51 karma · joined October 25, 2013
That implementation requires starting individual tasks on each node in your cluster.
>To create a cluster, you start one TensorFlow server per task in the cluster. Each task typically runs on a different machine, but you can run multiple tasks on the same machine (e.g. to control different GPU devices).
I'm used to using tools that can roll out to a cluster with more finesse than that. The Spark wrapper seems to provide some capability to do this automatically, but even the Spark wrapper requires installing python libraries on each node.
Am I missing something?
In theory, Storm can be set up to run with much lower latency than the others, but it is rare that an app will need that kind of response time.
The first time I read that, I thought it said "emails like they were from a six-year-old". Which is probably also true.
"Whizzed underneath aircraft as it approached a runway" leaves a lot of room for interpretation. Other examples in the article do sound more scary, but is that just cherry picking or typical?
We have Product Owners. And PMs. And Dev managers, and QA managers. It's a giant ball of red tape and regret.
At one point the official 'scrum coach' actually convinced everyone that he had a magic formula for converting points to hours and that he could take the pointed backlog and produce a project plan out of it to produce familiar reports for management.
Everyone says we are doing Scrum, even the agile coaching team. But its really just waterfall done using Rally.
http://krugman.blogs.nytimes.com/2012/07/24/they-didnt-build...