I’m getting 404s on these, maybe not public yet.
Thanks - try now.
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Thanks,
Sorry, I forgot that I forced my Notebook to not duplicate the images!
I mean, I don’t know why, when getting the images from the dataset, it gets two folders with the same images: Mushrooms and mushrooms/Mushrooms.
It can be seen in the copy you made:
But in my copy, I only used the folder Mushrooms:
So, I think now the is question would be, is the model that uses each image twice overfitting? or is the duplication producing an augmentation?
Sorry and thanks again.
Oh, sorry, I hadn’t seen your answer.
I would try that.
The difference was that one training was made with all the images duplicated.
I then asked about that in Jeremy’s response.
Thanks you.
Yeah sounds like the original had a bug so it’s not actually working correctly. Sorry I missed that!
Does anyone see any issue with installing packages to Paperspace persistent storage and then adding this location to system path?
To save time I have been doing the following:
- Create a directory in
/notebooks(the home dir) calledlibs - Install packages to this dir using the
--targetargument:!pip install --target=./libs wandb. - Set this location to the
sys.path:
import sys
pkg_path = "./libs"
sys.path.append(pkg_path)
The above cell is then the only cell you need to run each time you restart a Paperspace machine.
Does anyone see any fault in this approach? It saves me precious minutes (package install is a little slow in Paperspace) when I have a quick idea to try out, but unsure if setting sys.path has any other impacts (none that I can see so far).
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@stantonius The Paperspace images come with a certain amount pre-installed, but that’s just one thing which you have to reinstall / reload each time unfortunately.
Another approach would be to create a custom ‘notebook’ based on a custom docker image. You could potentially use the fastai docker container as a base (more on those here), but you might want to add some custom logic on top of that. I described how I did that with the IceVision library and connected it to a custom notebook on Paperspace in this blog post.
That said, I’m not sure I’d bother with it for just simple fastai experimentation. Even when I needed to do it for my side project, it still felt like a distraction from the main show in town: i.e. training my models. YMMV 
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@stantonius @strickvl please remember to keep non-beginner topics, like modifying the python sys path and creating docker images, away from any topic labelled “beginner”. I’ve moved your discussion to the non-beginner topic now.
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I’m about to use timm, but just making sure - I could use timm models in unet_learner as well as I could in vision_learner? (Or it’s just for vision_learner)
Another question from the tutorial. Say, we have this:
import timm
import torch
model = timm.create_model('resnet34')
x = torch.randn(1, 3, 224, 224)
model(x).shape
output:
torch.Size([1, 1000])
What does each axis of the model’s shape mean? I figure it out to be number of layers but I’m not sure.
right now unet_learner does not support timm.
The shape is the logits for each of the classes, and ImageNet has 1000 classes…
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Hey everyone,
I have been iterating through Jeremy’s notebook Iterate like a grandmaster! | Kaggle and trying to take on a the suggestion of using the patent-trained model + incorporating this with fastai. However I am hitting a wall: when I try to cut/slice/manipulate a Pytorch model
# example of what I thought was the simplest model modification,
# which is just deconstructing and reconstructing the model
model = nn.Sequential(*[l for l in model_d.children()])
and pass it a batch, I get this error:
TypeError: forward() got an unexpected keyword argument 'input_ids'
I know the batch is formatted correctly, because when I pass one batch to the HF-delivered model, I get the expected output.
Any ideas what I am doing wrong here? I have searched high and low and cannot find the answer, but that usually means I have overlooked something simple.
BTW this is clearly a basic example, but this is after I had tried experimenting with custom architectures and always eventually hit this same error. I ultimately wanted to write a blog for this group that outlines how to use models from other libs in fastai, but I have fallen at the first hurdle.
For reference, my notebook is here that describes the issues I am facing in more detail (same Kaggle competition data as JH’s notebook).
Extra points: I found when I was creating the Datasets object myself for this custom task, I had to specifically move the input tensors to the GPU via the .cuda() method in my custom Transform. However when I follow the HF or any fastai tutorial, it seems this is done automatically. I tried looking in both repos but can’t seem to see where this happens. Any ideas?
Many thanks for any guidance or feedback
I just found out about this intriguing project and it seems to resonate with what Jeremy was discussing in today’s lesson about Transformers being good for GPUs but ULMFiT/RNNs being better for larger contexts. It appears to be based on something called “Attention-Free Transformers”. Is anybody familiar with this type of work? Is it worth pursuing?
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Am I correct to understand that huggingface does not provide a facility to configure ssh-keys? Lots of googling hasn’t turned up anything. Is there some alternative way to configure that git push doesn’t ask my usename & password each time?
[Edit:] Heh! I discovered something new… Git - gitcredentials Documentation
So all I needed to do at my local machine WSL console was…
Read the help files to consider security implications
$ git help credential-cache
$ git help credential-store
Implement the one I chose…
$ git config --global credential.helper store
and then at the next push, entered my huggingface username/password for the last time.
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Maybe you can talk to BlinkDL in the EleutherAI discord, he is frequently sharing his progress there.
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Hey guys, any idea how to deploy my model into an online app? I just need a website where one can upload a photo, and get it predicted and decoded with the model.
I barely caught the thing with Binder, SeeMe.ai and others
How is what you’re looking to do different from what we covered in lesson 2?
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Oops! Might have forgotten that. I’ve probably looked again in an old tutorial. Thanks for addressing me to HuggingFace, Gradio… I will check these up
You don’t say that you’ve considered HuggingFaces and don’t want to use that, so use that!
Otherwise, I see lots of options googling for: gradio web hosting.
Can you report on three options you find there, so we can get a better feel for what sort of service you are looking for?
To be honest, I just didn’t know about HuggingFace. I was exploring different options but found only Binder and SeeMe.ai in an old version of the fastai tutorials/classes, and got lost, but rewatching again the class #2 of this year 2022, showed me new options such as HuggingFace, which is what I’m going to try first… Will keep you posted 
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a tip btw, in helping with the transcriptions I’m picking up details that I missed while just viewing them…
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