I want to ask a semi-theoretical question.
I'm using a Docker image which utilizing Nvidia-container-runtime to communicate with the GPU on my machine.
The Docker image purpose is to run an application which involves presentation of images (photos) at high rate (1 Hz - 10 Hz) (Gui application). However, as we noticed, there are some delays on the presentation rates in contrast to running the same application on bare OS (without the overhead of Docker container). Does anyone encountered this issue? Is this issue can be resolved somehow? As a note, the display rate should be as exact as possible, meaning we can't allow delays of more than 10 ms.
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For example, I have a 4vCPU, 8GB mem VM. At first, I ran a Nginx container on it and then used a stress test tool to continuously send requests to it and got some information like QPS, average latency. Then I ran three same Nginx containers on the VM and parallelly send the same requests above to these containers.I found that the respective QPS all decreased, and average latency all increased.
So what factors can affect different containers processing on one machine at the same time? I think the CPU and memory are enough to provide resources to these containers. What factors below the docker can affect these, firstly I think is network, but what else? And Specifically, why can network affect these QPS, average latency metrics?
So I have a worker docker images. I want to spin up a network of 500-50000 nodes to emulate what happens to a private blockchain such as etherium on different scales. What would be a recomendation for an opensource tool/library for such job:
a) one that would make sure that even on a low-endish (say one 40 cores node) all workers will be moved forward in time equaly (not realtime)
b) would allow (a) in a distributed setting (say 10 low-endish nodes on a single lan)
In other words I do not seek for realtime network emulation, so I can wait for 10 hours to simulate 1 minute and it would be good enough fro me. I thought about Kathara yet a problem still stands - how to make sure that say 10000 containers are given the same amount of ticks in a round-robin manner?
So how to emulate a complex network of docker workers?
I'm taking the assumption that you will run each inside of a container. To ensure each container runs with similar CPU access, you can configure CPU reservations and limits on each replica. These numbers get computed down to fractional slices of a core, so on an 8 core system, you could give each container 0.01 of a core to run upwards of 800 containers. See the compose documentation on how to set resource constraints. And with swarm mode, you could spread these replicas across multiple nodes, sharing a network.
That said, I think the advice to run shorter simulations on more hardware is good. You will find a significant portion of the time is spent in context switching between each process, possibly invalidating any measurements you want to take.
You will also encounter scalability issues with docker and the orchestration tool you choose. For example, you'll need to adjust the subnet size for any shared network which defaults to a /24 with around 253 available IP's. The docker engine itself will likely be spending a non-trivial amount of CPU time maintaining the state for all of the running containers.
Right after creating and starting up a data science virtual machine and connecting through ssh, I tried to use the nvidia-smi to see if the built-in nvidia and cuda were working property. The returned message read
NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA
driver. Make sure that the latest NVIDIA driver is installed and
running.
These were supposed to be part of the vm, yet when I tried to run the program I created, my local computer's default CPU was used instead of the vm's GPU. The ultimate goal of my project is to run an object detection model with the performance sped up from the my lousy 11 sec/image, so I figured I would use a vm and take advantage of its computing power. Yet it seems like this may not be the best option, so if anyone else has some advice there, I would appreciate it.
The issue you are seeing is because you are using a D Series VM. Only the N series VMs have GPUs. So in order to utilize the GPU you need to select one of the following sizes:
https://learn.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu
For this size family, the vCPU (core) quota in your subscription is initially set to 0 in each region. You will need to request a vCPU quota increase for this family in an available region.
I'm doing an internship focused on Docker and I have to load-balance an application which have a client, a server and a database. My goal is to dynamically scale the number of server containers according their CPU usage. For instance if the CPU usage is over 60% I add a new container on the fly to divide the CPU usage. My problem is that my simulation does not get the CPU usage higher than 20%, it is a very simple simulation where a random users register and go to random pages.
Question : How can I lower the CPU capacity of my server containers using my docker-compose file in order to artificially make the CPU go higher ? I tried to use the cpu_quota and cpu_shares instructions but it's not very documented and I don't know how it works or affects my containers.
I have recently came across an adaptor that would allow me to use laptop memory on my desktop. See item below:
http://www.amazon.co.uk/Laptop-Desktop-Adapter-Connector-Converter/dp/B009N7XX4Q/ref=sr_1_1?ie=UTF8&qid=1382361582&sr=8-1&keywords=Laptop+to+desktop+memory
Both the desktop and the laptop use DDR3.
My question is, are this adapters reliable?
I have 8 GB available and I was wondering if they could be put to use in my gaming rig.
The desktop is an i7 machine generally used for gaming and some basic development.
The adapter should be reliable based on how it looks. There is not much to it only that it extends the "mini" RAM block to a bigger one. You can make the analog with A-B USB cables.
What you should also consider is if both RAM devices use the same frequency and possible heat issues as you will have to cool down the laptop memory more that if it was desktop size. This is because a lot of current goes trough smaller size compared to the desktop based RAM blocks. Then again you have the extension board to handle and disperse some of the heat so if you are not having some really extensive RAM operations you should be fine but you should check what is the working frequency on both of them. For example if the laptop one is faster than the maximum one your computer can support then you won't get that faster performance and the RAM block will work with the frequency of the system bus but if it is slower then the system bus will work on that frequency.
Use standard things on this module as reference to calculate the width. Measure it on image and scale to a reference item and check on your system. Use contacts or the lock in grooves to do the scaling since they are of standard dimensions on all modules. Or the module length...