Namespace network

Below are some examples of how to use functions in the network namespace.

Inference

Want to expose your model to the network? Use network.serve:

import * as sm from '@shumai/shumai'
import { model } from './model'

sm.network.serve({
  run_model: (_, input) => {
    // output tensors are automatically serialized
    return model(input)
  }
})

A client can use network.tfetch (basically fetch but for Tensors):

import * as sm from '@shumai/shumai'

const input = sm.randn([128])
const url = 'localhost:3000/run_model'
const output = await sm.network.tfetch(url, input)

Training

Want to train your model over the network? Just add an endpoint for a backward pass to network.serve:

sm.network.serve({
  run_model: (user, input) => {
    const out = model(input)
    // capture a backward pass
    user.opt = (jacobian) => {
      sm.optim.sgd(out.backward(jacobian), 1e-3)
    }
    return out
  }
  optimize_model: (user, jacobian) => {
    // run it when that same user gives us a jacobian
    user.opt(jacobian)
  }
})

And the client can feed the jacobian with network.tfetch:

const url = 'localhost:3000'
for (let i of sm.range(100)) {
  const [input, ref_output] = get_data()
  const output = await sm.network.tfetch(`${url}/run_model`, input)

  // get the jacobian from a loss function
  output.requires_grad = true
  const loss = sm.loss.mse(output, ref_output)
  loss.backward()

  // send that jacobian back
  await sm.network.tfetch(`${url}/optimize_model`, output.grad)
}

Wrappers

Shumai provides wrapper for the above setup code. network.serve_model will create /forward and /optimize endpoints for you.

import * as sm from '@shumai/shumai'
import { model } from './model'

sm.network.serve_model(model, sm.optim.sgd)

And the client can attach with network.remote_model, which attaches a hook to backward for automatic gradients.

import * as sm from '@shumai/shumai'

const model = sm.network.remote_model('localhost:3000')

for (let i of sm.range(100)) {
  const [input, ref_output] = get_data()
  const output = await model(input)

  const loss = sm.loss.mse(ref_output, output)

  // async now, as it propagates through the network
  await loss.backward()
}

Composition

Want to run more than just a trivial remote trainer? Below is a distributed model parallel and pipelined server. We invoke multiple remote models and then make our own model server.

import * as sm from '@shumai/shumai'

const A = sm.network.remote_model('localhost:3001')
const B = sm.network.remote_model('localhost:3002')
const C = sm.network.remote_model('localhost:3003')

// no need to wrap this, autograd knows what's up
const weight = sm.randn([128, 128]).requireGrad()

function model(input) {
  // this will run in parallel
  const [a, b] = await sm.util.all(
    A(input),
    B(input)
  )
  // automatically pipelined (isn't async great?)
  const c = await C(a)
  return c.mul(b).matmul(weight)
}

sm.network.serve_model(model, sm.optim.sgd)

Same client as before :)

What about debugging?

All network.serve* methods automatically give us basic /statistics as JSON:

$ curl -s localhost:3000/statistics | jq
(env) bwasti@bwasti-mbp shumai % curl -s localhost:3000/statistics|jq
{
  "forward": {
    "hits": 1000,
    "seconds": 0.12337932200005891
  },
  "optimize": {
    "hits": 1000,
    "seconds": 0.16975503499999103
  }
}

but we can always add more:

sm.network.serve_model(model, sm.optim.sgd, {port:3000}, {
  statistics: () => {
    return {weight: weight.mean().toFloat32()}
  }
})

$ curl -s localhost:3000/statistics | jq .weight
0.1128062978386879

including recursively:

sm.network.serve_model(model, sm.optim.sgd, {port:3000}, {
  statistics: async () => {
    return {A: await (await fetch('localhost:3001/statistics')).json()}
  }
})