I am a very newbie and currently working for my Final Project. I watch a youtube video that teach me to code Abstractive Text Summarization with google's Pegasus library. It Works fine but I need it to be more efficient.
So here is the code
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
Everytime I run that code, it always download the "Google Pegasus-xsum" library which sized about 2.2 GB.
So here is the sample of the code in notebook : https://github.com/nicknochnack/PegasusSummarization/blob/main/Pegasus%20Tutorial.ipynb
and it will running download the library like picture below :
Is there any way to download the library first and then I saved it locally, and everytime I run the code it's just gonna call the library locally?
Something like caching or saving the library locally maybe?
Thanks.
Mac
Using inspect you can find and locate the modules easily.
import inspect
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
print(inspect.getfile(PegasusForConditionalGeneration))
print(inspect.getfile(PegasusTokenizer))
You will get their paths sth like this
/usr/local/lib/python3.9/site-packages/transformers/models/pegasus/modeling_pegasus.py
/usr/local/lib/python3.9/site-packages/transformers/models/pegasus/tokenization_pegasus.py
Now, if you go and see what is inside the tokenization_pegasus.py file, you will notice that the model of google/pegasus-xsum is being probably fetched by the following line
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}
where here if you open:
https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model
You will get the model downloaded directly to your machine.
UPDATE
After some search on Google, I've found sth important where you can get the used models and all their related files downloaded to your working directory by the following
tokenizer.save_pretrained("local_pegasus-xsum_tokenizer")
model.save_pretrained("local_pegasus-xsum_tokenizer_model")
Ref:
https://github.com/huggingface/transformers/issues/14561
So that after running it, you will see the following being saved automatically in your working directory. So, now you can call the models directly but you need to search how...
Also, the 12.2GB file that you wanted to know its path locally, it is being located here online
https://huggingface.co/google/pegasus-xsum/tree/main
And after downloading the models to your directory as you can see from the screenshot its name is pytorch_model.bin as it’s named online.
Related
I was following a tutorial online on using tensorflow and he used this code:
prepWork = tf.keras.utils.get_file('shakespeare.txt', urlToTextFile)
If I want to use this code for my own project, I need to read a local text file, let's say 'prepWork.txt', from my machine. I can't use get_file, because that only works for online files. How would I do this? Everything I've tried before doesn't work.
You can find a TextLoader class which reads a text file and transforms it into batches of consecutive fixed sized length of words in the following repository (in utils.py file) https://github.com/sherjilozair/char-rnn-tensorflow/blob/master/utils.py
I am trying to use a simple pipeline offline. I am only allowed to download files directly from the web.
I went to https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/tree/main and downloaded all the files in a local folder C:\\Users\\me\\mymodel
However, when I tried to load the model I get a strange error
from transformers import pipeline
classifier = pipeline(task= 'sentiment-analysis',
model= "C:\\Users\\me\\mymodel",
tokenizer = "C:\\Users\\me\\mymodel")
ValueError: unable to parse C:\Users\me\mymodel\modelcard.json as a URL or as a local path
What is the issue here?
Thanks!
Must be either of the two cases:
You didn't download all the required files properly
Folder path is wrong
FYI, I am listing out the required contents in the directory:
config.json
pytorch_model.bin/ tf_model.h5
special_tokens_map.json
tokenizer.json
tokenizer_config.json
vocab.txt
the solution was slightly indirect:
load the model on a computer with internet access
save the model with save_pretrained()
transfer the folder obtained above to the offline machine and point its path in the pipeline call
The folder will contain all the expected files.
I use AutoGluon to create ML models locally on my computer.
Now I want to deploy them through AWS, but I realized that all the pickle files created in the process use hardcoded path references to other pickle files:
/home/myname/Desktop/ETC_PATH/AutoGluon/
I use cloudpickle.dump(predictor, open('FINAL_MODEL.pkl', 'wb')) to pickle the final ensemble model, but AutoGluon creates numerous other pickle files of the individual models, which are then referenced as /home/myname/Desktop/ETC_PATH/AutoGluon/models/ and /home/myname/Desktop/ETC_PATH/AutoGluon/models/specific_model/ and so forth...
How can I achieve that all absolute paths everywhere are replaced by relative paths like root/AutoGluon/WHATEVER_PATH, where root could be set to anything, depending on where the model is later saved.
Any pointers would be helpful.
EDIT: I'm reasonably sure I found the problem. If, instead of loading FINAL_MODEL.pkl (that seems to hardcode paths) I use AutoGluon's predictor = task.load(model_dir) it should find all dependencies correctly, whether or not the AutoGluon folder as a whole was moved. This issue on github helped
EDIT: This solved the problem: If, instead of loading FINAL_MODEL.pkl (that seems to hardcode paths) I use AutoGluon's predictor = task.load(model_dir) it should find all dependencies correctly, whether or not the AutoGluon folder as a whole was moved. This issue on github helped
I,ve been trying to use a BERT model from tf-hub https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/2.
import tensorflow_hub as hub
bert_layer = hub.keras_layer('./bert_en_uncased_L-12_H-768_A-12_2', trainable=True)
But problem is that it is downloading data after every run.
