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What is the Trainer API?

The Trainer API lets you:
  • Create and upload training datasets
  • Submit AI Toolkit LoRA training jobs
  • Poll status/results and download training artifacts (checkpoints, config yaml, samples)

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Base URL

https://trainer-api.runcomfy.net

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Authentication

All requests require a Bearer token: Authorization: Bearer <token> Get your token from your Profile page.

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Minimum working flow

  1. Create a dataset (metadata)
  2. Upload dataset files (direct upload for small files, or signed URLs for large files)
  3. Wait until the dataset becomes READY
  4. Submit a training job with your AI Toolkit YAML config
  5. Poll status and fetch result artifacts

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Example

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1) Create a dataset

curl --request POST \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/datasets" \
  --header "Content-Type: application/json" \
  --header "Authorization: Bearer <token>" \
  --data '{
    "name": "<YOUR_DATASET_NAME>"
  }'
Save the returned dataset_id (you’ll use it in upload and status calls).

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2) Upload files

For small dataset files (≤150MB), upload directly: 
curl --request POST \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/datasets/{dataset_id}/upload" \
  --header "Authorization: Bearer <token>" \
  --form "file=@./dog_01.jpg"
For larger uploads, request signed upload URLs and PUT the bytes to object storage. See Get signed upload URLs.

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3) Wait until the dataset is READY

curl --request GET \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/datasets/{dataset_id}/status" \
  --header "Authorization: Bearer <token>"
Typical dataset lifecycle: DRAFT → UPLOADING → READY (or FAILED)

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4) Submit a training job

curl --request POST \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs" \
  --header "Content-Type: application/json" \
  --header "Authorization: Bearer <token>" \
  --data '{
    "config_file_format": "yaml",
    "config_file": "<YOUR_AI_TOOLKIT_YAML_AS_A_JSON_STRING>",
    "gpu_type": "ADA_80_PLUS"
  }'
Because config_file is a multiline YAML file, you’ll usually want to JSON-escape it with jq and pipe to curl. See Submit a training job.

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6) Poll status and fetch results

curl --request GET \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/status" \
  --header "Authorization: Bearer <token>"

curl --request GET \
  --url "https://trainer-api.runcomfy.net/prod/v1/trainers/ai-toolkit/jobs/{job_id}/result" \
  --header "Authorization: Bearer <token>"