I found it interesting that part of the vulnerability is that the PRNG takes the time the machine 'spins' as a parameter, thus introducing an attack vector.
In a lot of companies, including mine, Senior Developer is close to entry level. People with 3+ years experience are being hired at the Senior Developer level.
I have read that. I even re-read it before making my post.
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.
As I've been reading about tensorflow lately I feel like I'm missing something regarding distributed processing. How can Tensorflow 'scale up' easily if you are outside of Google? We have big datasets that I want to run learning on but it seems awkward to do with tensorflow. We're big enough that the team managing our cluster is separate than development and it is a huge pain if we need them to go install tools on each node. Even with Spark support it seems like the tensorflow python libraries need to be set up on each machine in the cluster ahead of time.
This always cracks me up. In reality they got the price correct and maximized the money that the company got for doing the IPO in the first place. The problem is that there is an expectation that IPO stocks will 'pop' and allow wall street to line their own pockets. Since they didn't, they consider it a failure.
My biggest question is what is a 'close call'? Is it something that a layperson would also agree is a close call? Some of the examples in the article don't sound too scary.
"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.
It is completely arbitrary. At our company the entry level developer is "Senior <language> Developer". It is literally the lowest developer title you can have.
Right. If they really cared about ratios they wouldn't offer unbalanced service such as 50/15. They have built their network around users predominately consuming content and then complain when there is too much consumption from specific providers that happen to compete with services they also offer.