Create Fine-Tune Job with Lamini API on New Upcoming Event Alert from Google Calendar API

Pipedream makes it easy to connect APIs for Lamini, Google Calendar and 2,700+ other apps remarkably fast.

Trigger workflow on

New Upcoming Event Alert from the Google Calendar API

Next, do this

Create Fine-Tune Job with the Lamini API

No credit card required

▶

Watch us build a workflow

8 min

Watch now ➜

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

Getting Started#

This integration creates a workflow with a Google Calendar 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 Upcoming Event Alert trigger
1. Connect your Google Calendar account
2. Connect your Google Calendar account
3. Optional- Select a Calendar ID
4. Optional- Select one or more Event Types
5. Configure Minutes Before
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 Upcoming Event Alert on Google Calendar

Description:Emit new event based on a time interval before an upcoming event in the calendar. This source uses Pipedream's Task Scheduler. [See the documentation](https://pipedream.com/docs/examples/waiting-to-execute-next-step-of-workflow/#step-1-create-a-task-scheduler-event-source) for more information and instructions for connecting your Pipedream account.

Version:0.0.10

Key:google_calendar-upcoming-event-alert

View on GitHub

Google Calendar Overview#

The Google Calendar API lets you dip into the powerhouse of scheduling, allowing for the reading, creation, and manipulation of events and calendars directly from your applications. Through Pipedream, you can seamlessly integrate Google Calendar into a myriad of workflows, automating event management, syncing with other services, setting up custom reminders, or even collating data for reporting. The key here is to streamline your calendar-related processes, ensuring that your time management is as efficient and automated as possible.

Trigger Code#

import taskScheduler from "../../../pipedream/sources/new-scheduled-tasks/new-scheduled-tasks.mjs";
import googleCalendar from "../../google_calendar.app.mjs";
import sampleEmit from "./test-event.mjs";

export default {
  key: "google_calendar-upcoming-event-alert",
  name: "New Upcoming Event Alert",
  description: `Emit new event based on a time interval before an upcoming event in the calendar. This source uses Pipedream's Task Scheduler.
    [See the documentation](https://pipedream.com/docs/examples/waiting-to-execute-next-step-of-workflow/#step-1-create-a-task-scheduler-event-source) 
    for more information and instructions for connecting your Pipedream account.`,
  version: "0.0.10",
  type: "source",
  props: {
    pipedream: taskScheduler.props.pipedream,
    googleCalendar,
    db: "$.service.db",
    http: "$.interface.http",
    calendarId: {
      propDefinition: [
        googleCalendar,
        "calendarId",
      ],
    },
    eventTypes: {
      propDefinition: [
        googleCalendar,
        "eventTypes",
      ],
    },
    time: {
      type: "integer",
      label: "Minutes Before",
      description: "Number of minutes to trigger before the start of the calendar event.",
      min: 0,
      reloadProps: true,
    },
  },
  async additionalProps() {
    const props = {};
    if (this.time > 0) {
      props.timer = {
        type: "$.interface.timer",
        description: "Poll the API to schedule alerts for any newly created events",
        default: {
          intervalSeconds: 60 * this.time,
        },
      };
    }
    return props;
  },
  hooks: {
    async deactivate() {
      const ids = this._getScheduledEventIds();
      if (!ids?.length) {
        return;
      }
      for (const id of ids) {
        if (await this.deleteEvent({
          body: {
            id,
          },
        })) {
          console.log("Cancelled scheduled event");
        }
      }
      this._setScheduledEventIds();
    },
  },
  methods: {
    ...taskScheduler.methods,
    _getScheduledEventIds() {
      return this.db.get("scheduledEventIds");
    },
    _setScheduledEventIds(ids) {
      this.db.set("scheduledEventIds", ids);
    },
    _getScheduledCalendarEventIds() {
      return this.db.get("scheduledCalendarEventIds");
    },
    _setScheduledCalendarEventIds(ids) {
      this.db.set("scheduledCalendarEventIds", ids);
    },
    _hasDeployed() {
      const result = this.db.get("hasDeployed");
      this.db.set("hasDeployed", true);
      return result;
    },
    subtractMinutes(date, minutes) {
      return date.getTime() - minutes * 60000;
    },
    async getCalendarEvents() {
      const calendarEvents = [];
      const params = {
        returnOnlyData: false,
        calendarId: this.calendarId,
        eventTypes: this.eventTypes,
      };
      do {
        const {
          data: {
            items, nextPageToken,
          },
        } = await this.googleCalendar.listEvents(params);
        if (items?.length) {
          calendarEvents.push(...items);
        }
        params.pageToken = nextPageToken;
      } while (params.pageToken);
      return calendarEvents;
    },
  },
  async run(event) {
    // self subscribe only on the first time
    if (!this._hasDeployed()) {
      await this.selfSubscribe();
    }

