Create Fine-Tune Job with Lamini API on New or changed appointments from SuperSaaS API

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New or changed appointments from the SuperSaaS API

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Create Fine-Tune Job with the Lamini API

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Getting Started#

This integration creates a workflow with a SuperSaaS trigger and Lamini action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New or changed appointments trigger
1. Connect your SuperSaaS account
2. Select one or more Schedules
Configure the Create Fine-Tune Job action
1. Connect your Lamini account
2. Select a Model Name
3. Configure Dataset ID
4. Optional- Configure Finetune Arguments
5. Optional- Configure GPU Config
6. Optional- Configure Is Public
7. Optional- Configure Custom Model Name
8. Optional- Configure Wait for Completion
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New or changed appointments on SuperSaaS

Description:Emits an event for every changed appointments from the selected schedules.

Version:0.0.1

Key:supersaas-changed-appointments

View on GitHub

SuperSaaS Overview#

The SuperSaaS API unlocks the power to automate and streamline your appointment scheduling processes. This tool is tailored for businesses and individuals who manage bookings, reservations, or rentals. By leveraging the API with Pipedream's integration capabilities, you can craft custom workflows that handle everything from syncing appointment data with your calendar, sending customized notifications, to processing payments or follow-ups based on booking activities.

Trigger Code#

const dayjs = require('dayjs');
const makeEventSummary = require('../utils/makeEventSummary.js');
const supersaas = require('../supersaas.app.js');

module.exports = {
  key: 'supersaas-changed-appointments',
  name: 'New or changed appointments',
  description: `Emits an event for every changed appointments from the selected schedules.`,
  version: '0.0.1',
  props: {
    supersaas,
    schedules: { propDefinition: [supersaas, 'schedules'] },
    db: "$.service.db",
    http: '$.interface.http',
  },
  hooks: {
    async activate() {
      const { http, schedules } = this;

      this.db.set('activeHooks', await this.supersaas.createHooks(schedules.map(x => ({
        event: 'C', // change_appointment
        parent_id: Number(x),
        target_url: http.endpoint,
      }))));
    },
    async deactivate() {
      await this.supersaas.destroyHooks(this.db.get('activeHooks') || []);
      this.db.set('activeHooks', []);
    },
  },
  async run(ev) {
    const outEv = {
      meta: {
        summary: makeEventSummary(ev),
        ts: dayjs(ev.body.created_on).valueOf(),
      },
      body: ev.body,
    };

    console.log('Emitting:', outEv);
    this.$emit(outEv, outEv.meta);
  },
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
SuperSaaS	`supersaas`	`app`	This component uses the SuperSaaS app.
Schedules	`schedules`	`string[]`	Select a value from the drop down menu.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
N/A	`http`	`$.interface.http`	This component uses `$.interface.http` to generate a unique URL when the component is first instantiated. Each request to the URL will trigger the `run()` method of the component.

Trigger Authentication#

SuperSaaS uses API keys for authentication. When you connect your SuperSaaS account, Pipedream securely stores the keys so you can easily authenticate to SuperSaaS APIs in both code and no-code steps.

You can find your account name and API key in the Account Info section of your settings. See the Authentication docs for more info.

About SuperSaaS#

Appointment Scheduling for Every Situation

Action#

Create Fine-Tune Job on Lamini

Description:Create a fine-tuning job with a dataset. [See the documentation](https://docs.lamini.ai/api/).

Version:0.0.2

Key:lamini-create-fine-tune-job

View on GitHub

Action Code#

import app from "../../lamini.app.mjs";
import constants from "../../common/constants.mjs";
import utils from "../../common/utils.mjs";

export default {
  key: "lamini-create-fine-tune-job",
  name: "Create Fine-Tune Job",
  description: "Create a fine-tuning job with a dataset. [See the documentation](https://docs.lamini.ai/api/).",
  version: "0.0.2",
  type: "action",
  props: {
    app,
    modelName: {
      description: "Base model to be fine-tuned.",
      propDefinition: [
        app,
        "modelName",
        () => ({
          includeFineTunedModels: false,
        }),
      ],
    },
    datasetId: {
      type: "string",
      label: "Dataset ID",
      description: "Previously uploaded dataset to use for training. Please use the **Upload Dataset** action to upload a dataset.",
    },
    fineTuneArgs: {
      type: "object",
      label: "Finetune Arguments",
      description: "Optional hyperparameters for fine-tuning. Each property is optional:\n- `index_pq_m`: Number of subquantizers for PQ (eg. 8)\n- `index_max_size`: Maximum index size (eg. 65536)\n- `max_steps`: Maximum number of training steps (eg. 60)\n- `batch_size`: Training batch size (eg. 1)\n- `learning_rate`: Learning rate (eg. 0.0003)\n- `index_pq_nbits`: Number of bits per subquantizer (eg. 8)\n- `max_length`: Maximum sequence length (eg. 2048)\n- `index_ivf_nlist`: Number of IVF lists (eg. 2048)\n- `save_steps`: Steps between checkpoints (eg. 60)\n- `args_name`: Name for the argument set (eg. \"demo\")\n- `r_value`: R value for LoRA (eg. 32)\n- `index_hnsw_m`: Number of neighbors in HNSW (eg. 32)\n- `index_method`: Indexing method (eg. \"IndexIVFPQ\")\n- `optim`: Optimizer to use (eg. \"adafactor\")\n- `index_hnsw_efConstruction`: HNSW construction parameter (eg. 16)\n- `index_hnsw_efSearch`: HNSW search parameter (eg. 8)\n- `index_k`: Number of nearest neighbors (eg. 2)\n- `index_ivf_nprobe`: Number of IVF probes (eg. 48)\n- `eval_steps`: Steps between evaluations (eg. 30)\n[See the documentation](https://docs.lamini.ai/tuning/hyperparameters/#finetune_args).",
      optional: true,
    },
    gpuConfig: {
      type: "object",
      label: "GPU Config",
      description: "Optional GPU configuration for fine-tuning. [See the documentation](https://docs.lamini.ai/tuning/hyperparameters/#gpu_config).",
      optional: true,
    },
    isPublic: {
      type: "boolean",
      label: "Is Public",
      description: "Whether this fine-tuning job and dataset should be publicly accessible.",
      optional: true,
    },
    customModelName: {
      type: "string",
      label: "Custom Model Name",
      description: "A human-readable name for the fine-tuned model.",
      optional: true,
    },
    waitForCompletion: {
      type: "boolean",
      label: "Wait for Completion",
      description: "If set to `true`, the action will wait and poll until the fine-tuning job is `COMPLETED`. If is set to `false`, it will return immediately after creating the job.",
      default: false,
      optional: true,
    },
  },
  methods: {
    createFineTuneJob(args = {}) {
      return this.app.post({
        versionPath: constants.VERSION_PATH.V1,
        path: "/train",
        ...args,
      });
    },
  },
  async run({ $ }) {
    const {
      app,
      createFineTuneJob,
      modelName,
      datasetId,
      fineTuneArgs,
      gpuConfig,
      isPublic,
      customModelName,
      waitForCompletion,
    } = this;

