Sky pilot run l l ms and a i on any cloud with one click

A New Framework for Running AI and Batch Jobs Across Cloud Platforms

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AI
by guoxudong on Oct 8, 2023 updated on Oct 10, 2023

With the growing prominence of cloud computing, there's a pressing demand for seamless, cost-effective deployment of large language models (LLMs), AI, and batch jobs across various cloud platforms. Enter SkyPilot, designed to tackle this challenge. It not only streamlines cloud infrastructure operations, allowing users to deploy and scale jobs effortlessly across any cloud platform, but also intelligently compares real-time GPU prices from multiple providers. This ensures users select the most cost-effective platform for their tasks, leading to substantial cost savings and optimized GPU availability. Consequently, SkyPilot responds to the market's call for efficient and affordable cloud resource utilization. By leveraging SkyPilot, both enterprises and individual developers can harness the full potential of GPUs, thereby advancing artificial intelligence and big data technologies and ushering in new horizons for the cloud computing industry.

SkyPilot

SkyPilot is a framework designed for large language models (LLMs), AI, and batch jobs that can run on any cloud platform. It's a CLI tool and is very convenient for users familiar with the command line. With just one command, a complete cloud environment can be initiated without delving into the specifics of VMs, networks, or security group configurations. Compared with Terraform, SkyPilot offers faster speed and a better experience. Most importantly, it allows users to use the same configuration on multiple cloud platforms, saving a lot of learning and adaptation time.

Key advantages of SkyPilot include:

  1. Cloud Infrastructure Abstraction: Simplifies the process of launching jobs and clusters on any cloud, making it easy to scale and access object storage.
  2. Maximized GPU Availability: Automatically allocates resources in all accessible regions and clouds, achieving automatic failover.
  3. Reduced Cloud Costs: Uses spot VMs to save costs, automatically selects the cheapest resources, and shuts down idle clusters.
  4. No Code Changes Needed: Compatible with existing GPU, TPU, and CPU workloads without code modifications.

In addition to these advantages, a core feature of SkyPilot is simplifying cloud infrastructure management. Its key features include:

  • Managed Spot: Optimizes resource allocation by leveraging spot VMs (temporary virtual machines), offering users a 3-6x cost saving. It also ensures stable job operation in case of preemption events.
  • Smarter Optimizer: Smartly chooses the cheapest virtual machine, region, or cloud platform to further save user costs.
  • Other Features and Characteristics:
    • Cross-Cloud Platform Support: Runs on AWS, Azure, GCP, and other cloud platforms.
    • Easy Expansion: Easily runs multiple jobs, which are automatically managed to ensure effective resource utilization.
    • Object Storage Access: Simplifies access to object storage like S3, GCS, R2, etc., facilitating data management and storage.

Currently supported cloud providers include AWS, Azure, GCP, Lambda Cloud, IBM, Samsung, OCI, Cloudflare, and Kubernetes:

Supported Cloud Platforms

Quick Start

Here's how to use SkyPilot to deploy the Llama-2 Chatbot on Azure.

Installation

First, ensure you have Python 3.7 or higher installed on your system. For Apple Silicon, it's recommended to use Python 3.8 or higher. Here's how to install SkyPilot:

  • Create and activate a new conda environment (recommended but not mandatory):
conda create -y -n sky python=3.8
conda activate sky
  • Install SkyPilot using pip. Depending on the cloud service providers you plan to use with SkyPilot, you can choose additional installation options. For instance, if you wish to use SkyPilot on AWS and Azure, run:
pip install "skypilot[aws,azure]"

Alternatively, you can install all available additional options:

pip install "skypilot[all]"

Moreover, you can opt to install the latest nightly build:

pip install -U "skypilot-nightly[all]"

Or install SkyPilot from source:

git clone https://github.com/skypilot-org/skypilot.git
cd skypilot
pip install ".[all]"

Configuration

After installation, some initial configurations are necessary to connect to your cloud provider. These configuration steps may vary depending on the cloud provider. The overall configuration process is relatively simple. If you've already set up the corresponding cloud CLI locally, you can use the following command to check if SkyPilot can access it properly:

sky check

You'll see output indicating the access status for each cloud:

sky check

Next, we'll briefly cover how to configure for Azure. For configurations for other clouds, refer to the official documentation.

  • Download and install the Azure CLI.

  • Log in to the Azure CLI with az login.

  • Retrieve your account's subscription_id list using az account subscription list.

  • Set your desired subscription with az account set -s <subscription_id>.

    # Login
az login
# Set the subscription to use
az account set -s <subscription_id>

  • Finally, run sky check again to verify the successful configuration.

Creating and Running Llama-2 Chatbot

Outlined below are the preliminary steps. For a comprehensive guide, please consult the official documentation.

Pre-requisites

Running Llama-2 Chatbot with SkyPilot

  • Create a new YAML file named chatbot-meta.yaml and add the following content:
resources:
  accelerators: V100:1
  disk_size: 1024

envs:
  MODEL_SIZE: 7
  HF_TOKEN: <your-huggingface-token> # TODO: Replace with huggingface token

setup: |
  set -ex

  git clone https://github.com/facebookresearch/llama.git || true
  cd ./llama
  pip install -e .
  cd -

  git clone https://github.com/skypilot-org/sky-llama.git || true
  cd sky-llama
  pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
  pip install -r requirements.txt
  pip install -e .
  cd -