    const scheduledEventIds = this._getScheduledEventIds() || [];

    // incoming scheduled event
    if (event.$channel === this.selfChannel()) {
      const remainingScheduledEventIds = scheduledEventIds.filter((id) => id !== event["$id"]);
      this._setScheduledEventIds(remainingScheduledEventIds);
      this.emitEvent(event, `Upcoming ${event.summary} event`);
      return;
    }

    // schedule new events
    const scheduledCalendarEventIds = this._getScheduledCalendarEventIds() || {};
    const calendarEvents = await this.getCalendarEvents();

    for (const calendarEvent of calendarEvents) {
      const startTime = calendarEvent.start
        ? (new Date(calendarEvent.start.dateTime || calendarEvent.start.date))
        : null;
      if (!startTime
        || startTime.getTime() < Date.now()
        || scheduledCalendarEventIds[calendarEvent.id])
      {
        continue;
      }
      const later = new Date(this.subtractMinutes(startTime, this.time));

      const scheduledEventId = this.emitScheduleEvent(calendarEvent, later);
      scheduledEventIds.push(scheduledEventId);
      scheduledCalendarEventIds[calendarEvent.id] = true;
    }
    this._setScheduledEventIds(scheduledEventIds);
    this._setScheduledCalendarEventIds(scheduledCalendarEventIds);
  },
  sampleEmit,
};

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
Google Calendar	`pipedream`	`app`	This component uses the Google Calendar app.
Google Calendar	`googleCalendar`	`app`	This component uses the Google Calendar app.
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.
Calendar ID	`calendarId`	`string`	Select a value from the drop down menu.
Event Types	`eventTypes`	`string[]`	Select a value from the drop down menu:`defaultfocusTimeoutOfOfficeworkingLocation`
Minutes Before	`time`	`integer`	Number of minutes to trigger before the start of the calendar event.

Trigger Authentication#

Google Calendar uses OAuth authentication. When you connect your Google Calendar account, Pipedream will open a popup window where you can sign into Google Calendar and grant Pipedream permission to connect to your account. Pipedream securely stores and automatically refreshes the OAuth tokens so you can easily authenticate any Google Calendar API.

Pipedream requests the following authorization scopes when you connect your account:

https://www.googleapis.com/auth/calendar.eventshttps://www.googleapis.com/auth/calendar.readonlyhttps://www.googleapis.com/auth/calendar.settings.readonlyemailprofile

About Google Calendar#

With Google Calendar, you can quickly schedule meetings and events and get reminders about upcoming activities, so you always know what’s next.

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.

Action Authentication#

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

About Lamini#

Enterprise LLM Platform

More Ways to Connect Lamini + Google Calendar#

Other Popular Integrations#

Generate Completion with Lamini API on New Cancelled Event from Google Calendar API

Google Calendar + Lamini

Create Fine-Tune Job with Lamini API on New Upcoming Event Alert from Google Calendar API

Pipedream makes it easy to connect APIs for Lamini, Google Calendar and 2,700+ other apps remarkably fast.

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

1-24of2,700+apps by most popular

1
-
24
of
2,700+
apps by most popular