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Documentation Index

Fetch the complete documentation index at: https://runcrate.ai/docs/llms.txt

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Walk through a full training workflow without leaving your editor. Your AI agent handles GPU provisioning, file transfers, training execution, and cleanup.

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”Deploy an A100, upload my dataset, and start training.”

The agent orchestrates the entire setup:
  1. list_gpu_types — confirms A100 80GB availability and pricing ($1.60/hr)
  2. create_instance — deploys an instance named fine-tune-run with an A100
  3. instance_status — polls every 10 seconds until the instance is running
  4. file_upload — uploads train.py to /workspace/train.py
  5. file_upload — uploads dataset.jsonl to /workspace/dataset.jsonl
  6. ssh_execute — runs pip install torch transformers datasets accelerate
  7. ssh_execute — runs nohup python /workspace/train.py > /root/train.log 2>&1 &
Deployed fine-tune-run (1x A100 80GB, $1.60/hr). Uploaded train.py and dataset.jsonl. Training started — check progress with “how’s the training going?"

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"How’s the training going? Show me GPU usage and the last few log lines.”

The agent checks two things in parallel:
  1. ssh_execute — runs nvidia-smi --query-gpu=utilization.gpu,memory.used,memory.total --format=csv,noheader
  2. ssh_execute — runs tail -20 /root/train.log
GPU utilization: 97% | Memory: 68,420 / 81,920 MiB (83%) 
Epoch 2/5 | Step 1240/3100 | Loss: 0.4812 | LR: 2.1e-5
Epoch 2/5 | Step 1260/3100 | Loss: 0.4756 | LR: 2.0e-5
Epoch 2/5 | Step 1280/3100 | Loss: 0.4701 | LR: 2.0e-5
Loss is decreasing steadily. At the current rate, training will finish in ~2.5 hours.

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”Training is done. Download the model checkpoint and tear down the instance.”

The agent wraps up the workflow:
  1. ssh_execute — runs ls -lh /workspace/output/ to confirm checkpoint files
  2. file_download — downloads /workspace/output/adapter_model.safetensors
  3. file_download — downloads /workspace/output/adapter_config.json
  4. file_download — downloads /workspace/output/training_args.json
  5. billing_usage — checks total spend for this session
  6. delete_instance — terminates fine-tune-run
Downloaded 3 files (adapter weights, config, training args). Total cost for this training run: $6.40 (4 hours on A100). Instance fine-tune-run terminated. Billing stopped.

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Tools used in this workflow

ToolPurpose
list_gpu_typesCheck GPU availability and pricing
create_instanceProvision the training machine
instance_statusWait for deployment to complete
file_uploadTransfer training code and data
ssh_executeInstall dependencies, start training, check logs
file_downloadRetrieve trained model artifacts
billing_usageVerify session cost
delete_instanceClean up and stop billing