Note: The vgg16 model put forward in the course added batch normalization to the model, which wasn’t available in the original VGG16. Also, there are better options for GPUs available now, obviously, such as the 1080 Ti or the new 1070 Ti cards. The next genration Volta GPU, which has “tensor units” optimized for operations needed by cuDNN is currently only available in pro-level Tesla cards. Apparently, NVIDIA likes to sell off the Pascal GPUs for as long as possible until the large Volta dies can be produced with satisfactory yield.
Just posted my completed build to PcPartPicker (Intel 8700K, 1080Ti, Intel Optane 900P 480GB XPoint SSD).
I have written my first blog post summarizing my learning’s that i have found while building my DL machine.
Please do Check it out: Choosing Components for Personal Deep Learning Machine
Feedback and Suggestions are Welcome.
Hello @ gokkulnath, it is great post. very detailed. I can see a lot of effort went into it.
Can you also share the part of installtion of softwars and libraries.
Thanks for Reading. I have just installed Windows 10 and Ubuntu with drivers and then I used the setup scripts provided in the Fast.ai Github repo
If you still want a detailed version check out these posts :
Build your own top-spec remote-access Machine Learning rig
Setting up a Deep learning machine in a lazy yet quick way
/install-gpu-part1-v2-cuda8.sh does not install theano, which is at least talked and used in the first lesson.
shouldn’t we install it? @radek
Yes, this is on purpose. The script is supposed to be for part 1 v2 where we only use PyTorch - added keras with tensorflow as an extra. Also, I am not sure for how much longer Theano will be maintained (or what the status of the project is atm) nor whether the objections to using TF from over a year ago still hold - TF is being actively developed and I would imagine a lot has changed since then and likely will change especially as I believe that the dynamic computational graph functionality has been announced that it will be worked on. Might be that the reasons we were unable to use TF easily for RNNs in part 1 v1 are not valid anymore.
Well, lots of ‘ifs’ here Sorry I do not have a concrete answer, but likely most if not the entire part 1 v1 can be completed on a TF backend and I would recommend going that route. Otherwise, you could try modifying the script to use Theano or one other possibility would be to comment out the part where TF is installed and install Theano after the install completes manually.
got it. needed your thought behind this, and well explained
I calculated the electricity bill using this formula
watts = 600
price_per_kwh = 0.12
cost_per_day = watts/1000.0 * 24 * price_per_kwh
print(“cost per month max”, cost_per_day*30)
Comes around 51 max and 20 on an average, ty.
Hi all! As I had promised earlier in this thread, I’ve put together a more detailed blog post on the mATX deep learning rig I spec’d, bought, assembled, configured, and benchmarked. You can find that blog post linked here.
I would really love any feedback you might have on the build and post, as I’m always trying to learn and improve. (and I’d also love to hear any suggestions for how I might continue to put this little guy to work after I run through my current backlog of personal projects!)
Lastly, I just wanted to express my sincere, immense gratitude for all of the posts that come before me in this thread – you’ve been incredibly helpful in guiding my thoughts, and to get me to this point of contentment!
Hi, any thoughts on following machine available at costco
It has 1070 8 GB graphics
32 GB RAM
and intel core i7
Will it be good to
- Participate with decent chance of getting ranking at kaggle for competitions like https://www.kaggle.com/c/cdiscount-image-classification-challenge
- Future proof for 2 years.
I just wrote a blog post about how GPUs help with deep learning. The post also includes parts of Jeremy’s lesson 3 lecture. Could you guys please go through it and review?
For me is best of devbox will be the server platform, they are much cheaper in our days (you can buy all stuff on ebay or https://natex.us/), for example:
Intel Xeon E5-2670 4 years old 2.6 8 cores 20M cach stepping 2 ~ 50$-150$ for each (ebay or natex)
DD3 2x 128GB 16x8GB ~ 500$ (ebay)
Matherboard? You have to find server matherboard for you process with PCIe x16, for example (natex) ~ 150-200$
2x geforce gtx 1080 ti or 2x geforce pascal titan 2
Server power supply, find youself ~ 50$ (natex)
Summary: 2 Xeon E5-2670 (16 core, 32 freads), 128GB from 800$ + Video ~ 600$
You can see all of this beauty here:
Dual Xeon E5-2670s Benchmarks - Build The BEST 16 Core USED Workstation
Dual Intel Xeon E5-2670 PC Server Build - 16 Cores, 32 hardware threads
Hi, everyone. Apart from the hardware things, does anyone use his/her server remotely?
I’m considering to add a graphic card to my desktop in the dorm, and would love to access it remotely from the library or cafeteria. However my machine is behind a NAT (managed by the dorm/uni) + a WLAN router (mine), so there seems to be no simple way to ssh into it using my laptop from the library network (which I suppose is another unknown layer of routers/NATs). Does anyone have a similar network situation, and have come up with a solution?
I’ve thought about joining the two machines together inside a VPN, and have looked into a virtual LAN gaming service Hamachi, but their linux client seems very buggy and useless. TeamViewer is also not a good option as what I really need is only Jupyter Notebook, not a remote desktop (laggy and different resolutions).
Hello, what about router port forwarding? And all the problems go away ))
I can configure my own router in my room, but that router itself is behind a NAT I don’t have access to. So I guess that won’t do.
(correct me if I’m wrong, I’m not very familiar with networks )
Ask your provider (if this possible) to give you public IP adress.
It is also possible to buy static IP from many VPN providers. (google it).
I use a Fritz!Box (a DSL modem+router) which has its own VPN software (FRITZ!VPN) to connect to the router from anywhere in the world. I have not tested with desktop yet but with my mobile i can connect to my home network. It doesn’t has any recurring charges.
Unfortunately, I was just informed by my network admins that they couldn’t set up a port forwarding for me. Meanwhile I have done some googling on buying static IPs from VPN providers, but all of them seem to be much more expensive (all require subscriptions) than a low end VPS with static IPs.
Perhaps I’ll try setting up reverse SSH tunneling or a VPN server on a VPS after the holidays.
Thanks again and happy holidays!