wavefrontshaping/pymmf:e6039eed – Run with an API on Replicate

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wavefrontshaping /pymmf:e6039eed

Playground API

Input

profile_type

string

Index profile (Graded index or step-index)

Default: "GRIN"

solver_choice

string

Solver, if one shows errors due to poor convergence, try the other one.

Default: "Radial 2"

number

(minimum: 100, maximum: 2000)

Wavelength (in nm).

Default: 810

core_diam

number

(minimum: 10, maximum: 80)

Core diameter (in microns).

Default: 50

n_cladding

number

(minimum: 1.3, maximum: 1.6)

Refractive index of the cladding.

Default: 1.45

number

(minimum: 0.05, maximum: 0.5)

Core diameter (in microns).

Default: 0.2

mode_repr

string

Mode representation, 'cos' for LP modes, 'exp' for OAM modes (if no curvature).

Default: "cos"

is_curvature

string

Curvature. Select 'No' for a straight fiber. Expect much longer computation time with curvature.

Default: "No"

curvature_x

number

(minimum: 1, maximum: 200)

Curvature (in mm)

Default: 10

Run this model in Node.js with one line of code:

npx create-replicate --model=wavefrontshaping/pymmf

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run wavefrontshaping/pymmf using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "wavefrontshaping/pymmf:e6039eed08a689f312350f145a1a755292021b8b85bab226c2bb0924bb4007e0",
  {
    input: {
      NA: 0.2,
      wl: 810,
      core_diam: 50,
      mode_repr: "cos",
      n_cladding: 1.45,
      curvature_x: 10,
      is_curvature: "No",
      profile_type: "GRIN",
      solver_choice: "Radial 2"
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run wavefrontshaping/pymmf using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "wavefrontshaping/pymmf:e6039eed08a689f312350f145a1a755292021b8b85bab226c2bb0924bb4007e0",
    input={
        "NA": 0.2,
        "wl": 810,
        "core_diam": 50,
        "mode_repr": "cos",
        "n_cladding": 1.45,
        "curvature_x": 10,
        "is_curvature": "No",
        "profile_type": "GRIN",
        "solver_choice": "Radial 2"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run wavefrontshaping/pymmf using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "e6039eed08a689f312350f145a1a755292021b8b85bab226c2bb0924bb4007e0",
    "input": {
      "NA": 0.2,
      "wl": 810,
      "core_diam": 50,
      "mode_repr": "cos",
      "n_cladding": 1.45,
      "curvature_x": 10,
      "is_curvature": "No",
      "profile_type": "GRIN",
      "solver_choice": "Radial 2"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/wavefrontshaping/pymmf@sha256:e6039eed08a689f312350f145a1a755292021b8b85bab226c2bb0924bb4007e0 \
  -i 'NA=0.2' \
  -i 'wl=810' \
  -i 'core_diam=50' \
  -i 'mode_repr="cos"' \
  -i 'n_cladding=1.45' \
  -i 'curvature_x=10' \
  -i 'is_curvature="No"' \
  -i 'profile_type="GRIN"' \
  -i 'solver_choice="Radial 2"'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 r8.im/wavefrontshaping/pymmf@sha256:e6039eed08a689f312350f145a1a755292021b8b85bab226c2bb0924bb4007e0
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "NA": 0.2,
      "wl": 810,
      "core_diam": 50,
      "mode_repr": "cos",
      "n_cladding": 1.45,
      "curvature_x": 10,
      "is_curvature": "No",
      "profile_type": "GRIN",
      "solver_choice": "Radial 2"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

No output yet! Press "Submit" to start a prediction.