I have a vagrant box that has about 6gb of disk space. How do I change this so that the vm takes up as much space as needed? I see plenty of space on my laptop:
Vagrant:
[vagrant#localhost ~]$ df
Filesystem 1K-blocks Used Available Use% Mounted on
/dev/mapper/VolGroup-lv_root
6744840 6401512 700 100% /
tmpfs 749960 0 749960 0% /dev/shm
/dev/sda1 495844 40707 429537 9% /boot
v-root 487385240 332649828 154735412 69% /vagrant
Laptop:
~ $ df
Filesystem 512-blocks Used Available Capacity iused ifree %iused Mounted on
/dev/disk1 974770480 662925656 311332824 69% 82929705 38916603 68% /
devfs 390 390 0 100% 675 0 100% /dev
map -hosts 0 0 0 100% 0 0 100% /net
map auto_home 0 0 0 100% 0 0 100% /home
There is no way to make dynamically sized disks with virtual box as far as I am aware... They always need to have a max size even though they can be dynamically allocated.
Resizing them is also quite complicated but there are several tutorials around on the net, for example something like this should get you on the right track: http://tanmayk.wordpress.com/2011/11/02/resizing-a-virtualbox-partition/
I usually make sure that the vagrant box I choose to use or create has about 40 gigs as the size for the dynamically allocated disk.
Related
I am experiencing outofmemory issue while joining 2 datasets; one contains 39M rows other contain 360K rows.
I have 2 worker nodes, each of the worker node has maximum memory of 125 GB.
In Yarn Memory allocated for all YARN containers on a node = 96GB
Minimum Container Size (Memory) = 3072
In Hive settings :
hive.tez.java.opts=-Xmx2728M -Xms2728M -Djava.net.preferIPv4Stack=true -XX:NewRatio=8 -XX:+UseNUMA -XX:+UseG1GC -XX:+ResizeTLAB
hive.tez.container.size=3410
What values I should set to get rid of outofmemory issue.
I solved it by using increasing the Yarn Memory allocated
Minimum Container Size (Memory) = 3072 to 3840
Memory allocated for all YARN containers on a node 96 to 120 GB ( each node had 120GB)
Percentage of physical CPU allocated for all containers on a node 80%
Number of virtual cores 8
https://learn.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-hive-out-of-memory-error-oom
As you can see in the below command I have assigned a total of 500 GB disk space to my VM. But I am seeing 14.4 GB actual space available to the disk and once it gets used completely. I got an error there isn't much space to use? How to extend space for /dev/mapper/centos-root.
I am using VMware ESXi and using centOS for this VM.
[root#localhost Apr]# fdisk -l
Disk /dev/sda: 536.9 GB, 536870912000 bytes, 1048576000 sectors
Units = sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disk label type: dos
Disk identifier: 0x00064efd
Device Boot Start End Blocks Id System
/dev/sda1 * 2048 2099199 1048576 83 Linux
/dev/sda2 2099200 33554431 15727616 8e Linux LVM
Disk /dev/mapper/centos-root: 14.4 GB, 14382268416 bytes, 28090368 sectors
Units = sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 bytes
Disk /dev/mapper/centos-swap: 1719 MB, 1719664640 bytes, 3358720 sectors
Units = sectors of 1 * 512 = 512 bytes
Sector size (logical/physical): 512 bytes / 512 bytes
I/O size (minimum/optimal): 512 bytes / 512 byte
Execute bellow Steps to increase your Linux Disk after adding space in VMWare :
Step 1 - update partition table
fdisk /dev/sda
Press p to print the partition table to identify the number of partitions.
Press n to create a new primary partition.
Press p for primary.
Press 3 for the partition number, depending on the output of the partition table print.
Press Enter two times.
Press t to change the system's partition ID.
Press 3 to select the newly creation partition.
Type 8e to change the Hex Code of the partition for Linux LVM.
Press w to write the changes to the partition table.
Step 2 - Restart the virtual machine.
Step 3 - verify that the changes were saved
fdisk -l
Step 4 - convert the new partition to a physical volume
pvcreate /dev/sda3
Step 5 - extend the physical volume (centos is your VG name, if not use your VG name in place of centos )
vgextend centos /dev/sda3
Step 6 - extend the Logical Volume (500G is the size you want to add , if not use the right size in place of 500G)
lvextend -L+500G /dev/mapper/centos-root
Step 7 - expand the ext filesystem online
resize2fs /dev/mapper/centos-root
extend disk without reboot
echo 1 > /sys/block/sda/device/rescan
echo 1 > /sys/block/sdb/device/rescan
echo 1 > /sys/block/nvme0n1/device/rescan_controller
partprobe
gdisk fix warnging
parted change partion size
## parted can executed as command line. but this is very dangerous
parted -s /dev/sdb "resizepart 2 -1" quit
parted -s /dev/sdb "resizepart 3 100%" quit
resizepart 3 100%
pvresize /dev/sda3
lvextend -l +100%FREE cs/root
xfs_growfs /dev/cs/root
Loading 1500 images of size (1000,1000,3) breaks the code and throughs kill 9 without any further error. Memory used before this line of code is 16% of system total memory. Total size of images direcotry is 7.1G.
