Code Server Use Case

Step 1: Create a GPU Container using Code Server

Step 2: install python3, pip

sudo apt update && sudo apt install -y python3 python3-pip python3-venv git 

Step 3: using virtual environment to install required python packages and run training code

python3 -m venv ~/venv  
source ~/venv/bin/activate 

Step 4: Install required python packages

pip install --upgrade pip  
pip install torch torchvision torchaudio scikit-learn scipy --index-url 
https://download.pytorch.org/whl/cu121
  
pip install datasets evaluate accelerate

Step 5: Clone Hugging Face transformers from Github

 cd /workspace    
 git clone https://github.com/huggingface/transformers.git  
 pip install –e . 

Step 6: Install python package

pip install scikit-learn scipy 
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 

Step 7: Finetune BERT on GLUE MRPC. Your output will be stored at /tmp/bert-finetuned

In this step, you will fine-tune the pre-trained BERT model on the Microsoft Research Paraphrase Corpus (MRPC) task from the GLUE benchmark. This means the model will learn to classify whether two sentences are paraphrases (have the same meaning) or not.

cd /workspace/transformers/examples/pytorch/text-classification 
python3 run_glue.py  
--model_name_or_path bert-base-uncased  
--task_name mrpc  
--do_train  
--do_eval  
--per_device_train_batch_size 16  
--learning_rate 2e-5  
--num_train_epochs 3  
--output_dir /tmp/bert-finetuned  
--overwrite_output_dir 

Step 7: Create a file contains test script called test.py. Insert the code below.

from transformers import BertTokenizer, BertForSequenceClassification 
import torch 
 
# Load fine-tuned model and tokenizer 
model_path = "/tmp/bert-finetuned" 
model = BertForSequenceClassification.from_pretrained(model_path) 
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") 
 
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
model.to(device) 
model.eval() 
 
sentence1 = "This is a great example!" 
sentence2 = "This is a demo for code server GPU Container!" 
 
inputs = tokenizer(sentence1, sentence2, return_tensors="pt").to(device) 
 
with torch.no_grad(): 
outputs = model(**inputs) 
logits = outputs.logits 
predicted_class = torch.argmax(logits, dim=1).item() 
 
label_map = {0: "not paraphrase", 1: "paraphrase"} 
print(f"Sentence: {sentence1}") 
print(f"Sentence: {sentence2}") 
print(f"Predicted Class: {predicted_class} ({label_map[predicted_class]})")  

Step 8: Run test.py to test the finetuned model

python3 test.py 

---

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the ask query parameter:

GET https://ai-docs.fptcloud.com/fpt-gpu-cloud/gpu-container/use-cases/code-server-use-case.md?ask=<question>

The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.