Skip to main content

Amazon + Blender 2.74

I’ve been branching out doing some 3D modelling at work recently and wanted to share the rendering pipeline I settled on. I began on my laptop Dell XPS 15 (XPS15-6846sLV), GPU rendering worked straight away from Windows 7, but took some setting up with Archlinux. This setup was pretty good for modeling, but I quickly realized that I needed some more serious heat dissipation for rendering. I then moved to my workstation which has much more CPU power and a dedicated NVS 310 graphics card, not too shabby but sample renders were taking too long. I borrowed a work colleague's GeForce GTX TITAN with 12GB GDDR5 ram - unsurprisingly this beast sped things up considerably.

I still had to render more frames than would make sense on one machine - especially in the time I had to work with; so I set up Blender on Amazon Web Services to take advantage of the EC2 instances with GPUs (g2.2xlarge). Taking advantage of favourable spot prices in North Virginia one can run GPU instances for between 5 and 10 cents per hour. In the EC2 Management console there is a section Limits where you may need to put in a request to be allowed more than 5 g2.2xlarge instances. Amazon came back with an approval the same day for me.

Getting Blender to render remotely from the command line with the GPU took some fiddling so I've created a public AMI which I hope will save some hassles.

I've had small jobs of between 5 and 20 machines running at once for up to 48 hours. I couldn't think of an easier way to get short term access to 20 high end machines with $2000 graphics cards in them!

To get started with an AWS Blender render farm, follow the excellent instructions on the Brenda documentation but substitute the AMI with my image which has CUDA enabled and Blender 2.74 installed:

North Virginia (us-east-1): ami-7207351a
Sydney (ap-southeast-2): ami-9f5220a5


You can happily launch a spot machine, SSH in and render from the command line.
E.g. to run the BMW27 benchmark:
$ blender -b -P /opt/ BMW27.blend -F PNG -o out.png -s 1 -e 1 -a
Time: 02:47:48
Note if you don’t include -P /opt/ it will still run but will use the CPU instead of GPU.

Remotely with brenda:

Use a frame-template with the addition -P /opt/cuda\
brenda-work -T frame-template -s 1 -e 1 push
Tip run nvidia-smi to see GPU utilization during rendering.
Note the AMI is based on StarCluster which uses Ubuntu 12.04 as the underlying operating system. Blender 2.73 is also present under /opt/blender-2.73.

Popular posts from this blog

My setup for downloading & streaming movies and tv

I recently signed up for Netflix and am retiring my headless home media pc. This blog will have to serve as its obituary. The box spent about half of its life running FreeNAS, and half running Archlinux. I’ll briefly talk about my experience with FreeNAS, the migration, and then I’ll get to the robust setup I ended up with.

The machine itself cost around $1000 in 2014. Powered by an AMD A4-7300 3.8GHz cpu with 8GB of memory. A SilverStone DS380 case is both functional, quiet and looks great. The hard drives have been updated over the last two years until it had a full compliment of 6 WD Green 4TiB drives - all spinning bits of metal though.

Initially I had the BSD based FreeNAS operating system installed. I had a single hard drive in its own ZFS pool for TV and Movies, and a second ZFS pool comprised of 5 hard drives for documents and photos.

FreeNAS is straight forward to use and setup, provided you only want to do things supported out of the box or by plugins. Each plugin is install…

Driveby contribution to Python Cryptography

While at PyConAU 2016 I attended the Monday sprints and spent some time looking at a proposed feature I hoped would soon be part of cryptography. As most readers of this blog will know, cryptography is a very respected project within the Python ecosystem and it was an interesting experience to see how such a prominent open source project handles contributions and reviews.

The feature in question is the Diffie-Hellman Key Exchange algorithm used in many cryptography applications. Diffie-Helman Key Exchange is a way of generating a shared secret between two parties where the secret can't be determined by an eavesdropper observing the communication. DHE is extremely common - it is one of the primary methods used to provide "perfect forward secrecy" every time you initiate a TLS connection to an HTTPS website. Mathematically it is extremely elegant and the inventors were the recipients of the 2015 Turing award.

I wanted to write about this particular contribution because man…

Python, Virtualenv and Docker

Unsurprisingly I use some very popular Scientific Python packages like Numpy, Scipy and Scikit Learn. These packages don't get on that well with virtualenv and pip as they take a lot of external dependencies to build. These dependencies can be optional libraries like libblas and libatlas which if present will make Numpy run faster, or required dependencies like a fortran compiler.

Back in the good old days you wouldn't pin all your dependency versions down and you'd end up with a precarious mix of apt-get installed and pip installed packages. Working with other developers, especially on different operating system update schedules could be a pain. It was time to update your project when it breaks because of a dependency upgraded by the operating system.

Does virtualenv fully solve this? No, not when you have hard requirements on the binaries that must be installed at a system level.

Docker being at a lower level gives you much more control without adding too much extra comp…