I have a Google Colab notebook with PyTorch code running in it.
At the beginning of the train function, I create, save and download word_to_ix and tag_to_ix dictionaries without a problem, using the following code:
from google.colab import files
torch.save(tag_to_ix, pos_dict_path)
files.download(pos_dict_path)
torch.save(word_to_ix, word_dict_path)
files.download(word_dict_path)
I train the model, and then try to download it with the code:
torch.save(model.state_dict(), model_path)
files.download(model_path)
Then I get a MessageError: TypeError: Failed to fetch.
Obviously, the problem is not with the third party cookies (as suggested here), because the first files are downloaded without a problem. (I actually also tried adding the link in my Allow section, but, surprise surprise, it made no difference.)
I was originally trying to save the model as is (which, to my understanding, saves it as a Pickle), and I thought maybe Colab files doesn't handle downloading Pickles well, but as you can see above, I'm now trying to save a dict object (which is also what word_to_ix and tag_to_ix) are, and it's still not working.
Downloading the file manually with right-click isn't a solution, because sometimes I leave the code running while I do other things, and by the time I get back to it, the runtime has disconnected, and the files are gone.
Any suggestions?
Related
I am trying to import models from hugging face and use them in Visual Studio Code.
I installed transformers, tensorflow, and torch.
I have tried looking at multiple tutorials online but have found nothing.
I am trying to run the following code:
from transformers import pipeline
classifier = pipeline('sentiment-analysis')
result = classifier("I hate it when I'm sitting under a tree and an apple hits my head.")
print(result)
However, I get the following error:
No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).
Using a pipeline without specifying a model name and revision in production is not recommended.
Traceback (most recent call last):
File "c:\Users\user\Desktop\Artificial Intelligence\transformers\Workshops\workshop_3.py", line 4, in <module>
classifier = pipeline('sentiment-analysis')
File "C:\Users\user\Desktop\Artificial Intelligence\transformers\src\transformers\pipelines\__init__.py", line 702, in pipeline
framework, model = infer_framework_load_model(
File "C:\Users\user\Desktop\Artificial Intelligence\transformers\src\transformers\pipelines\base.py", line 266, in infer_framework_load_model
raise ValueError(f"Could not load model {model} with any of the following classes: {class_tuple}.")
ValueError: Could not load model distilbert-base-uncased-finetuned-sst-2-english with any of the following classes: (<class 'transformers.models.auto.modeling_auto.AutoModelForSequenceClassification'>, <class 'transformers.models.auto.modeling_tf_auto.TFAutoModelForSequenceClassification'>, <class 'transformers.models.distilbert.modeling_distilbert.DistilBertForSequenceClassification'>, <class 'transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertForSequenceClassification'>).
I have already searched online for ways to set up transformers to use in Visual Studio Code but nothing is helping.
Do you know how to fix this error, or if someone knows how to successfully use models from Hugging Face into my code, it would be appreciated?
This question is a little less about Hugging Face itself and likely more about installation and the installation steps you took (and potentially your program's access to the cache file where the models are automatically downloaded to.).
From what I am seeing either:
1/ your program is unable to access the model
2/ your program is throwing specific value errors in a bit of an edge case
If 1/ Take a look here: [https://huggingface.co/docs/transformers/installation#cache-setup][1]
Notice that it the docs walks through where the pre-trained models are downloaded. Check that it was downloaded here: C:\Users\username\.cache\huggingface\hub (of course with your own username on your computer instead. Check in the cache location to make sure it was downloaded? (You can check in the cache locations mentioned.)
Second, if for some reason, there is an issue with downloading, you can try downloading manually and doing it via offline mode (this is more to get it up and running): https://huggingface.co/docs/transformers/installation#offline-mode
Third, if it is downloaded, do you have the right permissions to access the .cache? (Try running your program (if it is a program that you trust) on Windows Terminal as an administrator.). Various ways - find one that you're comfortable with, here are a couple hints from Stackoverflow/StackExchange: Opening up Windows Terminal with elevated privileges, from within Windows Terminal or this: https://superuser.com/questions/1560049/open-windows-terminal-as-admin-with-winr
If 2/ I have seen people bring up very specific issues on not finding specific values (not the same as yours but similar) and the issue was solved by installing PyTorch because some models only exist as PyTorch models. You can see the full response from #YokoHono here: Transformers model from Hugging-Face throws error that specific classes couldn t be loaded
I am using Google Colabs for my research in machine learning.
I do many variations on a network and run them saving the results.
