Spaces:
Sleeping
Sleeping
File size: 8,390 Bytes
490d677 4d3c39e 490d677 57002ac 490d677 4d3c39e 57002ac 4d3c39e 490d677 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 | from __future__ import annotations
import os
from pathlib import Path
from typing import Any, Optional, Sequence, Tuple
import pandas as pd
import gradio as gr
from space_utils import SpaceBundle, analyze_path, coerce_upload_path, find_local_sample
APP_TITLE = "nsys-llm-explainer — Instant Nsight Trace Analyzer for Cloud LLM Inference"
CSS = """
.gradio-container {
background:
radial-gradient(circle at top left, rgba(42, 93, 142, 0.35), transparent 30%),
radial-gradient(circle at top right, rgba(20, 104, 117, 0.22), transparent 26%),
linear-gradient(180deg, #081018 0%, #0b111a 42%, #090e15 100%);
color: #e6eef7;
font-family: "Aptos", "Segoe UI", sans-serif;
}
.hero-card {
border: 1px solid rgba(115, 145, 180, 0.28);
border-radius: 22px;
background: linear-gradient(135deg, rgba(14, 22, 34, 0.95), rgba(10, 14, 20, 0.92));
box-shadow: 0 24px 70px rgba(0, 0, 0, 0.28);
padding: 22px 24px;
margin-bottom: 16px;
}
.hero-kicker {
text-transform: uppercase;
letter-spacing: 0.18em;
color: #8fb4d9;
font-size: 11px;
font-weight: 700;
}
.hero-title {
margin: 10px 0 10px;
font-size: 34px;
line-height: 1.05;
font-weight: 800;
color: #f3f8ff;
}
.hero-subtitle {
color: #b2c5d9;
font-size: 15px;
line-height: 1.6;
max-width: 980px;
}
.badge-row {
display: flex;
flex-wrap: wrap;
gap: 8px;
margin-top: 16px;
}
.badge {
display: inline-flex;
align-items: center;
padding: 6px 12px;
border-radius: 999px;
border: 1px solid rgba(137, 171, 207, 0.28);
background: rgba(13, 21, 31, 0.82);
color: #d8e6f5;
font-size: 12px;
}
.upload-card {
border: 1px solid rgba(88, 113, 143, 0.26);
border-radius: 18px;
background: rgba(10, 16, 24, 0.86);
padding: 14px;
margin-bottom: 14px;
}
.section-title {
color: #f4f8fd;
font-size: 16px;
font-weight: 700;
margin: 0 0 10px 0;
}
.gr-markdown, .prose {
color: #e8eff7;
}
.wrap-long {
white-space: pre-wrap;
word-break: break-word;
}
"""
HEADER = """
<div class="hero-card">
<div class="hero-kicker">Cloud ML trace intelligence</div>
<div class="hero-title">nsys-llm-explainer — Instant Nsight Trace Analyzer for Cloud LLM Inference</div>
<div class="hero-subtitle">
Upload a `trace.sqlite` or `report.json` and get prioritized findings, NCCL/NVLink correlation, launch storm diagnosis,
per-process breakdowns, and downloadable analysis artifacts. The same code path powers the CLI, dashboard, and this Space.
