Traditional machine learning tools built on top of Nx. Scholar implements several algorithms for classification, regression, clustering, dimensionality reduction, metrics, and preprocessing.
For deep learning, see Axon. For decision trees/forests, see EXGBoost.
Add to your mix.exs
:
def deps do
[
{:scholar, "~> 0.3.0"}
]
end
Besides Scholar, you will most likely want to use an existing Nx compiler/backend, such as EXLA:
def deps do
[
{:scholar, "~> 0.3.0"},
{:exla, ">= 0.0.0"}
]
end
And then in your config/config.exs
file:
import Config
config :nx, :default_backend, EXLA.Backend
# Client can also be set to :cuda / :rocm
config :nx, :default_defn_options, [compiler: EXLA, client: :host]
JIT required! {: .warning}
It is important you set the
default_defn_options
as shown in the snippet above, as many algorithms in Scholar use loops which are much more memory efficient when JIT compiled.If for some reason you cannot set a default
defn
compiler, you can explicitly JIT any function, for example:EXLA.jit(&Scholar.Cluster.AffinityPropagation.fit/1)
.
To use Scholar inside code notebooks, run:
Mix.install([
{:scholar, "~> 0.3.0"},
{:exla, ">= 0.0.0"}
])
Nx.global_default_backend(EXLA.Backend)
# Client can also be set to :cuda / :rocm
Nx.Defn.global_default_options(compiler: EXLA, client: :host)
JIT required! {: .warning}
It is important you set the
Nx.Defn.global_default_options/1
as shown in the snippet above, as many algorithms in Scholar use loops which are much more memory efficient when JIT compiled.If for some reason you cannot set a default
defn
compiler, you can explicitly JIT any function, for example:EXLA.jit(&Scholar.Cluster.AffinityPropagation.fit/1)
.
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