I have a part of my notebook that used to be separate file (network.py) At the start of a training session I used to save this file in a directory that has the results and logs etc. Now that this part of the code is in the notebook it is easier to edit etc, BUT I do not have a file to copy to the output directory that describes the model. how to i take a section of a google colab notebook and save the raw code as a python file?
Things I have tried:
%%writefile "my_file.py" - is able to write the file however the classes are not available to the runtime.
I was simply trying to generate a summary that would show the run_metadata as follows:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary = sess.run([x, y], options=run_options, run_metadata=run_metadata)
train_writer.add_run_metadata(paths.logs, 'step%d' % step)
train_writer.add_summary(paths.logs, step)
I made sure the path to the logs folder exists, this is confirmed by the fact the the summary file is generated but no metadata is presetn. Now I am not sure a file is actually generated to be honest (for the metadata), but when I open tensorboard, the graph looks fine and the session runs dropdown menu is populated. Now when I select any of the runs it shows a progress bar "Parsing metadata.pbtxt" that stops and hangs right half way through.
This prevents me from gathering any more additional info about my graph. Am I missing something ? A similar issue happened when trying to run this tutorial locally (MNIST summary tutorial). I feel like I am missing something simple. Does anyone have an idea about what could cause this issue ? Why would my tensorboard hang when trying to load a session run data ?
I can't believe I made it work right after posting the question but here it goes. I noticed that this line:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
was giving me an error so I removed the params and turned it into
run_options = tf.RunOptions()
without realizing that this is what caused the metadata not to be parsed. Once I researched the error message:
Couldn't open CUDA library cupti64_90.dll
I looked into this Github Thread and moved the file into the bin folder. After that I ran again my code with the trace_level param, had no errors and the metadata was successfully parsed.
I have set up a google cloud account
I want to perform my deep learning much more faster on a jupyter notebook, but
I cannot find a way to read my csv file
I downloaded it with wget from my github account and afterwards I tried
dataset = pd.read_csv('/home/user/.jupyter/SIEMENSTRAIN.csv')
but I get the following error
pandas.parser.CParserError: Error tokenizing data. C error: Expected 2 fields in line 3, saw 12
Why? When I read it on my laptop using my jupyter notebooks, everything runs well
Any suggestions?
I tried the recommended solutions for this error and I got the next warning
/home/user/anaconda3/lib/python3.5/site-packages/ipykernel/main.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators; you can avoid this warning by specifying engine='python'.
if name == 'main':
When I ran dataset.head() this is what appeared
Any help please?
There are a number of possibilities that could be causing the problem... I would first always make sure that Pandas (pd)'s version is updated and compatible.
The more likely cause is that the CSV itself is not right, so pd.read_csv() is not able to work correctly (thus a Parse Error). This may have something to do with the headers, though I'm not sure what your original CSV file looks like. It's worth playing around with read_csv, for example:
df = pandas.read_csv(fileName, sep='delimiter', header=None)
This tampers with 2 things - the delimiter, and if pd is reading a header from CSV or not.
I go through some pd.read_csv() stuff in my book about Stock Prediction (another cool Machine Learning problem) and Deep Learning, feel free to check it out.
Good Luck!
I tried what you proposed and this is what I got
So, any suggestions?
I suppose that the path is ok, but it just won't be read properly, or am I wrong?
I am using a simple (not necessarily efficient) method for Pytorch model saving.
import torch
from google.colab import files
torch.save(model, filename) # save a trained model on the VM
files.download(filename) # download the model to local
best_model = files.upload() # select the model just downloaded
best_model[filename] # access the model
Colab disconnects during execution of the last line, and hitting RECONNECT tab always shows ALLOCATING -> CONNECTING (fails, with "unable to connect to the runtime" message in the left bottom corner) -> RECONNECT. At the same time, executing any one of the cells gives Error message "Failed to execute cell, Could not send execute message to runtime: [object CloseEvent]"
I know it is related to the last line, because I can successfully connect with my other google accounts which doesn't execute that.
Why does it happen? It seems the google accounts which have executed the last line can no longer connect to the runtime.
Edit:
One night later, I can reconnect with the google account after session expiration. I just attempted the approach in the comment, and found that just files.upload() the Pytorch model would lead to the problem. Once the upload completes, Colab disconnects.
Try disabling your ad-blocker. Worked for me
(I wrote this answer before reading your update. Think it may help.)
files.upload() is just for uploading files. We have no reason to expect it to return some pytorch type/model.
When you call a = files.upload(), a is a dictionary of filename - a big bytes array.
{'my_image.png': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR....' }
type(a['my_image.png'])
Just like when you do open('my_image', 'b').read()
So, I think the next line best_model[filename] try to print the whole huge bytes array, which bugs the colab.