diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index cb8ec7367..a673ebb13 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -67,9 +67,9 @@ To create a new release: - Publish the curated release notes - Build and publish the package to PyPI -Then follow the SkyPilot or Local Training instructions below. +Then follow the GPU training instructions below. -### SkyPilot +### GPU Training (Local or Cloud VM) Copy the `.env.example` file to `.env` and set the environment variables: @@ -77,21 +77,9 @@ Copy the `.env.example` file to `.env` and set the environment variables: cp .env.example .env ``` -Ensure you have a valid SkyPilot cloud available: +Make sure you're on a machine with at least one H100 or A100-80GB GPU. Machines equipped with lower-end GPUs may work, but training will be slower. -```bash -uv run sky check -``` - -Launch a cluster: - -```bash -./scripts/launch-cluster.sh # you can pass any sky launch arguments here -``` - -Make sure you are on a machine with at least one H100 or A100-80GB GPU. Machines equipped with lower-end GPUs may work, but training will be slower. - -You can now SSH into the `art` cluster, using either VSCode or the command line. +If you're using a cloud VM, you can SSH into the machine using either VSCode or the command line. ### Connecting via Command Line @@ -145,8 +133,4 @@ If you run into any issues, the training output is set to maximum verbosity. Cop ### Cleaning Up -When you're done, you can tear down the cluster with: - -```bash -uv run sky down art -``` +When you're done, shut down your GPU instance (if using a cloud VM) or stop the local training process. diff --git a/README.md b/README.md index 643dc223d..5a8b0deaa 100644 --- a/README.md +++ b/README.md @@ -157,7 +157,6 @@ ART stands on the shoulders of giants. While we owe many of the ideas and early - [vLLM](https://github.com/vllm-project/vllm) - [trl](https://github.com/huggingface/trl) - [torchtune](https://github.com/pytorch/torchtune) -- [SkyPilot](https://github.com/skypilot-org/skypilot) Finally, thank you to our partners who've helped us test ART in the wild! We're excited to see what you all build with it. diff --git a/dev/test_skypilot/launch.py b/dev/test_skypilot/launch.py deleted file mode 100644 index 4f5a8bcee..000000000 --- a/dev/test_skypilot/launch.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Training example for MCP agent using rollout with AlphaMcpServer in scenarios.""" - -import asyncio - -from dotenv import load_dotenv - -from art.skypilot.backend import SkyPilotBackend - -load_dotenv() - - -async def launch(): - backend = await SkyPilotBackend().initialize_cluster( - cluster_name="test-skypilot", - gpu="H100-SXM", - env_path=".env", - force_restart=True, - ) - - print("successfully initialized skypilot server") - - -if __name__ == "__main__": - asyncio.run(launch()) diff --git a/dev/test_skypilot/launch_tail.py b/dev/test_skypilot/launch_tail.py deleted file mode 100644 index 73df245cd..000000000 --- a/dev/test_skypilot/launch_tail.py +++ /dev/null @@ -1,27 +0,0 @@ -"""Training example for MCP agent using rollout with AlphaMcpServer in scenarios.""" - -import asyncio - -from dotenv import load_dotenv - -from art.skypilot.backend import SkyPilotBackend - -load_dotenv() - - -async def launch_tail(): - backend = await SkyPilotBackend().initialize_cluster( - cluster_name="test-skypilot", - gpu="H100-SXM", - env_path=".env", - force_restart=True, - tail_logs=True, - ) - print("successfully initialized skypilot server") - - # unforunately, we can't cancel the task programmatically, so we have to ctrl+c - # to exit - - -if __name__ == "__main__": - asyncio.run(launch_tail()) diff --git a/dev/test_skypilot/register_model.py b/dev/test_skypilot/register_model.py deleted file mode 100644 index 8ed01c60b..000000000 --- a/dev/test_skypilot/register_model.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Training example for MCP agent using rollout with AlphaMcpServer in scenarios.""" - -import asyncio - -from dotenv import load_dotenv -from pydantic import BaseModel - -import art -from art.skypilot.backend import SkyPilotBackend - -load_dotenv() - - -class ComplexModelConfig(BaseModel): - max_turns: int = 5 - max_tokens: int = 2048 - - base_model: str = "Qwen/Qwen2.5-14B-Instruct" - # Random seed to control which subset of the training data is sampled - training_dataset_seed: int | None = None - - # Training configuration - scale_rewards: bool = True - - -async def register_model(): - backend = await SkyPilotBackend().initialize_cluster( - cluster_name="test-skypilot", - gpu="H100-SXM", - env_path=".env", - # force_restart=True, - ) - - model = art.TrainableModel( - name="complex-model", - project="test-skypilot", - base_model="Qwen/Qwen2.5-14B-Instruct", - config=ComplexModelConfig( - num_epochs=160, - ), - ) - - await backend.register(model) - - print("model registered") - - -if __name__ == "__main__": - asyncio.run(register_model()) diff --git a/docs/features/checkpoint-deletion.mdx b/docs/features/checkpoint-deletion.mdx index e8d81f8d4..dd725fa80 100644 --- a/docs/features/checkpoint-deletion.mdx +++ b/docs/features/checkpoint-deletion.mdx @@ -11,7 +11,7 @@ To delete all but the most recent and best-performing checkpoints of a model, ca ```python import art -# also works with LocalBackend and SkyPilotBackend +# also works with LocalBackend from art.serverless.backend import ServerlessBackend model = art.TrainableModel( diff --git a/docs/fundamentals/art-backend.mdx b/docs/fundamentals/art-backend.mdx index ea9413c37..f65473a99 100644 --- a/docs/fundamentals/art-backend.mdx +++ b/docs/fundamentals/art-backend.mdx @@ -20,15 +20,6 @@ While the backend's training and inference settings are highly configurable, the arrow={true} > -
- -
-## Managed, remote, or local training +## Managed or local training -ART provides three backend classes: +ART provides two backend classes: * `ServerlessBackend` - train remotely on autoscaling GPUs -* `SkyPilotBackend` - train remotely on self-managed infra * `LocalBackend` - run your agent and training code on the same machine -If your agent is already set up on a machine equipped with an advanced GPU and you want to run training on the same machine, use `LocalBackend`. If your agent is running on a machine without an advanced GPU (this includes most personal computers and production servers), use `SkyPilotBackend` or `ServerlessBackend` instead. `ServerlessBackend` optimizes speed and cost by autoscaling across managed clusters. `SkyPilotBackend` lets you use your own infra. - -All three backend types implement the `art.Backend` class and the client interacts with all three in the exact same way. Under the hood, `SkyPilotBackend` configures a remote machine equipped with a GPU to run `LocalBackend`, and forwards requests from the client to the remote instance. `ServerlessBackend` runs within W&B Training clusters and autoscales GPUs to meet training and inference demand. +If your agent is already set up on a machine equipped with an advanced GPU and you want to run training on the same machine, use `LocalBackend`. If your agent is running on a machine without an advanced GPU (this includes most personal computers and production servers), use `ServerlessBackend` instead. `ServerlessBackend` optimizes speed and cost by autoscaling across managed clusters. ### ServerlessBackend @@ -67,59 +55,6 @@ backend = ServerlessBackend( As your training job progresses, `ServerlessBackend` automatically saves your LoRA checkpoints as W&B Artifacts and deploys them for production inference on W&B Inference. -### SkyPilotBackend - -To use SkyPilotBackend, you'll need to install the optional dependency: - -```bash -pip install openpipe-art[skypilot] -``` - -When a `SkyPilotBackend` instance is initialized, it does a few things: - -- Provisions a remote machine with an advanced GPU (by default on RunPod) -- Installs `openpipe-art` and its dependencies -- Initializes a `LocalBackend` instance with vLLM and a training server (unsloth or torchtune) -- Registers the `LocalBackend` instance to forward requests to it over http - -To initialize a `SkyPilotBackend` instance, follow the code sample below: - -```python -from art.skypilot import SkyPilotBackend - -backend = await SkyPilotBackend.initialize_cluster( - # name of the cluster in SkyPilot's registry - cluster_name="my-cluster", - # version of openpipe-art that should be installed on the remote cluster - # default to version installed on the client - art_version="0.3.12", - # path to environment variables (e.g. WANDB_API_KEY) to set on the remote cluster - env_path=".env", - # the GPU the cluster is equipped with - gpu="H100" - # alternatively, more complicated requirements can be specified in - # the `resources` argument -) -``` - -When a training job is finished, you can shut down a cluster either through code or the CLI. - -**Code:** - -```python -backend = await SkyPilotBackend.initialize_cluster(...) - -# ...training code... - -backend.down() -``` - -**CLI:** - -```bash -uv run sky down my-cluster -``` - ### LocalBackend The `LocalBackend` class runs a vLLM server and either an Unsloth or torchtune instance on whatever machine your agent itself is executing. This is a good fit if you're already running your agent on a machine with a GPU. @@ -148,12 +83,6 @@ BACKEND_TYPE = "serverless" if BACKEND_TYPE == "serverless": from art.serverless.backend import ServerlessBackend backend = await ServerlessBackend() -else if BACKEND_TYPE="remote": - from art.skypilot import SkyPilotBackend - backend = await SkyPilotBackend.initialize_cluster( - cluster_name="my-cluster", - gpu="H100" - ) else: from art.local import LocalBackend backend = LocalBackend() @@ -182,12 +111,12 @@ To see `LocalBackend` and `ServerlessBackend` in action, try the examples below.
- Use SkyPilotBackend to train a SOTA summarizing agent. + Use LocalBackend to train a SOTA summarizing agent.