  # Download the model weights from the huggingface hub, as the official
  # download script has some issues.
  git config --global credential.helper cache
  sudo apt -y install git-lfs
  pip install transformers
  python -c "import huggingface_hub; huggingface_hub.login('${HF_TOKEN}', add_to_git_credential=True)"
  git clone https://huggingface.co/meta-llama/Llama-2-${MODEL_SIZE}b-chat

  wget https://github.com/tsl0922/ttyd/releases/download/1.7.2/ttyd.x86_64
  sudo mv ttyd.x86_64 /usr/local/bin/ttyd
  sudo chmod +x /usr/local/bin/ttyd

run: |
  cd sky-llama
  ttyd /bin/bash -c "torchrun --nproc_per_node $SKYPILOT_NUM_GPUS_PER_NODE chat.py --ckpt_dir ~/sky_workdir/Llama-2-${MODEL_SIZE}b-chat --tokenizer_path ~/sky_workdir/Llama-2-${MODEL_SIZE}b-chat/tokenizer.model"

  • Run the following command to start the cluster and execute the task:

    sky launch -c llama chatbot-meta.yaml

    launch

    In the above steps, llama is the name of the cluster, and chatbot-meta.yaml is the task configuration file. Within a few minutes, SkyPilot will complete cluster creation, configuration, and task execution on Azure's V100 GPU.

  • Open a new terminal and execute the following command to bind local

port 7681 to the cluster's 7681 port:

```bash
ssh -L 7681:localhost:7681 llama
```

  • Open http://localhost:7681 in your browser and start chatting!

    ttyd chat

Cleaning Up

When you are done, you can use the following commands to stop or tear down the cluster:

  • Stop the cluster, run:

    sky stop lama  # or pass your custom name if you used "-c <other name>"

  • Restart a stopped cluster and relaunch the Chatbot:

    sky launch chatbot-meta.yaml -c llama --no-setup

    Using --no-setup aims to skip the setup step since the stopped cluster has retained its disk contents.

  • To tear down the cluster (non-restartable), run:

    sky down llama  # or pass your custom name if you used "-c <other name>"

To see your clusters, run sky status, which is a single pane of glass for all your clusters across regions/clouds.

Advanced Usage

Beyond the basic methods mentioned above, SkyPilot has numerous advanced features. Here's a brief introduction to some of them.

Show Supported GPU/TPU/Accelerators and Their Prices

The names and counts shown can be set in the accelerators field in task YAMLs, or in the --gpus flag in CLI commands. For example, if this table shows 8x V100s are supported, then the string V100:8 will be accepted by the above.

Different clouds have confusing GPU model names and their prices. SkyPilot has standardized the naming and pricing of the same GPU model and provides a show-gpus command to display currently supported GPU/TPU/accelerators and their prices:

sky show-gpus <gpu>

Public IP and Ports

If you prefer to access your clusters using ssh, you can use the following command to get the cluster's Public IP:

sky status --ip <your-custom-name>

Moreover, if you wish to open some ports, you can use resources.ports to specify ports to open:

resources:
# ports: 8888
  ports:
    - 8888
    - 10020-10040
    - 20000-20010

SkyPilot also support opening ports through the CLI:

sky launch -c jupyter --ports 8888 jupyter_lab.yaml

Interactive Nodes

SkyPilot also offers interactive nodes, allowing users to quickly start specified single node VMs on clouds with simple CLI commands, without needing a YAML configuration file.

  • sky gpunode
  • sky cpunode
  • sky tpunode
# Launch a default gpunode.
sky gpunode

# Do work, then log out. The node is kept running. Attach back to the
# same node and do more work.
sky gpunode

# Create many interactive nodes by assigning names via --cluster (-c).
sky gpunode -c node0
sky gpunode -c node1

# Port forward.
sky gpunode --port-forward 8080 --port-forward 4650 -c cluster_name
sky gpunode -p 8080 -p 4650 -c cluster_name

# Sync current working directory to ~/workdir on the node.
rsync -r . cluster_name:~/workdir

Show Estimated Cost of Launched Clusters

SkyPilot also provides a command to display the estimated cost of running clusters:

sky cost-report

Note: This CLI is experimental. The estimated cost is calculated based on the local cache of cluster status and may not be accurate.

Fetch Global Region Information for Azure and GCP

By default, SkyPilot supports most global regions on AWS, but only US regions on GCP and Azure. If you specify a region outside of the US, you might encounter an error like:

ValueError: Invalid region 'australiaeast'
List of supported azure regions: 'centralus, eastus, eastus2, northcentralus, southcentralus, westcentralus, westus, westus2, westus3'

If you wish to use all global regions, additional commands are required to fetch global region information for Azure and GCP:

version=$(python -c 'import sky; print(sky.clouds.service_catalog.constants.CATALOG_SCHEMA_VERSION)')
mkdir -p ~/.sky/catalogs/${version}
cd ~/.sky/catalogs/${version}
# GCP
pip install lxml
# Fetch all regions for GCP
python -m sky.clouds.service_catalog.data_fetchers.fetch_gcp --all-regions

# Azure
# Fetch all regions for Azure
python -m sky.clouds.service_catalog.data_fetchers.fetch_azure --all-regions

Conclusion

SkyPilot is a highly versatile and powerful tool that brings a new level of simplicity and efficiency to cloud infrastructure management, especially for large language models and AI batch jobs. By automatically selecting the cheapest and most effective resources, and providing a smooth user experience, SkyPilot is poised to be a game-changer for enterprises and developers in the cloud computing space. Whether you're a solo developer or part of a large organization, SkyPilot offers features and benefits that can streamline your cloud operations, save costs, and ensure maximum efficiency. With an active and growing community, the future of SkyPilot looks very promising. Give it a try and experience the future of cloud computing today!

References

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