The annual Middleware conference is a major forum for the discussion
of innovations and recent scientific advances of middleware systems with
a focus on the design, implementation, deployment, and evaluation of
distributed systems, platforms and architectures for computing, storage,
and communication. Highlights of the conference will include a high
quality single-track technical program, invited speakers, an industrial
track, panel discussions involving academic and industry leaders, poster
and demonstration presentations, a doctoral symposium, tutorials and
workshops.
ということから、ミドルウェアに関する技術要素についてのカンファレンスと理解。
キーノートスピーカー
Brian F. Cooper (Google)
Title: Why can't the bits sit still? The never ending challenge of
data migrations.
Monica Lam (STANFORD UNIVERSITY)
Title: Building the Smartest and Open Virtual Assistant to Protect
Privacy
LISA is the premier conference for operations professionals, where we
share real-world knowledge about designing, building, securing, and
maintaining the critical systems of our interconnected world.
[pipenv.exceptions.ResolutionFailure]: req_dir=requirements_dir [pipenv.exceptions.ResolutionFailure]: File "/home/dobachi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/pipenv/utils.py", line 726, in resolve_deps [pipenv.exceptions.ResolutionFailure]: req_dir=req_dir, [pipenv.exceptions.ResolutionFailure]: File "/home/dobachi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/pipenv/utils.py", line 480, in actually_resolve_deps [pipenv.exceptions.ResolutionFailure]: resolved_tree = resolver.resolve() [pipenv.exceptions.ResolutionFailure]: File "/home/dobachi/.pyenv/versions/3.7.5/lib/python3.7/site-packages/pipenv/utils.py", line 395, in resolve [pipenv.exceptions.ResolutionFailure]: raise ResolutionFailure(message=str(e)) [pipenv.exceptions.ResolutionFailure]: pipenv.exceptions.ResolutionFailure: ERROR: ERROR: Could not find a version that matches scikit-learn<0.20,<0.22,>=0.18.0,>=0.19,>=0.21.0
----------------------------------------', 'ERROR: Command errored out with exit status 1: /home/centos/.local/share/virtualenvs/auto-sklearn-example-hzZc_yaE/bin/python3.7m -u -c \'import sys, setuptools, tokenize; sys.argv[0] = \'"\'"\'/tmp/pip-install-53cbwkis/pyrfr/setup.py\'"\'"\'; __file__=\'"\'"\'/tmp/pip-install-53cbwkis/pyrfr/setup.py\'"\'"\';f=getattr(tokenize, \'"\'"\'open\'"\'"\', open)(__file__);code=f.read().replace(\'"\'"\'\\r\\n\'"\'"\', \'"\'"\'\\n\'"\'"\');f.close();exec(compile(code, __file__, \'"\'"\'exec\'"\'"\'))\' install --record /tmp/pip-record-udozwz5v/install-record.txt --single-version-externally-managed --compile --install-headers /home/centos/.local/share/virtualenvs/auto-sklearn-example-hzZc_yaE/include/site/python3.7/pyrfr Check the logs for full command output.']
(snip)
メモリエラー
EC2インスタンス上で改めて実行したところ、以下のようなエラーを生じた。
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(snip)
File "/home/centos/.local/share/virtualenvs/auto-sklearn-example-hzZc_yaE/lib/python3.7/site-packages/autosklearn/smbo.py", line 352, in _calculate_metafeatures_encoded (snip)
ImportError: /home/centos/.local/share/virtualenvs/auto-sklearn-example-hzZc_yaE/lib/python3.7/site-packages/sklearn/tree/_criterion.cpython-37m-x86_64-linux-gnu.so: failed to map segment from shared object: Cannot allocate memory
The 2019 USENIX Conference on Operational Machine Learning (OpML '19)
provides a forum for both researchers and industry practitioners to
develop and bring impactful research advances and cutting edge solutions
to the pervasive challenges of ML production lifecycle management. ML
production lifecycle is a necessity for wide-scale adoption and
deployment of machine learning and deep learning across industries and
for businesses to benefit from the core ML algorithms and research
advances.
The Conference on Systems and Machine Learning (SysML) targets
research at the intersection of systems and machine learning. The
conference aims to elicit new connections amongst these fields,
including identifying best practices and design principles for learning
systems, as well as developing novel learning methods and theory
tailored to practical machine learning workflows.
$ ./bin/docker-image-tool.sh --help Usage: ./bin/docker-image-tool.sh [options] [command] Builds or pushes the built-in Spark Docker image.
Commands: build Build image. Requires a repository address to be provided if the image will be pushed to a different registry. push Push a pre-built image to a registry. Requires a repository address to be provided.
Options: -f file Dockerfile to build for JVM based Jobs. By default builds the Dockerfile shipped with Spark. -p file Dockerfile to build for PySpark Jobs. Builds Python dependencies and ships with Spark. -R file Dockerfile to build for SparkR Jobs. Builds R dependencies and ships with Spark. -r repo Repository address. -t tag Tag to apply to the built image, or to identify the image to be pushed. -m Use minikube's Docker daemon. -n Build docker image with --no-cache -b arg Build arg to build or push the image. For multiple build args, this option needs to be used separately for each build arg.
Using minikube when building images will do so directly into minikube's Docker daemon. There is no need to push the images into minikube in that case, they'll be automatically available when running applications inside the minikube cluster.
Check the following documentation for more information on using the minikube Docker daemon:
$ sudo docker run --rm -it dobachi/spark-py:v2.4.3 /bin/bash
(snip)
bash-4.4# /opt/spark/bin/pyspark Python 2.7.16 (default, May 6 2019, 19:35:26) [GCC 8.3.0] on linux2 Type "help", "copyright", "credits" or "license" for more information. Could not open PYTHONSTARTUP IOError: [Errno 2] No such file or directory: '/opt/spark/python/pyspark/shell.py' >>>
val genericResults = new ArrayBuffer[GenericModelReturn]
val (resultArray, modelStats, modelSelection, dataframe) = _mainConfig.modelFamily match { case "RandomForest" => val (results, stats, selection, data) = runRandomForest(payload) results.foreach { x => genericResults += GenericModelReturn( hyperParams = extractPayload(x.modelHyperParams), model = x.model, score = x.score, metrics = x.evalMetrics, generation = x.generation ) } (genericResults, stats, selection, data)
(snip)
なお、2019/8/25現在対応しているモデルは以下の通り。（caseになっていたのを抜粋）
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case "RandomForest" => case "XGBoost" => case "GBT" => case "MLPC" => case "LinearRegression" => case "LogisticRegression" => case "SVM" => case "Trees" =>
val initialize = new RandomForestTuner(cachedData, payload.modelType) .setLabelCol(_mainConfig.labelCol) .setFeaturesCol(_mainConfig.featuresCol) .setRandomForestNumericBoundaries(_mainConfig.numericBoundaries) .setRandomForestStringBoundaries(_mainConfig.stringBoundaries)
