Add GPU-accelerated notebook and Zotero desktop services
- notebooks: Marimo notebook server with CUDA/Polars GPU support - Uses nvidia/cuda base with uv package manager - Includes cudf-polars for GPU-accelerated dataframes - GPU benchmark comparing Polars CPU/GPU and Pandas - zotero: Web-accessible Zotero via Selkies EGL desktop - Uses nvidia-egl-desktop with KasmVNC for browser access - GPU-accelerated desktop streaming - Persistent Zotero data directory - compose.yml: Docker Compose orchestration for all services Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
kert
6
.gitignore
vendored
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.gitignore
vendored
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# Data directories (user data, not tracked)
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data/
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zotero/data/
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# Marimo cache
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notebooks/__marimo__/
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52
compose.yml
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compose.yml
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services:
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notebooks:
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build: ./notebooks
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container_name: notebooks
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ports:
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- "2718:2718"
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volumes:
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- ./notebooks:/home/kert/notebooks
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- ./data:/home/kert/data
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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shm_size: "1g"
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restart: unless-stopped
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zotero:
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build: ./zotero
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container_name: zotero
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runtime: nvidia
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stdin_open: true
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tty: true
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ports:
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- "8080:8080"
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- "3478:3478"
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- "3478:3478/udp"
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volumes:
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- ./zotero/data:/home/ubuntu/Zotero
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- ./data:/home/ubuntu/data
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tmpfs:
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- /dev/shm:rw
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environment:
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- TZ=UTC
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- DISPLAY_SIZEW=1920
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- DISPLAY_SIZEH=1080
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- DISPLAY_REFRESH=60
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- DISPLAY_DPI=96
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- DISPLAY_CDEPTH=24
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- PASSWD=zotero
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- KASMVNC_ENABLE=true
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- SELKIES_ENABLE_BASIC_AUTH=false
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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restart: unless-stopped
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47
notebooks/Dockerfile
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notebooks/Dockerfile
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# syntax=docker/dockerfile:1
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FROM nvidia/cuda:12.6.0-runtime-ubuntu24.04
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ARG USERNAME=kert
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ARG USER_UID=1000
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ARG USER_GID=1000
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ARG PYTHON_VERSION=3.13
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ENV DEBIAN_FRONTEND=noninteractive \
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PATH="/home/${USERNAME}/.local/bin:${PATH}" \
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NVIDIA_VISIBLE_DEVICES=all \
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NVIDIA_DRIVER_CAPABILITIES=compute,utility
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# System dependencies
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RUN apt-get update && apt-get install -y --no-install-recommends \
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curl \
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ca-certificates \
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git \
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build-essential \
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&& rm -rf /var/lib/apt/lists/*
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# Rename existing ubuntu user/group to kert and fix home ownership
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RUN groupmod -n ${USERNAME} ubuntu \
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&& usermod -l ${USERNAME} -d /home/${USERNAME} -m -s /bin/bash ubuntu \
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&& chown -R ${USER_UID}:${USER_GID} /home/${USERNAME}
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# Install uv
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
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COPY --from=ghcr.io/astral-sh/uv:latest /uvx /usr/local/bin/uvx
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# Switch to user
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USER ${USERNAME}
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WORKDIR /home/${USERNAME}
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# Initialize uv project and install dependencies
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RUN uv python install ${PYTHON_VERSION} \
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&& uv init workspace --python ${PYTHON_VERSION} \
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&& cd workspace \
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&& uv add "marimo[recommended]" polars cudf-polars-cu12 pandas numpy pyarrow \
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--extra-index-url https://pypi.nvidia.com
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EXPOSE 2718
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CMD ["uv", "run", "--project", "/home/kert/workspace", \
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"marimo", "edit", \
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"--host", "0.0.0.0", "--port", "2718", "--headless", "--no-token", \
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"/home/kert/notebooks"]
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236
notebooks/gpu_test.py
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notebooks/gpu_test.py
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import marimo
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__generated_with = "0.19.7"
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app = marimo.App(width="medium")
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with app.setup:
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import marimo as mo
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import subprocess
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import platform
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import os
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import sys
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import time
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import numpy as np
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import polars as pl
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import pandas as pd
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@app.cell(hide_code=True)
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def gpu_diagnostics():
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try:
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smi_result = subprocess.run(
