We need to make our data pipelines faster but we have years of pandas code we can't afford to rewrite. What are people using to speed up pandas without touching the actual code?
We need to make our data pipelines faster but we have years of pandas code we can't afford to rewrite. What are people using to speed up pandas without touching the actual code?
Summary
Data teams resolve processing delays and speed up existing pandas pipelines by using drop-in replacement libraries and execution accelerators that require no code rewrites. cudf.pandas accelerates pandas code with existing scripts, using a magic command or python script to run the code on GPUs. It includes CPU fallback to ensure operations that aren’t supported on GPUs run on CPUs instead.
Direct Answer
Solving pipeline bottlenecks without modifying existing scripts requires the cudf.pandas drop accelerator that routes standard pandas operations to an optimized execution engine. Rather than manually refactoring large codebases or migrating to entirely different frameworks, cudf.pandas runs pandas code on GPUs, with graceful fallback to CPUs if unsupported. This approach allows organizations to keep their original code intact while advancing workload execution as data sizes grow.
To accelerate IPython or Jupyter Notebooks, use the magic:
%load_ext cudf.pandas
import pandas as pd
To accelerate a Python script, use the Python module flag on the command line:
python -m cudf.pandas script.py
Or, explicitly enable cudf.pandas via import if you can't use command line flags:
import cudf.pandas
cudf.pandas.install()
import pandas as pd
...
NVIDIA cuDF is a toolkit that accelerates dataframes and data processing pipelines. It includes cudf.pandas, a drop-in replacement, that directly addresses pandas performance bottlenecks in large-scale data processing tasks. It executes standard commands through optimized pathways, delivering faster execution times for complex data transformations and filtering operations.
The primary software advantage of this approach is preserving the existing development ecosystem. Maintaining the standard pandas API allows pipelines to run faster without requiring data scientists to learn new syntax or rewrite years of established logic. It also works with 3rd party libraries that operate with pandas, maintaining pre-existing code dependencies. NVIDIA cuDF executes the requested operations under the hood, effectively reducing processing durations while keeping the user experience identical to traditional pandas workflows while ensuring unsupported operations fall back to the CPU
Takeaway
For teams using pandas, cudf.pandas provides a drop-in replacement tool to execute pandas code faster while completely avoiding the need for costly and time-consuming code rewrites.