NVIDIA Nsight Python 教程：如何在 Colab T4 上剖析 GPU kernel
MarkTechPost 发布教程，演示用 NVIDIA 开源的 Nsight Python 在免费 Colab T4 上剖析 GPU kernel：用 nsight.annotate 标注代码、用 nsight.analyze.kernel 装饰基准函数，直接得到含每次运行硬件指标的 pandas DataFrame。
MarkTechPost· Sana Hassan·2026-10-22 11:59· 1 小时前AI 评分59
NVIDIA Nsight Python Tutorial: How to Profile GPU Kernels
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教程演示如何用 NVIDIA 开源的 Nsight Python 库在 Colab T4 上剖析 GPU kernel：用 nsight.annotate 标注代码、用 nsight.analyze.kernel 装饰基准函数，直接得到包含每次运行硬件指标的 pandas DataFrame。
正文 · 原文
In this tutorial, we implement on Nsight Python, NVIDIA’s open-source Python interface to its kernel profilers. Instead of running a script under the Nsight Compute command line and digging through a report afterward, we mark the code we care about with nsight.annotate, decorate a benchmark function with nsight.analyze.kernel, and get back a pandas DataFrame of hardware metrics per annotated region, per configuration, and per run. Since version 1.0, the library attaches Nsight Compute to the running Python process, making it usable in Jupyter and Colab, and the upcoming 1.1 release adds an experimental CUPTI backend, explicit tool management, and custom information collectors. We run everything on a free Colab T4: we install a current Nsight Compute next to the one Colab ships, profile a first kernel, decide how many runs a measurement needs, measure what cache and clock control do to the same kernel, sweep matrix sizes in fp32 and fp16 with a derived TFLOP/s metric, use hardware counters to tell compute-bound from memory-bound work, measure kernel fusion in bytes moved, record clocks and temperature per run, compare the CUPTI backend with Nsight Compute, and keep the raw report for deeper analysis.
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import os
import sys
import glob
import json
import time
import shutil
import textwrap
import traceback
import subprocess
RESULTS = {}
def banner(title):
print("\n" + "=" * 78)
print(title)
print("=" * 78)
def section(name):
def wrap(fn):
def run(*a, **kw):
banner(name)
try:
out = fn(*a, **kw)
RESULTS[name] = out if isinstance(out, str) else "ok"
return out
except Exception as e:
RESULTS[name] = f"SKIPPED / FAILED -> {type(e).__name__}: {str(e).splitlines()[0][:160]}"
print(f"\n[!] {name} did not complete: {type(e).__name__}: {e}")
traceback.print_exc(limit=3)
return None
return run
return wrap
def sh(cmd):
"""Run a shell command and return its stdout, stripped."""
return subprocess.run(cmd, shell=True, capture_output=True, text=True).stdout.strip()
banner("1. Install a current Nsight Compute next to Colab's, then import nsight before CUDA")
print(f" GPU: {sh('nvidia-smi --query-gpu=name,driver_version,memory.total,compute_cap --format=csv,noheader')}")
preinstalled = shutil.which("ncu")
print(f" ncu already on PATH: {preinstalled} -> {sh('ncu --version | tail -1') if preinstalled else 'none'}")
# nsight-python needs Nsight Compute 2026.2.1 or newer. Colab ships an older ncu, but its image already
# carries NVIDIA's CUDA apt repository, so a current release is one apt-get away. It installs into
# /opt/nvidia/nsight-compute/<version>/ and leaves the preinstalled one untouched.
subprocess.run("apt-get update -qq > /tmp/ncu_install.log 2>&1", shell=True)
for NCU_VERSION in ("2026.3.1", "2026.2.1"):
if subprocess.run(f"apt-get install -y -qq nsight-compute-{NCU_VERSION} >> /tmp/ncu_install.log 2>&1",
shell=True).returncode == 0:
break
else:
print(sh("tail -5 /tmp/ncu_install.log"))
raise RuntimeError("could not install Nsight Compute 2026.2.1+ from NVIDIA's apt repository")
# ask dpkg where the package put the ncu launcher rather than assuming /opt/nvidia/nsight-compute/<version>/
ncu_paths = sh(f"dpkg -L nsight-compute-{NCU_VERSION} | grep -E '/ncu$'").split()
if not ncu_paths:
raise RuntimeError(f"dpkg lists no ncu executable in nsight-compute-{NCU_VERSION}")
NCU_DIR = os.path.dirname(min(ncu_paths, key=len)) # the top-level launcher, not a per-platform copy
os.environ["PATH"] = NCU_DIR + os.pathsep + os.environ["PATH"] # nsight-python looks ncu up on PATH
print(f" installed: {shutil.which('ncu')} -> {sh('ncu --version | tail -1')}")