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["nvidia-smi"],
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capture_output=True, text=True, timeout=10
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)
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nvidia_smi_output = smi_result.stdout
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gpu_detected = smi_result.returncode == 0
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except Exception as exc:
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nvidia_smi_output = str(exc)
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gpu_detected = False
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visible_devices = os.environ.get("NVIDIA_VISIBLE_DEVICES", "not set")
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driver_capabilities = os.environ.get("NVIDIA_DRIVER_CAPABILITIES", "not set")
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mo.md(f"""
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# GPU & System Check
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**GPU Available:** `{gpu_detected}`
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**NVIDIA_VISIBLE_DEVICES:** `{visible_devices}`
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**NVIDIA_DRIVER_CAPABILITIES:** `{driver_capabilities}`
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```
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{nvidia_smi_output}
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```
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""")
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return
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@app.cell(hide_code=True)
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def polars_gpu_benchmark():
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benchmark_rows = 50_000_000
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bench_rng = np.random.default_rng(42)
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bench_gen_start = time.perf_counter()
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bench_df = pl.DataFrame({
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"id": np.arange(benchmark_rows),
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"group": bench_rng.choice(["A", "B", "C", "D", "E"], size=benchmark_rows),
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"value_1": bench_rng.standard_normal(benchmark_rows),
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"value_2": bench_rng.uniform(0, 1000, size=benchmark_rows),
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"value_3": bench_rng.integers(0, 100, size=benchmark_rows),
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})
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bench_gen_elapsed = time.perf_counter() - bench_gen_start
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# Pre-create lazy frame to exclude setup from timing
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bench_lazy = bench_df.lazy()
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# GPU-supported aggregations only (no quantile/median which cause fallback)
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bench_agg_expr = [
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pl.col("value_1").mean().alias("mean_v1"),
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pl.col("value_1").std().alias("std_v1"),
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pl.col("value_2").sum().alias("sum_v2"),
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pl.col("value_2").min().alias("min_v2"),
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pl.col("value_2").max().alias("max_v2"),
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pl.col("value_3").mean().alias("mean_v3"),
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pl.len().alias("count"),
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]
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# --- GPU collect ---
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bench_gpu_agg_start = time.perf_counter()
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bench_gpu_agg_result = (
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bench_lazy
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.group_by("group")
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.agg(*bench_agg_expr)
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.sort("group")
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.collect(engine="gpu")
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)
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bench_gpu_agg_elapsed = time.perf_counter() - bench_gpu_agg_start
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# --- CPU collect ---
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bench_cpu_agg_start = time.perf_counter()
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bench_cpu_agg_result = (
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bench_lazy
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.group_by("group")
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.agg(*bench_agg_expr)
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.sort("group")
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.collect()
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)
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bench_cpu_agg_elapsed = time.perf_counter() - bench_cpu_agg_start
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bench_agg_speedup = bench_cpu_agg_elapsed / bench_gpu_agg_elapsed if bench_gpu_agg_elapsed > 0 else float("inf")
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# GPU-supported window functions only (no rank which causes fallback)
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bench_window_expr = [
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pl.col("value_1").mean().over("group").alias("group_mean"),
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pl.col("value_2").sum().over("group").alias("group_sum"),
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]
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# --- GPU window ---
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bench_gpu_window_start = time.perf_counter()
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bench_gpu_window_result = (
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bench_lazy
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.with_columns(*bench_window_expr)
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.head(5)
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.collect(engine="gpu")
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)
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bench_gpu_window_elapsed = time.perf_counter() - bench_gpu_window_start
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# --- CPU window ---
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bench_cpu_window_start = time.perf_counter()
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bench_cpu_window_result = (
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bench_lazy
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.with_columns(*bench_window_expr)
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.head(5)
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.collect()
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)
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bench_cpu_window_elapsed = time.perf_counter() - bench_cpu_window_start
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bench_window_speedup = bench_cpu_window_elapsed / bench_gpu_window_elapsed if bench_gpu_window_elapsed > 0 else float("inf")
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mo.md(f"""
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# Polars GPU vs CPU — {benchmark_rows:,} rows
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| Operation | GPU | CPU | Speedup |
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|---|---|---|---|
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| Data generation | `{bench_gen_elapsed:.3f}s` | — | — |
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| GroupBy aggregation | `{bench_gpu_agg_elapsed:.3f}s` | `{bench_cpu_agg_elapsed:.3f}s` | **{bench_agg_speedup:.1f}x** |
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| Window functions | `{bench_gpu_window_elapsed:.3f}s` | `{bench_cpu_window_elapsed:.3f}s` | **{bench_window_speedup:.1f}x** |
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""")
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mo.hstack([
