Quadro Penetration
🔞 ALL INFORMATION CLICK HERE 👈🏻👈🏻👈🏻
Quadro Penetration
Animal42069
/
BetterPenetration
Code cleanup, bug fixes, and adding a simplified penetration option
Code cleanup, bug fixes, and adding a simplified penetration option
Code cleanup, bug fixes, and adding a simplified penetration option
Code cleanup, bug fixes, and adding a simplified penetration option
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Animal42069
ManlyMarco
ScrewThisNoise
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This plugin seeks to solve to "telescoping" issue that occurs in HScenes in AI Shoujo and Honey Select 2. It also adds some additional features to overall improve the HScene experience. This plugin does not work for studio.
Replaces the default "telescoping" behavior by allowing the head to move past the point of penetration.
The head will reposition to maintain length, optionally you can allow the penis to begin to telescope after it has penetrated by a specified percentage
Works for vaginal, anal, and oral penetraion.
Maintain proper rotation of the penis, no more spinning shafts during certain positions.
Adjust the length and girth of penis.
Adjust the overall sack size.
Can add softness to the penis, to add a certain amount of squishiness after penetration.
Offset options to further tweak things when using characters with abnormal body shapes.
Supporst multiple male and multiple female positions present in Honey Select 2.
This plugin does not work for studio. I have a few ideas on how to make something similar work in studio, but it would be a major undertaking.
Male uncensor requires a shaft bone and a head bone, which most uncensors have.
No special female uncensors are needed, but if you use Roy12's vagina uncensor it will utilize the dynamic bones if they are present. This uncensor only works for AI Shoujo.
The mod works by keeping track of certain bones on the girl and using that information to set boundaries. Any character made in the game will have these bones. If somehow these bones aren't present then it will revert to default behavior. The mod tries to place the head inside the girl at a position that pierces the original target (vagina, anus, mouth). Due to sizes, lengths, angles and different positions this isn't always possible. It is recommended to use Mantas' BetterHScenes to adjust the characters in the scene to make the geometry involved more favorable
Requires BepInEx
Copy the dll to your install directory /BepInEx/plugins
Home - Quadro
GitHub - Animal42069/BetterPenetration
Choosing between GeForce or Quadro GPUs to do machine... - Stack Overflow
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machine-learning gpu gpgpu tensorflow
589 1 1 gold badge 4 4 silver badges 8 8 bronze badges
This is 100% not opinion based.
– Goddard
Jul 30 '16 at 16:05
This question is not opinion based, tensorflow is a specific application and there are specific hardware differences between these cards. The question even points to one of the main differences and asks whether the programming library uses that technology.
– user359135
Nov 30 '16 at 14:18
3,680 5 5 gold badges 28 28 silver badges 42 42 bronze badges
1,773 15 15 silver badges 17 17 bronze badges
"Quadro GPUs aren't for scientific computation, Tesla GPUs are" +1
– Guy Coder
Jan 11 '16 at 12:26
but the quadros have 24 gb GPU memory, which would be great for future deep learning models. Why doesn't anybody mention that? or is it actually so slow that its not worth it at all
– harveyslash
Jun 11 '17 at 15:17
Yeah, in know speed-wise the quadro is not as good, but more often than not the limiting factor for deep learning models is the amount of data you can stuff in the graphic card's memory. Shouldn't a slower GPU which can process bigger training batch train faster?
– PhilMacKay
Mar 22 '18 at 13:34
@Guy Coder Don't fully agree. What matters is the underlying chip which often is exactly the same between tesla, quadro or GeForce. Only advantage a Tesla might have is more RAM and ECC RAM.
– beginner_
Apr 12 '18 at 5:16
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Is there any noticeable difference in TensorFlow performance if using Quadro GPUs vs GeForce GPUs?
e.g. does it use double precision operations or something else that would cause a drop in GeForce cards?
I am about to buy a GPU for TensorFlow, and wanted to know if a GeForce would be ok. Thanks and appreciate your help
I think GeForce TITAN is great and is widely used in Machine Learning (ML). In ML, single precision is enough in most of cases.
More detail on the performance of the GTX line (currently GeForce 10) can be found in Wikipedia, here .
Other sources around the web support this claim. Here is a quote from doc-ok in 2013 ( permalink ).
For comparison, an “entry-level” $700 Quadro 4000 is significantly slower than a $530 high-end GeForce GTX 680, at least according to my measurements using several Vrui applications, and the closest performance-equivalent to a GeForce GTX 680 I could find was a Quadro 6000 for a whopping $3660.
Specific to ML, including deep learning, there is a Kaggle forum discussion dedicated to this subject (Dec 2014, permalink ), which goes over comparisons between the Quadro, GeForce, and Tesla series:
Quadro GPUs aren't for scientific computation, Tesla GPUs are. Quadro
cards are designed for accelerating CAD, so they won't help you to
train neural nets. They can probably be used for that purpose just
fine, but it's a waste of money.
Tesla cards are for scientific computation, but they tend to be pretty
expensive. The good news is that many of the features offered by Tesla
cards over GeForce cards are not necessary to train neural networks.
For example, Tesla cards usually have ECC memory, which is nice to
have but not a requirement. They also have much better support for
double precision computations, but single precision is plenty for
neural network training, and they perform about the same as GeForce
cards for that.
One useful feature of Tesla cards is that they tend to have is a lot
more RAM than comparable GeForce cards. More RAM is always welcome if
you're planning to train bigger models (or use RAM-intensive
computations like FFT-based convolutions).
If you're choosing between Quadro and GeForce, definitely pick
GeForce. If you're choosing between Tesla and GeForce, pick GeForce,
unless you have a lot of money and could really use the extra RAM.
NOTE: Be careful what platform you are working on and what the default precision is in it. For example, here in the CUDA forums (August 2016), one developer owns two Titan X's (GeForce series) and doesn't see a performance gain in any of their R or Python scripts. This is diagnosed as a result of R being defaulted to double precision, and has a worse performance on new GPU than their CPU (a Xeon processor). Tesla GPUs are cited as the best performance for double precision. In this case, converting all numbers to float32 increases performance from 12.437s with nvBLAS 0.324s with gmatrix+float32s on one TITAN X (see first benchmark). Quoting from this forum discussion:
Double precision performance of Titan X is pretty low.
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