diff --git a/docs/fundamentals/art-client.mdx b/docs/fundamentals/art-client.mdx index 7374354d4..b99ee0514 100644 --- a/docs/fundamentals/art-client.mdx +++ b/docs/fundamentals/art-client.mdx @@ -18,15 +18,6 @@ If you're curious about how ART allows you to run training and inference either > Run training and inference on autoscaling GPUs. - - Run training and inference on a separate ephemeral machine. - diff --git a/docs/integrations/langgraph-integration.mdx b/docs/integrations/langgraph-integration.mdx index 79c661f93..5ba5214e0 100644 --- a/docs/integrations/langgraph-integration.mdx +++ b/docs/integrations/langgraph-integration.mdx @@ -15,7 +15,7 @@ To use ART with LangGraph, install ART with the required extras: uv pip install -U openpipe-art[backend,langgraph]>=0.4.9 ``` -The `langgraph` extra includes the LangGraph integration dependencies, while `backend` provides the training backend components. If running using the [SkyPilotBackend](/fundamentals/art-backend#skypilotbackend), substitute `skypilot` for `backend` in the extras array. +The `langgraph` extra includes the LangGraph integration dependencies, while `backend` provides the training backend components. ## Why Use ART with LangGraph? @@ -264,7 +264,7 @@ from art.utils import iterate_dataset # Initialize model and backend model = art.Model(name="Qwen/Qwen2.5-7B-Instruct") -backend = art.backends.SkyPilotBackend() +backend = art.LocalBackend() # Data models class EmailResult(BaseModel): diff --git a/docs/tutorials/open-deep-research.mdx b/docs/tutorials/open-deep-research.mdx index 27f63254f..451fc614b 100644 --- a/docs/tutorials/open-deep-research.mdx +++ b/docs/tutorials/open-deep-research.mdx @@ -40,13 +40,15 @@ Once the repository is cloned, install dependencies. If you haven't already, ins Then install the project dependencies by running `uv sync`. -### Step 2: Install SkyPilot/RunPod +### Step 2: Install backend dependencies and provision a GPU -We'll be using `LocalBackend` to manage the GPU that your model will be trained on. In order to provision a GPU for your training run, you'll need to have SkyPilot installed on your machine and provide it with the credentials to spin up machines on at least one infra provider. +You'll be using `LocalBackend` to manage the GPU that your model will be trained on. Install ART with the backend dependencies: -We recommend using RunPod because of their ease of use, but any infra provider that SkyPilot [supports](https://docs.skypilot.co/en/latest/overview.html#bringing-your-infra) will work. +```bash +pip install openpipe-art[backend] +``` -Follow RunPod's **Getting Started** guide [here](https://docs.runpod.io/integrations/skypilot/). You'll have to provide a credit card to use RunPod, but you'll only pay for the time your GPUs are running. +Make sure you have access to a machine with one or more modern NVIDIA GPUs. This can be your local workstation or a cloud VM. If you're using a cloud provider, launch the GPU instance and run the rest of this tutorial on that machine. ### Step 3: Set up optional environment variables found in `.env.example` @@ -74,18 +76,13 @@ uv run run_train.py # Run your rl training run. 1+ Day This is the main GRPO training loop where the model learns to optimize its research strategies based on feedback from the DeepResearch Bench evaluation framework. -The following steps execute when a training run on a new cluster begins: +The first training run will: -- **Spin up a cluster with 1 or more H200 GPUs.** - - This usually takes about 10 minutes, but RunPod occasionally has network throughput issues that can cause the cluster to take up to 30 minutes to spin up. -- **Register the model with ART.** - - This usually takes less than 5 minutes, though it can require up to 30 minutes if RunPod experiences network issues. -- **Download the model checkpoint.** - - Usually takes a few minutes depending on the model size. -- **Train the model for a specified number of steps.** - - Each RL step involves running the research agent on a subset of benchmark questions, and updating the model based on the rewards. We hold out another subset of test set questions to evalutate model progress every 10 steps that we do not train on. Training time depends on the number of steps and the complexity of each research task. -- **Upload the final model checkpoint.** - - This usually takes a few minutes. +- Register the model with ART. +- Download the model checkpoint. +- Start vLLM and the training service on your GPU. +- Train the model for a specified number of steps. +- Upload the final model checkpoint (if configured). ### Step 5: Generate the benchmarks @@ -103,15 +100,9 @@ This script will: Then run the `display_benchmarks.ipynb` notebook to visualize the results and generate comparison charts. -### Step 6: Shutting down the cluster - -When you're done training and running benchmarks, you can shut down the cluster by running: - -```bash -uv run sky down [cluster-name] -``` +### Step 6: Shutting down your GPU instance -However, since spinning up clusters is a time-intensive process, we recommend keeping clusters alive until you're sure you won't be using them in the near future. +When you're done training and running benchmarks, shut down your GPU instance through your cloud provider's console or CLI. If you're running locally, you can stop the training process. ## Training Results diff --git a/docs/tutorials/summarizer.mdx b/docs/tutorials/summarizer.mdx index 3b44c37bf..031025208 100644 --- a/docs/tutorials/summarizer.mdx +++ b/docs/tutorials/summarizer.mdx @@ -31,19 +31,15 @@ Once the repository is cloned, install dependencies. If you haven't already, ins Then install the project dependencies by running `uv sync`. -### 2. Install SkyPilot/RunPod +### 2. Install backend dependencies and provision a GPU -We'll be using `SkyPilotBackend` to manage the GPU that your model will be trained on. You'll need to install ART with the SkyPilot optional dependency: +You'll be using `LocalBackend` to manage the GPU that your model will be trained on. Install ART with the backend dependencies: ```bash -pip install openpipe-art[skypilot] +pip install openpipe-art[backend] ``` -In order for the backend to work, you'll need to have SkyPilot installed on your machine and provide it with the credentials to spin up machines on at least one infra provider. - -We recommend using RunPod because of their ease of use, but any infra provider that SkyPilot [supports](https://docs.skypilot.co/en/latest/overview.html#bringing-your-infra) will work. - -Follow RunPod's **Getting Started** guide [here](https://docs.runpod.io/integrations/skypilot/). You'll have to provide a credit card to use RunPod, but you'll only pay for the time your GPUs are running. +Make sure you have access to a machine with a modern NVIDIA GPU. This can be your local workstation or a cloud VM. If you're using a cloud provider (e.g. RunPod, Lambda, or GCP), launch the GPU instance and run the rest of this tutorial on that machine. ### 3. Set up optional environment variables found in `.env.example`. @@ -66,39 +62,17 @@ To enable model and logging backup to S3, you'll also need to provide AWS creden uv run python src/summarizer/train.py ``` -The following steps execute when a training run on a new cluster begins: - -- **Spin up a cluster with 1 H100 GPU.** - - This usually takes about 10 minutes, but RunPod occasionally has network throughput issues that can cause the cluster to take up to 30 minutes to spin up. Once the cluster is provisioned, it can be used for subsequent training runs without going through this process again. -- **Register the model with ART.** - - This usually takes less than 5 minutes, though it can require up to 30 minutes if RunPod experiences network issues. -- **Download the model checkpoint from S3.** - - Usually takes a few seconds. -- **Train the model for a specified number of steps.** - - Training itself should be pretty quick (each step takes less than a minute), but the total training time will depend on how many steps you run for. During training, the model checkpoint is saved to S3 after each step. -- **Upload the final model checkpoint to S3.** - - This usually takes a few seconds. +The first training run will: -### 5. Shutting down the cluster +- Register the model with ART. +- Download the model checkpoint from S3 (if configured). +- Start vLLM and the training service on your GPU. +- Train the model for a specified number of steps. +- Upload the final model checkpoint to S3 (if configured). -When you're done training and running benchmarks, you can shut down the cluster in two ways: - -Through the CLI: - -```bash -uv run sky down -``` - -or through code: - -```python -DESTROY_AFTER_RUN = True - -if DESTROY_AFTER_RUN: - await backend.down() -``` +### 5. Shutting down your GPU instance -However, since spinning up clusters is a time-intensive process, we recommend keeping clusters alive until you're sure you won't be using them in the near future. +When you're done training and running benchmarks, shut down your GPU instance through your cloud provider's console or CLI. If you're running locally, you can simply stop the training process. ### Running Benchmarks diff --git a/examples/hn_title_generator/skypilot-reference-grpo-trainer.yaml b/examples/hn_title_generator/skypilot-reference-grpo-trainer.yaml deleted file mode 100644 index 39cd93043..000000000 --- a/examples/hn_title_generator/skypilot-reference-grpo-trainer.yaml +++ /dev/null @@ -1,43 +0,0 @@ -# To launch, run the following command from the root directory of the art repository: -# `uv run sky launch examples/hn_title_generator/skypilot-reference-grpo-trainer.yaml --cluster=kyle-hn-title-generator-002 --env-file=.env --yes --retry-until-up --down --idle-minutes-to-autostop 60` - -resources: - image_id: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel - cloud: runpod - region: US - accelerators: - - "H100-SXM" - -workdir: . - -envs: - HF_HUB_ENABLE_HF_TRANSFER: 1 - VLLM_CONFIGURE_LOGGING: 0 - -setup: | - apt-get update && apt-get install -y git - - curl -LsSf https://astral.sh/uv/install.sh | sh - - # Source the environment to make uv available - source $HOME/.local/bin/env - - uv pip install --system \ - unsloth==2025.3.19 \ - vllm==0.8.2 \ - bitsandbytes==0.45.4 \ - datasets==3.3.2 \ - s3fs==2024.12.0 \ - hf-transfer==0.1.9 \ - typer==0.15.2 \ - fastapi==0.115.11 \ - python-dotenv==1.0.1 \ - polars==1.24.0 \ - wandb==0.19.8 \ - git+https://github.com/corbt/panza.git \ - - echo "Setup complete" - -run: | - echo "Running train_grpo.py" - uv run python examples/hn_title_generator/reference_grpo_trainer.py diff --git a/examples/hn_title_generator/skypilot.yaml b/examples/hn_title_generator/skypilot.yaml deleted file mode 100644 index 59f27c9be..000000000 --- a/examples/hn_title_generator/skypilot.yaml +++ /dev/null @@ -1,19 +0,0 @@ -# To launch, run the following command from the root directory of the art repository: -# `uv run sky launch examples/hn_title_generator/skypilot.yaml --cluster=kyle-hn-title-generator-001 --env-file=.env --yes --retry-until-up --down --idle-minutes-to-autostop 10` - -workdir: . -resources: - accelerators: ["H100-SXM:1"] -envs: - HF_HUB_ENABLE_HF_TRANSFER: 1 - -setup: | - curl -LsSf https://astral.sh/uv/install.sh | sh - - source $HOME/.local/bin/env - - uv sync - -run: | - echo "Running training script..." - uv run python examples/hn_title_generator/train.py diff --git a/examples/just-the-facts/just_the_facts/generate_benchmarks.py b/examples/just-the-facts/just_the_facts/generate_benchmarks.py index 36cd60c63..c6f5e6256 100644 --- a/examples/just-the-facts/just_the_facts/generate_benchmarks.py +++ b/examples/just-the-facts/just_the_facts/generate_benchmarks.py @@ -6,7 +6,7 @@ from dotenv import load_dotenv import art -from art.skypilot import SkyPilotBackend +from art.local import LocalBackend from just_the_facts.rollout import rollout from just_the_facts.scenarios import val_scenarios @@ -77,13 +77,7 @@ async def log_comparison_model(comparison_model: art.Model): async def run_benchmarks(): global backend - backend = await