> ## Documentation Index
> Fetch the complete documentation index at: https://runcrate.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Run Quick ML Experiments with AI Agents

> Use MCP tools to deploy a GPU, run an experiment, get results, and tear down — all in one conversation. Zero infrastructure left behind.

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Run a one-off experiment without any setup overhead. Tell your AI agent what you want to test, and it handles deploy, execute, collect, and cleanup in a single conversation.

***

## "Run a quick benchmark comparing PyTorch and JAX matmul performance on an RTX 4090."

The agent handles everything end-to-end:

1. **`create_instance`** — deploys `bench-run` with RTX 4090
2. **`instance_status`** — waits until running
3. **`ssh_execute`** — `pip install torch jax[cuda12]`
4. **`file_upload`** — uploads the benchmark script
5. **`ssh_execute`** — `python /root/benchmark.py`
6. **`delete_instance`** — terminates immediately

> **Benchmark results (RTX 4090, 4096x4096 matmul, 1000 iterations):**
>
> | Framework   | Mean time | Std dev | TFLOPS |
> | ----------- | --------- | ------- | ------ |
> | PyTorch 2.4 | 2.31 ms   | 0.04 ms | 59.4   |
> | JAX 0.4.31  | 2.28 ms   | 0.03 ms | 60.2   |
>
> Both frameworks perform nearly identically on this workload. JAX is marginally faster (\~1.3%) due to XLA compilation.
>
> \*\*Total cost: $0.12** (20 minutes on RTX 4090 at $0.35/hr).
> Instance terminated. Nothing left running.

***

## "Test if my fine-tuned model loads correctly. Deploy a GPU, load it, run 5 prompts, and shut down."

The agent runs the validation:

1. **`create_instance`** — deploys `model-test` with RTX 4090
2. **`instance_status`** — waits until running
3. **`ssh_execute`** — `pip install torch transformers`
4. **`file_upload`** — uploads `test_model.py` with 5 test prompts
5. **`ssh_execute`** — `python /root/test_model.py`
6. **`billing_usage`** — checks cost
7. **`delete_instance`** — terminates

> **Model validation results:**
>
> | Prompt                     | Output                         | Quality |
> | -------------------------- | ------------------------------ | ------- |
> | "Summarize this paper..."  | 3-sentence summary, accurate   | pass    |
> | "Translate to French..."   | Correct translation            | pass    |
> | "Extract entities from..." | Found 4/4 entities             | pass    |
> | "Generate SQL for..."      | Valid query, correct joins     | pass    |
> | "Classify sentiment..."    | Correct label, 0.94 confidence | pass    |
>
> All 5 test prompts passed. Model loads in 8.2 seconds, inference at \~45 tok/s.
> **Cost: \$0.06** (10 minutes). Instance terminated.

***

## "I want to try 3 different LoRA ranks and see which gives the best eval loss. Quick and dirty."

The agent runs a fast comparison:

1. **`create_instance`** — deploys `lora-test` with A100 80GB
2. **`instance_status`** — waits until running
3. **`ssh_execute`** — installs dependencies
4. **`file_upload`** — uploads training script and small eval dataset
5. **`ssh_execute`** — runs with `--lora-rank 8` (500 steps)
6. **`ssh_execute`** — runs with `--lora-rank 16` (500 steps)
7. **`ssh_execute`** — runs with `--lora-rank 32` (500 steps)
8. **`delete_instance`** — terminates

> **LoRA rank comparison (500 steps each, Llama 8B, 1K sample dataset):**
>
> | Rank | Trainable params | Eval loss | Train time |
> | ---- | ---------------- | --------- | ---------- |
> | 8    | 3.4M             | 1.842     | 4 min      |
> | 16   | 6.8M             | 1.687     | 5 min      |
> | 32   | 13.6M            | 1.691     | 7 min      |
>
> **Rank 16 wins.** It achieves the lowest eval loss — rank 32 doesn't improve further and trains 40% slower. The extra parameters at rank 32 are likely overfitting on this small dataset.
>
> \*\*Total cost: $0.43** (16 minutes on A100 at $1.60/hr). Instance terminated.

***

## Tools used in this workflow

| Tool                                  | Purpose                             |
| ------------------------------------- | ----------------------------------- |
| `create_instance` / `instance_status` | Provision and wait for GPU          |
| `ssh_execute`                         | Install packages, run experiments   |
| `file_upload`                         | Transfer experiment scripts         |
| `delete_instance`                     | Tear down immediately after results |