RunPod•3mo ago

Workers deployed with wrong GPU

In 'worker configuration', I've selected '48 GB GPU' (A6000, A40). Upon executing an 'endpoints query' (from the documentation: https://docs.runpod.io/sdks/graphql/manage-endpoints "View your Endpoints") to view all of them, the corresponding endpoint ID shows RTX 4090 and A40 as the worker's GPUs. I tried using a POST request through CURL with the corresponding IDs (from the documentation: https://docs.runpod.io/sdks/python/apis "Get GPUs"), but the workers do not any GPUs assigned to them. They do get GPUs assigned when specifying an RTX 4090 instead of an A6000.

Manage Endpoints | RunPod Documentation

Create, modify, or delete serverless endpoints using GraphQL queries and mutations with RunPod API, specifying GPU IDs, template IDs, and other endpoint settings.

API Wrapper | RunPod Documentation

Learn how to manage computational resources with the RunPod API, including endpoint configurations, template creation, and GPU management, to optimize your project's performance.

Solution:

Hi i tried the graphql and it works with this request: ```graphql mutation { saveEndpoint(input: { # options for gpuIds are "AMPERE_16,AMPERE_24,AMPERE_48,AMPERE_80,ADA_24"...

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2 Replies

Solution

nerdylive•3mo ago

Hi i tried the graphql and it works with this request:

mutation {
  saveEndpoint(input: {
    # options for gpuIds are "AMPERE_16,AMPERE_24,AMPERE_48,AMPERE_80,ADA_24"
    gpuIds: "AMPERE_48",
    idleTimeout: 5,
    # leave locations as an empty string or null for any region
    # options for locations are "CZ,FR,GB,NO,RO,US"
    # locations: "",
    # append -fb to your endpoint's name to enable FlashBoot
    name: "Generated Endpoint -fb",
    # uncomment below and provide an ID to mount a network volume to your workers
    # networkVolumeId: "",
    scalerType: "QUEUE_DELAY",
    scalerValue: 4,
    templateId: "yccyuy2aeh",
    workersMax: 3,
    workersMin: 0
  }) {
    gpuIds
    id
    idleTimeout
    locations
    name
    # networkVolumeId
    scalerType
    scalerValue
    templateId
    workersMax
    workersMin
  }
}

mutation {
  saveEndpoint(input: {
    # options for gpuIds are "AMPERE_16,AMPERE_24,AMPERE_48,AMPERE_80,ADA_24"
    gpuIds: "AMPERE_48",
    idleTimeout: 5,
    # leave locations as an empty string or null for any region
    # options for locations are "CZ,FR,GB,NO,RO,US"
    # locations: "",
    # append -fb to your endpoint's name to enable FlashBoot
    name: "Generated Endpoint -fb",
    # uncomment below and provide an ID to mount a network volume to your workers
    # networkVolumeId: "",
    scalerType: "QUEUE_DELAY",
    scalerValue: 4,
    templateId: "yccyuy2aeh",
    workersMax: 3,
    workersMin: 0
  }) {
    gpuIds
    id
    idleTimeout
    locations
    name
    # networkVolumeId
    scalerType
    scalerValue
    templateId
    workersMax
    workersMin
  }
}

nerdylive•3mo ago

It results like this

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Workers deployed with wrong GPU