SkyPilotBackend.initialize_cluster( - cluster_name="just-the-facts", - gpu="H100-SXM", - tail_logs=False, - env_path="../../.env", - art_version="../../", - ) + backend = LocalBackend() await gpt_4o_mini.register(backend) await gpt_4o.register(backend) await gpt_4_1.register(backend) diff --git a/examples/just-the-facts/just_the_facts/train.py b/examples/just-the-facts/just_the_facts/train.py index 621132adb..c6bbfdf38 100644 --- a/examples/just-the-facts/just_the_facts/train.py +++ b/examples/just-the-facts/just_the_facts/train.py @@ -17,24 +17,10 @@ weave.init(project_name="just-the-facts") -async def train( - model: art.TrainableModel[JustTheFactsConfig], use_skypilot: bool = False -): - if use_skypilot: - from art.skypilot.backend import SkyPilotBackend - - backend = await SkyPilotBackend.initialize_cluster( - cluster_name="just-the-facts", - gpu="H100-SXM", - tail_logs=False, - env_path="../../.env", - force_restart=True, - art_version="../../", - ) - else: - from art.local import LocalBackend +async def train(model: art.TrainableModel[JustTheFactsConfig]): + from art.local import LocalBackend - backend = LocalBackend() + backend = LocalBackend() print(f"Pulling latest checkpoint from S3 bucket: `{os.environ['BACKUP_BUCKET']}`") await backend._experimental_pull_from_s3( @@ -99,9 +85,8 @@ async def train( parser = argparse.ArgumentParser() parser.add_argument("--model", required=True, help="Name of the model to train") - parser.add_argument("--use-skypilot", action="store_true", help="Use Skypilot") args = parser.parse_args() model = models[args.model] - asyncio.run(train(model=model, use_skypilot=args.use_skypilot)) + asyncio.run(train(model=model)) diff --git a/examples/just-the-facts/pyproject.toml b/examples/just-the-facts/pyproject.toml index ee2e874c5..826d377be 100644 --- a/examples/just-the-facts/pyproject.toml +++ b/examples/just-the-facts/pyproject.toml @@ -3,7 +3,7 @@ name = "just-the-facts" version = "0.1.0" description = "Add your description here" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.11" dependencies = [ "aiohttp>=3.12.14", "asyncio>=3.4.3", @@ -15,7 +15,7 @@ dependencies = [ "mcp>=1.11.0", "newspaper3k>=0.2.8", "openai>=1.74.0", - "openpipe-art[skypilot]", + "openpipe-art[backend]", "python-dotenv>=1.1.1", "tenacity>=9.1.2", "weave>=0.51.56", diff --git a/examples/just-the-facts/run_remote.py b/examples/just-the-facts/run_remote.py deleted file mode 100644 index ba5f3d018..000000000 --- a/examples/just-the-facts/run_remote.py +++ /dev/null @@ -1,112 +0,0 @@ -# Usage: -# uv run run_training.py --models=207 --fast - -import argparse -from datetime import datetime -import textwrap - -from dotenv import dotenv_values, load_dotenv -import sky -from sky import ClusterStatus - -load_dotenv() - -parser = argparse.ArgumentParser( - description="Run a Python module remotely on a SkyPilot cluster. All arguments after the flags will be treated as the module path (and any additional arguments) to execute remotely." -) - -# Required/optional flags that we want to parse explicitly -parser.add_argument( - "--cluster-name", - type=str, - required=True, - help="Name of the SkyPilot cluster to run the command on.", -) -parser.add_argument( - "--cancel-all", - action="store_true", - help="Cancel all jobs on the cluster before submitting the new one.", -) - -# Everything that comes after the above flags will be captured here and treated as the command to run -parser.add_argument( - "command", - nargs=argparse.REMAINDER, - help="Python module path (and optional arguments) to execute remotely, e.g. 'mcp_rl.benchmarks.generate_benchmarks'.", -) - -args = parser.parse_args() - -cluster_name = args.cluster_name -cancel_all = args.cancel_all - -# Join the remaining arguments into a single string to pass to `python -m ...` -if len(args.command) == 0: - parser.error("No command specified. Provide a module path to run after the flags.") - -remote_command = " ".join(args.command).strip() - - -def run_remote(command: str): - print(f"Running command: '{command}'") - setup_script = textwrap.dedent( - """ - echo 'Setting up environment...' - apt install -y nvtop - curl -LsSf https://astral.sh/uv/install.sh | sh - source $HOME/.local/bin/env - """ - ) - - run_script = textwrap.dedent(f""" - cd ~/ART && uv add "transformers==4.53.2" --no-sync - cd ~/sky_workdir - uv remove openpipe-art - uv add --editable ~/ART --extra backend - uv sync --locked - - uv run python {command} - """) - - seconds_str = datetime.now().strftime("%Y%m%d%H%M%S") - - # Create a SkyPilot Task - task = sky.Task( - name=f"art-remote-{seconds_str}", - setup=setup_script, - run=run_script, - workdir=".", # Sync the project directory - envs=dict(dotenv_values()), # type: ignore - ) - task.set_resources(sky.Resources(accelerators="H100-SXM:1")) - task.set_file_mounts({"~/ART": "../.."}) - - print("Checking for existing cluster and jobs…") - cluster_status = sky.stream_and_get(sky.status(cluster_names=[cluster_name])) - if ( - len(cluster_status) > 0 - and cluster_status[0]["status"] == ClusterStatus.UP - and cancel_all - ): - print(f"Cluster {cluster_name} is UP. Canceling any active jobs…") - sky.stream_and_get(sky.cancel(cluster_name, all=True)) - - # Launch the task; stream_and_get blocks until the task starts running, but - # running this in its own thread means all models run in parallel. - job_id, _ = sky.stream_and_get( - sky.launch( - task, - cluster_name=cluster_name, - retry_until_up=True, - idle_minutes_to_autostop=120, - down=True, - ) - ) - - print(f"Job submitted for {cluster_name} (ID: {job_id}). 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a/examples/mcp-rl/mcp_rl/train.py b/examples/mcp-rl/mcp_rl/train.py index 020fc0e8d..49a79dafc 100644 --- a/examples/mcp-rl/mcp_rl/train.py +++ b/examples/mcp-rl/mcp_rl/train.py @@ -30,7 +30,7 @@ weave.init("mcp-agent-training") -async def train_mcp_agent(model: art.TrainableModel, use_skypilot: bool = False): +async def train_mcp_agent(model: art.TrainableModel): """Example training function that creates AlphaMcpServer and passes it in scenarios.""" load_dotenv() @@ -89,16 +89,9 @@ async def train_mcp_agent(model: art.TrainableModel, use_skypilot: bool = False) f"Using config: max_turns={max_turns}, trajectories_per_group={trajectories_per_group}, groups_per_step={groups_per_step}, num_epochs={num_epochs}, learning_rate={learning_rate}" ) - if use_skypilot: - from art.skypilot.backend import SkyPilotBackend + from art.local.backend import LocalBackend - backend = await SkyPilotBackend().initialize_cluster( - cluster_name="mcp-agent-training", gpu="H100-SXM" - ) - else: - from art.local.backend import LocalBackend - - backend = LocalBackend() + backend = LocalBackend() await backend._experimental_pull_from_s3( model, @@ -186,12 +179,6 @@ def main(): required=True, help="Model key to train from all_experiments.py (e.g. 001, 002, etc.). If not provided, uses default model.", ) - parser.add_argument( - "--use-skypilot", - action="store_true", - help="Whether to use SkyPilot backend instead of local backend.", - ) - args = parser.parse_args() if args.models not in models: @@ -222,7 +209,7 @@ def main(): print("Starting MCP agent training...") try: - asyncio.run(train_mcp_agent(model, use_skypilot=args.use_skypilot)) + asyncio.run(train_mcp_agent(model)) except Exception as e: print(f"Training failed with error: {e}") import traceback diff --git a/examples/mcp-rl/pyproject.toml b/examples/mcp-rl/pyproject.toml index 963a5cebc..5fbf50473 100644 --- a/examples/mcp-rl/pyproject.toml +++ b/examples/mcp-rl/pyproject.toml @@ -3,14 +3,14 @@ name = "art-mcp" version = "0.1.0" description = "Add your description here" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.11" dependencies = [ "aiohttp>=3.12.14", "asyncio>=3.4.3", "click>=8.1.8", "mcp>=1.11.0", "openai>=1.74.0", - "openpipe-art[skypilot]", + "openpipe-art[backend]", "python-dotenv>=1.1.1", "tenacity>=9.1.2", "weave>=0.51.56", diff --git a/examples/mcp-rl/run_remote.py b/examples/mcp-rl/run_remote.py deleted file mode 100644 index 0705f02a2..000000000 --- a/examples/mcp-rl/run_remote.py +++ /dev/null @@ -1,112 +0,0 @@ -# Usage: -# uv run run_training.py --models=207 --fast - -import argparse -from datetime import datetime -import textwrap - -from dotenv import dotenv_values, load_dotenv -import sky -from sky import ClusterStatus - -load_dotenv() - -parser = argparse.ArgumentParser( - description="Run a Python module remotely on a SkyPilot cluster. All arguments after the flags will be treated as the module path (and any additional arguments) to execute remotely." -) - -# Required/optional flags that we want to parse explicitly -parser.add_argument( - "--cluster-name", - type=str, - required=True, - help="Name of the SkyPilot cluster to run the command on.", -) -parser.add_argument( - "--cancel-all", - action="store_true", - help="Cancel all jobs on the cluster before submitting the new one.", -) - -# Everything that comes after the above flags will be captured here and treated as the command to run -parser.add_argument( - "command", - nargs=argparse.REMAINDER, - help="Python module path (and optional arguments) to execute remotely, e.g. 'mcp_rl.benchmarks.generate_benchmarks'.", -) - -args = parser.parse_args() - -cluster_name = args.cluster_name -cancel_all = args.cancel_all - -# Join the remaining arguments into a single string to pass to `python -m ...` -if len(args.command) == 0: - parser.error("No command specified. Provide a module path to run after the flags.") - -remote_command = " ".join(args.command).strip() - - -def run_remote(command: str): - print(f"Running command: '{command}'") - setup_script = textwrap.dedent( - """ - echo 'Setting up environment...' - apt install -y nvtop - curl -LsSf https://astral.sh/uv/install.sh | sh - source $HOME/.local/bin/env - """ - ) - - run_script = textwrap.dedent(f""" - cd ~/ART && uv add "transformers==4.53.2" --no-sync - cd ~/sky_workdir - uv remove openpipe-art - uv add --editable ~/ART --extra backend - uv sync --locked - - uv run python -m {command} - """) - - seconds_str = datetime.now().strftime("%Y%m%d%H%M%S") - - # Create a SkyPilot Task - task = sky.Task( - name=f"art-remote-{seconds_str}", - setup=setup_script, - run=run_script, - workdir=".", # Sync the project directory - envs=dict(dotenv_values()), # type: ignore - ) - task.set_resources(sky.Resources(accelerators="H100-SXM:1")) - task.set_file_mounts({"~/ART": "../.."}) - - print("Checking for existing cluster and jobs…") - cluster_status = sky.stream_and_get(sky.status(cluster_names=[cluster_name])) - if ( - len(cluster_status) > 0 - and cluster_status[0]["status"] == ClusterStatus.UP - and cancel_all - ): - print(f"Cluster {cluster_name} is UP. Canceling any active jobs…") - sky.stream_and_get(sky.cancel(cluster_name, all=True)) - - # Launch the task; stream_and_get blocks until the task starts running, but - # running this in its own thread means all models run in parallel. - job_id, _ = sky.stream_and_get( - sky.launch( - task, - cluster_name=cluster_name, - retry_until_up=True, - idle_minutes_to_autostop=120, - down=True, - ) - ) - - print(f"Job submitted for {cluster_name} (ID: {job_id}). 