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‎.justfile‎

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@@ -37,8 +37,8 @@ check:
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uv run pre-commit run -a
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# Run mypy
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uv run mypy .
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# Run deptry
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uv run deptry .
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# Run deptry with ignored issues
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uv run deptry . --ignore=DEP002,DEP003
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# Add scripts
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add_scripts:

‎README.md‎

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@@ -29,8 +29,10 @@ A web dashboard for training machine learning models with PyTorch.
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### Built With
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[![Python][Python-shield]][Python-url]
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[![GitHub Actions][github-actions-shield]][github-actions-url]
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[![Pytest][Pytest-shield]][Pytest-url]
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[![Codecov][Codecov-shield]][Codecov-url]
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[![Docker][Docker-shield]][Docker-url]
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[![GitHub Actions][github-actions-shield]][github-actions-url]
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<!-- GETTING STARTED -->
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@@ -107,9 +109,13 @@ The source code for this project is distributed under the terms of the MIT Licen
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<!-- MARKDOWN SHIELD BAGDES & LINKS -->
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<!-- https://github.com/Ileriayo/markdown-badges -->
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[Python-shield]: https://img.shields.io/badge/Python-%23008080.svg?style=for-the-badge&logo=python&logoColor=306998&labelColor=222222&color=306998
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[Python-shield]: https://img.shields.io/badge/Python-%23008080.svg?style=for-the-badge&logo=python&logoColor=FFDD54&labelColor=222222&color=306998
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[Python-url]: https://www.python.org/
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[github-actions-shield]: https://img.shields.io/badge/github%20actions-%232671E5.svg?style=for-the-badge&logo=githubactions&logoColor=2671E5&labelColor=222222&color=2671E5
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[github-actions-url]: https://github.com/features/actions
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[Pytest-shield]: https://img.shields.io/badge/pytest-%23008080.svg?style=for-the-badge&logo=pytest&logoColor=2F9FE3&labelColor=222222&color=2F9FE3
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[Pytest-url]: https://docs.pytest.org
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[Docker-shield]: https://img.shields.io/badge/docker-%232671E5.svg?style=for-the-badge&logo=docker&logoColor=1D63ED&labelColor=222222&color=1D63ED
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[Docker-url]: https://www.docker.com/
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[Codecov-shield]: https://img.shields.io/badge/codecov-%23008080.svg?style=for-the-badge&logo=codecov&logoColor=FF0077&labelColor=222222&color=FF0077
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[Codecov-url]: https://codecov.io/
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[github-actions-shield]: https://img.shields.io/badge/github%20actions-%232671E5.svg?style=for-the-badge&logo=githubactions&logoColor=2671E5&labelColor=222222&color=2671E5
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[github-actions-url]: https://github.com/features/actions

‎src/dashboard/pyproject.toml‎

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@@ -3,16 +3,18 @@ name = "dashboard"
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version = "0.0.0"
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description = "Dashboard functionality"
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authors = [
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{name="Miguel Villa Floran", email="miguel.villafloran@gmail.com"}
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{ name = "Miguel Villa Floran", email = "miguel.villafloran@gmail.com" },
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]
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requires-python = ">=3.10"
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readme = "README.md"
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dependencies = [
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"utils",
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"typer>=0.12.5",
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"uvicorn>=0.34.0",
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"fastapi>=0.115.8",
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"jinja2>=3.1.5",
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"utils",
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"typer>=0.12.5",
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"uvicorn>=0.34.0",
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"fastapi>=0.115.8",
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"jinja2>=3.1.5",
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"pynvml>=11.5.0",
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"torch>=2.6.0",
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]
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[project.scripts]

