# Frequently Asked Questions

## How does `cuml.accel` work with `cudf.pandas`?

`cuml.accel` provides zero-code-change acceleration for scikit-learn and
similar packages, while `cudf.pandas` does the same for pandas. They can be
used together from the command line:

```console
python -m cudf.pandas -m cuml.accel script.py
```

In Jupyter, load both extensions before importing the libraries they
accelerate:

```ipython
%load_ext cudf.pandas
%load_ext cuml.accel
```

## Can I load a serialized model without `cuml.accel`?

Models serialized while `cuml.accel` is active may be loaded without the
accelerator. They are then restored as their normal scikit-learn, UMAP, or
HDBSCAN counterparts. If loaded while `cuml.accel` is active, they are
restored as accelerated proxy models.

Serialization formats are not guaranteed to remain compatible across cuML or
dependency versions. For long-term portability, record the training
environment and validate loading and inference in the target environment.

#### WARNING
Only unpickle or deserialize models from trusted sources. Python’s
`pickle` module is not secure; malicious data can execute arbitrary code.
See the [Python pickle documentation](https://docs.python.org/3/library/pickle.html) for details.

## How do I report a bug?

Open a report in the [cuML issue tracker](https://github.com/NVIDIA/cuml/issues/new?template=bug_report.md). Include a minimal reproducer,
dependency versions, input characteristics, and logs from an `info` or
`debug` run when possible. Please report exceptions, incorrect fallback,
measurably worse model quality, and regressions in end-to-end runtime. Import
time is expected to be longer with `cuml.accel` and should be excluded from
runtime comparisons.

For help determining what ran on the GPU and why an operation fell back, see
[Logging and Profiling](../logging-and-profiling/index.html.md). The estimator inventory is in
[Accelerated Estimator Support](../compatibility/index.html.md), along with detailed estimator-specific
conditions.