = fakeredis.FakeStrictRedis(server=server, decode_responses=False) + return redis_client + + def get_prepoc_input(data_path: str) -> Messages: out = Messages() data = get_stream_data(data_path) for obj in data: - handler_ = HandlerFactory.get_handler("window") - _out = handler_("", get_datum(obj)) + _out = window("", get_datum(obj)) if _out.items()[0].key != DROP: out.append(_out.items()[0]) return out @@ -64,12 +65,12 @@ def get_prepoc_input(data_path: str) -> Messages: def get_inference_input(data_path: str, prev_clf_exists=True) -> Messages: out = Messages() preproc_input = get_prepoc_input(data_path) + print("PREPROC", preproc_input, prev_clf_exists) _mock_return = return_preproc_clf() if prev_clf_exists else None - with patch.object(MLflowRegistry, "load", Mock(return_value=_mock_return)): + with patch.object(RedisRegistry, "load", Mock(return_value=_mock_return)): for msg in preproc_input.items(): _in = get_datum(msg.value) - handler_ = HandlerFactory.get_handler("preprocess") - _out = handler_("", _in) + _out = preprocess("", _in) if _out.items()[0].key != DROP: out.append(_out.items()[0]) return out @@ -84,7 +85,7 @@ def get_threshold_input(data_path: str, prev_clf_exists=True, prev_model_stale=F _mock_return = return_mock_lstmae() else: _mock_return = None - with patch.object(MLflowRegistry, "load", Mock(return_value=_mock_return)): + with patch.object(RedisRegistry, "load", Mock(return_value=_mock_return)): for msg in inference_input.items(): _in = get_datum(msg.value) handler_ = HandlerFactory.get_handler("inference") @@ -98,7 +99,7 @@ def get_postproc_input(data_path: str, prev_clf_exists=True, prev_model_stale=Fa out = Messages() thresh_input = get_threshold_input(data_path, prev_model_stale=prev_model_stale) _mock_return = return_threshold_clf() if prev_clf_exists else None - with patch.object(MLflowRegistry, "load", Mock(return_value=_mock_return)): + with patch.object(RedisRegistry, "load", Mock(return_value=_mock_return)): for msg in thresh_input.items(): _in = get_datum(msg.value) handler_ = HandlerFactory.get_handler("threshold") @@ -109,34 +110,13 @@ def get_postproc_input(data_path: str, prev_clf_exists=True, prev_model_stale=Fa return out -def return_mock_vanilla(*_, **__): - return { - "primary_artifact": VanillaAE(2), - "metadata": torch.load(os.path.join(MODEL_DIR, "model_cpu.pth")), - "model_properties": ModelVersion( - creation_timestamp=1656615600000, - current_stage="Production", - description="", - last_updated_timestamp=datetime.datetime.now().timestamp() * 1000, - name="sandbox_numalogic_demo:metric_1", - run_id="6f1e582fb6194bbdaa4141feb2ce8e27", - run_link="", - source="mlflow-artifacts:/0/6f1e582fb6194bbdaa4141feb2ce8e27/artifacts/model", - status="READY", - status_message="", - tags={}, - user_id="", - version="125", - ), - } - - def return_mock_lstmae(*_, **__): return ArtifactData( artifact=LSTMAE(seq_len=2, no_features=1, embedding_dim=4), metadata={}, extras={ "creation_timestamp": 1653402941169, + "timestamp": 1653402941, "current_stage": "Production", "description": "", "last_updated_timestamp": 1645369200000, @@ -159,6 +139,7 @@ def return_stale_model(*_, **__): metadata={}, extras={ "creation_timestamp": 1653402941169, + "timestamp": 1653402941, "current_stage": "Production", "description": "", "last_updated_timestamp": 1656615600000, @@ -252,23 +233,3 @@ def mock_rollout_query_metric2(*_, **__): ) df.rename(columns={"hash_id": "rollouts_pod_template_hash"}, inplace=True) return df - - -def mock_configs(): - schema: DataConf = OmegaConf.structured(DataConf) - - conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "config.yaml")) - app_configs = OmegaConf.merge(schema, conf).configs - - conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "default-config.yaml")) - default_configs = OmegaConf.merge(schema, conf).configs - - conf = OmegaConf.load(os.path.join(TESTS_RESOURCES, "configs", "numalogic_config.yaml")) - schema: NumalogicConf = OmegaConf.structured(NumalogicConf) - default_numalogic = OmegaConf.merge(schema, conf) - - conf = OmegaConf.load(os.path.join(DEFAULT_CONFIG_DIR, "pipeline_config.yaml")) - schema: PipelineConf = OmegaConf.structured(PipelineConf) - pipeline_config = OmegaConf.merge(schema, conf) - - return app_configs, default_configs, default_numalogic, pipeline_config diff --git a/tests/udf/test_inference.py b/tests/udf/test_inference.py index adbfbb4..e297998 100644 --- a/tests/udf/test_inference.py +++ b/tests/udf/test_inference.py @@ -4,20 +4,18 @@ from freezegun import freeze_time from numalogic.models.autoencoder import AutoencoderTrainer -from numalogic.registry import MLflowRegistry +from numalogic.registry import RedisRegistry from orjson import orjson +from pynumaflow.function import Messages from numaprom._constants import TESTS_DIR from numaprom.entities import Status, StreamPayload, Header -from numaprom.udf.inference import inference -from numaprom.watcher import ConfigManager -from tests import redis_client +from tests import redis_client, inference from tests.tools import ( get_inference_input, return_stale_model, return_mock_lstmae, get_datum, - mock_configs, ) DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") @@ -25,19 +23,20 @@ STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") -@patch("numaprom.tools.set_aws_session", Mock(return_value=None)) -@patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) class TestInference(unittest.TestCase): + inference_input: Messages = None + @classmethod - @patch("numaprom.tools.set_aws_session", Mock(return_value=None)) - @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) def setUpClass(cls) -> None: redis_client.flushall() cls.inference_input = get_inference_input(STREAM_DATA_PATH) assert cls.inference_input.items(), print("input items is empty", cls.inference_input) + def setUp(self) -> None: + redis_client.flushall() + @freeze_time("2022-02-20 12:00:00") - @patch.object(MLflowRegistry, "load", Mock(return_value=return_mock_lstmae())) + @patch.object(RedisRegistry, "load", Mock(return_value=return_mock_lstmae())) def test_inference(self): for msg in self.inference_input.items(): _in = get_datum(msg.value) @@ -52,7 +51,7 @@ def test_inference(self): self.assertTrue(payload.win_ts_arr) @freeze_time("2022-02-20 12:00:00") - @patch.object(MLflowRegistry, "load", Mock(return_value=return_mock_lstmae())) + @patch.object(RedisRegistry, "load", Mock(return_value=return_mock_lstmae())) @patch.object(AutoencoderTrainer, "predict", Mock(side_effect=RuntimeError)) def test_inference_err(self): for msg in self.inference_input.items(): @@ -67,7 +66,7 @@ def test_inference_err(self): self.assertTrue(payload.win_arr) self.assertTrue(payload.win_ts_arr) - @patch.object(MLflowRegistry, "load", Mock(return_value=None)) + @patch.object(RedisRegistry, "load", Mock(return_value=None)) def test_no_model(self): for msg in self.inference_input.items(): _in = get_datum(msg.value) @@ -79,10 +78,10 @@ def test_no_model(self): self.assertIsInstance(payload, StreamPayload) @freeze_time("2022-02-20 12:00:00") - @patch.object(MLflowRegistry, "load", Mock(return_value=return_mock_lstmae())) + @patch.object(RedisRegistry, "load", Mock(return_value=return_mock_lstmae())) def test_no_prev_model(self): inference_input = get_inference_input(STREAM_DATA_PATH, prev_clf_exists=False) - assert self.inference_input.items(), print("input items is empty", self.inference_input) + assert inference_input.items(), print("input items is empty", inference_input) for msg in inference_input.items(): _in = get_datum(msg.value) _out = inference("", _in) @@ -92,7 +91,7 @@ def test_no_prev_model(self): self.assertEqual(payload.header, Header.STATIC_INFERENCE) self.assertIsInstance(payload, StreamPayload) - @patch.object(MLflowRegistry, "load", Mock(return_value=return_stale_model())) + @patch.object(RedisRegistry, "load", Mock(return_value=return_stale_model())) def test_stale_model(self): for msg in self.inference_input.items(): _in = get_datum(msg.value) @@ -102,3 +101,7 @@ def test_stale_model(self): self.assertTrue(payload) self.assertEqual(payload.status, Status.INFERRED) self.assertEqual(payload.header, Header.MODEL_STALE) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/udf/test_postprocess.py b/tests/udf/test_postprocess.py index 7a0ae0b..4be7cb7 100644 --- a/tests/udf/test_postprocess.py +++ b/tests/udf/test_postprocess.py @@ -3,13 +3,13 @@ import orjson from freezegun import freeze_time -from unittest.mock import patch, Mock + +from pynumaflow.function import Messages from numaprom._constants import TESTS_DIR from numaprom.entities import PrometheusPayload, StreamPayload, Header -from numaprom.watcher import ConfigManager from tests import redis_client -from tests.tools import get_postproc_input, get_datum, mock_configs +from tests.tools import get_postproc_input, get_datum from numaprom.udf.postprocess import postprocess DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") @@ -17,10 +17,9 @@ STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") -@patch("numaprom.tools.set_aws_session", Mock(return_value=None)) -@patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) +# @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) class TestPostProcess(unittest.TestCase): - postproc_input = None + postproc_input: Messages = None def setUp(self) -> None: redis_client.flushall() diff --git a/tests/udf/test_preprocess.py b/tests/udf/test_preprocess.py index 5d44543..ce97d64 100644 --- a/tests/udf/test_preprocess.py +++ b/tests/udf/test_preprocess.py @@ -1,31 +1,28 @@ import os import unittest -import numpy as np -from orjson import orjson from unittest.mock import patch, Mock -from numalogic.registry import MLflowRegistry +import numpy as np +from numalogic.registry import RedisRegistry +from orjson import orjson +from pynumaflow.function import Messages from numaprom._constants import TESTS_DIR from numaprom.entities import Status, StreamPayload, Header -from numaprom.watcher import ConfigManager -from tests.tools import get_prepoc_input, get_datum, return_preproc_clf, mock_configs # Make sure to import this in the end -from tests import redis_client -from numaprom.udf.preprocess import preprocess +from tests import redis_client, preprocess +from tests.tools import get_prepoc_input, get_datum, return_preproc_clf DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") STREAM_NAN_DATA_PATH = os.path.join(DATA_DIR, "stream_nan.json") -@patch("numaprom.tools.set_aws_session", Mock(return_value=None)) class TestPreprocess(unittest.TestCase): - preproc_input = None + preproc_input: Messages = None @classmethod - @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) def setUpClass(cls) -> None: redis_client.flushall() cls.preproc_input = get_prepoc_input(STREAM_DATA_PATH) @@ -34,7 +31,7 @@ def setUpClass(cls) -> None: def setUp(self) -> None: redis_client.flushall() - @patch.object(MLflowRegistry, "load", Mock(return_value=return_preproc_clf())) + @patch.object(RedisRegistry, "load", Mock(return_value=return_preproc_clf())) def test_preprocess(self): for msg in self.preproc_input.items(): _in = get_datum(msg.value) @@ -49,7 +46,7 @@ def test_preprocess(self): self.assertTrue(payload.win_ts_arr) self.assertIsInstance(payload, StreamPayload) - @patch.object(MLflowRegistry, "load", Mock(return_value=None)) + @patch.object(RedisRegistry, "load", Mock(return_value=None)) def test_preprocess_no_clf(self): for msg in self.preproc_input.items(): _in = get_datum(msg.value) @@ -60,8 +57,7 @@ def test_preprocess_no_clf(self): self.assertEqual(payload.header, Header.STATIC_INFERENCE) self.assertIsInstance(payload, StreamPayload) - @patch.object(MLflowRegistry, "load", Mock(return_value=return_preproc_clf())) - @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) + @patch.object(RedisRegistry, "load", Mock(return_value=return_preproc_clf())) def test_preprocess_with_nan(self): preproc_input = get_prepoc_input(STREAM_NAN_DATA_PATH) assert