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mo.ui.table(bench_gpu_agg_result, label="GPU Aggregation Results"),
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mo.ui.table(bench_gpu_window_result, label="GPU Window Functions (head 5)"),
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])
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return
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@app.cell(hide_code=True)
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def pandas_vs_polars_gpu():
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cmp_rows = 50_000_000
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cmp_rng = np.random.default_rng(99)
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# Generate data once, outside timing
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cmp_categories = cmp_rng.choice(["X", "Y", "Z"], size=cmp_rows)
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cmp_amounts = cmp_rng.standard_normal(cmp_rows).astype(np.float64)
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# --- Pandas (CPU only) - time only the compute, not DataFrame creation ---
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cmp_pandas_df = pd.DataFrame({"category": cmp_categories, "amount": cmp_amounts})
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cmp_pandas_start = time.perf_counter()
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cmp_pandas_agg = cmp_pandas_df.groupby("category")["amount"].agg(["mean", "std", "sum"])
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cmp_pandas_elapsed = time.perf_counter() - cmp_pandas_start
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# --- Polars GPU - time only the compute ---
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cmp_polars_df = pl.DataFrame({"category": cmp_categories, "amount": cmp_amounts})
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cmp_lazy = cmp_polars_df.lazy()
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cmp_gpu_start = time.perf_counter()
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cmp_gpu_agg = (
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cmp_lazy
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.group_by("category")
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.agg(
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pl.col("amount").mean().alias("mean"),
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pl.col("amount").std().alias("std"),
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pl.col("amount").sum().alias("sum"),
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)
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.sort("category")
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.collect(engine="gpu")
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)
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cmp_gpu_elapsed = time.perf_counter() - cmp_gpu_start
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# --- Polars CPU ---
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cmp_cpu_start = time.perf_counter()
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cmp_cpu_agg = (
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cmp_lazy
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.group_by("category")
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.agg(
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pl.col("amount").mean().alias("mean"),
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pl.col("amount").std().alias("std"),
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pl.col("amount").sum().alias("sum"),
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)
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.sort("category")
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.collect()
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)
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cmp_cpu_elapsed = time.perf_counter() - cmp_cpu_start
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cmp_gpu_vs_pandas = cmp_pandas_elapsed / cmp_gpu_elapsed if cmp_gpu_elapsed > 0 else float("inf")
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cmp_cpu_vs_pandas = cmp_pandas_elapsed / cmp_cpu_elapsed if cmp_cpu_elapsed > 0 else float("inf")
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mo.md(f"""
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# Three-Way Comparison — {cmp_rows:,} rows
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| Engine | GroupBy Time | vs Pandas |
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|---|---|---|
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| Pandas (CPU) | `{cmp_pandas_elapsed:.3f}s` | 1.0x |
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| Polars (CPU) | `{cmp_cpu_elapsed:.3f}s` | **{cmp_cpu_vs_pandas:.1f}x** |
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| Polars (GPU) | `{cmp_gpu_elapsed:.3f}s` | **{cmp_gpu_vs_pandas:.1f}x** |
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""")
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return
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@app.cell(hide_code=True)
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def environment_info():
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python_version = sys.version
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platform_info = platform.platform()
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cpu_cores = os.cpu_count()
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marimo_version = mo.__version__
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polars_version = pl.__version__
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pandas_version = pd.__version__
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numpy_version = np.__version__
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mo.md(f"""
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# Environment
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| Component | Version |
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|---|---|
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| Python | `{python_version}` |
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| Platform | `{platform_info}` |
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| CPU cores | `{cpu_cores}` |
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| Marimo | `{marimo_version}` |
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| Polars | `{polars_version}` |
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| Pandas | `{pandas_version}` |
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| NumPy | `{numpy_version}` |
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""")
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return
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if __name__ == "__main__":
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app.run()
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21
zotero/Dockerfile
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21
zotero/Dockerfile
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# syntax=docker/dockerfile:1
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FROM ghcr.io/selkies-project/nvidia-egl-desktop:latest
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USER root
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# Install Zotero
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RUN apt-get update && apt-get install -y --no-install-recommends \
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curl \
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ca-certificates \
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&& curl -sL https://raw.githubusercontent.com/retorquere/zotero-deb/master/install.sh | bash \
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&& apt-get update && apt-get install -y --no-install-recommends \
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zotero \
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&& rm -rf /var/lib/apt/lists/*
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# Create desktop shortcut for Zotero
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RUN mkdir -p /home/ubuntu/Desktop \
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&& cp /usr/share/applications/zotero.desktop /home/ubuntu/Desktop/ \
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&& chmod +x /home/ubuntu/Desktop/zotero.desktop \
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&& chown -R ubuntu:ubuntu /home/ubuntu/Desktop
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USER ubuntu
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Block a user