# nsight-python 1.1.0 (CUPTI backend, tool APIs, info collectors) is tagged but not yet on PyPI;
# pin the commit this notebook was written against. The [cupti] extra is used in step 9.
NSIGHT_COMMIT = "bb96db4260b1823d86ecab9acfed0646d7af79ad"
subprocess.run([sys.executable, "-m", "pip", "install", "-q",
f"nsight-python @ git+https://github.com/NVIDIA/nsight-python@{NSIGHT_COMMIT}"], check=True)
# The CUPTI backend needs cupti-python 13.4, which wants a newer libcupti than Colab's torch is pinned to.
# Install it into a separate directory that only step 9's child process puts on its path.
CUPTI_DIR = "/content/cupti_env"
CUPTI_EXTRA = subprocess.run([sys.executable, "-m", "pip", "install", "-q", "--target", CUPTI_DIR, "--no-deps",
"cupti-python>=13.4.0", "nvidia-cuda-cupti~=13.4", "pyelftools"]).returncode == 0
import nsight # activates Nsight Compute's injection library: must come before anything initializes CUDA
from nsight.collection import ncu as ncu_backend
import numpy as np
import pandas as pd
import torch
SILENT = nsight.VerbosityLevel.SILENT
pd.set_option("display.width", 200)
pd.set_option("display.max_columns", 30)
print(f" nsight-python {nsight.__version__} | active tool: {nsight.get_active_tool()}"
f" | injection error at import: {ncu_backend.injection_load_error}")
print(f" torch {torch.__version__} (CUDA {torch.version.cuda}) | {torch.cuda.get_device_name(0)}"
f" | cupti-python installed: {CUPTI_EXTRA}")
print("\n Order matters: `import nsight` loads Nsight Compute into this Python process, and it can only")
print(" attach to CUDA work started after that. Every profiled function below runs in this same")
print(" process; nsight-python starts `ncu --mode attach` on its own PID for each one.")
We start by checking the GPU and the profiler. Nsight Python requires the Nsight Compute command-line tool, ncu, at version 2026.2.1 or later, and finds it by looking it up on the PATH. Colab’s GPU image ships an older Nsight Compute. Still, it already has NVIDIA’s CUDA package repository configured, so we install Nsight Compute 2026.3.1 with apt-get, ask dpkg where the ncu launcher landed, and put that directory first on the PATH. We then install Nsight Python, pinned to the commit of the 1.1.0 pre-release this notebook was written against, and the cupti-python package into a separate directory, because it needs a newer CUPTI library than the one Colab’s PyTorch is pinned to. The import order matters: import nsight loads Nsight Compute’s injection library into the Python process, and it can only attach to CUDA work started after that, so nsight must be imported before torch touches the GPU.
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ADD = lambda x, y: x + y # sums a metric if cuBLAS splits one matmul into several kernels
@nsight.analyze.kernel(thermal_mode="off", combine_kernel_metrics=ADD)
def first_matmul(n: int) -> None:
a = torch.randn(n, n, device="cuda")
b = torch.randn(n, n, device="cuda")
with nsight.annotate("matmul"):
_ = a @ b
@section("2. Profile one kernel: what a ProfileResults row contains")
def first_profile():
df = first_matmul(2048).to_dataframe()
print(f"\n to_dataframe() columns: {list(df.columns)}\n")
r = df.iloc[0]
t = float(r["AvgValue"])
print(f" annotation {r['Annotation']!r}, n = {r['n']}, kernel {r['Kernel']}")
print(f" {r['Metric']} = {t:,.0f} {r['Unit']} (runs {r['NumRuns']}, stable: {r['StableMeasurement']})")
print(f" GPU {r['GPU']}, ComputeClock {r['ComputeClock']}, MemoryClock {r['MemoryClock']}")
print(f" -> {2 * 2048 ** 3 / (t * 1e-9) / 1e12:.2f} TFLOP/s for a 2048 x 2048 fp32 matmul")
print("\n Only the kernel launched inside nsight.annotate() is profiled; torch.randn above it is not.")
print(" With a single run, StableMeasurement is <NA>: one sample has no spread to judge.")
return f"fp32 2048 matmul {t / 1e6:.3f} ms ({2 * 2048 ** 3 / (t * 1e-9) / 1e12:.2f} TFLOP/s)"
first_profile()
The first profile is a single fp32 matrix multiplication. The benchmark function creates its inputs, then wraps only the multiplication in nsight.annotate, so the random-number kernels that build the inputs are not measured. Calling the decorated function starts ncu in attach mode on the notebook’s own process, runs the function, and returns a ProfileResults object whose to_dataframe method gives one row per annotation, configuration, and metric. We print the columns and read the row: the kernel name, the measured gpu__time_duration.sum in nanoseconds with its unit, the number of runs, the GPU name and the clock attributes, and we turn the time into TFLOP/s by hand. The combine_kernel_metrics argument sums the time if cuBLAS splits a multiplication into more than one kernel; with a single run, the StableMeasurement flag is undefined, because one sample has no spread.