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argparse.ArgumentParser(description="Train a model to play Tic-Tac-Toe") -parser.add_argument( - "--backend", - choices=["skypilot", "local"], - default="local", - help="Backend to use for training (default: local)", -) -args = parser.parse_args() weave.init("tic-tac-toe", global_postprocess_output=strip_logprobs) async def main(): - # Avoid import unnecessary backend dependencies - if args.backend == "skypilot": - from art.skypilot.backend import SkyPilotBackend - - backend = await SkyPilotBackend.initialize_cluster( - cluster_name="art3", art_version=".", env_path=".env", gpu="H100" - ) - else: - from art.local.backend import LocalBackend + from art.local.backend import LocalBackend - backend = LocalBackend() + backend = LocalBackend() model = art.TrainableModel( name="llama-8b-007", @@ -115,9 +96,6 @@ async def main(): print(traj) - if DESTROY_AFTER_RUN: - await backend.down() - if GENERATE_BENCHMARKS: gpt_4o_mini = art.Model( name="gpt-4o-mini", diff --git a/examples/tic_tac_toe_self_play/deploy_step.py b/examples/tic_tac_toe_self_play/deploy_step.py index c95946196..9394fd249 100644 --- a/examples/tic_tac_toe_self_play/deploy_step.py +++ b/examples/tic_tac_toe_self_play/deploy_step.py @@ -3,7 +3,7 @@ import os from rollout import ModelConfig, TicTacToeScenario, rollout -from train import BASE_MODEL, CLUSTER_NAME, MODEL_NAME, PROJECT_NAME +from train import BASE_MODEL, MODEL_NAME, PROJECT_NAME import art from art.utils.deployment import TogetherDeploymentConfig, deploy_model @@ -11,12 +11,6 @@ async def deploy_step(): parser = argparse.ArgumentParser(description="Train a model to play Tic-Tac-Toe") - parser.add_argument( - "--backend", - choices=["skypilot", "local"], - default="local", - help="Backend to use for training (default: local)", - ) parser.add_argument( "--step", type=int, @@ -30,20 +24,9 @@ async def deploy_step(): base_model=BASE_MODEL, ) - # Avoid import unnecessary backend dependencies - if args.backend == "skypilot": - from art.skypilot.backend import SkyPilotBackend - - backend = await SkyPilotBackend.initialize_cluster( - cluster_name=CLUSTER_NAME, - art_version=".", - env_path=".env", - gpu="H100", - ) - else: - from art.local.backend import LocalBackend + from art.local.backend import LocalBackend - backend = LocalBackend() + backend = LocalBackend() # Pull checkpoint from S3 checkpoint_path = await backend._experimental_pull_model_checkpoint( diff --git a/examples/tic_tac_toe_self_play/train.py b/examples/tic_tac_toe_self_play/train.py index d18112942..8cb5a86fb 100644 --- a/examples/tic_tac_toe_self_play/train.py +++ b/examples/tic_tac_toe_self_play/train.py @@ -1,4 +1,3 @@ -import argparse import asyncio import os import random @@ -18,9 +17,6 @@ PULL_FROM_S3 = False STEP = 300 -DESTROY_AFTER_RUN = False - -CLUSTER_NAME = "art4" PROJECT_NAME = "tic-tac-toe" BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct" MODEL_NAME = "llama-8b-student-001" @@ -29,35 +25,9 @@ async def main(): - parser = argparse.ArgumentParser(description="Train a model to play Tic-Tac-Toe") - parser.add_argument( - "--backend", - choices=["skypilot", "local"], - default="local", - help="Backend to use for training (default: local)", - ) - parser.add_argument( - "--restart", - action="store_true", - help="Restart the ART server", - ) - args = parser.parse_args() - - # Avoid import unnecessary backend dependencies - if args.backend == "skypilot": - from art.skypilot.backend import SkyPilotBackend - - backend = await SkyPilotBackend.initialize_cluster( - cluster_name=CLUSTER_NAME, - art_version=".", - env_path=".env", - gpu="H100", - force_restart=args.restart, - ) - else: - from art.local.backend import LocalBackend + from art.local.backend import LocalBackend - backend = LocalBackend() + backend = LocalBackend() model = art.TrainableModel( name=MODEL_NAME, diff --git a/examples/tic_tac_toe_self_play/train_o4_mini.py b/examples/tic_tac_toe_self_play/train_o4_mini.py index 325c84109..1cc054395 100644 --- a/examples/tic_tac_toe_self_play/train_o4_mini.py +++ b/examples/tic_tac_toe_self_play/train_o4_mini.py @@ -1,4 +1,3 @@ -import argparse import asyncio import os import random @@ -17,44 +16,15 @@ PULL_FROM_S3 = False STEP = 200 -DESTROY_AFTER_RUN = False - -CLUSTER_NAME = "art5" PROJECT_NAME = "tic-tac-toe" BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct" MODEL_NAME = "llama-8b-o4-mini-001" async def main(): - parser = argparse.ArgumentParser(description="Train a model to play Tic-Tac-Toe") - parser.add_argument( - "--backend", - choices=["skypilot", "local"], - default="local", - help="Backend to use for training (default: local)", - ) - parser.add_argument( - "--restart", - action="store_true", - help="Restart the ART server", - ) - args = parser.parse_args() - - # Avoid import unnecessary backend dependencies - if args.backend == "skypilot": - from art.skypilot.backend import SkyPilotBackend - - backend = await SkyPilotBackend.initialize_cluster( - cluster_name=CLUSTER_NAME, - art_version=".", - env_path=".env", - gpu="H100", - force_restart=args.restart, - ) - else: - from art.local.backend import LocalBackend + from art.local.backend import LocalBackend - backend = LocalBackend() + backend = LocalBackend() model = art.TrainableModel( name=MODEL_NAME, diff --git a/pyproject.toml b/pyproject.toml index efd4fc45f..9f33dd860 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,11 +41,6 @@ backend = [ "gql<4", ] -skypilot = [ - "semver>=3.0.4", - "skypilot[cudo,do,fluidstack,gcp,lambda,kubernetes,paperspace,runpod]==0.10.5", -] - langgraph = [ "langchain-core>=0.3.51", "langgraph>=0.6.2", @@ -66,7 +61,6 @@ tinker = [ [project.scripts] art = "art.cli:app" -stop-server = "art.skypilot.stop_server:main" [build-system] requires = ["hatchling"] @@ -151,10 +145,6 @@ allowed-unresolved-imports = [ "uvicorn.**", "vllm.**", "wandb.**", - # skypilot deps - "semver.**", - "sky.**", - "skypilot.**", # langgraph deps "langchain_core.**", "langchain_openai.**", diff --git a/scripts/deploy-model.py b/scripts/deploy-model.py index 004afb715..3feaca254 100644 --- a/scripts/deploy-model.py +++ b/scripts/deploy-model.py @@ -18,9 +18,7 @@ def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser( - description="Deploy a model checkpoint using ART & SkyPilot" - ) + parser = argparse.ArgumentParser(description="Deploy a model checkpoint using ART") parser.add_argument("--project", required=True, help="ART project name") parser.add_argument("--model", required=True, help="Name of the model to deploy") diff --git a/scripts/launch-cluster.sh b/scripts/launch-cluster.sh deleted file mode 100755 index 682eed5c9..000000000 --- a/scripts/launch-cluster.sh +++ /dev/null @@ -1,48 +0,0 @@ -#!/bin/bash - -CLUSTER_NAME="art" - -# Parse arguments -ARGS=() -PULL_LATEST=true -while [[ $# -gt 0 ]]; do - case "$1" in - -c) - CLUSTER_NAME="$2" - shift 2 - ;; - --no-pull) - PULL_LATEST=false - shift 1 - ;; - *) - ARGS+=("$1") - shift - ;; - esac -done - -# Check for unstaged changes -if ! git diff --quiet; then - echo "Warning: You have unstaged changes. Unstaged changes will be discarded from the cluster working directory." -fi - -# Check for uncommitted changes -if ! git diff --cached --quiet; then - echo "Warning: You have uncommitted changes. Uncommitted changes will be discarded from the cluster working directory." -fi - -if [[ "$PULL_LATEST" == true ]]; then - echo "Pulling latest changes..." - if ! git pull; then - echo "Error: Failed to pull latest changes." - exit 1 - fi -else - echo "Skipping git pull (deploying current working tree). To pull latest, omit --no-pull." - # Preserve synced working tree on remote by disabling reset/clean. - ARGS+=(--env "GIT_RESET_CLEAN=false") -fi - -# Launch the cluster -uv run sky launch skypilot-config.yaml -c "$CLUSTER_NAME" --env-file .env -y "${ARGS[@]}" \ No newline at end of file diff --git a/scripts/launch-test-cluster.sh b/scripts/launch-test-cluster.sh deleted file mode 100755 index 7875fc98d..000000000 --- a/scripts/launch-test-cluster.sh +++ /dev/null @@ -1,91 +0,0 @@ -#!/bin/bash - -# Usage: ./launch-test-cluster.sh [options] -# Options: -# -c CLUSTER_NAME Set cluster name (default: art-integration-test) -# --no-pull Skip git pull before launching -# -n NOTEBOOKS Comma-separated notebook indexes to run (e.g., '0,2' or '1-3') -# -# Examples: -# ./launch-test-cluster.sh --no-pull # Run all tests -# ./launch-test-cluster.sh -n 0 --no-pull # Run only notebook 0 -# ./launch-test-cluster.sh -n 0,2 --no-pull # Run notebooks 0 and 2 -# ./launch-test-cluster.sh -c my-cluster -n 1-3 --no-pull # Custom cluster, run notebooks 1-3 - -CLUSTER_NAME="art-integration-test" - -# Parse arguments -LAUNCH_ARGS=() -NOTEBOOKS="" -PULL_LATEST=true - -while [[ $# -gt 0 ]]; do - case "$1" in - -c) - CLUSTER_NAME="$2" - shift 2 - ;; - --no-pull) - PULL_LATEST=false - shift 1 - ;; - -n) - NOTEBOOKS="$2" - shift 2 - ;; - *) - LAUNCH_ARGS+=("$1") - shift - ;; - esac -done - -# Always attempt to tear down the cluster on exit -trap 'echo "Tearing down cluster \"$CLUSTER_NAME\"..."; uv run sky down -y "$CLUSTER_NAME" || true' EXIT - -# Check for unstaged changes -if ! git diff --quiet; then - echo "Warning: You have unstaged changes. Unstaged changes will be discarded from the cluster working directory." -fi - -# Check for uncommitted changes -if ! git diff --cached --quiet; then - echo "Warning: You have uncommitted changes. Uncommitted changes will be discarded from the cluster working directory." -fi - -if [[ "$PULL_LATEST" == true ]]; then - echo "Pulling latest changes..." - if ! git pull; then - echo "Error: Failed to pull latest changes." - exit 1 - fi -else - echo "Skipping git pull (deploying current working tree). To pull latest, omit --no-pull." - # Preserve synced working tree on remote by disabling reset/clean. - LAUNCH_ARGS+=(--env "GIT_RESET_CLEAN=false") -fi - -echo "Launching cluster \"$CLUSTER_NAME\"..." -uv run sky launch skypilot-config.yaml -c "$CLUSTER_NAME" --env-file .env -y "${LAUNCH_ARGS[@]}" -LAUNCH_EXIT=$? -if [[ $LAUNCH_EXIT -ne 0 ]]; then - echo "Error: Cluster launch failed with exit code $LAUNCH_EXIT" - exit $LAUNCH_EXIT -fi - -echo "Running tests on \"$CLUSTER_NAME\"..." -if [[ -n "$NOTEBOOKS" ]]; then - echo "Running notebooks: $NOTEBOOKS" - TEST_CMD="uv run tests/integration.py -n '$NOTEBOOKS'" -else - TEST_CMD="uv run tests/integration.py" -fi - -uv run sky exec -c "$CLUSTER_NAME" --env-file .env \ - "bash -lc 'export CUDA_VISIBLE_DEVICES=\${CUDA_VISIBLE_DEVICES:-0}; $TEST_CMD || true; pkill -f python || true; exit'" -TEST_EXIT_CODE=$? - -echo "Test completed." - -# Exit with the test command's exit code; trap will down the cluster. -exit $TEST_EXIT_CODE \ No newline at end of file diff --git a/skypilot-config.yaml b/skypilot-config.yaml deleted file mode 100644 index 638faec5b..000000000 --- a/skypilot-config.yaml +++ /dev/null @@ -1,401 +0,0 @@ -# Title: Task YAML — SkyPilot documentation - -# URL Source: https://docs.skypilot.co/en/latest/reference/yaml-spec.html - -# Markdown Content: -# SkyPilot provides an intuitive YAML interface to specify a task (resource requirements, setup commands, run commands, file mounts, storage mounts, and so on). - -# Task YAMLs can be used with the [CLI](https://docs.skypilot.co/en/latest/reference/cli.html#cli), or the programmatic API ([`sky.Task.from_yaml()`](https://docs.skypilot.co/en/latest/reference/api.html#sky.Task.from_yaml "sky.Task.from_yaml")). - -# Available fields: - -# \# Task name (optional), used for display purposes. -# name: my-task - -# \# Working directory (optional), synced to ~/sky\_workdir on the remote cluster -# \# each time launch or exec is run with the yaml file. -# # -# \# Commands in "setup" and "run" will be executed under it. -# # -# \# If a relative path is used, it's evaluated relative to the location from -# \# which \`sky\` is called. -# # -# \# To exclude files from syncing, see -# \# https://docs.skypilot.co/en/latest/examples/syncing-code-artifacts.html#exclude-uploading-files -# workdir: ~/my-task-code - -# \# Number of nodes (optional; defaults to 1) to launch including the head node. -# # -# \# A task can set this to a smaller value than the size of a cluster. -# num\_nodes: 4 - -# \# Per-node resource requirements (optional). -# resources: -# cloud: aws \# The cloud to use (optional). - -# \# The region to use (optional). Auto-failover will be disabled -# \# if this is specified. -# region: us-east-1 - -# \# The zone to use (optional). Auto-failover will be disabled -# \# if this is specified. -# zone: us-east-1a - -# \# Accelerator name and count per node (optional). -# # -# \# Use \`sky show-gpus\` to view available accelerator configurations. -# # -# \# The following three ways are valid for specifying accelerators for a cluster: -# # -# \# To specify a single type of accelerator: -# \# Format: : (or simply , short for a count of 1). -# \# accelerators: H100:4 -# # -# \# To specify an ordered list of accelerators (try the accelerators in -# \# the specified order): -# \# Format: \[:, ...\] -# \# accelerators: \['L4:1', 'H100:1', 'A100:1'\] -# # -# \# To specify an unordered set of accelerators (optimize all specified -# \# accelerators together, and try accelerator with lowest cost first): -# \# Format: {:, ...} -# \# accelerators: {'L4:1', 'H100:1', 'A100:1'} -# accelerators: H100:8 - -# \# Number of vCPUs per node (optional). -# # -# \# Format: -# \# : exactly vCPUs -# \# +: at least vCPUs -# # -# \# E.g., 4+ means first try to find an instance type with \>\= 4 vCPUs. If -# \# not found, use the next cheapest instance with more than 4 vCPUs. -# cpus: 4+ - -# \# Memory in GiB per node (optional). -# # -# \# Format: -# \# : exactly GiB -# \# +: at least GiB -# # -# \# E.g., 32+ means first try to find an instance type with \>\= 32 GiB. If -# \# not found, use the next cheapest instance with more than 32 GiB. -# memory: 32+ - -# \# Instance type to use (optional). If 'accelerators' is specified, -# \# the corresponding instance type is automatically inferred. -# instance\_type: p3.8xlarge - -# \# Whether the cluster should use spot instances (optional). -# \# If unspecified, defaults to False (on-demand instances). -# use\_spot: False - -# \# The recovery strategy for managed jobs (optional). -# # -# \# In effect for managed jobs. Possible values are \`FAILOVER\` and \`EAGER\_NEXT\_REGION\`. -# # -# \# If \`FAILOVER\` is specified, the job will be restarted in the same region -# \# if the node fails, and go to the next region if no available resources -# \# are found in the same region. -# # -# \# If \`EAGER\_NEXT\_REGION\` is specified, the job will go to the next region -# \# directly if the node fails. This is useful for spot instances, as in -# \# practice, preemptions in a region usually indicate a shortage of resources -# \# in that region. -# # -# \# default: EAGER\_NEXT\_REGION -# job\_recovery: none -# \# Or, to allow up to 3 restarts (default: 0) on user code errors: -# \# job\_recovery: -# \# strategy: EAGER\_NEXT\_REGION -# \# max\_restarts\_on\_errors: 3 - -# \# Disk size in GB to allocate for OS (mounted at /). Increase this if you -# \# have a large working directory or tasks that write out large outputs. -# disk\_size: 256 - -# \# Disk tier to use for OS (optional). -# \# Could be one of 'low', 'medium', 'high', 'ultra' or 'best' (default: 'medium'). -# \# if 'best' is specified, use the best disk tier enabled. -# \# Rough performance estimate: -# \# low: 1000 IOPS; read 90 MB/s; write 90 MB/s -# \# medium: 3000 IOPS; read 220 MB/s; write 220 MB/s -# \# high: 6000 IOPS; read 400 MB/s; write 400 MB/s -# \# ultra: 60000 IOPS; read 4000 MB/s; write 3000 MB/s -# \# Measured by examples/perf/storage\_rawperf.yaml -# disk\_tier: medium - -# \# Ports to expose (optional). -# # -# \# All ports specified here will be exposed to the public Internet. Under -# \# the hood, a firewall rule / inbound rule is automatically added to allow -# \# inbound traffic to these ports. Applies to all VMs of a cluster created -# \# with this field set. -# # -# \# Currently only TCP protocol is supported. -# # -# \# Ports Lifecycle: -# \# A cluster's ports will be updated whenever \`sky launch\` is executed. -# \# When launching an existing cluster, any new ports specified will be -# \# opened for the cluster, and the firewall rules for old ports will never -# \# be removed until the cluster is terminated. -# # -# \# Could be an integer, a range, or a list of integers and ranges: -# \# To specify a single port: -# \# ports: 8081 -# \# To specify a port range: -# \# ports: 10052-10100 -# \# To specify multiple ports / port ranges: -# \# ports: -# \# - 8080 -# \# - 10022-10040 -# ports: 8081 - -# \# Additional accelerator metadata (optional); only used for TPU node -# \# and TPU VM. -# \# Example usage: -# # -# \# To request a TPU VM: -# \# accelerator\_args: -# \# tpu\_vm: True (optional, default: True) -# # -# \# To request a TPU node: -# \# accelerator\_args: -# \# tpu\_name: ... -# \# tpu\_vm: False -# # -# \# By default, the value for "runtime\_version" is decided based on which is -# \# requested and should work for either case. If passing in an incompatible -# \# version, GCP will throw an error during provisioning. -# accelerator\_args: -# \# Default is "tpu-vm-base" for TPU VM and "2.12.0" for TPU node. -# runtime\_version: tpu-vm-base -# \# tpu\_name: mytpu -# \# tpu\_vm: True # True to use TPU VM (the default); False to use TPU node. - -# \# Custom image id (optional, advanced). The image id used to boot the -# \# instances. Only supported for AWS, GCP, OCI and IBM (for non-docker image). -# \# If not specified, SkyPilot will use the default debian-based image -# \# suitable for machine learning tasks. -# # -# \# Docker support -# \# You can specify docker image to use by setting the image\_id to -# \# \`docker:\` for Azure, AWS and GCP. For example, -# \# image\_id: docker:ubuntu:latest -# \# Currently, only debian and ubuntu images are supported. -# \# If you want to use a docker image in a private registry, you can specify your -# \# username, password, and registry server as task environment variable. For -# \# details, please refer to the \`envs\` section below. -# # -# \# AWS -# \# To find AWS AMI ids: https://leaherb.com/how-to-find-an-aws-marketplace-ami-image-id -# \# You can also change the default OS version by choosing from the -# \# following image tags provided by SkyPilot: -# \# image\_id: skypilot:gpu-ubuntu-2004 -# \# image\_id: skypilot:k80-ubuntu-2004 -# \# image\_id: skypilot:gpu-ubuntu-1804 -# \# image\_id: skypilot:k80-ubuntu-1804 -# # -# \# It is also possible to specify a per-region image id (failover will only -# \# go through the regions specified as keys; useful when you have the -# \# custom images in multiple regions): -# \# image\_id: -# \# us-east-1: ami-0729d913a335efca7 -# \# us-west-2: ami-050814f384259894c -# # -# \# GCP -# \# To find GCP images: https://cloud.google.com/compute/docs/images -# \# image\_id: projects/deeplearning-platform-release/global/images/common-cpu-v20230615-debian-11-py310 -# \# Or machine image: https://cloud.google.com/compute/docs/machine-images -# \# image\_id: projects/my-project/global/machineImages/my-machine-image -# # -# \# Azure -# \# To find Azure images: https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage -# \# image\_id: microsoft-dsvm:ubuntu-2004:2004:21.11.04 -# # -# \# OCI -# \# To find OCI images: https://docs.oracle.com/en-us/iaas/images -# \# You can choose the image with OS version from the following image tags -# \# provided by SkyPilot: -# \# image\_id: skypilot:gpu-ubuntu-2204 -# \# image\_id: skypilot:gpu-ubuntu-2004 -# \# image\_id: skypilot:gpu-oraclelinux9 -# \# image\_id: skypilot:gpu-oraclelinux8 -# \# image\_id: skypilot:cpu-ubuntu-2204 -# \# image\_id: skypilot:cpu-ubuntu-2004 -# \# image\_id: skypilot:cpu-oraclelinux9 -# \# image\_id: skypilot:cpu-oraclelinux8 -# # -# \# It is also possible to specify your custom image's OCID with OS type, -# \# for example: -# \# image\_id: ocid1.image.oc1.us-sanjose-1.aaaaaaaaywwfvy67wwe7f24juvjwhyjn3u7g7s3wzkhduxcbewzaeki2nt5q:oraclelinux -# \# image\_id: ocid1.image.oc1.us-sanjose-1.aaaaaaaa5tnuiqevhoyfnaa5pqeiwjv6w5vf6w4q2hpj3atyvu3yd6rhlhyq:ubuntu -# # -# \# IBM -# \# Create a private VPC image and paste its ID in the following format: -# \# image\_id: -# \# To create an image manually: -# \# https://cloud.ibm.com/docs/vpc?topic=vpc-creating-and-using-an-image-from-volume. -# \# To use an official VPC image creation tool: -# \# https://www.ibm.com/cloud/blog/use-ibm-packer-plugin-to-create-custom-images-on-ibm-cloud-vpc-infrastructure -# \# To use a more limited but easier to manage tool: -# \# https://github.com/IBM/vpc-img-inst -# image\_id: ami-0868a20f5a3bf9702 - -# \# Labels to apply to the instances (optional). -# # -# \# If specified, these labels will be applied to the VMs or pods created -# \# by SkyPilot. These are useful for assigning metadata that may be -# \# used by external tools. Implementation depends on the chosen cloud - -# \# On AWS, labels map to instance tags. On GCP, labels map to instance -# \# labels. On Kubernetes, labels map to pod labels. On other clouds, -# \# labels are not supported and will be ignored. -# # -# \# Note: Labels are applied only on the first launch of the cluster. They -# \# are not updated on subsequent launches. -# labels: -# my-label: my-value - -# \# Candidate resources (optional). If specified, SkyPilot will only use -# \# these candidate resources to launch the cluster. The fields specified -# \# outside of \`any\_of\`, \`ordered\` will be used as the default values for -# \# all candidate resources, and any duplicate fields specified inside -# \# \`any\_of\`, \`ordered\` will override the default values. -# \# \`any\_of:\` means that SkyPilot will try to find a resource that matches -# \# any of the candidate resources, i.e. the failover order will be decided -# \# by the optimizer. -# \# \`ordered:\` means that SkyPilot will failover through the candidate -# \# resources with the specified order. -# \# Note: accelerators under \`any\_of\` and \`ordered\` cannot be a list or set. -# any\_of: -# \- cloud: aws -# region: us-west-2 -# accelerators: H100 -# \- cloud: gcp -# accelerators: H100 - -# \# Environment variables (optional). These values can be accessed in the -# \# \`file\_mounts\`, \`setup\`, and \`run\` sections below. -# # -# \# Values set here can be overridden by a CLI flag: -# \# \`sky launch/exec --env ENV=val\` (if ENV is present). -# # -# \# If you want to use a docker image as runtime environment in a private -# \# registry, you can specify your username, password, and registry server as -# \# task environment variable. For example: -# \# envs: -# \# SKYPILOT\_DOCKER\_USERNAME: -# \# SKYPILOT\_DOCKER\_PASSWORD: -# \# SKYPILOT\_DOCKER\_SERVER: -# # -# \# SkyPilot will execute \`docker login --username --password -# \# \` before pulling the docker image. For \`docker -# \# login\`, see https://docs.docker.com/engine/reference/commandline/login/ -# # -# \# You could also specify any of them through the CLI flag if you don't want -# \# to store them in your yaml file or if you want to generate them for -# \# constantly changing password. For example: -# \# sky launch --env SKYPILOT\_DOCKER\_PASSWORD=$(aws ecr get-login-password --region us-east-1). -# # -# \# For more information about docker support in SkyPilot, please refer to the \`image\_id\` section above. -# envs: -# MY\_BUCKET: skypilot-temp-gcs-test -# MY\_LOCAL\_PATH: tmp-workdir -# MODEL\_SIZE: 13b - -# file\_mounts: -# \# Uses rsync to sync local files/directories to all nodes of the cluster. -# # -# \# If a relative path is used, it's evaluated relative to the location from -# \# which \`sky\` is called. -# # -# \# If symlinks are present, they are copied as symlinks, and their targets -# \# must also be synced using file\_mounts to ensure correctness. -# /remote/dir1/file: /local/dir1/file -# /remote/dir2: /local/dir2 - -# \# Create a S3 bucket named sky-dataset, uploads the contents of -# \# /local/path/datasets to the bucket, and marks the bucket as persistent -# \# (it will not be deleted after the completion of this task). -# \# Symlinks and their contents are NOT copied. -# # -# \# Mounts the bucket at /datasets-storage on every node of the cluster. -# /datasets-storage: -# name: sky-dataset \# Name of storage, optional when source is bucket URI -# source: /local/path/datasets \# Source path, can be local or bucket URI. Optional, do not specify to create an empty bucket. -# store: s3 \# Could be either 's3', 'gcs', 'azure', 'r2', 'oci', or 'ibm'; default: None. Optional. -# persistent: True \# Defaults to True; can be set to false to delete bucket after cluster is downed. Optional. -# mode: MOUNT \# Either MOUNT or COPY. Defaults to MOUNT. Optional. - -# \# Copies a cloud object store URI to the cluster. Can be private buckets. -# /datasets-s3: s3://my-awesome-dataset - -# \# Demoing env var usage. -# /checkpoint/${MODEL\_SIZE}: ~/${MY\_LOCAL\_PATH} -# /mydir: -# name: ${MY\_BUCKET} \# Name of the bucket. -# mode: MOUNT - -# \# Setup script (optional) to execute on every \`sky launch\`. -# \# This is executed before the 'run' commands. -# # -# \# The '|' separator indicates a multiline string. To specify a single command: -# \# setup: pip install -r requirements.txt -# setup: | -# echo "Begin setup." -# pip install -r requirements.txt -# echo "Setup complete." - -# \# Main program (optional, but recommended) to run on every node of the cluster. -# run: | -# echo "Beginning task." -# python train.py - -# \# Demoing env var usage. -# echo Env var MODEL\_SIZE has value: ${MODEL\_SIZE} - -# Experimental Configurations[#](https://docs.skypilot.co/en/latest/reference/yaml-spec.html#experimental-configurations "Permalink to this heading") -# --------------------------------------------------------------------------------------------------------------------------------------------------- - -# Note - -# Experimental features and APIs may be changed or removed without any notice. - -# In additional to the above fields, SkyPilot also supports the following experimental fields in the task YAML: - -# experimental: -# \# Override the configs in ~/.sky/config.yaml from a task level. -# # -# \# The following fields can be overridden. Please refer to docs of Advanced -# \# Configuration for more details of those fields: -# \# https://docs.skypilot.co/en/latest/reference/config.html -# config\_overrides: -# docker: -# run\_options: ... -# kubernetes: -# pod\_config: ... -# provision\_timeout: ... -# gcp: -# managed\_instance\_group: ... -# nvidia\_gpus: -# disable\_ecc: ... - -workdir: . -resources: - accelerators: ["H100-SXM:1", "H100:1", "A100-80GB:1"] - ports: - - 7999 # main ART server - - 8000 # vLLM server -envs: - ALL_EXTRAS: "false" - GIT_USER_NAME: "Your Name" - GIT_USER_EMAIL: "your.email@example.com" - GITHUB_TOKEN: "" - PYTHONUTF8: "1" #Enforce UTF-8 Encoding to fix ASCII encoding issue in unsloth-zoo -setup: | - ./scripts/setup.sh -config: - kubernetes: - pod_config: - spec: - schedulerName: gpu-binpack diff --git a/src/art/backend.py b/src/art/backend.py index c9a8bed81..3aa77a6bd 100644 --- a/src/art/backend.py +++ b/src/art/backend.py @@ -1,17 +1,4 @@ -import json -from typing import TYPE_CHECKING, Any, AsyncIterator, Iterable, Literal, TypeAlias -import warnings - -import httpx -from tqdm import auto as tqdm - -from art.utils import log_http_errors -from art.utils.deployment import ( - DeploymentResult, - Provider, - TogetherDeploymentConfig, - WandbDeploymentConfig, -) +from typing import TYPE_CHECKING, Any, AsyncIterator, Iterable, Protocol, TypeAlias from . import dev from .trajectories import TrajectoryGroup @@ -25,232 +12,41 @@ AnyTrainableModel: TypeAlias = "TrainableModel[Any, Any]" -class Backend: - def __init__( - self, - *, - base_url: str = "http://0.0.0.0:7999", - ) -> None: - self._base_url = base_url - self._client = httpx.AsyncClient(base_url=base_url) +class Backend(Protocol): + """Protocol for backend implementations.""" - async def close(self) -> None: - """ - If running vLLM in a separate process, this will kill that process and close the communication threads. - """ - response = await self._client.post("/close", timeout=None) - response.raise_for_status() + def _model_inference_name( + self, model: AnyModel, step: int | None = None + ) -> str: ... - async def register( - self, - model: AnyModel, - ) -> None: - """ - Registers a model with the Backend for logging and/or training. + async def close(self) -> None: ... - Args: - model: An art.Model instance. - """ - response = await self._client.post("/register", json=model.safe_model_dump()) - response.raise_for_status() + async def register(self, model: AnyModel) -> None: ... - async def _get_step(self, model: AnyTrainableModel) -> int: - response = await self._client.post("/_get_step", json=model.safe_model_dump()) - response.raise_for_status() - return response.json() + async def _get_step(self, model: AnyTrainableModel) -> int: ... async def _delete_checkpoint_files( - self, - model: AnyTrainableModel, - steps_to_keep: list[int], - ) -> None: - response = await self._client.post( - "/_delete_checkpoint_files", - json={"model": model.safe_model_dump(), "steps_to_keep": steps_to_keep}, - ) - response.raise_for_status() + self, model: AnyTrainableModel, steps_to_keep: list[int] + ) -> None: ... async def _prepare_backend_for_training( self, model: AnyTrainableModel, config: dev.OpenAIServerConfig | None, - ) -> tuple[str, str]: - response = await self._client.post( - "/_prepare_backend_for_training", - json={"model": model.safe_model_dump(), "config": config}, - timeout=600, - ) - response.raise_for_status() - base_url, api_key = tuple(response.json()) - return base_url, api_key - - def _model_inference_name(self, model: AnyModel, step: int | None = None) -> str: - """Return the inference name for a model checkpoint. - - Override in subclasses to provide backend-specific naming. - Default implementation returns model.name with optional @step suffix. - """ - base_name = model.inference_model_name or model.name - if step is not None: - return f"{base_name}@{step}" - return base_name + ) -> tuple[str, str]: ... async def train( self, model: AnyTrainableModel, trajectory_groups: Iterable[TrajectoryGroup], **kwargs: Any, - ) -> TrainResult: - """Train the model on the given trajectory groups. - - This method is not implemented in the base Backend class. Use - LocalBackend, ServerlessBackend, or TinkerBackend directly for training. + ) -> TrainResult: ... - Raises: - NotImplementedError: Always raised. Use a concrete backend instead. - """ - raise NotImplementedError( - "The base Backend class does not support the train() method. " - "Use LocalBackend, ServerlessBackend, or TinkerBackend directly. " - "If you are using the 'art run' server, consider using LocalBackend " - "in-process instead." - ) - - async def _train_model( + def _train_model( self, model: AnyTrainableModel, trajectory_groups: list[TrajectoryGroup], config: TrainConfig, dev_config: dev.TrainConfig, verbose: bool = False, - ) -> AsyncIterator[dict[str, float]]: - async with self._client.stream( - "POST", - "/_train_model", - json={ - "model": model.safe_model_dump(), - "trajectory_groups": [tg.model_dump() for tg in trajectory_groups], - "config": config.model_dump(), - "dev_config": dev_config, - "verbose": verbose, - }, - timeout=None, - ) as response: - response.raise_for_status() - pbar: tqdm.tqdm | None = None - async for line in response.aiter_lines(): - result = json.loads(line) - yield result - num_gradient_steps = result.pop("num_gradient_steps") - if pbar is None: - pbar = tqdm.tqdm(total=num_gradient_steps, desc="train") - pbar.update(1) - pbar.set_postfix(result) - if pbar is not None: - pbar.close() - - # ------------------------------------------------------------------ - # Experimental support for S3 - # ------------------------------------------------------------------ - - @log_http_errors - async def _experimental_pull_from_s3( - self, - model: AnyModel, - *, - s3_bucket: str | None = None, - prefix: str | None = None, - verbose: bool = False, - delete: bool = False, - only_step: int | Literal["latest"] | None = None, - ) -> None: - """Download the model directory from S3 into file system where the LocalBackend is running. Right now this can be used to pull trajectory logs for processing or model checkpoints. - - .. deprecated:: - This method is deprecated. Use `_experimental_pull_model_checkpoint` instead. - - Args: - only_step: If specified, only pull this specific step. Can be an int for a specific step, - or "latest" to pull only the latest checkpoint. If None, pulls all steps. - """ - warnings.warn( - "_experimental_pull_from_s3 is deprecated. Use _experimental_pull_model_checkpoint