    const MAX_RETRIES = 15;
    const DELAY = 1000 * 30; // 30 seconds
    const { run } = $.context;

    // First run: Create the fine-tune job
    if (run.runs === 1) {
      const { upload_base_path: uploadBasePath } =
        await app.getUploadBasePath({
          $,
        });

      await app.getExistingDataset({
        $,
        data: {
          dataset_id: datasetId,
          upload_base_path: uploadBasePath,
        },
      });

      const response = await createFineTuneJob({
        $,
        data: {
          model_name: modelName,
          dataset_id: datasetId,
          upload_file_path: `${uploadBasePath}/${datasetId}.jsonlines`,
          finetune_args: utils.parseJson(fineTuneArgs),
          gpu_config: utils.parseJson(gpuConfig),
          is_public: isPublic,
          custom_model_name: customModelName,
        },
      });

      $.export("$summary", `Successfully created a fine-tune job with ID \`${response.job_id}\`.`);

      // If user doesn't want to wait, return immediately
      if (!waitForCompletion) {
        return response;
      }

      // Store job_id for polling and start rerun
      $.flow.rerun(DELAY, {
        jobId: response.job_id,
      }, MAX_RETRIES);
      return response;
    }

    // Subsequent runs: Poll for job status
    if (run.runs > MAX_RETRIES) {
      throw new Error("Max retries exceeded - fine-tuning job may still be running");
    }

    const { jobId } = run.context;

    // Poll for status
    const statusResponse = await app.getJobStatus({
      $,
      jobId,
    });

    // If job is completed, return the final status
    if (statusResponse.status === "COMPLETED") {
      $.export("$summary", `Fine-tuning job \`${jobId}\` completed successfully.`);
      return statusResponse;
    }

    // If job failed, throw error
    if (statusResponse.status === "FAILED") {
      throw new Error(`Fine-tuning job \`${jobId}\` failed.`);
    }

    // Otherwise, continue polling
    $.flow.rerun(DELAY, {
      jobId,
    }, MAX_RETRIES);
    return {
      status: statusResponse.status,
      jobId,
      message: `Job is still running. Current status: ${statusResponse.status}`,
    };
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
Lamini	`app`	`app`	This component uses the Lamini app.
Model Name	`modelName`	`string`	Select a value from the drop down menu.
Dataset ID	`datasetId`	`string`	Previously uploaded dataset to use for training. Please use the Upload Dataset action to upload a dataset.
Finetune Arguments	`fineTuneArgs`	`object`	Optional hyperparameters for fine-tuning. Each property is optional: `index_pq_m`: Number of subquantizers for PQ (eg. 8) `index_max_size`: Maximum index size (eg. 65536) `max_steps`: Maximum number of training steps (eg. 60) `batch_size`: Training batch size (eg. 1) `learning_rate`: Learning rate (eg. 0.0003) `index_pq_nbits`: Number of bits per subquantizer (eg. 8) `max_length`: Maximum sequence length (eg. 2048) `index_ivf_nlist`: Number of IVF lists (eg. 2048) `save_steps`: Steps between checkpoints (eg. 60) `args_name`: Name for the argument set (eg. "demo") `r_value`: R value for LoRA (eg. 32) `index_hnsw_m`: Number of neighbors in HNSW (eg. 32) `index_method`: Indexing method (eg. "IndexIVFPQ") `optim`: Optimizer to use (eg. "adafactor") `index_hnsw_efConstruction`: HNSW construction parameter (eg. 16) `index_hnsw_efSearch`: HNSW search parameter (eg. 8) `index_k`: Number of nearest neighbors (eg. 2) `index_ivf_nprobe`: Number of IVF probes (eg. 48) `eval_steps`: Steps between evaluations (eg. 30) See the documentation
GPU Config	`gpuConfig`	`object`	Optional GPU configuration for fine-tuning. See the documentation
Is Public	`isPublic`	`boolean`	Whether this fine-tuning job and dataset should be publicly accessible.
Custom Model Name	`customModelName`	`string`	A human-readable name for the fine-tuned model.
Wait for Completion	`waitForCompletion`	`boolean`	If set to `true`, the action will wait and poll until the fine-tuning job is `COMPLETED`. If is set to `false`, it will return immediately after creating the job.