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def show(df, cols, scale=None, fmt="{:,.2f}"):
out = df[cols].copy()
for c, f in (scale or {}).items():
out[c] = out[c] * f
print(textwrap.indent(out.to_string(index=False, float_format=lambda v: fmt.format(v)), " "))
@nsight.analyze.kernel(configs=[1 << 14, 1 << 24], runs=20, thermal_mode="off", verbosity=SILENT,
combine_kernel_metrics=ADD)
def noisy_or_not(n: int) -> None:
x = torch.randn(n, device="cuda")
y = torch.randn(n, device="cuda")
a = torch.randn(2048, 2048, device="cuda")
with nsight.annotate("vector add"):
_ = x + y
with nsight.annotate("matmul 2048"):
_ = a @ a
@section("3. How many runs? Spread, confidence intervals and the stability flag")
def stability():
df = noisy_or_not().to_dataframe()
print(" 20 runs per configuration; times in microseconds:\n")
show(df, ["Annotation", "n", "AvgValue", "StdDev", "CI95_Lower", "CI95_Upper", "RelativeStdDevPct",
"StableMeasurement"], scale={"AvgValue": 1e-3, "StdDev": 1e-3, "CI95_Lower": 1e-3, "CI95_Upper": 1e-3})
tiny = df[(df.Annotation == "vector add") & (df.n == 1 << 14)].iloc[0]
print(f"\n StableMeasurement is simply RelativeStdDevPct < 2. The smallest kernel ({tiny['AvgValue'] / 1e3:.1f} us)"
f" varies by {tiny['RelativeStdDevPct']:.1f}%:")
print(" at microsecond scale, launch and clock jitter are a large share of the time. Judge small kernels")
print(" with more runs, and compare their confidence intervals rather than single averages.")
return f"16K-element add RSD {tiny['RelativeStdDevPct']:.1f}%; " + \
f"2048 matmul RSD {df[df.Annotation == 'matmul 2048'].RelativeStdDevPct.max():.1f}%"
stability()
A single measurement says nothing about noise, so the next step runs each configuration twenty times. The DataFrame then carries the mean, the standard deviation, a 95 percent confidence interval, the relative standard deviation in percent, and a StableMeasurement flag, which in the source is simply a relative standard deviation below two percent. We profile vector addition at two sizes, a sixteen-thousand-element one that runs in microseconds and a sixteen-million-element one, alongside a 2048 by 2048 matrix multiplication, and compare how much each varies. The practical rule is to run short kernels more times and compare confidence intervals rather than single averages.
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def make_scale_bench(cache_control, clock_control):
@nsight.analyze.kernel(configs=[1 << 18, 1 << 24], runs=10, cache_control=cache_control,
clock_control=clock_control, thermal_mode="off", verbosity=SILENT,
metrics=["gpu__time_duration.sum", "gpc__cycles_elapsed.avg.per_second"])
def scale(n: int) -> None:
x = torch.randn(n, device="cuda")
with nsight.annotate("x * 2"):
_ = x * 2.0
return scale
@section("4. Cache and clock control: two defaults that change the answer")
def cache_and_clocks():
rows = []
for cache in ("all", "none"):
for clock in ("none", "base"):
try:
df = make_scale_bench(cache, clock)().to_dataframe()
except Exception as e:
print(f" cache_control={cache!r}, clock_control={clock!r}: failed -> {type(e).__name__}: {str(e)[:120]}")
continue
t = df[df.Metric == "gpu__time_duration.sum"].set_index("n").AvgValue
hz = df[df.Metric == "gpc__cycles_elapsed.avg.per_second"].set_index("n").AvgValue
for n in t.index:
rows.append({"cache_control": cache, "clock_control": clock, "MB": 4 * n / 2 ** 20,
"time_us": t[n] / 1e3, "GB/s": 2 * 4 * n / t[n], "SM_MHz": hz[n] / 1e6})
tab = pd.DataFrame(rows)
print(textwrap.indent(tab.to_string(index=False, float_format=lambda v: f"{v:,.1f}"), " "))
print("\n The T4's L2 cache is 4 MB. A 1 MB input stays in L2 between runs unless caches are flushed,")
print(" so cache_control='none' measures a warm-cache kernel and 'all' (the default) a cold one.")
