I'm trying to run the train file from this Unet with their default hyperparameters, batch size = 1.
I have a GTX970 with 4GB and made Windows use the integrated graphics.
When I run nvidia-smi, it says that the memory of the GPU is almost free (52MiB / 4096MiB), "No running processes found " and pytorch uses the GPU not the integrated graphics
I do not understand what is using the memory:
RuntimeError: CUDA out of memory. Tried to allocate 150.00 MiB (GPU 0; 4.00 GiB total capacity; 2.77 GiB already allocated; 72.46 MiB free; 2.82 GiB reserved in total by PyTorch).
GPU memory allocation is not done all at once. As the program loads the data and the model, GPU memory usage gradually increases until the training actually starts. In your case, the program has allocated 2.7GB and tries to get more memory before training starts, but there is not enough space. 4GB GPU memory is usually too small for CV deep learning algorithms.
$ man top
CPU Percentage of processor usage, broken into user, system, and idle components. The time period for which
these percentages are calculated depends on the event counting mode.
Disks Number and total size of disk reads and writes.
LoadAvg Load average over 1, 5, and 15 minutes. The load average is the average number of jobs in the run
queue.
MemRegions Number and total size of memory regions, and total size of memory regions broken into private (broken
into non-library and library) and shared components.
Networks Number and total size of input and output network packets.
PhysMem Physical memory usage, broken into wired, active, inactive, used, and free components.
Procs Total number of processes and number of processes in each process state.
SharedLibs Resident sizes of code and data segments, and link editor memory usage.
Threads Number of threads.
Time Time, in H:MM:SS format. When running in logging mode, Time is in YYYY/MM/DD HH:MM:SS format by
default, but may be overridden with accumulative mode. When running in accumulative event counting
mode, the Time is in HH:MM:SS since the beginning of the top process.
VirtMem Total virtual memory, virtual memory consumed by shared libraries, and number of pageins and pageouts.
Swap Swap usage: total size of swap areas, amount of swap space in use and amount of swap space available.
Purgeable Number of pages purged and number of pages currently purgeable.
Below the global state fields, a list of processes is displayed. The fields that are displayed depend on the
options that are set. The pid field displays the following for the architecture:
+ for 64-bit native architecture, or - for 32-bit native architecture, or * for a non-native architecture.
I see the following output of top on Mac. I don't quite understand as the manual is not very detailed.
For example, I only have 8GB of memory. Why it shows 15G PhysMem? What are wired, active, inactive, used, and free components?
For Disks, are the numbers '21281572/769G read' the size of disk read since the machine starts?
For Networks, are the numbers since the machine starts?
For VM, what are vsize, framework vsize, swapins, swapouts?
$ top -l 1 | head
Processes: 797 total, 4 running, 1 stuck, 792 sleeping, 1603 threads
2019/05/08 09:48:40
Load Avg: 54.32, 41.08, 34.69
CPU usage: 62.2% user, 36.89% sys, 1.8% idle
SharedLibs: 258M resident, 65M data, 86M linkedit.
MemRegions: 78888 total, 6239M resident, 226M private, 2045M shared.
PhysMem: 15G used (2220M wired), 785M unused.
VM: 3392G vsize, 1299M framework vsize, 0(0) swapins, 0(0) swapouts.
Networks: packets: 24484543/16G in, 24962180/7514M out.
Disks: 21281572/769G read, 20527776/242G written.
$ df -h
Filesystem Size Used Avail Use% Mounted on
/dev/disk1s1 466G 444G 19G 97% /
/dev/disk1s4 466G 3.1G 19G 14% /private/var/vm
/dev/disk2s1 932G 546G 387G 59% /Volumes/usbhd
com.apple.TimeMachine.2019-05-06-225547#/dev/disk1s1 466G 441G 19G 96% /Volumes/com.apple.TimeMachine.localsnapshots/Backups.backupdb/py???s MacBook Air/2019-05-06-225547/Macintosh HD
com.apple.TimeMachine.2019-05-02-082105#/dev/disk1s1 466G 440G 19G 96% /Volumes/com.apple.TimeMachine.localsnapshots/Backups.backupdb/py???s MacBook Air/2019-05-02-082105/Macintosh HD
I only have 8GB of memory. Why it shows 15G PhysMem?