preproc_input.items(), print("input items is empty", preproc_input) diff --git a/tests/udf/test_threshold.py b/tests/udf/test_threshold.py index cf8f699..1c9fbf0 100644 --- a/tests/udf/test_threshold.py +++ b/tests/udf/test_threshold.py @@ -4,29 +4,25 @@ from freezegun import freeze_time from unittest.mock import patch, Mock -from numalogic.registry import MLflowRegistry +from numalogic.registry import RedisRegistry from numaprom._constants import TESTS_DIR from numaprom.entities import Status, StreamPayload, TrainerPayload, Header -from numaprom.watcher import ConfigManager -from tests import redis_client -from tests.tools import get_threshold_input, get_datum, return_threshold_clf, mock_configs +from tests import redis_client, threshold +from tests.tools import get_threshold_input, get_datum, return_threshold_clf -# Make sure to import this in the end -from numaprom.udf import threshold DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") -@patch("numaprom.tools.set_aws_session", Mock(return_value=None)) -@patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) +# @patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) class TestThreshold(unittest.TestCase): def setUp(self) -> None: redis_client.flushall() @freeze_time("2022-02-20 12:00:00") - @patch.object(MLflowRegistry, "load", Mock(return_value=return_threshold_clf())) + @patch.object(RedisRegistry, "load", Mock(return_value=return_threshold_clf())) def test_threshold(self): thresh_input = get_threshold_input(STREAM_DATA_PATH) assert thresh_input.items(), print("input items is empty", thresh_input) @@ -43,7 +39,7 @@ def test_threshold(self): self.assertTrue(payload.win_arr) self.assertTrue(payload.win_ts_arr) - @patch.object(MLflowRegistry, "load", Mock(return_value=return_threshold_clf())) + @patch.object(RedisRegistry, "load", Mock(return_value=return_threshold_clf())) def test_threshold_prev_stale_model(self): thresh_input = get_threshold_input(STREAM_DATA_PATH, prev_model_stale=True) assert thresh_input.items(), print("input items is empty", thresh_input) @@ -57,7 +53,7 @@ def test_threshold_prev_stale_model(self): self.assertEqual(payload.status, Status.THRESHOLD) self.assertIsInstance(train_payload, TrainerPayload) - @patch.object(MLflowRegistry, "load", Mock(return_value=None)) + @patch.object(RedisRegistry, "load", Mock(return_value=None)) def test_threshold_no_prev_clf(self): thresh_input = get_threshold_input(STREAM_DATA_PATH, prev_clf_exists=False) assert thresh_input.items(), print("input items is empty", thresh_input) @@ -72,10 +68,9 @@ def test_threshold_no_prev_clf(self): self.assertIsInstance(train_payload, TrainerPayload) @freeze_time("2022-02-20 12:00:00") - @patch.object(MLflowRegistry, "load", Mock(return_value=None)) + @patch.object(RedisRegistry, "load", Mock(return_value=None)) def test_threshold_no_clf(self): thresh_input = get_threshold_input(STREAM_DATA_PATH) - print(thresh_input) assert thresh_input.items(), print("input items is empty", thresh_input) for msg in thresh_input.items(): diff --git a/tests/udf/test_window.py b/tests/udf/test_window.py index 21e2870..6403234 100644 --- a/tests/udf/test_window.py +++ b/tests/udf/test_window.py @@ -1,22 +1,21 @@ import os import orjson import unittest -from unittest.mock import patch, Mock from pynumaflow.function._dtypes import DROP from numaprom._constants import TESTS_DIR from numaprom.entities import StreamPayload -from numaprom.watcher import ConfigManager -from tests.tools import get_datum, get_stream_data, mockenv, mock_configs +from tests.tools import get_datum, get_stream_data, mockenv from tests import redis_client, window DATA_DIR = os.path.join(TESTS_DIR, "resources", "data") STREAM_DATA_PATH = os.path.join(DATA_DIR, "stream.json") -@patch.object(ConfigManager, "load_configs", Mock(return_value=mock_configs())) class TestWindow(unittest.TestCase): + input_stream: dict = None + @classmethod def setUpClass(cls) -> None: cls.input_stream = get_stream_data(STREAM_DATA_PATH)