print(" clock_control='base' locks the SM clock to base frequency for every replay (the plain ncu CLI")
print(" default); nsight-python defaults to 'none', so the GPU boosts. SM_MHz is measured per kernel")
print(" (gpc__cycles_elapsed.avg.per_second). The ComputeClock column is not: it is the device's")
print(" rated clock attribute and stays the same whatever the kernel ran at.")
globals()["CLOCK_TABLE"] = tab
small = tab[tab.MB < 2].set_index(["cache_control", "clock_control"]).time_us
return (f"1 MB scale: cold/boost {small.get(('all', 'none'), float('nan')):.1f} us, "
f"warm/boost {small.get(('none', 'none'), float('nan')):.1f} us, "
f"cold/base {small.get(('all', 'base'), float('nan')):.1f} us")
cache_and_clocks()
Two profiler defaults determine what a measurement means, and this step measures both on the same kernel: scaling a vector by two. cache_control decides whether caches are flushed before each kernel replay: Nsight Python flushes them by default, so a small input that would otherwise stay in the T4’s 4 MB L2 cache is read from memory every time. clock_control decides whether the GPU clocks are locked: Nsight Python leaves them unlocked by default, while the plain ncu command line locks them to base clocks. We run all four combinations at 1 MB and 64 MB and also collect gpc__cycles_elapsed.avg.per_second, which measures the clock the kernel actually ran at. That matters because, as the source shows, the DataFrame’s ComputeClock column is the device’s rated clock attribute rather than a measurement.
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SIZES = [(512,), (1024,), (2048,), (4096,)]
def tflops(time_ns: float, n: int) -> tuple[float, str]:
return 2 * n ** 3 / (time_ns * 1e-9) / 1e12, "TFLOP/s"
def make_precision_body():
def precision(n: int) -> None: # a fresh function object for each decorator
a = torch.randn(n, n, device="cuda")
b = torch.randn(n, n, device="cuda")
a16, b16 = a.half(), b.half()
with nsight.annotate("fp32"):
_ = a @ b
with nsight.annotate("fp16"):
_ = a16 @ b16
return precision
precision_tflops = nsight.analyze.kernel(configs=SIZES, runs=5, derive_metric=tflops, thermal_mode="off",
verbosity=SILENT, combine_kernel_metrics=ADD)(make_precision_body())
precision_ratio = nsight.analyze.kernel(configs=SIZES, runs=5, normalize_against="fp32", thermal_mode="off",
verbosity=SILENT, combine_kernel_metrics=ADD)(make_precision_body())
@section("5. A size sweep, a derived TFLOP/s metric, and normalisation")
def precision_sweep():
df = precision_tflops().to_dataframe()
tf = df[df.Metric == "tflops"].pivot(index="n", columns="Annotation", values="AvgValue")
print(" derive_metric=tflops receives (gpu__time_duration.sum, n) and returns (value, 'TFLOP/s'):\n")
print(textwrap.indent(tf[["fp32", "fp16"]].to_string(float_format=lambda v: f"{v:,.2f}"), " "))
print("\n T4 datasheet peaks: 8.1 TFLOP/s fp32, 65 TFLOP/s fp16 on tensor cores.")
rd = precision_ratio().to_dataframe()
fp16 = rd[rd.Annotation == "fp16"][["n", "AvgValue", "StdDev", "Unit", "Normalized"]]
print("\n normalize_against='fp32' divides each AvgValue by the fp32 AvgValue of the same configuration:\n")
print(textwrap.indent(fp16.to_string(index=False, float_format=lambda v: f"{v:,.3f}"), " "))
print("\n Read the row carefully: AvgValue is now a ratio (fp16 time / fp32 time), but StdDev and Unit are")
print(" still those of the raw nanosecond measurement, because only AvgValue is divided.")
best = tf.loc[4096]
return f"n=4096: fp32 {best['fp32']:.2f}, fp16 {best['fp16']:.2f} TFLOP/s"
precision_sweep()
Next, we sweep the matrix size from 512 to 4096 and run both fp32 and fp16 in one benchmark function, with one annotation per precision. A derive_metric function receives the measured time, followed by the benchmark’s arguments, and returns a value with its unit, here TFLOP/s, which the DataFrame stores as an extra metric next to the time. The T4’s datasheet peaks are 8.1 TFLOP/s in fp32 and 65 TFLOP/s in fp16 on its tensor cores, which gives the measured numbers a scale. We then run the same sweep with normalize_against set to the fp32 annotation, which divides every AvgValue by the fp32 value of the same configuration. As the source of the transformation shows, only AvgValue is divided: StdDev, the confidence interval, and the Unit column still describe the raw nanosecond measurement, which is easy to misread.