instead.", - DeprecationWarning, - stacklevel=2, - ) - response = await self._client.post( - "/_experimental_pull_from_s3", - json={ - "model": model.safe_model_dump(), - "s3_bucket": s3_bucket, - "prefix": prefix, - "verbose": verbose, - "delete": delete, - "only_step": only_step, - }, - timeout=600, - ) - response.raise_for_status() - - @log_http_errors - async def _experimental_push_to_s3( - self, - model: AnyModel, - *, - s3_bucket: str | None = None, - prefix: str | None = None, - verbose: bool = False, - delete: bool = False, - ) -> None: - """Upload the model directory from the file system where the LocalBackend is running to S3.""" - response = await self._client.post( - "/_experimental_push_to_s3", - json={ - "model": model.safe_model_dump(), - "s3_bucket": s3_bucket, - "prefix": prefix, - "verbose": verbose, - "delete": delete, - }, - timeout=600, - ) - response.raise_for_status() - - @log_http_errors - async def _experimental_fork_checkpoint( - self, - model: AnyModel, - from_model: str, - from_project: str | None = None, - from_s3_bucket: str | None = None, - not_after_step: int | None = None, - verbose: bool = False, - prefix: str | None = None, - ) -> None: - """Fork a checkpoint from another model to initialize this model. - - Args: - model: The model to fork to. - from_model: The name of the model to fork from. - from_project: The project of the model to fork from. Defaults to model.project. - from_s3_bucket: Optional S3 bucket to pull the checkpoint from. If provided, - will pull from S3 first. Otherwise, will fork from local disk. - not_after_step: Optional step number. If provided, will copy the last saved - checkpoint that is <= this step. Otherwise, copies the latest checkpoint. - verbose: Whether to print verbose output. - prefix: Optional S3 prefix for the bucket. - """ - response = await self._client.post( - "/_experimental_fork_checkpoint", - json={ - "model": model.safe_model_dump(), - "from_model": from_model, - "from_project": from_project, - "from_s3_bucket": from_s3_bucket, - "not_after_step": not_after_step, - "verbose": verbose, - "prefix": prefix, - }, - timeout=600, - ) - response.raise_for_status() + ) -> AsyncIterator[dict[str, float]]: ... diff --git a/src/art/local/backend.py b/src/art/local/backend.py index 4d46df28b..dfc5a5c6f 100644 --- a/src/art/local/backend.py +++ b/src/art/local/backend.py @@ -31,7 +31,7 @@ from mp_actors import close_proxy, move_to_child_process from .. import dev -from ..backend import Backend +from ..backend import AnyTrainableModel, Backend from ..model import Model, TrainableModel from ..preprocessing.pack import ( PackedTensors, @@ -174,7 +174,7 @@ async def _get_service(self, model: TrainableModel) -> ModelService: def _get_packed_tensors( self, - model: TrainableModel, + model: AnyTrainableModel, trajectory_groups: list[TrajectoryGroup], advantage_balance: float, allow_training_without_logprobs: bool, @@ -238,7 +238,7 @@ def _get_packed_tensors( ) return packed_tensors - async def _get_step(self, model: TrainableModel) -> int: + async def _get_step(self, model: AnyTrainableModel) -> int: return self.__get_step(model) def __get_step(self, model: Model) -> int: @@ -250,7 +250,7 @@ def __get_step(self, model: Model) -> int: async def _delete_checkpoint_files( self, - model: TrainableModel, + model: AnyTrainableModel, steps_to_keep: list[int], ) -> None: """Delete checkpoint files, keeping only the specified steps.""" @@ -265,7 +265,7 @@ async def _delete_checkpoint_files( async def _prepare_backend_for_training( self, - model: TrainableModel, + model: AnyTrainableModel, config: dev.OpenAIServerConfig | None = None, ) -> tuple[str, str]: service = await self._get_service(model) @@ -368,7 +368,7 @@ def _trajectory_log(self, trajectory: Trajectory) -> str: async def train( # type: ignore[override] self, - model: TrainableModel, + model: AnyTrainableModel, trajectory_groups: Iterable[TrajectoryGroup], *, # Core training parameters diff --git a/src/art/serverless/backend.py b/src/art/serverless/backend.py index f431d4b17..8620d30cc 100644 --- a/src/art/serverless/backend.py +++ b/src/art/serverless/backend.py @@ -8,7 +8,7 @@ from art.serverless.client import Client, ExperimentalTrainingConfig from .. import dev -from ..backend import Backend +from ..backend import AnyTrainableModel, Backend from ..trajectories import TrajectoryGroup from ..types import ServerlessTrainResult, TrainConfig @@ -21,7 +21,7 @@ def __init__( self, *, api_key: str | None = None, base_url: str | None = None ) -> None: client = Client(api_key=api_key, base_url=base_url) - super().__init__(base_url=str(client.base_url)) + self._base_url = str(client.base_url) self._client = client async def close(self) -> None: @@ -101,7 +101,7 @@ async def _get_step(self, model: "Model") -> int: async def _delete_checkpoint_files( self, - model: "TrainableModel", + model: AnyTrainableModel, steps_to_keep: list[int], ) -> None: """Delete checkpoint files, keeping only the specified steps.""" @@ -119,7 +119,7 @@ async def _delete_checkpoint_files( async def _prepare_backend_for_training( self, - model: "TrainableModel", + model: AnyTrainableModel, config: dev.OpenAIServerConfig | None, ) -> tuple[str, str]: return str(self._base_url), self._client.api_key # ty:ignore[possibly-missing-attribute] @@ -129,7 +129,7 @@ async def _prepare_backend_for_training( async def train( # type: ignore[override] self, - model: "TrainableModel", + model: AnyTrainableModel, trajectory_groups: Iterable[TrajectoryGroup], *, # Core training parameters @@ -249,7 +249,7 @@ async def train( # type: ignore[override] async def _train_model( self, - model: "TrainableModel", + model: AnyTrainableModel, trajectory_groups: list[TrajectoryGroup], config: TrainConfig, dev_config: dev.TrainConfig, diff --git a/src/art/skypilot/__init__.py b/src/art/skypilot/__init__.py deleted file mode 100644 index d6da67a0b..000000000 --- a/src/art/skypilot/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .backend import SkyPilotBackend - -__all__ = ["SkyPilotBackend"] diff --git a/src/art/skypilot/backend.py b/src/art/skypilot/backend.py deleted file mode 100644 index 5844b8cc4..000000000 --- a/src/art/skypilot/backend.py +++ /dev/null @@ -1,383 +0,0 @@ -import asyncio -from importlib.metadata import PackageNotFoundError, version -import os -from typing import TYPE_CHECKING, Literal, cast - -from dotenv import dotenv_values -import semver -import sky -import sky.clouds - -from .. import dev -from ..backend import Backend -from .utils import ( - get_art_server_base_url, - get_task_job_id, - get_vllm_base_url, - is_task_created, - to_thread_typed, - wait_for_art_server_to_start, -) - -if TYPE_CHECKING: - from ..model import Model, TrainableModel - - -class SkyPilotBackend(Backend): - _cluster_name: str - _envs: dict[str, str] - _art_server_job_id: int | None - - @classmethod - async def initialize_cluster( - cls, - *, - cluster_name: str = "art", - gpu: str | None = None, - resources: sky.Resources | None = None, - art_version: str | None = None, - env_path: str | None = None, - force_restart: bool = False, - tail_logs: bool = True, - ) -> "SkyPilotBackend": - self = cls.__new__(cls) - self._cluster_name = cluster_name - self._envs = {} - self._art_server_job_id = None - - if env_path is not None: - self._envs = { - k: v for k, v in dotenv_values(env_path).items() if v is not None - } - print(f"Loading envs from {env_path}") - print(f"{len(self._envs)} environment variables found") - - if gpu is None and resources is None: - raise ValueError("Either gpu or resources must be provided") - - if resources is None: - resources = sky.Resources( - cloud=sky.clouds.RunPod(), - accelerators={"H100": 1}, - ports=[], - ) - - if gpu is not None: - resources = resources.copy(accelerators={gpu: 1}) - - # ensure ports 7999 and 8000 are open - updated_ports = resources.ports - if updated_ports is None: - updated_ports = [] - updated_ports += ["7999", "8000"] - resources = resources.copy(ports=updated_ports) - - # check if cluster already exists - cluster_status = await to_thread_typed( - lambda: sky.stream_and_get(sky.status(cluster_names=[self._cluster_name])) - ) - if ( - len(cluster_status) == 0 - or cluster_status[0]["status"] != sky.ClusterStatus.UP - ): - await self._launch_cluster(resources, art_version) - - else: - print(f"Cluster {self._cluster_name} exists, using it...") - - art_server_running = await is_task_created( - cluster_name=self._cluster_name, task_name="art_server" - ) - - if art_server_running and force_restart: - print("force_restart=True; cancelling existing art_server task…") - await to_thread_typed( - lambda: sky.cancel(cluster_name=self._cluster_name, all=True) - ) - # wait 5 seconds to ensure the server finishes winding down - await asyncio.sleep(5) - art_server_running = False - - if art_server_running: - self._art_server_job_id = await get_task_job_id( - cluster_name=self._cluster_name, task_name="art_server" - ) - print("Art server task already running, using it…") - else: - art_server_task = sky.Task(name="art_server", run="uv run art") - - clusters = await to_thread_typed( - lambda: sky.stream_and_get( - sky.status(cluster_names=[self._cluster_name]) - ) - ) - resources = clusters[0]["handle"].launched_resources - if resources is None: - raise ValueError("Cluster handle has no launched resources") - - # For some reason, skypilot doesn't support the region and zone set - resources = resources.copy(region=None, zone=None) - - # If a local path was provided for art_version, ensure it is mounted so the latest - # code is synced to the remote cluster every time we (re)launch the art_server task. - if art_version is not None and os.path.exists(art_version): - art_server_task.workdir = art_version - - # print(clusters[0]["handle"].launched_resources) - art_server_task.set_resources(cast(sky.Resources, resources)) - art_server_task.update_envs(self._envs) - - # run art server task - job_id, _ = await to_thread_typed( - lambda: sky.stream_and_get( - sky.exec( - task=art_server_task, - cluster_name=self._cluster_name, - ) - ) - ) - self._art_server_job_id = job_id - - print("Task launched, waiting for it to start...") - await wait_for_art_server_to_start(cluster_name=self._cluster_name) - print("Art server task started") - - base_url = await get_art_server_base_url(self._cluster_name) - print(f"Using base_url: {base_url}") - - # Manually call the real __init__ now that base_url is ready - super(cls, self).