Hi Jeremy, not a question just a short thank you note. I think the value you produce with fast.ai is absolutely fantastic, and I really want to communicate my gratefulness for all of the excellent content and courses and blog posts. Especially having access to them for free. Personally, it has helped me to learn so much and to actually implement these techniques in a workplace setting. Again, thanks.

Regards

Great - can't wait to try out the new version! I am running fast.ai in a virtual environment. How does one upgrade to the latest version for both Fast.ai and PyTorch. Are the new versions backwards comaptible with the earlier version - would it break any code? If not, would you recommend I clone the current install and upgrade that to be on the safe side. Thanks!

None of fastai is backwards compatible with v0.7 - although the API has a similar structure, so it shouldn't be hard to rewrite. PyTorch v1.0 also has some big differences to the version we used in the course.

I suggest you create a new conda environment and then follow the instructions in the readme to install both.

Thanks for all your hard work Jeremy. In Cleveland, we are running a study group with about 30 people on the 2018 version of the course. Because of logistics, we aren't planning on rescheduling to match the new course, but its still nice knowing that the library is being invested into.

I have a gaming system (Ryzen 1950X w/ GTX 1080Ti). Is there an easy way to create a bootable external media with most of the stuff preconfigured already?

I remember it being a pain to get all the drivers working and there was a lot of version incompatibility in the Python ecosystem.

I'd rather not mess with the bootloader and just have an external drive I can select to boot to in the BIOS. For some reason Windows wiped out GRUB and I can't boot my old Ubuntu distro anymore.

You have a couple of options to consider: 1. Install a modern Linux distribution on the bare metal, get https://github.com/NVIDIA/nvidia-docker and run the GPU-accelerated container https://github.com/Paperspace/fastai-docker 2. Try running the Ubuntu Hyper-V image provided by Canonical/Microsoft

P.S. I'm considering building a DL box based on Ryzen 2600 w/ GTX 1070Ti, so could you please share your experience with Ryzen and Linux.

My DL box has a Ryzen 1800x and a GTX 1080ti running Ubuntu 18.04. The Ryzen has given me very few issues, Nvidia gave me some setup issues, but now that it's all configured its rock solid.

A Hyper-V image would be great if it can take advantage of the GPU and not cause driver issues. Didn't know they had one for DL. Or is it just pure Ubuntu you mean?

The story on python is much better nowadays. You can install cuda and drivers from nvidia, and then install anaconda, then just run:

    conda install -c pytorch -c fastai fastai pytorch-nightly cuda92

If you don't use conda, things can get more complicated, depending on what libs you have installed.

Shameless plug, but another option for running your experiments on a GPU is Spell (https://spell.run). Very simple to use and will allow you to use as many GPUs as you want at once and only pay for the compute time. You can also run GPU-enabled kernels for Jupiter.

Fast.ai instructions here: https://learn.spell.run/fast_ai

Hey Jeremy, just wanted to say thanks very much for making the course and the library, both have been invaluable for us in getting started with deep learning (and achieving useful results) quickly. Both parts of the 2018 course have been a really great tour through the different areas of deep learning, and provided good jumping off points for digging deeper on the parts we got interested in.

Hey Jeremy, are you going to be recommending GCP/AWS hosted fastai as the best way to get started now over Paperspace etc?

Blog announcement here: https://cloud.google.com/blog/products/ai-machine-learning/i...

For a little latitude, I believe there are PyTorch VMs available on the other cloud providers as well.

Hello and thank you for the great work! Hopefully, the new version will work on MacOS with Anaconda, because 0.7 failed there due to the unsatisfied dependency on PIL. Apparently, the default Anaconda channels doesn't include the python 3.x-compiliant PIL package for Darwin. That blocked my progress on ML course.

MacOS is really hard to work with for deep learning. Try https://salamander.ai/ . $0.19/hour for a server with NVIDIA GPU, fastai v1 pre-installed with notebooks and datasets. Or try Google Cloud preemptible instances - similar cost IIRC, but a little more setup to do.

Oh, it's $0.36/hour for salamander. the equivalent on gcp (n1-16-standard with attached k80) is $0.29/hour. though as you said, Salamander is easier to get started with (just 1 click after signing up!)

ps: i'm the creator of salamander

Are you sure you didn’t want pillow, which provides the PIL import and is the follow-on from the original PIL?

You can always get a server with a GPU instance in the cloud somewhere. That might cost a few $ but then again, so does a GPU.

Hey Jeremy, will you update your courses to reflect the new API changes? I just started on your deep learning course, using pip to install the fastai library.

The live version of the course, updated for fastai v1, is beginning later this month, with publicly available videos to follow in early 2019.

I was just listening to the the podcast when I saw this post. It's great to hear you can make a lot of progress on ML without a PHD. :)

When someone has a very high IQ, like Jeremy Howard obviously does, PhD becomes just a superfluous credential.

What podcast?

I assume they are referring to the TWiMLAI podcast that was posted this morning https://twimlai.com/twiml-talk-186-the-fastai-v1-deep-learni...

Hi Jeremy!

What is the main difference with Keras+TF, I've only worked with them (still not mastering them), why should I consider fastai?

Thank you!

Possibly addressed in a passage from this post: http://www.fast.ai/2018/10/02/fastai-ai/

> It’s important to understand that these improved results over Keras in no way suggest that Keras isn’t an excellent piece of software. Quite the contrary! If you tried to complete this task with almost any other library, you would need to write far more code, and would be unlikely to see better speed or accuracy than Keras. That’s why we’re showing Keras in this comparison - because we’re admirers of it, and it’s the strongest benchmark we know of!

Why do you hate variable names longer than 4 characters so much? :b