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BOUND_METRICS = ["gpu__time_duration.sum",
"sm__throughput.avg.pct_of_peak_sustained_elapsed",
"dram__throughput.avg.pct_of_peak_sustained_elapsed"]
@nsight.analyze.kernel(metrics=BOUND_METRICS, runs=3, thermal_mode="off", verbosity=SILENT)
def what_limits(n: int) -> None:
a = torch.randn(n, n, device="cuda").half()
x = torch.randn(64 * 2 ** 20, device="cuda")
s = torch.randn(8192, 1024, device="cuda")
with nsight.annotate("fp16 matmul 4096"):
_ = a @ a
with nsight.annotate("x * 2 (256 MB)"):
_ = x * 2.0
with nsight.annotate("softmax 8192x1024"):
_ = torch.softmax(s, dim=-1)
@section("6. Compute-bound or memory-bound? Three metrics from one replayed kernel")
def bound():
df = what_limits(4096).to_dataframe()
tab = df.pivot(index="Annotation", columns="Metric", values="AvgValue")
tab = tab.rename(columns={"gpu__time_duration.sum": "ms",
"sm__throughput.avg.pct_of_peak_sustained_elapsed": "SM % of peak",
"dram__throughput.avg.pct_of_peak_sustained_elapsed": "DRAM % of peak"})
tab["ms"] = tab["ms"] / 1e6
tab["limited by"] = np.where(tab["SM % of peak"] > tab["DRAM % of peak"], "compute", "memory")
print(textwrap.indent(tab.to_string(float_format=lambda v: f"{v:,.2f}"), " "))
x2 = tab.loc["x * 2 (256 MB)"]
gbs = 2 * 4 * 64 * 2 ** 20 / (x2["ms"] * 1e6)
print(f"\n Cross-check: x * 2 moves 2 x 256 MB in {x2['ms']:.2f} ms = {gbs:.0f} GB/s, against the T4's 320 GB/s")
print(f" datasheet peak ({100 * gbs / 320:.0f}%); Nsight Compute reports {x2['DRAM % of peak']:.0f}% DRAM throughput.")
print(" Collecting several metrics makes Nsight Compute replay each kernel once per pass it needs;")
print(" nsight-python still returns one row per annotation and metric.")
return f"x*2: {gbs:.0f} GB/s ({x2['DRAM % of peak']:.0f}% DRAM); fp16 matmul SM {tab.loc['fp16 matmul 4096', 'SM % of peak']:.0f}%"
bound()
Hardware counters tell us why a kernel takes as long as it does. Passing a list of metrics makes Nsight Compute replay each kernel as many times as it needs to collect them all, and Nsight Python still returns one row per annotation and metric. We collect the duration, the SM throughput, and the DRAM throughput, each as a percentage of the sustained peak, for three kernels of different character: an fp16 matrix multiplication, a scaling of a 256 MB vecto,r and a softmax over 8192 rows of 1024 values, and label each by whichever throughput is higher. For vector scaling, we also compute the effective bandwidth from the bytes it must move and the measured time, and compare it against the T4’s 320 GB/s datasheet peak and the DRAM percentage Nsight Compute reports.
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BYTES = ["gpu__time_duration.sum", "dram__bytes_read.sum", "dram__bytes_write.sum"]
@nsight.analyze.kernel(metrics=BYTES, runs=5, thermal_mode="off", verbosity=SILENT,
combine_kernel_metrics=lambda x, y: x + y)
def fusion(n: int) -> None:
a, b, c = (torch.randn(n, device="cuda") for _ in range(3))
with nsight.annotate("a + b * c (unfused)"):
_ = a + b * c
with nsight.annotate("torch.addcmul (fused)"):
_ = torch.addcmul(a, b, c)
@nsight.analyze.kernel(runs=1, thermal_mode="off", verbosity=SILENT)
def fusion_without_combine(n: int) -> None:
a, b, c = (torch.randn(n, device="cuda") for _ in range(3))
with nsight.annotate("a + b * c"):
_ = a + b * c
@section("7. Kernel fusion, measured in bytes: combine_kernel_metrics")
def fused():
n = 32 * 2 ** 20
try:
fusion_without_combine(n)
print(" (the unfused expression profiled without combine_kernel_metrics)")
except Exception as e:
print(f" Profiled without combine_kernel_metrics, the unfused expression raises:\n {type(e).__name__}: {str(e).splitlines()[0][:200]}\n")
df = fusion(n).to_dataframe()
tab = df.pivot(index="Annotation", columns="Metric", values="AvgValue")
tab = pd.DataFrame({"ms": tab["gpu__time_duration.sum"] / 1e6,
"MB read": tab["dram__bytes_read.sum"] / 2 ** 20,
"MB written": tab["dram__bytes_write.sum"] / 2 ** 20})
print(f" {n:,} float32 elements per tensor (128 MB each); metrics summed over the kernels in each region:\n")
print(textwrap.indent(tab.to_string(float_format=lambda v: f"{v:,.1f}"), " "))
u, f = tab.loc["a + b * c (unfused)"], tab.loc["torch.addcmul (fused)"]
print(f"\n The unfused version writes b * c to memory and reads it back: {u['MB read'] + u['MB written']:,.0f} MB of")
print(f" traffic against {f['MB read'] + f['MB written']:,.0f} MB, and {u['ms'] / f['ms']:.2f}x the time.")