__init__(base_url=base_url) - - if self._art_server_job_id is not None and tail_logs: - await asyncio.to_thread( - sky.tail_logs, - cluster_name=self._cluster_name, - job_id=self._art_server_job_id, - follow=True, - ) - print( - "Tailing logs. This process will only automatically exit after the backend is down. To exit before then, you'll have to manually close the process (e.g. ctrl+C)." - ) - return self - - async def _launch_cluster( - self, - resources: sky.Resources, - art_version: str | None = None, - ) -> None: - print("Launching cluster...") - - task = sky.Task( - name=self._cluster_name, - ) - task.set_resources(resources) - task.update_envs(self._envs) - - # default to installing the version of art that is used by the client - if art_version is None: - try: - art_version = version("openpipe-art") - except PackageNotFoundError: - raise ValueError( - "No version of openpipe-art installed in project. Please provide an art_version." - ) - - art_version_is_semver = False - # check if art_version is valid semver - try: - semver.Version.parse(art_version) - art_version_is_semver = True - except Exception: - pass - - if art_version_is_semver: - art_installation_command = ( - f"uv pip install --system openpipe-art[backend]=={art_version}" - ) - elif os.path.exists(art_version): - # copy the contents of the art_path onto the new machine - task.workdir = art_version - art_installation_command = "uv sync --extra backend" - else: - raise ValueError( - f"Invalid art_version: {art_version}. Must be a semver or a path to a local directory." - ) - - setup_script = f""" - curl -LsSf https://astral.sh/uv/install.sh | sh - - source $HOME/.local/bin/env - - git config --global --add safe.directory /root/sky_workdir - - {art_installation_command} - """ - task.setup = setup_script - - try: - job_id, _ = await to_thread_typed( - lambda: sky.stream_and_get( - sky.launch( - task=task, cluster_name=self._cluster_name, retry_until_up=True - ) - ) - ) - - await to_thread_typed( - lambda: sky.tail_logs( - cluster_name=self._cluster_name, - job_id=job_id, - follow=True, - ) - ) - - except Exception as e: - print(f"Error launching cluster: {e}") - print() - raise e - - async def register( - self, - model: "Model", - ) -> None: - """ - Registers a model with the Backend for logging and/or training. - - Args: - model: An art.Model instance. - """ - - print("Registering model with server") - print(f"To view logs, run: 'uv run sky logs {self._cluster_name}'") - await super().register(model) - - async def _prepare_backend_for_training( - self, - model: "TrainableModel", - config: dev.OpenAIServerConfig | None, - ) -> tuple[str, str]: - response = await self._client.post( - "/_prepare_backend_for_training", - json={"model": model.model_dump(), "config": config}, - timeout=1200, - ) - response.raise_for_status() - [_, api_key] = tuple(response.json()) - - vllm_base_url = await get_vllm_base_url(self._cluster_name) - - return (vllm_base_url, api_key) - - async def _experimental_pull_model_checkpoint( - self, - model: "TrainableModel", - *, - step: int | Literal["latest"] | None = None, - local_path: str | None = None, - s3_bucket: str, - prefix: str | None = None, - verbose: bool = False, - ) -> str: - """Pull a model checkpoint to the client machine. - - For SkyPilotBackend, this always pulls from S3 to the client machine - (where the script is running), not to the remote cluster. - - Args: - model: The model to pull checkpoint for. - step: The step to pull. Can be an int for a specific step, - or "latest" to pull the latest checkpoint. If None, pulls latest. - local_path: Local directory to save the checkpoint. If None, uses default paths. - s3_bucket: S3 bucket to pull from. - prefix: S3 prefix. - verbose: Whether to print verbose output. - - Returns: - Path to the local checkpoint directory on the client machine. - """ - import os - - from art.utils.output_dirs import ( - get_default_art_path, - get_model_dir, - get_step_checkpoint_dir, - ) - from art.utils.s3 import pull_model_from_s3 - from art.utils.s3_checkpoint_utils import get_latest_checkpoint_step_from_s3 - - # Determine which step to use - resolved_step: int - if step is None or step == "latest": - # Get latest from S3 - latest_step = await get_latest_checkpoint_step_from_s3( - model_name=model.name, - project=model.project, - s3_bucket=s3_bucket, - prefix=prefix, - ) - if latest_step is None: - raise ValueError( - f"No checkpoints found in S3 for {model.project}/{model.name}" - ) - resolved_step = latest_step - else: - resolved_step = step - - # Determine target location - art_path = get_default_art_path() - if local_path is not None: - # Custom location - copy directly to local_path - checkpoint_dir = local_path - else: - # Standard ART structure - checkpoint_dir = get_step_checkpoint_dir( - get_model_dir(model=model, art_path=art_path), resolved_step - ) - - # Pull from S3 to client machine - if verbose: - print( - f"Pulling checkpoint step {resolved_step} from S3 to client machine..." - ) - - if local_path is not None: - # For custom location, pull to default location first, then copy - await pull_model_from_s3( - model_name=model.name, - project=model.project, - step=resolved_step, - s3_bucket=s3_bucket, - prefix=prefix, - verbose=verbose, - art_path=art_path, - exclude=["logs", "trajectories"], - ) - # Copy to custom location - temp_checkpoint_dir = get_step_checkpoint_dir( - get_model_dir(model=model, art_path=art_path), resolved_step - ) - import shutil - - os.makedirs(local_path, exist_ok=True) - shutil.copytree(temp_checkpoint_dir, local_path, dirs_exist_ok=True) - if verbose: - print(f"✓ Checkpoint copied to {local_path}") - else: - # Pull directly to standard location - await pull_model_from_s3( - model_name=model.name, - project=model.project, - step=resolved_step, - s3_bucket=s3_bucket, - prefix=prefix, - verbose=verbose, - art_path=art_path, - exclude=["logs", "trajectories"], - ) - - return checkpoint_dir - - async def down(self) -> None: - await to_thread_typed( - lambda: sky.stream_and_get(sky.down(cluster_name=self._cluster_name)) - ) diff --git a/src/art/skypilot/stop_server.py b/src/art/skypilot/stop_server.py deleted file mode 100644 index 6ada3dc8d..000000000 --- a/src/art/skypilot/stop_server.py +++ /dev/null @@ -1,41 +0,0 @@ -import argparse -import asyncio - -import sky - -from art.skypilot.backend import SkyPilotBackend -from art.skypilot.utils import is_task_created, to_thread_typed - -parser = argparse.ArgumentParser( - description="Close the art server hosted on a skypilot cluster" -) -parser.add_argument( - "--cluster", - type=str, - required=True, - help="The name of the skypilot cluster to close the art server on", -) -args = parser.parse_args() - - -async def stop_server() -> None: - cluster_status = await to_thread_typed( - lambda: sky.stream_and_get(sky.status(cluster_names=[args.cluster])) - ) - if len(cluster_status) == 0 or cluster_status[0]["status"] != sky.ClusterStatus.UP: - raise ValueError(f"Cluster {args.cluster} is not running") - - if not await is_task_created(cluster_name=args.cluster, task_name="art_server"): - raise ValueError(f"Art server task for cluster {args.cluster} is not running") - - backend = await SkyPilotBackend.initialize_cluster( - cluster_name=args.cluster, art_version=".", env_path=".env", gpu="H100" - ) - await backend.close() - - # cancel the art server task - await to_thread_typed(lambda: sky.cancel(cluster_name=args.cluster, all=True)) - - -def main() -> None: - asyncio.run(stop_server()) diff --git a/src/art/skypilot/utils.py b/src/art/skypilot/utils.py deleted file mode 100644 index 238e139ef..000000000 --- a/src/art/skypilot/utils.py +++ /dev/null @@ -1,107 +0,0 @@ -import asyncio -from typing import Callable, TypeVar - -import httpx -import sky -from sky.core import endpoints - -T = TypeVar("T") - - -async def to_thread_typed(func: Callable[[], T]) -> T: - return await asyncio.to_thread(func) - - -async def get_task_status(cluster_name: str, task_name: str) -> sky.JobStatus | None: - job_queue = await to_thread_typed( - lambda: sky.stream_and_get(sky.queue(cluster_name)) - ) - - for job in job_queue: - if job["job_name"] == task_name: - return job["status"] - return None - - -async def get_task_job_id(cluster_name: str, task_name: str) -> int | None: - job_queue = await to_thread_typed( - lambda: sky.stream_and_get(sky.queue(cluster_name)) - ) - - for job in job_queue: - if job["job_name"] == task_name: - return job["job_id"] - return None - - -async def is_task_created(cluster_name: str, task_name: str) -> bool: - task_status = await get_task_status(cluster_name, task_name) - if task_status is None: - return False - return task_status in ( - sky.JobStatus.INIT, - sky.JobStatus.PENDING, - sky.JobStatus.SETTING_UP, - sky.JobStatus.RUNNING, - ) - - -# wait for task to start running -# checks every 5 seconds for 1 minute -async def wait_for_task_to_start(cluster_name: str, task_name: str) -> None: - task_status = await get_task_status(cluster_name, task_name) - - num_checks = 0 - while num_checks < 12: - task_status = await get_task_status(cluster_name, task_name) - if task_status is None: - raise ValueError(f"Task {task_name} not found in cluster {cluster_name}") - if task_status == sky.JobStatus.RUNNING: - return - await asyncio.sleep(5) - num_checks += 1 - - raise ValueError( - f"Task {task_name} in cluster {cluster_name} failed to start within 60s" - ) - - -async def wait_for_art_server_to_start(cluster_name: str) -> str: - print(f"Waiting for art server task to start on cluster {cluster_name}...") - await wait_for_task_to_start(cluster_name, "art_server") - print( - f"Art server started on cluster {cluster_name}. Waiting for it to be ready..." - ) - - base_url = await get_art_server_base_url(cluster_name) - - num_checks = 0 - client = httpx.AsyncClient( - base_url=base_url, - timeout=10, - ) - while num_checks < 12: - try: - response = await client.get("/healthcheck") - if response.status_code == 200: - return base_url - except Exception: - pass - await asyncio.sleep(5) - num_checks += 1 - - return base_url - - -async def get_art_server_base_url(cluster_name: str) -> str: - art_endpoint = await to_thread_typed( - lambda: endpoints(cluster=cluster_name, port=7999)[7999] - ) - return f"http://{art_endpoint}" - - -async def get_vllm_base_url(cluster_name: str) -> str: - vllm_endpoint = await to_thread_typed( - lambda: endpoints(cluster=cluster_name, port=8000)[8000] - ) - return f"http://{vllm_endpoint}/v1" diff --git a/src/art/test/test_step_skipping.py b/src/art/test/test_step_skipping.py index 2c3398a88..f4c85a1b8 100755 --- a/src/art/test/test_step_skipping.py +++ b/src/art/test/test_step_skipping.py @@ -1,4 +1,4 @@ -#!/usr/bin/env -S uv run --with skypilot[runpod] sky launch --cluster=kyle-tss --gpus=H100-SXM:1 --yes --retry-until-up --down --fast --idle-minutes-to-autostop=20 --workdir=. --env-file=.env -- uv run src/art/test/test_step_skipping.py +#!/usr/bin/env -S uv run # /// script # dependencies = [ # "openpipe-art[backend]", diff --git a/uv.lock b/uv.lock index 80768c8f4..3970abe92 100644 --- a/uv.lock +++ b/uv.lock @@ -42,27 +42,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/f8/bb/be8146c196ad6e4dec78385d91e92591f8a433576c4e04c342a636fcd811/accelerate-1.7.0-py3-none-any.whl", hash = "sha256:cf57165cca28769c6cf2650812371c81b18e05743dfa3c748524b1bb4f2b272f", size = 362095, upload-time = "2025-05-15T10:00:49.914Z" }, ] -[[package]] -name = "aiodns" -version = "3.6.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - 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