There is 15G available, which is above the 8Gb in the machine due to the usage of Virtual Memory, where the OS can choose to swap (copy in / out) pages in memory to other storage (hard disk / SSD).
What are wired, active, inactive, used, and free components?
These are the states of physical pages of memory as used by Virtual Memory. So we have:
wired - Memory pages that are in use and can't be swapped (paged) out to disk (e.g. the OS itself)
active - Memory pages used for virtual memory that are in use that have been referenced recently. They are not likely to be paged out, unless no other pages are available
inactive - Memory pages that are used for virtual memory, but have not been referenced recently. They are likely to be swapped out if the need arises
used - Sometimes known as "speculative", physical memory that is speculatively mapped as the OS guesses about possibly requiring this, but it's not yet active
free - Physical memory pages not being used for virtual memory and is instantly available
For Disks, are the numbers '21281572/769G read' the size of disk read since the machine starts
For Networks, are the numbers since the machine starts?
Yes, I believe that these are since rebooting the OS.
For VM, what are vsize, framework vsize, swapins, swapouts?
I expect these are:
vsize - the amount of virtual space being used on disk
framework vsize - No idea about this one!
swapins - the number of memory pages loaded in from virtual memory to physical memory
swapout - the number of memory pages swapped out to physical memory from virtual memory
In Cassandra configuration memory limit is given.Suppose we have physical memory of 8 gb out of which 2 gb is allocated to Cassandra. So if Cassandra's memory usage goes upto 2gb, does additional memory from 8 gb will be allocated to Cassandra or not?
I am running a 32-bit version of Matlab R2013a on my computer (4GB RAM, and 32-bit Windows 7).
I have dataset (~ 60 MB) and I want to read it using
ds = dataset('File', myFile, 'Delimiter', ',');
And each time I face Out of Memory error. Theoretically, I should be able to use 2GB of RAM, so there should be no problem reading such small files.
Here is what I got when typed memory
Maximum possible array: 36 MB (3.775e+07 bytes) *
Memory available for all arrays: 421 MB (4.414e+08 bytes) **
Memory used by MATLAB: 474 MB (4.969e+08 bytes)
Physical Memory (RAM): 3317 MB (3.478e+09 bytes)
* Limited by contiguous virtual address space available.
** Limited by virtual address space available.
I followed every instructions I found (this is not a new issue), but for my case it seems rather weird, because I cannot run a simple program now.
System: Windows 7 32 bit
Matlab: R2013a
RAM: 4 GB
Clearly your issue is right here.
Maximum possible array: 36 MB (3.775e+07 bytes) *
You are either using a lot of memory in your system and/or you have a very low swap space.
I am using Matlab r2012b on win7 32-bit with 4GB RAM.
However, the memory limit on Matlab process is pretty low. On memory command, I am getting the following output:
Maximum possible array: 385 MB (4.038e+08 bytes) *
Memory available for all arrays: 1281 MB (1.343e+09 bytes) **
Memory used by MATLAB: 421 MB (4.413e+08 bytes)
Physical Memory (RAM): 3496 MB (3.666e+09 bytes)
* Limited by contiguous virtual address space available.
** Limited by virtual address space available.
I need to increase the limit to as much as possible.
System: Windows 7 32 bit
RAM: 4 GB
Matlab: r2012b
For general guidance with memory management in MATLAB, see this MathWorks article. Some specific suggestions follow.
Set the /3GB switch in the boot.ini to increase the memory available to MATLAB. Or set it with a properties dialog if you are averse to text editors. This is mentioned in this section of the above MathWorks page.
Also use pack to increase the Maximum possible array by compacting the memory. The 32-bit MATLAB memory needs blocks of contiguous free memory, which is where this first value comes from. The pack command saves all the variables, clears the workspace, and reloads them so that they are contiguous in memory.
More on overall memory, try disabling the virtual machine, closing programs, stopping unnecessary Windows services. No easy answer for this part.