Has anyone had success running through the Fastai tutorials on any Jetson boards?
What has your experience been with Getting your Jetson setup and configured?
My experience so far, is the ARM CPU architecture does not have as much development support as the x86 architecture. So it is sometimes challenging getting the setup just right. Although I feel I’ve finally have my Nano setup to run through Lesson 1, I’m hung up at the first place I run ‘learn.fit_once_cycle(4)’ in the Lesson 1 Pet tutorial. This makes me wonder if this is an issue of compute resources as opposed to software compatibility. More on this later.
Although working through the Fastai lessons are a top priority, I also find it useful to refresh my Linux skills, and learn more about the python infrastructure stack. I have over 7 years of experience developing Java middleware services on Linux, but it has been about 4 - 5 years since I’ve been seriously hands on. I say this, because I don’t mind putting in the extra work getting my local environment setup. However, I don’t want to waste my time if this configuration is not ideal to use with the Fastai learning track.
What has your experience been with Running through Lesson 1 of the Fastai course (and beyond)?
- The Nano has 4 gig of memory shared between the CPU and GPU
- I installed an 8 gig Swap file (good for the CPU, but not applicable for the GPU)
- I’m running headless, to conserve as much memory as possible
Another option is to go for the JETSON TX2 MODULE.
- GPU & CPU are at least twice that of the Nano
- (more importantly) 8 GB of Memory
- More impressive specs
The gearhead in me say’s - “Go for the TX2 man!” However, I already have the Nano and I’m hoping I could make it through Fastai’s 7 lesson’s with it, before I invest more dollars in hardware.
Very interested others thoughts and perspectives on this topic!