Early Experiences with Deep Learning on a Laptop with Nvidia GTX 1070 GPU – part 1

Last August Nvidia brought desktop-class graphics to laptops with GeForce 1060, 1070 and 1080. Laptops with such GPUs seems to be primarily targeted towards gaming, but they can also be used for Deep Learning, e.g. with TensorFlow, Pytorch or Keras. A laptop for Deep Learning can be a convenient supplement to using GPUs in the Cloud (Nvidia K80 or P100) or buying a desktop or server machine with perhaps even more powerful GPUs than in a laptop (e.g. the Pascal Titan X or the new 1080 TI).

1. Choice of GPU
I decided on the GTX 1070 GPU since it had

  1. Same amount of GPU RAM as GTX 1080 – 8 GB – enough to develop or test a large range of CNN and GAN models
  2. Was cheaper and used less energy than GTX 1080
  3. High performance

2. Choice of Laptop and Configuration
I chose the Acer Predator G9-593, it had a nice spec and was upgradable to several disks and up to 64 GB of RAM

There are several youtube videos of people unboxing the G9-593 and looking into how to upgrade hardware (e.g RAM and disks)

I first installed Ubuntu 14.04 with Cuda (8.0), cuDNN, Nvidia drivers, nvidia-docker and then later upgraded to Ubuntu 16.04 – check out the blog post (by Donald Kinghorn) Install Ubuntu 16.04 or 14.04 and CUDA 8 and 7.5 for NVIDIA Pascal GPU. Then I installed TensorFlow and Pytorch, got some issues with GPU support for Pytorch but I assume it is just finger trouble on my side, but Tensorflow worked nicely on the GPU as you can see in the section below.

3. Example training the Pix2Pix Conditional Adversarial Network in TensorFlow on the Laptop

To test Deep Learning on the laptop I chose the pix2pix-tensorflow project, see examples below followed by a gif of actual training on the laptop.pix2pix-tensorflow

Best regards,
Amund Tveit (@atveit)


Appendix – Deep Learning benchmark of Nvidia GTX 1070

The benchmark in the table below – from github.com/tobigithub/tensorflow-deep-learning/wiki/tf-benchmarks – was very favorable in the direction of 1070 (note that this compares 1080 and 1070 to older generation GPUs)


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  1. Timely to stumble upon this as I was just purchasing an Acer ROG with 64GB of RAM and the 1070 GPU. Curious if you still felt the performance was such as to allow training of moderately large nets and if you happened to run a dual boot with windows on the machine (which is what I am expected to do). Cheers!

  2. Hey I tried to set up the same on laptop having gpu Geforce 1060 but got some errors. like

    ERROR: An NVIDIA kernel module ‘nvidia-drm’ appears to already be loaded in
    your kernel. This may be because it is in use (for example, by an X
    server, a CUDA program, or the NVIDIA Persistence Daemon), but this
    may also happen if your kernel was configured without support for
    module unloading. Please be sure to exit any programs that may be
    using the GPU(s) before attempting to upgrade your driver. If no
    GPU-based programs are running, you know that your kernel supports
    module unloading, and you still receive this message, then an error
    may have occured that has corrupted an NVIDIA kernel module’s usage
    count, for which the simplest remedy is to reboot your computer

    Could guide me please for the set up? I really got exhausted searching on internet. Please help me here.

  3. Hi Amund,

    I’m a CS graduate student @Georgia Tech and I’m going with the ML concentration. I want to dive into deep learning. I need your advice regarding the hardware options to enter to this field. I don’t have the budget to have a medium budget laptop and a custom pc. I don’t have the money to use AWS(expenses are constrained ). I have some savings and I’m planning to buy this laptop because it seems well rounded.

    I want to start a startup with a colleague and I’m hoping this solution (either dyi PC or this laptop) will help me with the first iterations of this process.

    What do you think will be the best approach for short and medium terms?
    How’s this laptop holding up?

    Best regards,

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