</div>
<div class="badge-row">
<span class="badge">SQLite + report.json input</span>
<span class="badge">Evidence-backed findings</span>
<span class="badge">CSV + JSON downloads</span>
<span class="badge">Built for cloud LLM traces</span>
</div>
</div>
"""
def _empty_outputs(message: str) -> Tuple[Any, str, pd.DataFrame, str, str, list[str], pd.DataFrame]:
empty_df = pd.DataFrame(columns=["section", "metric", "value"])
empty_manifest = pd.DataFrame(columns=["artifact", "purpose", "path"])
return (
message,
message,
empty_df,
message,
message,
[],
empty_manifest,
)
def _bundle_to_outputs(bundle: SpaceBundle) -> Tuple[Any, str, pd.DataFrame, str, str, list[str], pd.DataFrame]:
summary_df = pd.DataFrame(bundle.summary_rows)
manifest_df = pd.DataFrame(bundle.manifest_rows)
bottleneck = next((row["value"] for row in bundle.summary_rows if row.get("metric") == "Top bottleneck"), "No bottleneck summary available")
summary_markdown = [
"### Quick read",
"",
"- Source: `{}` (`{}`)".format(bundle.source_path.name, bundle.source_kind),
"- {}".format(bundle.report.get("generated_at") or "Generated time unavailable"),
"- {}".format(bottleneck),
"- Warnings: `{}`".format(len(bundle.report.get("warnings") or [])),
]
files = [str(path) for path in bundle.artifact_paths]
return (
bundle.status_markdown,
"\n".join(summary_markdown),
summary_df,
bundle.findings_markdown,
bundle.markdown,
files,
manifest_df,
)
def _resolve_path(uploaded: Any, sample_path: str) -> Optional[Path]:
uploaded_path = coerce_upload_path(uploaded)
if uploaded_path:
return uploaded_path
if sample_path:
candidate = Path(sample_path)
if candidate.exists():
return candidate
return None
def _run_analysis(uploaded, sample_path):
path = _resolve_path(uploaded, sample_path)
if not path:
return _empty_outputs(
"Upload a `trace.sqlite`/`.db` file or a `report.json` to generate the report. "
"If you are using this Space as a demo, click `Load sample trace` first."
)
try:
bundle = analyze_path(path)
return _bundle_to_outputs(bundle)
except Exception as exc:
message = "Failed to analyze `{}`: `{}`".format(path.name, exc)
return _empty_outputs(message)
def _build_demo(sample_path: Optional[Path]) -> gr.Blocks:
with gr.Blocks(title=APP_TITLE, css=CSS, theme=gr.themes.Soft(primary_hue="blue", secondary_hue="slate")) as demo:
gr.HTML(HEADER)
with gr.Row(elem_classes=["upload-card"]):
with gr.Column(scale=6):
upload = gr.File(
label="Upload trace or report",
file_count="single",
file_types=[".sqlite", ".db", ".json"],
type="filepath",
)
with gr.Column(scale=2, min_width=180):
analyze_btn = gr.Button("Analyze trace", variant="primary")
with gr.Column(scale=2, min_width=180):
sample_btn = gr.Button(
"Load sample trace",
variant="secondary",
visible=bool(sample_path),
)
status = gr.Markdown("Upload a trace or report to begin.")
sample_state = gr.State(str(sample_path) if sample_path else "")
with gr.Tabs():
with gr.Tab("Summary"):
gr.Markdown("### Summary")
summary = gr.Markdown(elem_classes=["wrap-long"])
summary_table = gr.Dataframe(
headers=["section", "metric", "value"],
datatype=["str", "str", "str"],
interactive=False,
wrap=True,
label="Key metrics",
)
with gr.Tab("Findings"):
findings = gr.Markdown(elem_classes=["wrap-long"])
with gr.Tab("Markdown"):
report_markdown = gr.Markdown(elem_classes=["wrap-long"])
with gr.Tab("Downloads"):
gr.Markdown(
"### Generated artifacts\n"
"The analysis writes `report.md`, `report.json`, CSV tables, and a zip bundle."
)
manifest = gr.Dataframe(
headers=["artifact", "purpose", "path"],
datatype=["str", "str", "str"],
interactive=False,
wrap=True,
label="Artifact manifest",
)
downloads = gr.File(
label="Download files",
file_count="multiple",
type="filepath",
)
analyze_btn.click(
fn=_run_analysis,
inputs=[upload, sample_state],
outputs=[status, summary, summary_table, findings, report_markdown, downloads, manifest],
)
if sample_path:
sample_btn.click(
fn=lambda sp: _run_analysis(None, sp),
inputs=[sample_state],
outputs=[status, summary, summary_table, findings, report_markdown, downloads, manifest],
)
demo.load(
fn=lambda sp: _run_analysis(None, sp),
inputs=[sample_state],
outputs=[status, summary, summary_table, findings, report_markdown, downloads, manifest],
)
return demo
def main() -> None:
demo = _build_demo(find_local_sample())
demo.queue()
demo.launch(
server_name="0.0.0.0",
server_port=int(os.getenv("PORT", "7860")),
share=True,
)
if __name__ == "__main__":
main()
|