diff --git a/taskcluster/ci/browsertime/kind.yml b/taskcluster/ci/browsertime/kind.yml index 361b00d45..011764a63 100644 --- a/taskcluster/ci/browsertime/kind.yml +++ b/taskcluster/ci/browsertime/kind.yml @@ -13,7 +13,7 @@ kind-dependencies: primary-dependency: signing only-for-build-types: - - nightly + - performance-test only-for-abis: - armeabi-v7a @@ -81,7 +81,7 @@ job-defaults: - '--app=fenix' - '--browsertime' - '--cold' - - '--binary=org.mozilla.fenix.nightly' + - '--binary=org.mozilla.fenix.performancetest' - '--activity=org.mozilla.fenix.IntentReceiverActivity' - '--download-symbols=ondemand' - '--browsertime-node=$MOZ_FETCHES_DIR/node/bin/node' diff --git a/taskcluster/ci/raptor/kind.yml b/taskcluster/ci/raptor/kind.yml index 000658188..607a08c3f 100644 --- a/taskcluster/ci/raptor/kind.yml +++ b/taskcluster/ci/raptor/kind.yml @@ -11,7 +11,7 @@ kind-dependencies: - toolchain only-for-build-types: - - nightly + - performance-test only-for-abis: - armeabi-v7a @@ -76,7 +76,7 @@ job-defaults: - './test-linux.sh' - '--cfg=mozharness/configs/raptor/android_hw_config.py' - '--app=fenix' - - '--binary=org.mozilla.fenix.nightly' + - '--binary=org.mozilla.fenix.performancetest' - '--activity=org.mozilla.fenix.IntentReceiverActivity' - '--download-symbols=ondemand' fetches: diff --git a/taskcluster/ci/toolchain/gecko-derived.yml b/taskcluster/ci/toolchain/gecko-derived.yml index da1dccfaf..5595d6629 100644 --- a/taskcluster/ci/toolchain/gecko-derived.yml +++ b/taskcluster/ci/toolchain/gecko-derived.yml @@ -27,7 +27,7 @@ linux64-ffmpeg-4.1.4: linux64-geckodriver: attributes: - toolchain-artifact: public/build/geckodriver.tar.gz + toolchain-artifact: public/build/geckodriver.tar.xz description: "Geckodriver toolchain" run: index-search: diff --git a/taskcluster/docker/visual-metrics/Dockerfile b/taskcluster/docker/visual-metrics/Dockerfile index a96636252..742aed114 100644 --- a/taskcluster/docker/visual-metrics/Dockerfile +++ b/taskcluster/docker/visual-metrics/Dockerfile @@ -20,10 +20,10 @@ WORKDIR /builds/worker USER worker:worker COPY requirements.txt /builds/worker/requirements.txt +RUN pip3 install setuptools==46.0.0 RUN pip3 install --require-hashes -r /builds/worker/requirements.txt && \ rm /builds/worker/requirements.txt -COPY similarity.py /builds/worker/bin/similarity.py COPY run-visual-metrics.py /builds/worker/bin/run-visual-metrics.py COPY performance-artifact-schema.json /builds/worker/performance-artifact-schema.json diff --git a/taskcluster/docker/visual-metrics/requirements.txt b/taskcluster/docker/visual-metrics/requirements.txt index 560a0d008..936f3a2f5 100644 --- a/taskcluster/docker/visual-metrics/requirements.txt +++ b/taskcluster/docker/visual-metrics/requirements.txt @@ -1,23 +1,13 @@ -# Dependency hashes must be for python3.6 - # Direct dependencies attrs==19.1.0 --hash=sha256:69c0dbf2ed392de1cb5ec704444b08a5ef81680a61cb899dc08127123af36a79 structlog==19.1.0 --hash=sha256:db441b81c65b0f104a7ce5d86c5432be099956b98b8a2c8be0b3fb3a7a0b1536 voluptuous==0.11.5 --hash=sha256:303542b3fc07fb52ec3d7a1c614b329cdbee13a9d681935353d8ea56a7bfa9f1 jsonschema==3.2.0 --hash=sha256:4e5b3cf8216f577bee9ce139cbe72eca3ea4f292ec60928ff24758ce626cd163 -numpy==1.18.3 --hash=sha256:a551d8cc267c634774830086da42e4ba157fa41dd3b93982bc9501b284b0c689 -scipy==1.4.1 --hash=sha256:386086e2972ed2db17cebf88610aab7d7f6e2c0ca30042dc9a89cf18dcc363fa -matplotlib==3.0.3 --hash=sha256:e8d1939262aa6b36d0c51f50a50a43a04b9618d20db31e6c0192b1463067aeef -opencv-python==4.2.0.34 --hash=sha256:dcb8da8c5ebaa6360c8555547a4c7beb6cd983dd95ba895bb78b86cc8cf3de2b # Transitive dependencies importlib_metadata==1.1.0 --hash=sha256:e6ac600a142cf2db707b1998382cc7fc3b02befb7273876e01b8ad10b9652742 more_itertools==8.0.0 --hash=sha256:a0ea684c39bc4315ba7aae406596ef191fd84f873d2d2751f84d64e81a7a2d45 pyrsistent==0.15.6 --hash=sha256:f3b280d030afb652f79d67c5586157c5c1355c9a58dfc7940566e28d28f3df1b +setuptools==46.0.0 --hash=sha256:693e0504490ed8420522bf6bc3aa4b0da6a9f1c80c68acfb4e959275fd04cd82 six==1.12.0 --hash=sha256:3350809f0555b11f552448330d0b52d5f24c91a322ea4a15ef22629740f3761c zipp==0.6.0 --hash=sha256:f06903e9f1f43b12d371004b4ac7b06ab39a44adc747266928ae6debfa7b3335 -cycler==0.10.0 --hash=sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d -kiwisolver==1.1.0 --hash=sha256:400599c0fe58d21522cae0e8b22318e09d9729451b17ee61ba8e1e7c0346565c -pyparsing==2.4.7 --hash=sha256:ef9d7589ef3c200abe66653d3f1ab1033c3c419ae9b9bdb1240a85b024efc88b -python-dateutil==2.8.1 --hash=sha256:75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a -setuptools==46.1.3 --hash=sha256:4fe404eec2738c20ab5841fa2d791902d2a645f32318a7850ef26f8d7215a8ee diff --git a/taskcluster/docker/visual-metrics/run-visual-metrics.py b/taskcluster/docker/visual-metrics/run-visual-metrics.py index 4ae05172d..69f1c32d1 100644 --- a/taskcluster/docker/visual-metrics/run-visual-metrics.py +++ b/taskcluster/docker/visual-metrics/run-visual-metrics.py @@ -47,7 +47,6 @@ JOB_SCHEMA = Schema( {Required("test_name"): str, Required("browsertime_json_path"): str} ], Required("application"): {Required("name"): str, "version": str}, - Required("extra_options"): [str], } ) @@ -155,13 +154,13 @@ def read_json(json_path, schema): The contents of the file at ``json_path`` interpreted as JSON. """ try: - with open(str(json_path), "r", encoding="utf-8", errors="ignore") as f: + with open(str(json_path), "r") as f: data = json.load(f) except Exception: log.error("Could not read JSON file", path=json_path, exc_info=True) raise - log.info("Loaded JSON from file", path=json_path) + log.info("Loaded JSON from file", path=json_path, read_json=data) try: schema(data) @@ -203,9 +202,9 @@ def main(log, args): tar.extractall(path=str(fetch_dir)) except Exception: log.error( - "Could not read/extract browsertime results archive", + "Could not read extract browsertime results archive", path=browsertime_results_path, - exc_info=True + exc_info=True, ) return 1 log.info("Extracted browsertime results", path=browsertime_results_path) @@ -214,11 +213,6 @@ def main(log, args): jobs_json_path = fetch_dir / "browsertime-results" / "jobs.json" jobs_json = read_json(jobs_json_path, JOB_SCHEMA) except Exception: - log.error( - "Could not open the jobs.json file", - path=jobs_json_path, - exc_info=True - ) return 1 jobs = [] @@ -229,11 +223,6 @@ def main(log, args): try: browsertime_json = read_json(browsertime_json_path, BROWSERTIME_SCHEMA) except Exception: - log.error( - "Could not open a browsertime.json file", - path=browsertime_json_path, - exc_info=True - ) return 1 for site in browsertime_json: @@ -283,35 +272,6 @@ def main(log, args): "type": "vismet", "suites": suites, } - for entry in suites: - entry["extraOptions"] = jobs_json["extra_options"] - - # Try to get the similarity for all possible tests, this means that we - # will also get a comparison of recorded vs. live sites to check - # the on-going quality of our recordings. - similarity = None - if "android" in os.getenv("TC_PLATFORM", ""): - try: - from similarity import calculate_similarity - similarity = calculate_similarity(jobs_json, fetch_dir, OUTPUT_DIR, log) - except Exception: - log.info("Failed to calculate similarity score", exc_info=True) - - if similarity: - suites[0]["subtests"].append({ - "name": "Similarity3D", - "value": similarity[0], - "replicates": [similarity[0]], - "lowerIsBetter": False, - "unit": "a.u.", - }) - suites[0]["subtests"].append({ - "name": "Similarity2D", - "value": similarity[1], - "replicates": [similarity[1]], - "lowerIsBetter": False, - "unit": "a.u.", - }) # Validates the perf data complies with perfherder schema. # The perfherder schema uses jsonschema so we can't use voluptuous here. diff --git a/taskcluster/docker/visual-metrics/similarity.py b/taskcluster/docker/visual-metrics/similarity.py deleted file mode 100644 index 5820e531e..000000000 --- a/taskcluster/docker/visual-metrics/similarity.py +++ /dev/null @@ -1,251 +0,0 @@ -#!