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Studentenverbindung Concordia Wappen Zirkel Basisdaten Hochschule/n: Universität Bern Gründung: 1862 Farbenstatus: Farbentragend Farben: Grün-Rot-Gold Farben: Fuchsenfarben: Mütze: Dunkelgrüne Tellermütze Art des Bundes: Männerbund Stellung zur Mensur: Nichtschlagend Wahlspruch: Freiheit, Freundschaft, Fortschritt Website: www.concordiabern.ch Wappen der Studentenverbindung Concordia Bernensis. Die Studentenverbindung Concordia ist eine 1862 [1] an der Universität Bern gegründete Studentenverbindung [2] mit ca. 200 Mitgliedern: 30 Aktive, 170 Altherren (Stand 2013). Die Aktiven sind Mitglieder der Studentenverbindung Concordia, die Alten Herren treten nach Studienabschluss in die Männer Concordia über. Die Farben sind Grün-Rot-Gold, die Devise lautet: Freiheit Freundschaft Fortschritt. Inhaltsverzeichnis 1 Geschichte 2 Vereinszeitschrift 3 Personen 4 Freundschaftsverbindungen 5 Literatur 6 Siehe auch 7 Einzelnachweise 8 Weblinks Geschichte | Am 8. November 1858 erliess die

1 1 I am running pyspark application, v2.4.0, on Kubernetes, my spark application depends on numpy and tensorflow modules, please suggest the way to add these dependencies to Spark executors. I have checked the documentation, we can include the remote dependencies using --py-files, --jars etc. but nothing mentioned about library dependencies. apache-spark kubernetes share | improve this question asked Nov 13 '18 at 9:48 Lakshman Battini Lakshman Battini 1,099 3 15 add a comment  |  1 1 I am running pyspark application, v2.4.0, on Kubernetes, my spark application depends on numpy and tensorflow modules, please suggest the way to add these dependencies to Spark executors. I have checked the documentation, we can include the remote dependencies using --py-files, --jars etc. but nothing mentioned about library dependencies. apache-spark kubernetes share | improve this question asked Nov 13 '18 at 9:48

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1 In my tasks.py, I have two celery tasks A and B. Task B takes user id as parameter. Task A collects all the user ids and creates a group of Task B with each user id. @app.task def A(): user_ids = get_all_user_ids() job = group(B.s(uid) for uid in user_ids) job() @app.task def B(user_id): #some heavy processing In this case, I am directly calling the celery task group. Problem with this is that sometimes it gives a SoftTimeLimitExceeded exception in task A. My question is if I call don't directly call the job rather if call it with apply_async, will this resolve the issue? Also, What is the exact difference between calling a task group directly vs. calling it with apply_async? python celery share | improve this question asked Nov 13 '18 at 9:49 Vaibhav Jha Vaibhav Jha 6 1 add a comment  |  1 In my tasks.py, I have two celery tasks A and B. Task B takes user id as parameter. Task A collects all the user ids a