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QReserve Review

2

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Average

Revainrating 3 out of 5  
Rating 
3.0
Marketing, Event Management

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Description of QReserve

A flexible & user-friendly scheduling platform to easily manage equipment, labs, meeting rooms, amenities, people & more while providing a wide range of reporting & financial capabilities. With QReserve: -Set detailed resource access rules -Collect booking forms -Manage projects -Check-in/out of bookings & auto-cancel late or no-show bookings -Integrate with existing Outlook & Google calendars -Allow on-kiosk booking from live maps/floorplans -Book from defined time slots -Invite guests to bookings & request RSVP's -Invoice & process payments -Check-in/out equipment with integrated barcode reader support -Access activity, actual usage, capacity & utilization data -& so much more!

Reviews

Global ratings 2
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  • 2
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Type of review

Revainrating 5 out of 5

Excellent scheduling management: QReserve

The ease in which it can be used for our needs is what I like best about this system! It's fast - so much faster than my old scheduler program that was made before we started using computers. There are multiple ways to access information at once without having two screens open showing different things (this helps me keep an eye out!). My only dislike with qreserv has little doings w/the software itself; sometimes when you click something other times nothing happens or takes too long to respond…

Pros
  • Which means if someone works ill they don't accrue vacation days until after 2 weeks
Cons
  • Some disadvantages

Revainrating 2 out of 5

CUDA out of memory. Tried to allocate 18.00 MiB (GPU 0; 15.74 GiB total capacity; 13.33 GiB already allocated; 9.56 MiB free; 13.86 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.

See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF CUDA out of memory. Tried to allocate 36.00 MiB (GPU 0; 15.74 GiB total capacity; 13.58 GiB already allocated; 9.56 MiB free; 13.86 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.

Pros
  • Good integration between Python and GPUs, easy to get started with.
  • Can handle very large datasets without having to be memory-limited.
Cons
  • Python needs to be installed on the system where you want to run it, so that you can import its libraries.
  • The platform cannot handle data directly, so it must be prepared first.
  • There is no solution for this at the moment.