/usr/bin/env python3 -# -# This Source Code Form is subject to the terms of the Mozilla Public -# License, v. 2.0. If a copy of the MPL was not distributed with this -# file, You can obtain one at http://mozilla.org/MPL/2.0/. -import cv2 -import json -import numpy as np -import os -import pathlib -import shutil -import socket -import tarfile -import tempfile -import urllib - -from functools import wraps -from matplotlib import pyplot as plt -from scipy.stats import spearmanr - - -def open_data(file): - return cv2.VideoCapture(str(file)) - - -def socket_timeout(value=120): - """Decorator for socket timeouts.""" - def _socket_timeout(func): - @wraps(func) - def __socket_timeout(*args, **kw): - old = socket.getdefaulttimeout() - socket.setdefaulttimeout(value) - try: - return func(*args, **kw) - finally: - socket.setdefaulttimeout(old) - return __socket_timeout - return _socket_timeout - - -@socket_timeout(120) -def query_activedata(query_json, log): - """Used to run queries on active data.""" - active_data_url = "http://activedata.allizom.org/query" - - req = urllib.request.Request(active_data_url) - req.add_header("Content-Type", "application/json") - jsondata = json.dumps(query_json) - - jsondataasbytes = jsondata.encode("utf-8") - req.add_header("Content-Length", len(jsondataasbytes)) - - log.info("Querying Active-data...") - response = urllib.request.urlopen(req, jsondataasbytes) - log.info("Status: %s" % {str(response.getcode())}) - - data = json.loads(response.read().decode("utf8").replace("'", '"'))["data"] - return data - - -@socket_timeout(120) -def download(url, loc, log): - """Downloads from a url (with a timeout).""" - log.info("Downloading %s" % url) - try: - urllib.request.urlretrieve(url, loc) - except Exception as e: - log.info(str(e)) - return False - return True - - -def get_frames(video): - """Gets all frames from a video into a list.""" - allframes = [] - while video.isOpened(): - ret, frame = video.read() - if ret: - # Convert to gray to simplify the process - allframes.append(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)) - else: - video.release() - break - return allframes - - -def calculate_similarity(jobs_json, fetch_dir, output, log): - """Calculates the similarity score against the last live site test. - - The technique works as follows: - 1. Get the last live site test. - 2. For each 15x15 video pairings, build a cross-correlation matrix: - 1. Get each of the videos and calculate their histograms - across the full videos. - 2. Calculate the correlation coefficient between these two. - 3. Average the cross-correlation matrix to obtain the score. - - The 2D similarity score is the same, except that it builds a histogram - from the final frame instead of the full video. - - For finding the last live site, we use active-data. We search for - PGO android builds since this metric is only available for live sites that - run on android in mozilla-cental. Given that live sites currently - run on cron 3 days a week, then it's also reasonable to look for tasks - which have occurred before today and within the last two weeks at most. - But this is a TODO for future work, since we need to determine a better - way of selecting the last task (HG push logs?) - there's a lot that factors - into these choices, so it might require a multi-faceted approach. - - Args: - jobs_json: The jobs JSON that holds extra information. - fetch_dir: The fetch directory that holds the new videos. - log: The logger. - Returns: - Two similarity scores (3D, 2D) as a float, or None if there was an issue. - """ - app = jobs_json["application"]["name"] - test = jobs_json["jobs"][0]["test_name"] - splittest = test.split("-cold") - - cold = "" - if len(splittest) > 0: - cold = ".*cold" - test = splittest[0] - - # PGO vs. OPT shouldn't matter much, but we restrict it to PGO builds here - # for android, and desktop tests have the opt/pgo restriction removed - plat = os.getenv("TC_PLATFORM", "") - if "android" in plat: - plat = plat.replace("/opt", "/pgo") - else: - plat = plat.replace("/opt", "").replace("/pgo", "") - ad_query = { - "from": "task", - "limit": 1000, - "where": { - "and": [ - { - "regexp": { - "run.name": ".*%s.*browsertime.*-live.*%s%s.*%s.