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fix(docs): small fixes for customizer optimize throughput nb #848

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fix(docs): small fixes for customizer optimize throughput nb #848

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15 changes: 3 additions & 12 deletions docs/customizer/tutorials/optimize-throughput.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -77,9 +77,7 @@
"source": [
"Install additional dependencies if they are not installed in your Python environment.\n",
"\n",
"The cell below automatically detects your environment and uses:\n",
"- `uv pip install` if you're in a uv-managed virtual environment\n",
"- `pip install` otherwise\n",
"The cell below uses the `%pip` so the packages install into the same Python kernel that is running this notebook.\n",
"\n",
"Required packages:\n",
"- `datasets` - Download the public [rajpurkar/squad](https://huggingface.co/datasets/rajpurkar/squad) dataset\n",
Expand All @@ -98,11 +96,7 @@
},
"outputs": [],
"source": [
"if command -v uv >/dev/null 2>&1 && [ -n \"$VIRTUAL_ENV\" ]; then\n",
" uv pip install datasets pandas matplotlib nvidia-ml-py\n",
"else\n",
" pip install datasets pandas matplotlib nvidia-ml-py\n",
"fi"
"%pip install -q datasets pandas matplotlib nvidia-ml-py"
]
},
{
Expand Down Expand Up @@ -885,16 +879,13 @@
"outputs": [],
"source": [
"from nemo_platform.types.jobs import PlatformJobStep\n",
"from datetime import datetime\n",
"import pandas as pd\n",
"\n",
"STEP_NAME = \"training\"\n",
"\n",
"def get_elapsed_time(step: PlatformJobStep) -> float:\n",
" \"\"\"Calculate elapsed time in seconds from step's created_at to updated_at.\"\"\"\n",
" created_at = datetime.fromisoformat(step.created_at.replace(\"Z\", \"+00:00\"))\n",
" updated_at = datetime.fromisoformat(step.updated_at.replace(\"Z\", \"+00:00\"))\n",
" return (updated_at - created_at).total_seconds()\n",
" return (step.updated_at - step.created_at).total_seconds()\n",

@coderabbitai coderabbitai Bot Jul 22, 2026

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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win

🧩 Analysis chain

🏁 Script executed:

python3 - <<'PY'
import json, re, pathlib

paths = [
    "docs/customizer/tutorials/optimize-throughput.ipynb",
    "docs/customizer/tutorials/optimize-throughput.mdx",
]

for p in paths:
    path = pathlib.Path(p)
    print(f"\n## {p}")
    if not path.exists():
        print("MISSING")
        continue
    if p.endswith(".ipynb"):
        nb = json.loads(path.read_text())
        for i, cell in enumerate(nb.get("cells", [])):
            src = "".join(cell.get("source", []))
            if "created_at" in src or "updated_at" in src or "total_seconds()" in src:
                print(f"\n-- cell {i} ({cell.get('cell_type')}) --")
                print(src)
    else:
        text = path.read_text()
        for m in re.finditer(r".{0,120}(created_at|updated_at|total_seconds\(\)).{0,120}", text):
            print(m.group(0))
PY

Repository: NVIDIA-NeMo/nemo-platform

Length of output: 1953

Update docs/customizer/tutorials/optimize-throughput.mdx. It still parses created_at/updated_at as strings while the notebook subtracts the datetime fields directly, so the published tutorial keeps the wrong timing code.

🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@docs/customizer/tutorials/optimize-throughput.ipynb` at line 888, Update the
timing calculation in optimize-throughput.mdx to parse created_at and updated_at
into datetime values before subtracting them, matching the notebook’s direct
datetime-field subtraction behavior. Locate the relevant step timing code and
ensure it no longer treats either timestamp as a string.

"\n",
"step_with_sequence_packing = client.jobs.steps.retrieve(\n",
" name=STEP_NAME,\n",
Expand Down
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