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Support training for latency based anomalies specifically for perf-anomaly #409

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Support training for latency based anomalies specifically for perf-anomaly #409

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2045115
Support training for latency based anomalies specifically for perf-an…
May 13, 2024
7b42e2f
Delete ML_comp_10hrs.ipynb
shashank10456 May 13, 2024
bb07663
Support training for latency based anomalies specifically for perf-an…
May 14, 2024
883a32c
Cleanup docker file
Sep 10, 2024
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20 changes: 7 additions & 13 deletions Dockerfile
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,9 @@
####################################################################################################

ARG PYTHON_VERSION=3.11
FROM python:${PYTHON_VERSION}-slim-bookworm AS builder
FROM python:${PYTHON_VERSION}-bookworm AS builder
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The -slim-bookworm here replaced with -bookworm. However, the same -slim-bookworm at line 31 remains unchanged. Why is the difference?


ARG POETRY_VERSION=1.6
ARG POETRY_VERSION=1.8
ARG INSTALL_EXTRAS

ENV POETRY_NO_INTERACTION=1 \
Expand All @@ -16,28 +16,22 @@ ENV POETRY_NO_INTERACTION=1 \
POETRY_HOME="/opt/poetry" \
PATH="$POETRY_HOME/bin:$PATH"

RUN apt-get update \
&& apt-get install --no-install-recommends -y build-essential \
&& apt-get clean && rm -rf /var/lib/apt/lists/* \
&& pip install --no-cache-dir poetry==$POETRY_VERSION
RUN pip install --no-cache-dir poetry==$POETRY_VERSION

WORKDIR /app
COPY poetry.lock pyproject.toml ./

RUN poetry install --without dev --no-root --extras "${INSTALL_EXTRAS}" \
RUN poetry install --without dev --no-root --extras "redis druid rds" \
&& poetry run pip install --no-cache-dir "torch>=2.0,<3.0" --index-url https://download.pytorch.org/whl/cpu \
&& poetry run pip install --no-cache-dir "lightning[pytorch]" \
&& rm -rf $POETRY_CACHE_DIR \
&& pip cache purge \
&& apt-get purge -y --auto-remove build-essential
&& poetry run pip install --no-cache-dir "lightning[pytorch]<3.0"

####################################################################################################
# runtime: used for running the udf vertices
####################################################################################################
FROM python:${PYTHON_VERSION}-slim-bookworm AS runtime

RUN apt-get update \
&& apt-get install --no-install-recommends -y dumb-init \
&& apt-get install dumb-init \
&& apt-get clean && rm -rf /var/lib/apt/lists/* \
&& apt-get purge -y --auto-remove

Expand All @@ -50,4 +44,4 @@ COPY . /app
WORKDIR /app

ENTRYPOINT ["/usr/bin/dumb-init", "--"]
EXPOSE 5000
EXPOSE 5000
14 changes: 13 additions & 1 deletion numalogic/connectors/_config.py
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Expand Up @@ -43,15 +43,27 @@
datasource: str
dimensions: list[str] = field(default_factory=list)
aggregations: dict = field(default_factory=dict)
post_aggregations: dict = field(default_factory=dict)
group_by: list[str] = field(default_factory=list)
pivot: Pivot = field(default_factory=lambda: Pivot())
granularity: str = "minute"

def __post_init__(self):
from pydruid.utils.aggregators import doublesum
from pydruid.utils import postaggregator, aggregators
from numalogic.connectors.druid import postaggregator as _post_agg
from numalogic.connectors.druid import aggregators as _agg

if not self.aggregations:
self.aggregations = {"count": doublesum("count")}
self.aggregations = {
"agg_out": _agg.quantiles_doubles_sketch("valuesDoublesSketch", "agg0", 64)
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What does this value 64 imply? Does it need to be configurable for different latency anomaly use cases?

}

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numalogic/connectors/_config.py:60:47: F401 `pydruid.utils.aggregators.doublesum` imported but unused
if not self.post_aggregations:

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numalogic/connectors/_config.py:61:51: F401 `pydruid.utils.aggregators` imported but unused
self.post_aggregations = {
"p90": _post_agg.QuantilesDoublesSketchToQuantile(
output_name="agg_out", field=postaggregator.Field("agg_out"), fraction=0.90
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As a library feature, percentile is better to be configurable.

)
}


@dataclass
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1 change: 1 addition & 0 deletions numalogic/udfs/trainer/_druid.py
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Expand Up @@ -124,6 +124,7 @@ def fetch_data(self, payload: TrainerPayload) -> Optional[pd.DataFrame]:
delay=self.dataconn_conf.delay_hrs,
granularity=_fetcher_conf.granularity,
aggregations=dict(_fetcher_conf.aggregations),
post_aggregations=dict(_fetcher_conf.post_aggregations),
group_by=list(_fetcher_conf.group_by),
pivot=_fetcher_conf.pivot,
hours=_conf.numalogic_conf.trainer.train_hours,
Expand Down
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