ymt123

5 ms·

1. 1.▲
   by ymt123 10y ago

   I'm a fan of glances: https://nicolargo.github.io/glances/
2. 2.▲
   by ymt123 10y ago

   Have you tried Sacred[1]? It definitely doesn't answer the "infrastructure for deep learning" challenge but it is helpful for understanding what experiments have been run/where did this model come from (including what ve
3. 3.▲
   by ymt123 10y ago

   It's great to see people talking about the infrastructure they use to manage their deep learning workloads. One area where we've had trouble with other orchestration tools (e.g. Docker Swarm) was in managing resources at anything
4. 4.▲
   Imagenet for Space(techcrunch.com)

   4 points by ymt123 10y ago|0 comments
5. 5.▲
   by ymt123 10y ago

   If I'm understanding correctly that sounds similar to Pachyderm ( http://www.pachyderm.io )
6. 6.▲
   by ymt123 10y ago

   It's not quite the same (since it doesn't become a Map-Reduce job) but if you're mostly interested in the programming paradigm/scalability the Python API for Apache Spark might be a good alternative
7. 7.▲
   by ymt123 11y ago

   I think this was the problem [1] was targeting [1] http://engineeringblog.yelp.com/2016/01/dumb-init-an-init-fo...
8. 8.▲
   by ymt123 11y ago

   There are actually dockerized versions of many (maybe all) of the deep learning libraries. The docker containers can take advantage of the GPU for training. You still have to install CUDA on the box (outside the docker container) but then y