So i downloaded the .tar file from tf-hub https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/2
Now i'm trying to use this downloaded tar file(after untar)
I've followed this tutorial https://medium.com/#xianbao.qian/how-to-run-tf-hub-locally-without-internet-connection-4506b850a915
But it didn't work out well and there is no further infromation or script is provided in this blog post
if someone can provide complete script to use the dowloaded model locally(without internet) or can improve the above blog post(Medium).
I've also tried
untarredFilePath = './bert_en_uncased_L-12_H-768_A-12_2'
bert_lyr = hub.load(untarredFilePath)
print(bert_lyr)
Output
<tensorflow.python.saved_model.load.Loader._recreate_base_user_object.<locals>._UserObject object at 0x7f05c46e6a10>
Doesn't seems to work.
or is there any other method to do so..??
Hmm I cannot reproduce your problem. What worked for me:
script.sh
# download the model file using the 'wget' program
wget "https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/2?tf-hub-format=compressed"
# rename the downloaded file name to 'tar_file.tar.gz'
mv 2\?tf-hub-format\=compressed tar_file.tar.gz
# extract tar_file.tar.gz to the local directory
tar -zxvf tar_file.tar.gz
# turn off internet
# run a test script
python3 test.py
# running the last command prints some tensorflow warnings, and then '<tensorflow_hub.keras_layer.KerasLayer object at 0x7fd702a7d8d0>'
test.py
import tensorflow_hub as hub
print(hub.KerasLayer('.'))
I wrote this script using this medium article(https://medium.com/#xianbao.qian/how-to-run-tf-hub-locally-without-internet-connection-4506b850a915) as reference. I am creating a cache directory within my project and the tensorflow model is cached locally in this cached directory and I am able to load the model locally. Hope this helps you.
import os
os.environ["TFHUB_CACHE_DIR"] = r'C:\Users\USERX\PycharmProjects\PROJECTX\tf_hub'
import tensorflow as tf
import tensorflow_hub as hub
import hashlib
handle = "https://tfhub.dev/google/universal-sentence-encoder/4"
hashlib.sha1(handle.encode("utf8")).hexdigest()
embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")
def get_sentence_embeddings(paragraph_array):
embeddings=embed(paragraph_array)
return embeddings
After getting information from tf-hub team they provide this solution.
Let's say you have downloaded the .tar.gz file from official tf-hub model page from download button.
You have extracted it. You got a folder which contain assets, variable and model.
You put it in your working directory.
In script just add path to that folder:
import tensroflow-hub as hub
model_path ='./bert_en_uncased_L-12_H-768_A-12_2' # in my case
# one thing the path you have to provide is for folder which contain assets, variable and model
# not of the model.pb itself
lyr = hub.KerasLayer(model_path, trainable=True)
Hope it should work for you as well. Give it a try
The tensorflow_hub library caches downloaded and uncompressed models on disk to avoid repeated uploads. The documentation at tensorflow.org/hub/caching has been expanded to discuss this and other cases.
i am new to Microsoft Custom Vision and I am working on an intergration of Microsoft Azure Custom Vision API using jupyter notebooks/python. I was able to upload images, tag them automatically and train the first iterations. However, as I was trying to download a Docker file of the train iteration/model I got stuck while trying to export the model. Using the function export_iteration I ended up having an mst.rest.pipeline.clientrawresponse object. I think currently it is only stored in the exporting queue. How do I access this queue element to download it to my local system?
PS: I am working with a General (compact) model format so it should be exportable.
Example code:
# Initalize the Training Client
training_key = "your-training-key"
ENDPOINT = "your-endpoint"
c_plat = CustomVisionTrainingClient(training_key,ENDPOINT)
# List all projects you have
projects = c_plat.get_projects()
#Always take the newest project and its newest iteration and export it
iterations = c_plat.get_iterations(projects[0].id)
c_plat.export_iteration(project_id=projects[0].id, iteration_id=iterations[0].id, platform = "DockerFile", raw=True, flavor = "ARM")
After some trial and error I found a solution:
#Always takes the newest project and its newest iteration
iterations = c_plat.get_iterations(projects[0].id)
response = c_plat.export_iteration(project_id=projects[0].id, iteration_id=iterations[0].id, platform = "DockerFile", raw=False, flavor="ARM")
# Opnening the uri
import webbrowser
webbrowser.open(c_plat.get_exports(project_id=projects[0].id, iteration_id=iterations[0].id)[0].download_uri)
This opened the uri in a new tab and started the automatic download. Hope this might help somebody else.
Cheers!