X = np.asarray(images).astype('float64')
y = np.asarray(labels).astype('float64')
system spec is:
OS: macOS Catalina
processor: 2.2 GHz 6-Core Intel Core i7 16 GB 2
memory: 16 GB 2400 MHz DDR4
Update:
getting the bellow error while running the code on 32 vCPUs, 120 GB memory.
MemoryError: Unable to allocate 14.1 GiB for an array with shape (1200, 1024, 1024, 3) and data type float32
You would have to provide some more info/details for an exact answer but, assuming that this is a memory error(incredibly likely, size of the images on disk does not represent the size they would occupy in memory, so that is irrelevant. In 100% of all cases, the images in memory will occupy a lot more space due to pointers, objects that are needed and so on. Intuitively I would say that 16GB of ram is nowhere nearly enough to load 7GB of images. It's impossible to tell you how much you would need but from experience I would say that you'd need to bump it up to 64GB. If you are using Keras, I would suggest looking into the DirectoryIterator.
Edit:
As Cris Luengo pointed out, I missed the fact that you stated the size of the images.
I am training an image segmentation model on azure ML pipeline. During the testing step, I'm saving the output of the model to the associated blob storage. Then I want to find the IOU (Intersection over Union) between the calculated output and the ground truth. Both of these set of images lie on the blob storage. However, IOU calculation is extremely slow, and I think it's disk bound. In my IOU calculation code, I'm just loading the two images (commented out other code), still, it's taking close to 6 seconds per iteration, while training and testing were fast enough.
Is this behavior normal? How do I debug this step?
A few notes on the drives that an AzureML remote run has available:
Here is what I see when I run df on a remote run (in this one, I am using a blob Datastore via as_mount()):
Filesystem 1K-blocks Used Available Use% Mounted on
overlay 103080160 11530364 86290588 12% /
tmpfs 65536 0 65536 0% /dev
tmpfs 3568556 0 3568556 0% /sys/fs/cgroup
/dev/sdb1 103080160 11530364 86290588 12% /etc/hosts
shm 2097152 0 2097152 0% /dev/shm
//danielscstorageezoh...-620830f140ab 5368709120 3702848 5365006272 1% /mnt/batch/tasks/.../workspacefilestore
blobfuse 103080160 11530364 86290588 12% /mnt/batch/tasks/.../workspaceblobstore
The interesting items are overlay, /dev/sdb1, //danielscstorageezoh...-620830f140ab and blobfuse:
overlay and /dev/sdb1 are both the mount of the local SSD on the machine (I am using a STANDARD_D2_V2 which has a 100GB SSD).
//danielscstorageezoh...-620830f140ab is the mount of the Azure File Share that contains the project files (your script, etc.). It is also the current working directory for your run.
blobfuse is the blob store that I had requested to mount in the Estimator as I executed the run.
I was curious about the performance differences between these 3 types of drives. My mini benchmark was to download and extract this file: http://download.tensorflow.org/example_images/flower_photos.tgz (it is a 220 MB tar file that contains about 3600 jpeg images of flowers).
Here the results:
Filesystem/Drive Download_and_save Extract
Local_SSD 2s 2s
Azure File Share 9s 386s
Premium File Share 10s 120s
Blobfuse 10s 133s
Blobfuse w/ Premium Blob 8s 121s
In summary, writing small files is much, much slower on the network drives, so it is highly recommended to use /tmp or Python tempfile if you are writing smaller files.
For reference, here the script I ran to measure: https://gist.github.com/danielsc/9f062da5e66421d48ac5ed84aabf8535
And this is how I ran it: https://gist.github.com/danielsc/6273a43c9b1790d82216bdaea6e10e5c
In the neo4j-wrapper.conf file I see this:
# Java Heap Size: by default the Java heap size is dynamically
# calculated based on available system resources.
# Uncomment these lines to set specific initial and maximum
# heap size in MB.
#wrapper.java.initmemory=512
#wrapper.java.maxmemory=512
Does that mean that I should not worry about -Xms and -Xmx?
I've read elsewhere that -XX:ParallelGCThreads=4 -XX:+UseNUMA -XX:+UseConcMarkSweepGC would be good.
Should I add that on my Intel® Core™ i7-4770 Quad-Core Haswell 32 GB DDR3 RAM 2 x 240 GB 6 Gb/s SSD (Software-RAID 1) machine?
I would still configure it manually.
Set both to 12 GB and use the remaining 16GB for memory mapping in neo4j.properties. Try to match it to you store file sizes