IdeaPad 720s – Machine Learning For The Road

Even if the GPU is a mobile version, the performance gain compared to a CPU is very much noticeable. As a result, it makes a lot of sense to have a dedicated GPU in your notebook if you buy a new one. This allows you to play with more complex models, or even to train them, while you are traveling, which might mean that you don’t have easy access to servers with GPU cards all the time. And even if you just want to use a pre-trained model for feature extraction, you can spare a lot of time by using the GPU.

There is always the option to use a gamers notebook, but in case you want a lightweight companion there are much fewer options. Our choice was the IdeaPad 720s with a 14′ screen, because it is lightweight, but still powerful with enough RAM, and a dedicated GeForce 940mx GPU that comes with non-shared memory. Without a doubt this is no high-end configuration, but the CUDA capabilities are sufficient to run older nets, or to design your own one, might it be a ConvNet or a RNN. Plus, with the huge SSD, you can train on pretty large training sets with better I/O performance than SATA disks.

So far for the theory, but now comes the reality. Especially for newer, or more exotic notebooks, installing Linux on them is not always trivial. To spare others the pain, we summarize the steps we did to get it running. It’s still not perfect, there are minor problems with the WLAN, but we used it for quite some hours now without any problems and successfully tested PyTorch + CUDA.

The first thing you have to do is to switch from “UEFI” to “Legacy Support” but that’s nothing new. You can enter the BIOS by pressing F2 during boot, or use the Novo “button” on the left side of the notebook. If this worked, you should shrink the NTFS volume which is pretty straightforward to make room for a real OS. Just half the size, so you got about ~250 GB for Linux. After all settings were adjusted, we can start with the Linux installation. Lubuntu seems a good choice since it is also lightweight, but comes with excellent support for detecting more exotic hardware. Make sure you have chosen the correct boot order, so you can boot from a USB stick/DVD drive.

Long story short, the installation failed miserable because the SSD drive was not recognized. But there is no time for panic! Thanks to the active community, we found the answer to that problem pretty fast. You have to switch the “SATA Controller Mode” from “RAID” to “AHCI” in the BIOS. With the new setting, it was possible to create a ext4+swap partition in the free space of the SSD. Then, the actual installation could be done without any problems. Merely the GRUB installation seems not optimal, since we get no boot screen and thus, we don’t know if our Windows partition was correctly recognized. From the grub config it does not seem so, but this is not our major concern, since our focus is a working Linux system with GPU support. So, we are blind at startup, but since Linux starts correctly we do not investigate this any further right now.

The next step is to get PyTorch working which was no problem at all. We used pip, python 2.7 + cuda 8.0 and it worked like a charm. Only torchvision failed, but we solved it by using “pip install –no-deps torchvision” since one dependency is still pytorch 0.1.2. A quick test with ipython confirmed that everything is okay and working. The last step is the installation of the CUDA toolkit which was also no problem thanks to the apt sources we just had to uncomment in the sources.list file. After “apt-get update” we installed the cuda toolkit packages and all its dependencies. Since CUDA requires a kernel module that is compiled at the end of the installation, a restart is required. To check if the setup was done correctly, start “nvidia-smi” -after reboot- and see if at the device is listed there.

After we got a prompt again, we downloaded a pre-trained network from the model collection and hacked some code to perform an image classification. Compared to the early days of ConvNets, even the CPU version was pretty “fast”. Next, we checked that cuda is correctly recognized by PyTorch and after that, we moved the model to the GPU and also the tensor we use for classification. As we mentioned at the begin of the post, the performance boost was pretty much visible and except for the first call that triggered some background activities to setup CUDA, everything went smooth.

Bottom line, here is the check list again to enjoy the notebook with Linux:
– Shrink the size of your NTFS volumne by 50%
– Switch from “UEFI” to “Legacy Support”
– Switch the “SATA Controller Mode” from “RAID” to “AHCI”
Since there is still room for improvements, we might create a successor blog post with additional details.

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