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https://github.com/deepset-ai/haystack.git
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56cea8cbbd
* Add config files * log benchmarks to stdout * Add top-k and batch size to configs * Add batch size to configs * fix: don't download files if they already exist * Add batch size to configs * refine script * Remove configs using 1m docs * update run script * update run script * update run script * datadog integration * remove out folder * gitignore benchmarks output * test: send benchmarks to datadog * remove uncommented lines in script * feat: take branch/tag argument for benchmark setup script * fix: run.sh should ignore errors * Remove changes unrelated to datadog * Apply black * Update test/benchmarks/utils.py Co-authored-by: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com> * PR feedback * Account for reader benchmarks not doing indexing * Change key of reader metrics * Apply PR feedback * Remove whitespace --------- Co-authored-by: rjanjua <rohan.janjua@gmail.com> Co-authored-by: Silvano Cerza <3314350+silvanocerza@users.noreply.github.com>
71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
from time import perf_counter
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from typing import Dict
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from pathlib import Path
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import traceback
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import datetime
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import logging
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from haystack import Pipeline
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from haystack.nodes import BaseReader
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from haystack.utils import aggregate_labels
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from utils import load_eval_data, get_reader_config
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def benchmark_reader(pipeline: Pipeline, labels_file: Path) -> Dict:
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try:
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labels, queries = load_eval_data(labels_file)
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eval_labels = aggregate_labels(labels)
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eval_queries = []
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eval_docs = []
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for multi_label in eval_labels:
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eval_queries.append(multi_label.query)
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eval_docs.append([multi_label.labels[0].document])
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# Run querying
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start_time = perf_counter()
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# We use run_batch instead of eval_batch because we want to get pure inference time
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predictions = pipeline.run_batch(queries=eval_queries, documents=eval_docs, labels=eval_labels, debug=True)
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end_time = perf_counter()
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querying_time = end_time - start_time
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# Evaluate predictions
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eval_result = pipeline._generate_eval_result_from_batch_preds(predictions_batches=predictions)
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metrics = eval_result.calculate_metrics()["Reader"]
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reader_type, reader_model, reader_top_k = get_reader_config(pipeline)
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results = {
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"querying": {
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"exact_match": metrics["exact_match"],
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"f1": metrics["f1"],
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"n_queries": len(eval_labels),
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"querying_time": querying_time,
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"seconds_per_query": querying_time / len(eval_labels),
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"reader": reader_type,
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"reader_model": reader_model,
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"top_k": reader_top_k,
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"date_time": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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"error": None,
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}
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}
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except Exception:
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tb = traceback.format_exc()
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logging.error("##### The following Error was raised while running querying run:")
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logging.error(tb)
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reader_type, reader_model, reader_top_k = get_reader_config(pipeline)
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results = {
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"reader": {
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"exact_match": 0.0,
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"f1": 0.0,
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"n_queries": 0,
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"querying_time": 0.0,
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"seconds_per_query": 0.0,
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"reader": reader_type,
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"reader_model": reader_model,
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"date_time": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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"error": str(tb),
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}
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}
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return results
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