# Run your first job

Run a Python script locally, submit it to a remote worker and download its output files.

Run a Python script on your laptop first, then send the exact same file to a remote worker and download its output.

## 1. Install the CLI

Install the compute CLI (`ct-cli`) from the repository root:

```bash
uv sync --project compute/platform --frozen
alias ct-cli="$PWD/compute/platform/.venv/bin/ct-cli"
ct-cli --help
```

> **Note:** `alias ct-cli=...` applies to your current shell session. Add it to your shell profile or run `compute/platform/.venv/bin/ct-cli` directly in new terminals.

## 2. Run a script locally

We will use `compute/platform/examples/hello.py`, which writes its command-line arguments to `results/hello.txt`:


```python
"""A tiny ordinary script for trying the local compute CLI."""

from __future__ import annotations

import sys
from pathlib import Path

Path("results/hello.txt").write_text(" ".join(sys.argv[1:]) or "hello")
```


Run it locally with `ct-cli run`. Everything after `--` is passed to `sys.argv`:

```bash
ct-cli run compute/platform/examples/hello.py -- hello world
```

`ct-cli run` executes the script in an isolated attempt directory and prints the path to `results/hello.txt`:

```text
State: succeeded
Attempt directory: .ct-cli/local-runs/...
Output: results/hello.txt
```

No login or server connection is required for local runs.

## 3. Log in and select a worker pool

Authenticate with your account and inspect available workers:

```bash
ct-cli login
ct-cli worker list
```

```text
ID                                    NAME         POOL  PLATFORM                   ENABLED
0195f3a0-1234-7000-8000-000000000001  cpu-worker   cpu   linux-x86_64-py3.13-cpu    true
```

Save your default project, worker pool, and platform so you don't have to pass them on every command:

```bash
ct-cli login --project PROJECT_UUID --pool cpu --platform linux-x86_64-py3.13-cpu
ct-cli whoami
```

- **Working over SSH?** Add `--no-browser` to `ct-cli login` and open the printed URL on your laptop.
- **Using the local dev stack?** Skip `ct-cli login` and use `--config .local-dev/compute/client.json` instead (see [Use the local development stack](/operate/local-development/)).

## 4. Submit to a worker and download outputs

Submit the same script to your remote worker pool and wait for it to finish:

```bash
ct-cli submit compute/platform/examples/hello.py --wait -- hello worker
```

Copy the returned `JOB_UUID` to inspect logs or download output files:

```bash
ct-cli status JOB_UUID
ct-cli logs JOB_UUID
ct-cli fetch JOB_UUID --output ./job-output
```

Your downloaded file is saved at `./job-output/JOB_UUID/results/hello.txt`:

- Any file your script writes inside `results/` is automatically uploaded when the job finishes.
- Pressing `Ctrl-C` during `--wait` only stops waiting in your terminal—the worker keeps running. Run `ct-cli cancel JOB_UUID` if you want to stop the job itself.

## 5. Pipe JSON into scripts

By default, `ct-cli` prints human-readable summaries in a terminal. Pass `--json` (or pipe stdout to another command) when scripting:

```bash
ct-cli status JOB_UUID --json
ct-cli jobs --limit 10
```

Next, turn a Python function into a [reusable Program](/start/reusable-programs/) with validated inputs and outputs.