BitzFlex

BitzFlex

Hi!,

I have the Mac Studio and I want to use the GPU on it with EXLA.
I made the project that has config like below.

import Config
config :nx, :default_backend, EXLA.Backend

with iex ,
Nx.tensor [1,2,3,4] ,
it show
[info] TfrtCpuClient created.
#Nx.Tensor<
s64[4]
EXLA.Backend<host:0, 0.1189406505.872022046.161358>
[1, 2, 3, 4]

it seems not use GPU.

And, with below code (to make long Nx calculation) ----
url =
“https://pix4free.org/assets/library/2021-01-12/originals/san_francisco_california_golden_gate_bridge_water.jpg”

%{body: raw_image} = Req.get!(url)

imageNx = StbImage.read_binary!(raw_image)
            |> StbImage.to_nx()
            |> Nx.reshape({:auto,3})

IO.puts "Started --------"
start_ts =DateTime.utc_now() |> DateTime.to_unix(:second)

KMeans.fit(imageNx,
    num_clusters: 10,
    num_runs: 10,
    max_iterations: 200,
    key: Nx.Random.key(42)
  )

end_ts =DateTime.utc_now() |> DateTime.to_unix(:second)
IO.puts "DONE -- ellapse : #{end_ts - start_ts}"

It takes about 230 seconds, and the Mac Activity Monitor show 0 GPU usage of beam_smp.

Does the EXLA support Apple Silicon GPU ?

Any suggestion will be appreciated..

Thanks,

Showing Posts 1 to 4

polvalente

polvalente

Nx Core Team

We don’t currently support mac GPUs unfortunately.
There’s ongoing work on migrating EXLA to an MLIR-based implementation that will in the future enable support, though.

Torchx is closer to that, altough Pytorch still has a few issues to go on the ways of API coverage for Apple Metal.

polvalente

polvalente

Nx Core Team

Ah, there’s something else you should also configure, the default defn compiler!

config :nx, :default_defn_options, compiler: EXLA should make your code quite a bit faster too

BitzFlex

BitzFlex OP

Thank you so much!

Following your advice, adding the “config :nx, :default_defn_options, compiler: EXLA” setting dramatically reduced the computation time.

It went down from 220 seconds to 13 seconds. That’s really amazing.

Thank you once again.

polvalente

polvalente

Nx Core Team

Awesome!

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