Remove SkyPilot backend and docs

CONTRIBUTING.md

@@ -67,31 +67,19 @@ 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:
 
 ```bash
 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.

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.
 

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(

docs/fundamentals/art-backend.mdx

@@ -20,15 +20,6 @@ While the backend's training and inference settings are highly configurable, the
       arrow={true}
     ></Card>
   </div>
-  <div className="card-wrapper">
-    <Card
-      title="SkyPilotBackend"
-      icon="cloud"
-      href="/fundamentals/art-backend#skypilotbackend"
-      horizontal={true}
-      arrow={true}
-    ></Card>
-  </div>
   <div className="card-wrapper">
     <Card
       title="LocalBackend"
@@ -40,17 +31,14 @@ While the backend's training and inference settings are highly configurable, the
   </div>
 </div>
 
-## 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.
   <div className="card-wrapper">
     <Card
       title="Summarizer"
-      icon="cloud"
+      icon="laptop-code"
       href="/tutorials/summarizer"
       horizontal={true}
       arrow={true}
     >
-      Use SkyPilotBackend to train a SOTA summarizing agent.
+      Use LocalBackend to train a SOTA summarizing agent.
     </Card>
   </div>
 </div>

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.
 </Card>
-<Card
-  title="SkyPilotBackend"
-  icon="cloud"
-  href="/fundamentals/art-backend#skypilotbackend"
-  horizontal={true}
-  arrow={true}
->
-  Run training and inference on a separate ephemeral machine.
-</Card>
 <Card
   title="LocalBackend"
   icon="laptop-code"
@@ -63,11 +54,6 @@ Once you've initialized your [backend](/fundamentals/art-backend), you can regis
 # managed training
 backend = ServerlessBackend()
 
-# remote training
-backend = SkyPilotBackend.initialize_cluster(
-    cluster_name="art", gpu="H100"
-)
-
 # local training
 backend = LocalBackend()
 

docs/getting-started/installation-setup.mdx

@@ -65,36 +65,6 @@ await model.register(backend)
 ... the rest of your code ...
 ```
 
-### Running the server on remote dedicated GPUs
-
-The ART client can also be run locally and connected to a remote server, which ART will automatically provision for you. To use SkyPilot, you'll need to install the optional dependency:
-
-```bash
-pip install openpipe-art[skypilot]
-```
-
-Then you can use SkyPilotBackend in your code:
-
-```python
-from art import TrainableModel, gather_trajectory_groups
-from art.skypilot.backend import SkyPilotBackend
-
-backend = await SkyPilotBackend.initialize_cluster(
-    cluster_name="my-cluster",
-    gpu="H100"
-)
-
-model = TrainableModel(
-    name="agent-001",
-    project="my-agentic-task",
-    base_model="OpenPipe/Qwen3-14B-Instruct",
-)
-
-await model.register(backend)
-
-... the rest of your code ...
-```
-
 To learn more about the ART client and server, see the docs below.
 
 <div className="cards-container">

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):