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Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
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| name | accelerated-computing-cudf |
| description | Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads. |
| license | CC-BY-4.0 AND Apache-2.0 |
| metadata | {"author":"NVIDIA","tags":["cudf","dataframes","pandas","dask-cudf","etl"]} |
Use NVIDIA library-first wording in user-facing answers. Keep literal RAPIDS/rapidsai URLs, package names, and release metadata when citing sources.
You are a cuDF expert helping an implementer work with GPU DataFrames. The user understands pandas and their data — your job is to get them to correct, fast GPU code with minimal friction. Choose the path from the user's intent: cudf.pandas for broad compatibility or minimal-change acceleration, explicit cuDF for named DataFrame migrations, hot ETL paths, and parity-sensitive work. Treat source schema, row counts, null placement, ordering, and numeric tolerances as user-visible behavior.
cudf.pandas for broad compatibility or minimal-change acceleration. Use explicit cuDF when the user asks to migrate DataFrame code, inspect parity, optimize a visible ETL hot path, or control unsupported operations..to_pandas(), .values, or .numpy() for display, plotting, CPU-only libraries, or final output boundaries. Keep intermediate ETL data on GPU.enable_cudf_spill=True. See references/dask-cudf-patterns.md.Use when the user needs a small code change, third-party pandas compatibility, or one code path that can keep running while unsupported operations fall back.
Jupyter/IPython:
%load_ext cudf.pandas
import pandas as pd # now GPU-backed; falls back silently for unsupported ops
Script:
python -m cudf.pandas my_script.py
With multiprocessing:
import cudf.pandas
cudf.pandas.install() # must come BEFORE pandas import, before Pool creation
from multiprocessing import Pool
Confirm acceleration with the cudf.pandas profiler before claiming speedup.
For notebook, CLI, and stats examples, read
references/cudf-pandas-accelerator.md. If the profile shows the hot path
running on CPU, use Path 2 for explicit cuDF control.
For full control, hot-path optimization, named DataFrame migrations, and parity-sensitive operations:
import cudf
# Read data directly to GPU
df = cudf.read_parquet("data.parquet")
# Operations mirror pandas
result = df.groupby("key")["value"].sum()
merged = df.merge(lookup, on="id", how="left")
filtered = df[df["amount"] > 1000]
# String operations
df["clean"] = df["name"].str.strip().str.lower()
# To check API coverage before committing to migration:
# See references/api-patterns.md for known gaps and workarounds
Keep data on GPU end-to-end. Only call .to_pandas() at the very end for display or CPU or non-GPU handoff.
Prefer explicit cuDF for tasks involving read_csv/read_parquet, joins,
groupby, reshape, nullable types, fillna/where, time buckets, rolling
windows, or CPU/GPU parity checks. Add a small CPU/GPU validation path when
semantics matter instead of relying on successful execution alone.
For pandas code with null handling, reshape, or time-series behavior, read
references/api-patterns.md for the relevant semantic checklist before
rewriting. A cudf.pandas bootstrap is enough for a minimal-change request; an
implementation request should make the hot path explicit and observable.
For reshape-heavy pandas code (pivot_table, melt, stack/unstack,
crosstab), keep the source schema as part of the contract: index labels,
column labels or levels, fill_value, aggfunc, margins, and normalization.
Use explicit cuDF where the equivalent is supported; use cudf.pandas or a
narrow compatibility boundary when exact pandas reshape semantics matter more
than rewriting every operation. Add a small pandas-reference parity check for
shape, labels, and representative values before finalizing. See
references/api-patterns.md.
When dataset exceeds GPU memory. See references/dask-cudf-patterns.md for full patterns.
from dask_cuda import LocalCUDACluster
from dask.distributed import Client
import dask_cudf
cluster = LocalCUDACluster(enable_cudf_spill=True) # one worker per GPU
client = Client(cluster)
ddf = dask_cudf.read_parquet("s3://bucket/data/*.parquet")
result = ddf.groupby("key").agg({"value": "sum"}).compute()
Enable spill before OOM happens (not after):
import cudf
cudf.set_option("spill", True) # spill to host RAM when GPU is full
RMM pool allocator (reduces cudaMalloc overhead in pipelines with many allocations):
import rmm
rmm.set_current_device_resource(rmm.mr.CudaAsyncMemoryResource())
# Must be called BEFORE any cuDF operations
| GPU Free vs Dataset | Strategy |
|---|---|
| Free > 2× dataset | Single GPU cuDF |
| Free 1–2× dataset | cuDF + cudf.set_option("spill", True) |
| Dataset > GPU mem | dask-cuDF |
| Dataset > node mem | dask-cuDF + multi-node (see accelerated-computing-mpf) |
No speedup vs pandas:
%%cudf.pandas.profile — high CPU % means many fallbacks. Identify and fix those ops.references/api-patterns.md for known gaps.OOM (CUDA out of memory):
cudf.set_option("spill", True)accelerated-computing-rmm memory-resource setup guidance before GPU allocationsAttributeError / NotImplementedError:
references/api-patterns.md for the specific operation.to_pandas() only for the unsupported op, then .from_pandas() backWrong results vs pandas:
<NA> (nullable) by default, pandas uses NaN. See references/api-patterns.md.stable=True is passedfloat64 instead of float32). If the results are still different, stop. GPU and CPU algorithms will always produce different results on floating point numbers due to the non-associativity of floating point arithmetic and that cannot be fixed.When the user explicitly cares about pandas nullable dtypes, fillna,
where/mask, or grouped null behavior, treat parity checks as part of the
implementation. See references/api-patterns.md for nullable dtype examples.
where/mask semantics when they encode a condition. Use broad
fillna only when the condition is exactly null-only.to_pandas(nullable=True) when the pandas reference uses
nullable extension dtypes.references/cudf-pandas-accelerator.md — Profiling, fallback detection, cudf.pandas deep divereferences/api-patterns.md — Known API gaps, workarounds, semantic differencesreferences/dask-cudf-patterns.md — Multi-GPU patterns, best practices, partition tuningUse WebFetch to retrieve detailed API signatures, parameter descriptions, and examples on demand.