*" - % (plat, app, cold, test) - } - }, - {"not": {"prefix": {"run.name": "test-vismet"}}}, - {"in": {"repo.branch.name": ["mozilla-central"]}}, - {"gte": {"action.start_time": {"date": "today-week-week"}}}, - {"lt": {"action.start_time": {"date": "today-1day"}}}, - {"in": {"task.run.state": ["completed"]}}, - ] - }, - "select": ["action.start_time", "run.name", "task.artifacts"], - } - - # Run the AD query and find the browsertime videos to download - failed = False - try: - data = query_activedata(ad_query, log) - except Exception as e: - log.info(str(e)) - failed = True - if failed or not data: - log.info("Couldn't get activedata data") - return None - - log.info("Found %s datums" % str(len(data["action.start_time"]))) - maxind = np.argmax([float(t) for t in data["action.start_time"]]) - artifacts = data["task.artifacts"][maxind] - btime_artifact = None - for art in artifacts: - if "browsertime-results" in art["name"]: - btime_artifact = art["url"] - break - if not btime_artifact: - log.info("Can't find an older live site") - return None - - # Download the browsertime videos and untar them - tmpdir = tempfile.mkdtemp() - loc = os.path.join(tmpdir, "tmpfile.tgz") - if not download(btime_artifact, loc, log): - return None - tmploc = tempfile.mkdtemp() - try: - with tarfile.open(str(loc)) as tar: - tar.extractall(path=tmploc) - except Exception: - log.info( - "Could not read/extract old browsertime results archive", - path=loc, - exc_info=True, - ) - return None - - # Find all the videos - oldmp4s = [str(f) for f in pathlib.Path(tmploc).rglob("*.mp4")] - log.info("Found %s old videos" % str(len(oldmp4s))) - newmp4s = [str(f) for f in pathlib.Path(fetch_dir).rglob("*.mp4")] - log.info("Found %s new videos" % str(len(newmp4s))) - - # Finally, calculate the 2D/3D score - nhists = [] - nhists2d = [] - - total_vids = min(len(oldmp4s), len(newmp4s)) - xcorr = np.zeros((total_vids, total_vids)) - xcorr2d = np.zeros((total_vids, total_vids)) - - for i in range(total_vids): - datao = np.asarray(get_frames(open_data(oldmp4s[i]))) - - histo, _, _ = plt.hist(datao.flatten(), bins=255) - histo2d, _, _ = plt.hist(datao[-1, :, :].flatten(), bins=255) - - for j in range(total_vids): - if i == 0: - # Only calculate the histograms once; it takes time - datan = np.asarray(get_frames(open_data(newmp4s[j]))) - - histn, _, _ = plt.hist(datan.flatten(), bins=255) - histn2d, _, _ = plt.hist(datan[-1, :, :].flatten(), bins=255) - - nhists.append(histn) - nhists2d.append(histn2d) - else: - histn = nhists[j] - histn2d = nhists2d[j] - - rho, _ = spearmanr(histn, histo) - rho2d, _ = spearmanr(histn2d, histo2d) - - xcorr[i, j] = rho - xcorr2d[i, j] = rho2d - - similarity = np.mean(xcorr) - similarity2d = np.mean(xcorr2d) - - log.info("Average 3D similarity: %s" % str(np.round(similarity, 5))) - log.info("Average 2D similarity: %s" % str(np.round(similarity2d, 5))) - - if similarity < 0.5: - # For really low correlations, output the worst video pairing - # so that we can visually see what the issue was - minind = np.unravel_index(np.argmin(xcorr, axis=None), xcorr.shape) - - oldvid = oldmp4s[minind[0]] - shutil.copyfile(oldvid, str(pathlib.Path(output, "old_video.mp4"))) - - newvid = newmp4s[minind[1]] - shutil.copyfile(newvid, str(pathlib.Path(output, "new_video.mp4"))) - - return np.round(similarity, 5), np.round(similarity2d, 5) diff --git a/taskcluster/fenix_taskgraph/transforms/visual_metrics.py b/taskcluster/fenix_taskgraph/transforms/visual_metrics.py index 6a2a7950d..7b65ba2a9 100644 --- a/taskcluster/fenix_taskgraph/transforms/visual_metrics.py +++ b/taskcluster/fenix_taskgraph/transforms/visual_metrics.py @@ -75,10 +75,6 @@ def run_visual_metrics(config, jobs): symbol=treeherder_info['symbol'] ) - # Store the platform name so we can use it to calculate - # the similarity metric against other tasks - job['worker'].setdefault('env', {})['TC_PLATFORM'] = platform - # run-on-projects needs to be set based on the dependent task attributes = dict(dep_job.attributes) job['run-on-projects'] = attributes['run_on_projects']