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Wukong's pay-per-use pricing keeps costs low.
Wukong can be easily deployed in seconds on home computers or in the cloud.
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Wukong uses serverless computing to scale to thousands of executors in seconds.
From machine learning & linear algebra to data analytics, Wukong can do it all.
Wukong delivers best-in-class end-to-end performance for a variety of workloads.
Wukong is open source. Head over to GitHub to get started today.
# Generate random input data.
X = da.random.random((128000, 100), chunks = (10000, 100))
# Prepare SVD computation.
u, s, v = da.linalg.svd(X)
# Begin execution.
result = u.compute()
result = s.compute()
result = v.compute()
# Generate random input data.
X = da.random.random((10000, 10000), chunks = (2000, 2000))
# Prepare SVD computation.
u, s, v = da.linalg.svd_compressed(X, k = 5)
# Begin execution.
result = u.compute()
result = s.compute()
result = v.compute()
# Generate random input data.
X = da.random.random((10000, 10000), chunks = (1000, 1000))
# Prepare GEMM computation.
XX = da.matmul(X, X)
# Begin execution.
result = XX.compute()
# Generate random input data.
X = da.random.random((32768, 128), chunks = (8192, 128))
# Prepare GEMM computation.
q,r = da.linalg.tsqr(X)
# Begin execution.
res_q = q.compute()
res_r = r.compute()
# Prepare classifier.
X, y = sklearn.datasets.make_classification(n_samples=1000)
clf = ParallelPostFit(SVC(gamma='scale'))
clf.fit(X, y)
# Prepare the workload and begin execution.
X, y = dask_ml.datasets.make_classification(n_samples = 1024000, random_state = 1024000, chunks = 10240)
result = clf.predict(X).compute()
import pandas as pd
# Read dataframe from file.
df = pd.read_csv('employees.csv')
# Create Dask dataframe.
dask_df = dask.dataframe.from_pandas(df, npartitions = 64)
# Prepare and perform operation.
filtered_df = dask_df[dask_df['salary'] > 50000]
filtered_df.compute()