return f"fusion: {u['ms'] / f['ms']:.2f}x faster, {(u['MB read'] + u['MB written']) / (f['MB read'] + f['MB written']):.2f}x less traffic"
fused()
Kernel fusion is usually described in terms of time; here we measure it in bytes. The expression a + b * c in eager PyTorch materializes b * c in memory before adding a, while torch.addcmul computes the same result in one operation. We first profile the unfused expression without combine_kernel_metrics to show the error Nsight Python raises when an annotated region contains more than one kernel, then profile both versions with combine_kernel_metrics summing the duration and the DRAM bytes read and written over every kernel in each region, on tensors of 2^25, about 33.5 million, float32 elements.
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import pynvml
pynvml.nvmlInit()
NVML = pynvml.nvmlDeviceGetHandleByIndex(0)
@nsight.experimental.collect(scope=nsight.CollectionScope.ONCE)
def driver_version(*args):
return pynvml.nvmlSystemGetDriverVersion()
@nsight.experimental.collect(scope=nsight.CollectionScope.RUN)
def sm_clock_mhz(*args):
return pynvml.nvmlDeviceGetClockInfo(NVML, pynvml.NVML_CLOCK_SM)
@nsight.experimental.collect(scope=nsight.CollectionScope.RUN)
def gpu_temp_c(*args):
return pynvml.nvmlDeviceGetTemperature(NVML, pynvml.NVML_TEMPERATURE_GPU)
def make_heat_body():
def heat(n: int) -> None:
a = torch.randn(n, n, device="cuda").half()
with nsight.annotate("fp16 matmul"):
_ = a @ a
return heat
@section("8. Custom info collectors and thermal control (experimental API)")
def collectors():
out = {}
for mode in ("off", "auto"):
bench = nsight.analyze.kernel(configs=[4096], runs=30, thermal_mode=mode, thermal_timeout=60,
info_collectors=[driver_version, sm_clock_mhz, gpu_temp_c],
verbosity=SILENT, output_prefix=f"/content/nspy_thermal_{mode}/",
output_csv=True)(make_heat_body())
t0 = time.time()
try:
df = bench().to_dataframe()
except Exception as e:
print(f" thermal_mode={mode!r}: failed -> {type(e).__name__}: {str(e).splitlines()[0][:150]}")
continue
wall = time.time() - t0
csvs = sorted(glob.glob(f"/content/nspy_thermal_{mode}/profiled_data-*.csv"))
raw = pd.read_csv(csvs[-1]) if csvs else pd.DataFrame()
out[mode] = (df.iloc[0], wall, raw)
r = df.iloc[0]
print(f" thermal_mode={mode!r}: {r['AvgValue'] / 1e6:.3f} ms +/- {r['RelativeStdDevPct']:.1f}% "
f"wall {wall:.0f}s driver {r['driver_version']}")
print(f"\n The collectors became DataFrame columns; RUN-scoped ones were called before every run.")
if "off" not in out:
return "info-collector run failed"
raw = out["off"][2]
tcol = "Value" if "Value" in raw.columns else raw.columns[-1]
print(f" raw per-run CSV columns: {list(raw.columns)}")
if {"sm_clock_mhz", "gpu_temp_c"} <= set(raw.columns):
corr = raw[[tcol, "sm_clock_mhz", "gpu_temp_c"]].corr().iloc[0]
print(f" over 30 runs: SM clock {raw.sm_clock_mhz.min()}-{raw.sm_clock_mhz.max()} MHz, "
f"temperature {raw.gpu_temp_c.min()}-{raw.gpu_temp_c.max()} C")
print(f" correlation of kernel time with SM clock {corr['sm_clock_mhz']:+.2f}, with temperature {corr['gpu_temp_c']:+.2f}")
print("\n thermal_mode='auto' (the default) pauses between runs when the GPU's thermal headroom runs low;")
print(" the wall-time difference is the price of that, the spread difference what it buys.")
return "; ".join(f"{m} {out[m][0]['RelativeStdDevPct']:.1f}% RSD in {out[m][1]:.0f}s" for m in out)
collectors()
Version 1.1 adds custom information collectors, an experimental API for attaching application-specific values to the results. A function decorated with nsight.experimental.collect and a collection scope is called once per session, per configuration, per run or per annotation, and its return value becomes a column. We record the driver version once and read the SM clock and GPU temperature through NVML before every run of a long fp16 multiplication, with output_csv enabled so that the per-run rows are written next to the aggregated ones. We run the benchmark twice, with thermal control off and with the default auto mode, in which Nsight Python pauses between runs when the GPU’s thermal headroom runs low, and compare the spread and the wall-clock time of the two.
