With regards to GPU’s I thought I had issues with adding a 1080ti, the main thing is power. From what I remember of the 820 it’s likely to have a 1kw (1000w) power supply, then the next problem is power supply cables. The 1080ti needs 2 power connections a 6 way and an 8 way. As I had two cards only the 1080ti needed extra power connection. My box had two 6 way cables so all I had extra to buy was a 6 to 8 adapter cable. PCI 3 is good for cpu. So I don’t think you’ll have performance issues.
Be aware that the more you add to the PCI bus the more you interfere with the GPU, if you need power for a data ssd you’ll lose for you gpu
I was a bit worried about installing Ubuntu on a windows machine, but all went well. If it has windows already installed, install to a separate disk if you can. Then leave space in the windows partition for the swap. You can do this once Ubuntu is install, just install parted.
You can check HP for serial number maybe you have warranty left.
Thanks Roger - I already installed Ubuntu on my Win7 machine as a second boot option for flashing my Nividia Jetson. Like you, I was worried but all went well and it works far better than I expected! I am looking forward to the 820, and will order extra power cables if I need to.
Interesting how do you find the Jetson, I have a friend who is into FPGA’s. Not sure it is based on FPGA but I see they have ethernet connections. Looking to get away from GPU’s and create a server of some type
I haven’t really done much with it yet, but flash it and load stuff onto it. It was a real challenge to build Tensorflow. I have some apps in mind that I want to play with, I hope to do that soon.
Today I came up against a kernel update which blew away my nvidia so no gui and no cuda. It took awhile to find out what was wrong.
The unattended-update facility was to blame for the kernel update. this can be disable several ways.
The brilliant thing I learned is that all is not lost.
The option sudo sh ./NVIDIA-DRIVER.run -K rebuilds the nvidia kernel
see sudo sh ./NVIDIA-DRIVER.run --advanced-options
This will only work if ./NVIDIA-DRIVER.run version is already installed
Which is the situation when a linux kernel gets bumped up a version from a working system. As this happens the nvidia kernel module gets destroyed or lost when the new linux kernel is installed.
There are other ways to manage this.
Looks like a very nice system. I would install Ubuntu 16.04 LTS though to run the software (shrink the windows partition, then install Ubuntu “next” to it).
Second the Ubuntu comment. Also Costco has fantastic return policy, so really no risk. I’d check if the ssd is a nvme drive. Not a deal breaker, but good to have at this price.
Always look up “power supply tier list” before buying a power supply. Many of them are fires waiting to happen. You generally want to buy the best power supply you can justify, but at minimum stay in the first 2-3 tiers. There is an updated list frequently so make sure you look for the latest.
Somehow i am unable to invoke Jupyter notebook on my laptop.
Desktop
chetan@DeepLearning:~$ jupyter notebook –no-browser
[W 09:53:04.317 NotebookApp] server_extensions is deprecated, use nbserver_extensions
[I 09:53:04.319 NotebookApp] Writing notebook server cookie secret to /run/user/1000/jupyter/notebook_cookie_secret
[I 09:53:04.725 NotebookApp] [jupyter_nbextensions_configurator] enabled 0.2.4
[I 09:53:04.726 NotebookApp] Serving notebooks from local directory: /home/chetan
[I 09:53:04.726 NotebookApp] 0 active kernels
[I 09:53:04.726 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/
[I 09:53:04.726 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
Since the SSH command is not giving any error message , i am not able to figure out the reason why the localhost:8889 command not invoking jupyter notebook.
Get a z270, the z170 is not a good choice for Kaby Lake. Make sure you get a good PSU, Google “PSU Tier List” and pick the most recent post and try to get something in the top 3 brackets, as high as you can spend, this is not a black box part, it is very critical.
If you can, get 16GB sticks if you go with 32GB, that will allow you room to go to 64GB if you need to but ram prices are climbing so if it makes more sense to get 4x 8GB then do it. I wouldn’t waste time with a liquid cooler, it isn’t really necessary and they are not all that much better unless you go with a full liquid system.
A good fan (Noctua) will be pretty close to the closed liquid cooling systems. You can even go with a cheap Evo cooler for like $25. You don’t need much unless you go extreme overclocking, you can get the same overclock 90% of people use with a $25 cooler (if you get a good one). This is what I use and I overclock my cpu https://www.amazon.com/exec/obidos/ASIN/B005O65JXI/lexesto-20/ref=nosim/
For SSD, I would recommend sticking with Samsung EVO series, it is the best option regardless if you go SATA SSD or NVMe.
For the nVidia card I would recommend the MSI Gaming X edition or EVGA (not ideal but popular). I would avoid the founder cards as they have improper cooling, especially for extended 100% loads that you see with ML.
I would use Linux for ML and not Windows. Dual Booting to windows is fine if that is your plan, but Windows is too much of a problem once you get out of Part 1.
I am trying to use Azure NC6 machine and the speed is pretty slow (~600+ seconds for lesson 1 notebook training)
Any suggestion?
I am using python 3.6 and Keras 2 as in here.
Using tensorflow as backend with keras.json with:
update: I moved to NC12, two GPU and still very slow. When I do nvidia-smi it seems like only one GPU it working
Opened a dedicated thread for this problem here