merge

Dockerfile

@@ -1,13 +1,27 @@
 # For more information, please refer to https://aka.ms/vscode-docker-python
-FROM python:3.10-slim
+#FROM python:3.10-slim
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04
+
+#nvidia/cuda:12.6.3-cudnn-devel-ubuntu24.04
+
+#ENV DEBIAN_FRONTEND=noninteractive
+
+# Install Python and pip
+RUN apt-get update && apt-get install -y python3-pip python3-venv
 
 # Where we'll copy the code
 WORKDIR /code
 
 # Copy the current directory contents into the container at /code
 COPY ./requirements.txt /code/requirements.txt
-# Install pip requirements
-RUN pip install -r /code/requirements.txt
+
+# Install pip requirements without venv
+#RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Create a virtual environment and install pip requirements
+RUN python3 -m venv /code/venv
+RUN /code/venv/bin/pip install --no-cache-dir --upgrade pip
+RUN /code/venv/bin/pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
 # Creates a non-root user with an explicit UID
 # For more info, please refer to https://aka.ms/vscode-docker-python-configure-containers
@@ -30,4 +44,4 @@ COPY --chown=user . $HOME/app
 # Expose port 7860
 EXPOSE 7860
 ENV GRADIO_SERVER_NAME="0.0.0.0"
-CMD ["python", "app.py"]
+CMD ["python", "app.py"]

app.py

@@ -14,23 +14,23 @@ model = AutoModelForCausalLM.from_pretrained(model_name,device_map="auto",trust_
 
 #transfer model on GPU
 #model.to("cuda")
-pipe = pipeline("text-generation", model=model_name, tokenizer=tokenizer, 
-        max_new_tokens=512,
-        do_sample=True,
-        temperature=0.7,
-        top_p=0.95,
-        top_k=40,
-        repetition_penalty=1.1)
+# pipe = pipeline("text-generation", model=model_name, tokenizer=tokenizer, 
+#         max_new_tokens=512,
+#         do_sample=True,
+#         temperature=0.7,
+#         top_p=0.95,
+#         top_k=40,
+#         repetition_penalty=1.1)
 
 # Generate text using the model and tokenizer
 #@spaces.GPU(duration=60)
 def generate_text(input_text):
-    #input_ids = tokenizer.encode(input_text, return_tensors="pt")#.to("cuda")
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")#.to("cuda")
     #attention_mask = input_ids.ne(tokenizer.pad_token_id).long()
-    #output = model.generate(input_ids, max_new_tokens=512, top_k=50, top_p=0.95, temperature=0.7, do_sample=True)# attention_mask=attention_mask, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7, do_sample=True)
+    output = model.generate(input_ids, max_new_tokens=512, top_k=50, top_p=0.95, temperature=0.7, do_sample=True)# attention_mask=attention_mask, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7, do_sample=True)
     #output = model.generate(input_ids) #, attention_mask=attention_mask, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7, do_sample=True)
-    #return tokenizer.decode(output[0])
-    return pipe(input_text)[0]["generated_text"]
+    return tokenizer.decode(output[0])
+    #return pipe(input_text)[0]["generated_text"]
 
 interface = gr.Interface(fn=generate_text, inputs="text", outputs="text",title="TeLLMyStory",description="Enter your story idea and the model will generate the story based on it.")
 interface.launch()

requirements.txt

@@ -1,6 +1,8 @@
+--extra-index-url https://download.pytorch.org/whl/cu118
 transformers
-torch
+torch==2.0.1+cu118
 gradio
+#spaces
 huggingface_hub
 optimum
 #for running auto-gptq