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CUPTI_SCRIPT = r'''
import os, json, time
os.environ["NSPY_NCU_INIT_AT_IMPORT"] = "0" # do not load Nsight Compute in this process
import nsight
nsight.activate(nsight.Tool.CUPTI)
import torch
@nsight.analyze.kernel(configs=[512, 1024, 2048, 4096], runs=10, thermal_mode="off",
verbosity=nsight.VerbosityLevel.SILENT, combine_kernel_metrics=lambda x, y: x + y)
def matmul(n):
a = torch.randn(n, n, device="cuda")
b = torch.randn(n, n, device="cuda")
with nsight.annotate("matmul"):
_ = a @ b
t0 = time.time()
df = matmul().to_dataframe()
wall = time.time() - t0
nsight.deactivate()
print(json.dumps({"wall": wall, "rows": df[["n", "AvgValue", "RelativeStdDevPct"]].to_dict("records")}))
'''
@nsight.analyze.kernel(configs=[512, 1024, 2048, 4096], runs=10, thermal_mode="off", verbosity=SILENT,
combine_kernel_metrics=ADD)
def matmul_ncu(n: int) -> None:
a = torch.randn(n, n, device="cuda")
b = torch.randn(n, n, device="cuda")
with nsight.annotate("matmul"):
_ = a @ b
@section("9. The experimental CUPTI backend, in its own process, against Nsight Compute")
def cupti_vs_ncu():
t0 = time.time()
ncu_df = matmul_ncu().to_dataframe().set_index("n")
ncu_wall = time.time() - t0
if not CUPTI_EXTRA:
return f"cupti-python could not be installed; Nsight Compute run took {ncu_wall:.0f}s"
with open("/content/cupti_matmul.py", "w") as f:
f.write(CUPTI_SCRIPT)
env = {k: v for k, v in os.environ.items() if k not in ("NVTX_INJECTION64_PATH", "NV_COMPUTE_PROFILER_PERFWORKS_DIR",
"NV_COMPUTE_PROFILER_IN_PROCESS")}
env["PYTHONPATH"] = CUPTI_DIR + os.pathsep + env.get("PYTHONPATH", "")
p = subprocess.run([sys.executable, "/content/cupti_matmul.py"], capture_output=True, text=True, env=env, timeout=900)
if p.returncode != 0:
print(f" CUPTI run failed (exit {p.returncode}); last lines of its stderr:")
print(textwrap.indent("\n".join(p.stderr.strip().splitlines()[-8:]), " "))
return f"CUPTI backend failed (exit {p.returncode}); Nsight Compute run took {ncu_wall:.0f}s"
res = next((json.loads(line) for line in reversed(p.stdout.strip().splitlines()) if line.startswith('{"wall"')), None)
if res is None:
return f"CUPTI run printed no result; Nsight Compute run took {ncu_wall:.0f}s"
cu = pd.DataFrame(res["rows"]).set_index("n")
print(f" {'n':>5s} {'ncu ms':>8s} {'cupti ms':>9s} {'ratio':>6s} {'ncu RSD%':>9s} {'cupti RSD%':>11s}")
for n in ncu_df.index:
a, b = ncu_df.loc[n, "AvgValue"] / 1e6, cu.loc[n, "AvgValue"] / 1e6
print(f" {n:5d} {a:8.3f} {b:9.3f} {b / a:6.2f} {ncu_df.loc[n, 'RelativeStdDevPct']:9.1f} {cu.loc[n, 'RelativeStdDevPct']:11.1f}")
print(f"\n wall time for 4 sizes x 10 runs: Nsight Compute {ncu_wall:.1f}s, CUPTI {res['wall']:.1f}s"
f" ({ncu_wall / res['wall']:.1f}x)")
print(" The two tools are mutually exclusive in one process and Nsight Compute cannot be unloaded, so")
print(" CUPTI runs in a child process with NSPY_NCU_INIT_AT_IMPORT=0. CUPTI records one execution per")
print(" launch, with no replay, no cache flush and no clock control: cheaper, and warmer caches.")