‎src/dashboard/src/dashboard/api.py‎

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import os
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from fastapi import FastAPI, Request
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from fastapi.responses import HTMLResponse
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from fastapi.templating import Jinja2Templates
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api = FastAPI(
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title="Dashboard API",
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description="API for the dashboard application",
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version="1.0.0",
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)
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# Set up templates directory
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templates = Jinja2Templates(
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directory=os.path.join(os.path.dirname(__file__), "templates")
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)
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# Training metrics storage
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training_metrics = {
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"iteration": 0,
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"loss": 0.0,
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"perplexity": 0.0,
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"accuracy": 0.0,
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"learning_rate": 0.0,
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"gradient_norm": 0.0,
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"bits_memorized": 0.0,
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"bits_per_second": 0.0,
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"gpu_utilization": [0.0] * 1024, # For 1024 GPUs
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"skills": {
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"Translation": 20,
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"Summarization": 15,
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"Reasoning": 10,
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"Coding": 5,
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"Comprehension": 25,
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},
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}
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@api.get("/", response_class=HTMLResponse)
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async def root(request: Request):
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"""Serve the dashboard HTML"""
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return templates.TemplateResponse("dashboard.html", {"request": request})
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@api.post("/update_metrics")
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async def update_metrics(metrics: dict):
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"""Receive updated metrics from PyTorch training"""
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# Update our stored metrics
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training_metrics.update(metrics)
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return {"status": "success"}
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@api.get("/metrics")
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async def get_metrics():
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"""Return current training metrics"""
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return training_metrics

‎src/dashboard/src/dashboard/cli.py‎

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import os
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import typer
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import uvicorn
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from fastapi import FastAPI, Request
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from fastapi.responses import HTMLResponse
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from fastapi.templating import Jinja2Templates
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from utils import get_token
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app = typer.Typer()
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api = FastAPI(
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title="Dashboard API",
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description="API for the dashboard application",
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version="1.0.0",
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)
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# Set up templates directory
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templates = Jinja2Templates(
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directory=os.path.join(os.path.dirname(__file__), "templates")
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)
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# Training metrics storage
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training_metrics = {
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"iteration": 0,
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"loss": 0.0,
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"perplexity": 0.0,
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"accuracy": 0.0,
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"learning_rate": 0.0,
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"gradient_norm": 0.0,
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"bits_memorized": 0.0,
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"bits_per_second": 0.0,
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"gpu_utilization": [0.0] * 1024, # For 1024 GPUs
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"skills": {
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"Translation": 20,
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"Summarization": 15,
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"Reasoning": 10,
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"Coding": 5,
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"Comprehension": 25,
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},
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}
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@api.get("/", response_class=HTMLResponse)
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async def root(request: Request):
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"""Serve the dashboard HTML"""
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return templates.TemplateResponse("dashboard.html", {"request": request})
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@api.post("/update_metrics")
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async def update_metrics(metrics: dict):
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"""Receive updated metrics from PyTorch training"""
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# Update our stored metrics
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training_metrics.update(metrics)
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return {"status": "success"}
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@api.get("/metrics")
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async def get_metrics():
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"""Return current training metrics"""
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return training_metrics
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@app.command()
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def run():
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"""Start the FastAPI server"""
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uvicorn.run(
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"dashboard.cli:api",
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"dashboard.api:api",
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host=get_token("DASHBOARD_HOST"),
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port=int(get_token("DASHBOARD_PORT")),
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reload=True,

‎src/trainer/pyproject.toml‎

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@@ -3,19 +3,20 @@ name = "trainer"
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version = "0.0.0"
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description = "Trainer functionality"
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authors = [
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{name="Miguel Villa Floran", email="miguel.villafloran@gmail.com"}
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{ name = "Miguel Villa Floran", email = "miguel.villafloran@gmail.com" },
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]
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requires-python = ">=3.10"
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readme = "README.md"
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dependencies = [
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"utils",
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"typer>=0.12.5",
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"jinja2>=3.1.5",
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"torch>=2.6.0",
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"requests>=2.32.3",
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"numpy>=2.2.3",
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"torchvision>=0.21.0",
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"types-requests>=2.32.0.20250306",
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"utils",
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"typer>=0.12.5",
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"jinja2>=3.1.5",
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"torch>=2.6.0",
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"requests>=2.32.3",
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"numpy>=2.2.3",
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"torchvision>=0.21.0",
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"types-requests>=2.32.0.20250306",
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"pynvml>=11.5.0",
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]
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[project.scripts]

‎uv.lock‎

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