Create Fine-Tune Job with Lamini API on New Shipment (Instant) from Shopify API

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New Shipment (Instant) from the Shopify 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 Shopify 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 Shipment (Instant) trigger
1. Connect your Shopify account
2. Optional- Configure Metafield Namespaces
3. Optional- Configure Private Metafield Namespaces
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 Shipment (Instant) on Shopify

Description:Emit new event for each new fulfillment event for a store.

Version:0.0.4

Key:shopify_developer_app-new-shipment

View on GitHub

Shopify Overview#

The Shopify Admin REST & GraphQL API unleashes a myriad of possibilities to automate and enhance online store operations. It provides programmatic access to Shopify functionalities, allowing users to manage products, customers, orders, and more. Leveraging the Shopify Admin API within Pipedream, developers can create custom workflows that automate repetitive tasks, sync data across platforms, and respond dynamically to events in Shopify.

This integration can be used as a custom app on your store, or for automating actions on behalf of merchants through your Shopify app.

Looking for integrating into the Shopify Partner API for your apps, themes or referrals? Check out our Shopify Partner API integration

Trigger Code#

import constants from "../common/constants.mjs";
import common from "../common/webhook-metafields.mjs";

export default {
  ...common,
  key: "shopify_developer_app-new-shipment",
  name: "New Shipment (Instant)",
  type: "source",
  description: "Emit new event for each new fulfillment event for a store.",
  version: "0.0.4",
  dedupe: "unique",
  methods: {
    ...common.methods,
    getTopic() {
      return constants.EVENT_TOPIC.ORDERS_FULFILLED;
    },
    generateMeta(resource) {
      const ts = Date.parse(resource.updated_at);
      return {
        id: ts,
        summary: `New Shipped Order ${resource.id}.`,
        ts,
      };
    },
  },
};

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
Shopify	`app`	`app`	This component uses the Shopify 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.
Metafield Namespaces	`metafieldNamespaces`	`string[]`	Array of namespaces for any metafields that should be included with each webhook. Metafield definitions can be found in your store's Settings -> Custom Data. Select a metafield to view its namespace under "Namespace and key". For example, if the value is `custom.test_metafield`, the namespace is `custom`.
Private Metafield Namespaces	`privateMetafieldNamespaces`	`string[]`	Array of namespaces for any private metafields that should be included with each webhook. Metafield definitions can be found in your store's Settings -> Custom Data. Select a metafield to view its namespace under "Namespace and key". For example, if the value is `custom.test_metafield`, the namespace is `custom`.

Trigger Authentication#

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

Get your Access Token

Create a custom app in Shopify to obtain your Admin API access token. See our detailed instructions here

Enter your Admin API access token below, and save it in a secure location (we recommend using a password manager such as 1Password)

About Shopify#

Shopify is a complete commerce platform that lets anyone start, manage, and grow a business. You can use Shopify to build an online store, manage sales, market to customers, and accept payments in digital and physical locations.

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.

Create Fine-Tune Job with Lamini API on New Shipment (Instant) from Shopify API

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

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

Developers ♥ Pipedream

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