RAPIDS.jl
Unofficial Julia wrapper for the RAPIDS.ai ecosystem.
The goal of this library is to provide a simple method for accessing the GPU accelerated models withing RAPIDS from Julia, and integrating the models into MLJ. This library relies on PythonCall.jl and CondaPkg.jl for efficient installations of the Python dependencies.
This wrapper could be broken up into several libraries (cuDF
, cuML
, cuGraph
, cuSignal
, cuSpatial
), but there would be significant overlap between these libraries. Large dependencies such as cudatoolkit
would be repeated.
Long term, directly wrapping libcudf
, libcuml
... would greatly improve this library, but I don't have time to tackle that at this moment.
CUDA/GPU requirements
More information is available here.
- CUDA 11.2+
- NVIDIA driver 460.27.03+
- Pascal architecture or better (Compute Capability >=6.0)
- Ubuntu 20.04/22.04 or CentOS 7 / Rocky Linux 8 with gcc/++ 9.0+
Installation
From the Julia General Registry:
julia> ] # enters the pkg interface
pkg> add RAPIDS
julia> using Pkg; Pkg.add("RAPIDS")
From source:
julia> ]add https://github.com/tylerjthomas9/RAPIDS.jl
julia> using Pkg; Pkg.add(url="https://github.com/tylerjthomas9/RAPIDS.jl")
Python API
You can access the following python libraries with their standard syntax:
cupy
cudf
cuml
cugraph
cusignal
cuspatial
cuxfilter
dask
dask_cuda
dask_cudf
numpy
pickle
Here is an example of using LogisticRegression
, make_classification
via the Python API.
using RAPIDS
const make_classification = cuml.datasets.classification.make_classification
X_py, y_py = make_classification(n_samples=200, n_features=4,
n_informative=2, n_classes=2)
lr = cuml.LogisticRegression(max_iter=100)
lr.fit(X_py, y_py)
preds = lr.predict(X_py)
print(lr.coef_)
MLJ Interface
A MLJ interface is also available for supported models. The model hyperparameters are the same as described in the cuML docs. The only difference is that the models will always input/output numpy arrays, which will be converted back to Julia arrays (output_type="input"
).
using MLJBase
using RAPIDS.CuML
const make_classification = cuml.datasets.classification.make_classification
X_py, y_py = make_classification(n_samples=200, n_features=4,
n_informative=2, n_classes=2)
X = RAPIDS.pyconvert(Matrix{Float32}, X_py.get())
y = RAPIDS.pyconvert(Vector{Float32}, y_py.get().flatten())
lr = LogisticRegression(max_iter=100)
mach = machine(lr, X, y)
fit!(mach)
preds = predict(mach, X)
print(mach.fitresult.coef_)
MLJ Support:
- Clustering
KMeans
DBSCAN
AgglomerativeClustering
HDBSCAN
- Classification
LogisticRegression
MBSGDClassifier
RandomForestClassifier
SVC
LinearSVC
KNeighborsClassifier
- Regression
LinearRegression
Ridge
Lasso
ElasticNet
MBSGDRegressor
RandomForestRegressor
CD
SVR
LinearSVR
KNeighborsRegressor
- Dimensionality Reduction
PCA
IncrementalPCA
TruncatedSVD
UMAP
TSNE
GaussianRandomProjection
- Time Series
ExponentialSmoothing
ARIMA