Update app.py

app.py

@@ -1,155 +1,47 @@
-import gradio as gr
-import numpy as np
-import random
-
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "models/prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
+import sagemaker
+import boto3
+from sagemaker.huggingface import HuggingFace
+from datasets import load_dataset
+
+# Obtém o papel do SageMaker ou cria um caso ele não exista
+try:
+    role = sagemaker.get_execution_role()
+except ValueError:
+    iam = boto3.client('iam')
+    role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
+
+# Carrega o dataset
+dataset = load_dataset("practical-dreamer/RPGPT_PublicDomain-ShareGPT")
+
+# Configuração dos hiperparâmetros
+hyperparameters = {
+    'model_name_or_path': 'unsloth/Llama-3.2-11B-Vision-Instruct',
+    'dataset_name': 'practical-dreamer/RPGPT_PublicDomain-ShareGPT',
+    'output_dir': '/opt/ml/model',
+    'learning_rate': 5e-5,
+    'per_device_train_batch_size': 4,
+    'num_train_epochs': 3,
 }
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=1,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
+# Configuração do repositório Git para download do script de treinamento
+git_config = {
+    'repo': 'https://github.com/huggingface/transformers.git',
+    'branch': 'v4.37.0'
+}
 
-if __name__ == "__main__":
-    demo.launch()
+# Configura o Estimador do Hugging Face
+huggingface_estimator = HuggingFace(
+    entry_point='train.py',
+    source_dir='./path/to/script',  # atualize para o caminho correto do script
+    instance_type='ml.p3.2xlarge',
+    instance_count=1,
+    role=role,
+    git_config=git_config,
+    transformers_version='4.37.0',
+    pytorch_version='2.1.0',
+    py_version='py310',
+    hyperparameters=hyperparameters
+)
+
+# Inicia o trabalho de treinamento
+huggingface_estimator.fit()