tpucare 0.5.0
Automatically take good care of your preemptible TPUs
License: BSD
Requires Python: >=3.7

# TPU Care

Automatically take good care of your preemptible TPUs

## Table of Contents

* [TPU Care](#tpu-care)
    * [Table of Contents](#table-of-contents)
    * [Features](#features)
    * [Getting Started](#getting-started)
        * [Installation](#installation)
    * [Examples](#examples)
        * [Long-running preemptible training](#long-running-preemptible-training)
        * [Sweeps](#sweeps)
    * [Citation](#citation)

## Features

* **Reliable code execution**: TPU Care starts a TPU, ensures it's set up as specified and continues the experiment
  whenever the node dies. Think of it like [TerraForm](https://www.terraform.io/) + [Ansible](https://www.ansible.com/)
  for machine learning.
* **Maintenance of large swarms**: When running multiple nodes, TPU Care will automatically delete dead instances while
  keeping as many alive as possible.
* **Code generation**: To simplify setup, TPU Care efficiently clones your git repository and ensures trustable
  execution of your `run_command` that continues even during outages.
* **Optimized management**: When a node dies, TPU Care deletes it within five minutes and creates a new one the second
  there is capacity.

## Getting Started

### Installation

```BASH
python3 -m pip install tpucare
```

## Examples

We've been using TPU Care for a while at [HomebrewNLP](https://github.com/HomebrewNLP/). In fact, this library is just
the branched out core of the original production-ready HomebrewNLP code. At HomebrewNLP, there were two major use-cases
for this library. We started both massive hyperparameter sweeps which consumed 900,000 TPU-core hours within three
months and stable training on large TPU pods. Below, you can see a list of TPUs which are largely managed by TPU
Care: ![PU Output](https://i.imgur.com/LcOm0Bc.png)
<p align="center">Screenshot from <a href="https://github.com/shawwn/tpunicorn">TPUnicorn</a>, a CLI-based TPU managed software</p>
In the following sections, you'll learn how we use at massive scale with minimal code effort.

### Long-running preemptible training

For example, the following code can be used to create a production-ready v3-256 using
the [HomebrewNLP-Jax](https://github.com/HomebrewNLP/HomebrewNLP-Jax) codebase (
see [examples/pod.py](https://github.com/clashluke/tpucare/blob/main/examples/pod.py) for an executable version):

```PYTHON
import dataclasses
import typing
from netrc import netrc

import yaml

from tpucare import exec_command, exec_on_tpu, send_to_tpu, start_single


@dataclasses.dataclass
class Context:
    retry: int


ZONE = "europe-west4-a"
HOST = "big-pod"
RUN_NAME = "256-core-tpu"


def load_config(ctx: Context):
    with open("config.yaml", 'r') as f:
        config = f.read()
    config = yaml.safe_load(config)

    wandb_api = wandb.Api()
    config["training"]["do_checkpoint"] = True
    base_checkpoint_path = config["training"]["checkpoint_path"]

    start_step = 0
    for run in wandb_api.runs(f"{config['wandb']['entity']}/{config['wandb']['project']}"):
        if run.name == config['wandb']['name']:
            start_step = run.summary["_step"]
            break
    start_step -= start_step % config["training"]["checkpoint_interval"]

    config["training"]["start_step"] = start_step
    config["wandb"]["name"] = f"{RUN_NAME}-{ctx.retry}"
    if ctx.retry > 0:
        config["training"]["checkpoint_load_path"] = config["training"]["checkpoint_path"]
    config["training"]["checkpoint_path"] = f"{base_checkpoint_path}-{ctx.retry}"
    return yaml.dump(config)


def start_fn(ctx: Context, worker: int):
    """
    This function gets executed in threads to start a run on a new TPU. It receives the context object returned by 
    `creation_callback` as well as the worker id which corresponds to the slice id this code was executed on in a 
    multi-host setup. For single-host setups, such as v3-8s, the "worker" will always be set to 0.
    Ideally, it'd copy necessary files to the TPU and then run those. Here, `exec_command` can be used to create an 
    execution command that automatically spawns a `screen` session which persists even when the SSH connection gets cut.
    """
    send_to_tpu(HOST, ZONE, "config.yaml", load_config(ctx), worker)
    cmd = exec_command(repository="https://github.com/HomebrewNLP/HomebrewNLP-Jax", wandb_key=wandb_key)
    send_to_tpu(HOST, ZONE, "setup.sh", cmd, worker)
    exec_on_tpu(HOST, ZONE, "bash setup.sh", worker)


def creation_callback(host: str, ctx: typing.Optional[Context]) -> Context:
    """
    The `creation_callback` is called once whenever a new TPU gets created and can be used to persist state
    (such as retry counters) across multiple invocations.
    """
    if ctx is None:  # first invocation
        return Context(0)
    ctx.retry += 1
    return ctx


def main(service_account: str, tpu_version: int = 3, slices: int = 32, preemptible: bool = True):
    start_single(host=HOST, tpu_version=tpu_version, zone=ZONE, preemptible=preemptible,
                 service_account=service_account, slices=slices, start_fn=start_fn,
                 creation_callback=creation_callback)
```

### Sweeps

Similarly, large swarms of instances can be launched trivially using tpucare. Here, we largely do the same setup as
above, but call `launch_multiple` instead of `launch_single` which takes the additional argument `tpus` specifying the
number of TPUs that should be launched and babysit. Depending on capacity and quota, the actual number of TPUs you get
might be lower than the number of TPUs specified.

```PYTHON
def main(service_account: str, tpus: int, tpu_version: int = 3, slices: int = 32, preemptible: bool = True):
    start_multiple(prefix=HOST, tpu_version=tpu_version, zone=ZONE, preemptible=preemptible,
                   service_account=service_account, slices=slices, start_fn=start_fn,
                   creation_callback=creation_callback, tpus=tpus)
```

However, this would simply launch the same run many times. If you instead plan to register them with a
[WandB Sweep](https://docs.wandb.ai/guides/sweeps/configuration), we need to modify the `start_fn` to join the wandb
sweep.\
By patching in the code below, tpucare will start and maintain a large swarm of TPUs all working towards the same
hyperparameter optimization problem.

```PYTHON
import wandb

with open("sweep.yaml", 'r') as f:  # sweep config passed straight to wandb
    config = yaml.safe_load(f.read())
sweep_id = wandb.sweep(config, entity="homebrewnlp", project="gpt")


def start_fn(ctx: Context, worker: int):
    cmd = exec_command(repository="https://github.com/HomebrewNLP/HomebrewNLP-Jax", wandb_key=wandb_key,
                       run_command=f"/home/ubuntu/.local/bin/wandb agent {sweep_id}")
    send_to_tpu(HOST, ZONE, "setup.sh", cmd, worker)
    exec_on_tpu(HOST, ZONE, "bash setup.sh", worker)
```

The full executable code can be found
in [examples/sweep.py](https://github.com/clashluke/tpucare/blob/main/examples/sweep.py).

Similarly, the `start_fn` could be adapted to start an inference server
for [HomebrewNLP](https://github.com/HomebrewNLP/HomebrewNLP-Jax/)
or [Craiyon](https://huggingface.co/spaces/dalle-mini/dalle-mini) or even execute machine learning unit-tests in
parallel.

## Citation

```BIBTEX
@software{nestler_lucas_2022_6837312,
  author       = {Nestler, Lucas},
  title        = {TPU Care},
  month        = jul,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {0.0.2},
  doi          = {10.5281/zenodo.6837312},
  url          = {https://doi.org/10.5281/zenodo.6837312}
}```