I have a code that basically takes in a csv, which can be uploaded from streamlit and then pushes out a classification prediction.
Just as a context I use xgboost to create my model and I save it as following:
joblib.dump(model, 'C:\\Users\myname\classification\default_class_model.pkl')
To grab the model I do:
model_from_joblib =joblib.load('C:\\Users\myname\classification\default_class_model.pkl')
scoring = model_from_joblib.predict(X_test)
When I execute it in Jupyter notebooks it seems to work just fine, but when running on anaconda and do
streamlit run mymodel.py
I get the error:
XGBoostError: [13:38:10]
C:\Users\Administrator\workspace\xgboost-win64_release_1.1.0\include\xgboost/json.h:65:
Invalid cast, from Null to Array
Does anyone have an idea why this may be?
I solved the problem by updating the xgboost version I was using
Related
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.
I was practicing some project from Kaggle notebook which is machine learning related code. and i am facing problem at one part. the error is NameError: name 'ImageList' is not defined. the Kaggle notebook link is this and the cell link is this. How can i fix it.
If you are getting this error even after defining the ImageList, Run the default first cell in the kaggle or make sure that U have run all the cells.
Even I faced the same issue during my initial practice on kaggle.
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.
Will I be able to pickle all the .meta, .data and checkpoint files of a tensorflow model? That's because I want to run a prediction on my model and if i deploy it , the files can't be on disk right? I know about the tensorflow serving but I don't really understand it. I want to be able to load the tensforflow files without accessing the drive all the time.
Using pickle is not recommended. Instead, they have created a new format called "SavedModel format" that serves this exact purpose.
See: https://www.tensorflow.org/guide/saved_model
I am training a neural network for object detection using Google Colab. I wanted to visualize the learning process but every time I try to access tensorboard, it shows me the following:
No dashboards are active for the current data set. Probable causes: - You haven’t written any data to your event files. - TensorBoard can’t find your event files.
I am not training the model locally and have configured my google drive account with the colab notebook for the training data so user hpabst's answer does not seem useful.
I also tried setting up tensorboard using ngrok but that gave me a similar output.
I made sure I am generating summary data in a log directory by creating a summary writer:
import tensorflow as tf
sess = tf.Session()
file_writer = tf.summary.FileWriter('/content/logs/my_log_dir/', sess.graph)
and followed that with
tensorboard = TensorBoard(log_dir="/content/logs/my_log_dir/",batch_size=32, write_graph=True, update_freq='epoch')
model.fit_generator(
train_generator,
steps_per_epoch=(train_data/BS),
epochs=EPOCHS,
validation_data=validation_generator,
validation_steps=(test_data/BS),
callbacks=[tensorboard, checkpoint])
and finally
tensorboard --logdir /content/logs/my_log_dir/
The event files are in place. The path to the log directory is also correct.
Like I said, I was getting the same- No active dashboards error using ngrok. I moved over to the SCALARS menu in the Tensorboard GUI and to the left, under the runs section, at the bottom, I found out that the path to the log directory was being shown as '/content/ log /my_log_dir' although everywhere in my code I had only mentioned the path as -'/content/ logs /my_log_dir'. Maybe setting up tensorboard using ngrok expects the files to be in the 'log' and not the 'logs' directory. I made the change and now it works just fine.