Parallel Python with Dask
Tim Peters
- Unlock
- the
- Power
- of
- Parallel
- Python
- with
- Dask:
- A
- Perfect
- Learning
- Guide
- for
- Aspiring
- Data
- ScientistsDask
- has
- revolutionized
- parallel
- computing
- for
- Python,
- empowering
- data
- scientists
- to
- accelerate
- their
- workflows.
- This
- comprehensive
- guide
- unravels
- the
- intricacies
- of
- Dask
- to
- help
- you
- harness
- its
- capabilities
- for
- machine
- learning
- and
- data
- analysis.
- Across
- 10
- chapters,
- you'll
- master
- Dask's
- fundamentals,
- architecture,
- and
- integration
- with
- Python's
- scientific
- computing
- ecosystem.
- Step-by-step
- tutorials
- demonstrate
- parallel
- mapping,
- task
- scheduling,
- and
- leveraging
- Dask
- arrays
- for
- NumPy
- workloads.
- You'll
- discover
- how
- Dask
- seamlessly
- scales
- Pandas,
- Scikit-Learn,
- PyTorch,
- and
- other
- libraries
- for
- large
- datasets.Dedicated
- chapters
- explore
- scaling
- regression,
- classification,
- hyperparameter
- tuning,
- feature
- engineering,
- and
- more
- with
- clear
- examples.
- You'll
- also
- learn
- to
- tap
- into
- the
- power
- of
- GPUs
- with
- Dask,
- RAPIDS,
- and
- Google
- JAX
- for
- orders
- of
- magnitude
- speedups.
- This
- book
- places
- special
- emphasis
- on
- practical
- use
- cases
- related
- to
- scalability
- and
- distributed
- computing.
- You'll
- learn
- Dask
- patterns
- for
- cluster
- computing,
- managing
- resources
- efficiently,
- and
- robust
- data
- pipelines.
- The
- advanced
- chapters
- on
- DaskML
- and
- deep
- learning
- showcase
- how
- to
- build
- scalable
- models
- with
- PyTorch
- and
- TensorFlow.With
- this
- book,
- you'll
- gain
- practical
- skills
- to:Packed
- with
- hands-on
- examples
- and
- expert
- insights,
- this
- book
- provides
- the
- complete
- toolkit
- to
- harness
- Dask's
- capabilities.
- It
- will
- empower
- Python
- programmers,
- data
- scientists,
- and
- machine
- learning
- engineers
- to
- achieve
- faster
- workflows
- and
- operationalize
- parallel
- computing.Table
- of Content