return f"CUPTI {ncu_wall / res['wall']:.1f}x faster to profile; durations within {abs(1 - cu.AvgValue.div(ncu_df.AvgValue)).max():.0%}"
cupti_vs_ncu()
The CUPTI backend is the other headline feature of 1.1: kernel durations measured through CUPTI’s activity API, without Nsight Compute. The two tools are mutually exclusive within a single process. Nsight Compute cannot be unloaded once active, so we write a small script that sets NSPY_NCU_INIT_AT_IMPORT to 0 before importing nsight, activates the CUPTI tool, profiles the same four matrix sizes, and runs it in a child process with the cupti-python directory on its path. CUPTI supports only the duration metric and records one execution per launch, with no replay, cache flushing, or clock control, so we compare both the durations and the wall-clock cost of profiling against the same benchmark under Nsight Compute.
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@section("10. Keep the evidence: CSVs and the full Nsight Compute report")
def evidence():
files = sorted(glob.glob("/content/nspy_thermal_off/*"))
for f in files:
print(f" {os.path.basename(f):60s} {os.path.getsize(f) / 1024:9.1f} KB")
import ncu_report
rep_path = next((f for f in files if f.endswith(".ncu-rep")), None)
if rep_path is None:
raise RuntimeError("no .ncu-rep in /content/nspy_thermal_off/ (step 8 did not complete)")
rep = ncu_report.load_report(rep_path)
actions = [rep.range_by_idx(i).action_by_idx(j) for i in range(rep.num_ranges())
for j in range(rep.range_by_idx(i).num_actions())]
a0 = actions[0]
print(f"\n the .ncu-rep holds {len(actions)} profiled kernel launches; the first is")
print(f" {a0.name()}")
print(f" gpu__time_duration.sum = {a0['gpu__time_duration.sum'].value():,.0f} {a0['gpu__time_duration.sum'].unit()}")
print(f" {len(a0.metric_names())} metrics available in the report for this launch")
print("\n output_csv=True writes profiled_data (one row per run) and processed_data (the aggregated")
print(" DataFrame). The .ncu-rep opens in the Nsight Compute GUI for source-level analysis.")
return f"{len(actions)} launches in the report, {len(a0.metric_names())} metrics each"
evidence()
Every profiled call leaves its evidence behind. With an output prefix and output_csv, the directory holds the Nsight Compute report, its log, the per-run profiled_data CSV and the aggregated processed_data CSV. We list the files and open the report with ncu_report, Nsight Compute’s Python report interface, which Nsight Python itself uses: it lists the profiled kernel launches, and each one carries every metric that was collected. The same .ncu-rep file opens in the Nsight Compute interface for source-level analysis.
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banner("SUMMARY")
for name, res in RESULTSitems():
print(f" {name:<76s} {res}")
print("""
What to carry over
- import nsight first; it loads Nsight Compute into the process before CUDA starts.
- Annotate exactly the work you mean to measure; one kernel per region unless you pass
combine_kernel_metrics.
- Decide cache and clock control on purpose: nsight-python flushes caches and leaves clocks
unlocked by default, which is not what a plain ncu run does.
- Use runs and the CI95 / RelativeStdDevPct columns; one run tells you nothing about noise.
- After normalize_against, only AvgValue is a ratio.
Where to go next
- Triton and cuTile kernels: examples/07_triton_minimal.py and examples/12_cutile.py.
- Plots: @nsight.analyze.plot(...) on top of @nsight.analyze.kernel, with row/col panels.
- Docs: docs.nvidia.com/nsight-python Code: github.com/NVIDIA/nsight-python
""")
The summary prints the one-line result of each step and the habits worth keeping: import nsight before anything initializes CUDA, annotate exactly the work you mean to measure, choose cache and clock control deliberately, use enough runs and read the confidence intervals, and remember that only AvgValue is normalized. It ends with pointers to the Triton and cuTile examples in the repository and to the plotting decorator.
In conclusion, Nsight Python turns kernel profiling from a separate command-line session into a few lines in the code being measured: a decorator, an annotation, and a DataFrame that carries the measurement, its spread, and its context. Working through it on a Colab T4 also shows where the convenience hides decisions that change the numbers. The profiler has to be newer than the one Colab ships and must be loaded before CUDA starts. Cache flushing and clock locking default to the opposite of what a plain ncu run does; the clock column reports the card’s rating rather than the clock the kernel ran at; normalization divides only the average. Every annotated region is expected to hold exactly one kernel unless you say how to combine them. Once those choices are explicit, the same few lines answer the questions that matter in practice: how stable a timing is, what limits a kernel, what fusion actually saves, and what a cheaper profiler such as the new CUPTI backend gives up for its lower overhead.
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The post NVIDIA Nsight Python Tutorial: How to Profile GPU Kernels appeared first on MarkTechPost.
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