No particular problem. I am installing fastai on my new machine and I was trying to gather up a new shiny object
Having a setup on your own machine is a double edged sword. It is so very easy to get sidetracked.
In an effort to stick to the course work, I go back and forth between my rig and a paperspace setup. Jeremy had an exercise concerning bears and said we should acquire and load our own data. I had hours of pain trying to follow the process of getting urls of oaks, elms, and beech trees and loading them to paperspace. I was able skip all that and directly load my images on my home rig in about 15 minutes.
On other exercises, Wimpy runs out of memory and I have no choice but to use the rental.
Are you a live classroom participant or taking the courses online?
Both. I am reviewing Course 1v3 online and looking forward to the live classroom Course 2v3 beginning next week. I also have been using paperspace, but am frustrated by the unreasonable overhead to get connected, and the consequences of forgetting to close the session.
This script might have some error which can be easily resolved by the assistance of https://netgears.support/netgear-genie-support and installation will become easy.
I have the same issue, I am running also pytorch and fastai on Windows 10, and got result:
But during training (learn.fit(1)) it only use one CPU core, as it take 6 times longer then in on linux with the same card, I guess it only use cpu.
After a bit of digging, I found some additional information (learned a lot).
I ran Task Manager and watched CPU and GPU utilization in the Processes tab. For all the exercises in Lesson 1, I saw CPU up around 100%. In one of the exercises, with one of the NN models (maybe resnet34) I’d see GPU utilization as high as 29% when a process named Python was running. In almost all other cases I’d see a much lower reading for GPU…maybe as high as 10%. I never saw Python as the Process name on any of the other lessens that I ran. This bolsters the observation that I read earlier that effective GPU utilization is a matter of software implementation.
Some time ago I found a utility called Specy. It organizes and presents details about a PC that I didn’t know existed. I used it to monitor CPU and GPU temperature.
Even when Task Manager shows only 10% utilization, the GPunit temperature would go up to about 70 C (about 160F) from a bit over room temperature.
I know that Msft tools have been used by millions, but I wonder about the accuracy of the GPU info which Task Manager reports. TM was written long ago and GPU reporting is an afterthought. The TM Performance tab can sometimes be informative, too, but I have the same suspicion about accuracy.
I think if torch.cuda.is_available() comes back true, then you are probably getting as much bang for the GPU buck as is possible, from the driving software.
Wimpy has taught me another lesson. I had to experement but if I set bs = 2, I can run every lesson. I reliably get out of memory if I set the batch size bigger. It doesn’t seem to affect the results at all. Wimpy is a 1030 GT with 2G memory.
How do you monitor the separate cores of the CPU?
Let me toss in two more observation.
If you are resource constained…(or financially, like me) and go the cheap Charley route, you might be storage (c drive) constrained. You should consider putting things like dogscats, dogbreed, and weights (you know, everything you download after you git fastai and update conda env.
Don’t forget to defragment! The wimpier you are, the more it will affect performance.
I monitor by Task Manager and seen 26% cpu utilisation, and I have 4 cores. I see only 5% utilisation of GPU. Also training time is 6 time slower then on the same GPU on linux.