> ## Documentation Index
> Fetch the complete documentation index at: https://docs.usechamber.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Python SDK
> Official Python client library for the Chamber GPU platform
The Chamber Python SDK provides a simple, intuitive interface for submitting and managing GPU workloads programmatically.
## Features
Auto-containerize and submit workloads without Docker or Kubernetes expertise
Submit GPU workloads with full control over resources, priority, and scheduling
Track workload status, retrieve GPU metrics, and get workload statistics
Query available GPU capacity, budgets, and manage allocations
Support for multi-node distributed training with gang and elastic scheduling
Manage teams and use workload templates for consistent configurations
## Quick Examples
### One-Liner: Auto-Containerize & Submit
The fastest way to get your training code running on GPUs. No Dockerfile or Kubernetes knowledge required:
```python theme={null}
from chamber_sdk import ChamberClient
client = ChamberClient.from_config()
# Configure registries once (persisted to ~/.chamber/config.json)
ChamberClient.add_registry("prod", "us-east1-docker.pkg.dev/my-project/prod", set_default=True)
ChamberClient.add_registry("dev", "us-east1-docker.pkg.dev/my-project/dev")
# Submit to default registry
job = client.run("./my-project", gpus=4, team="ml-research")
# Or specify a registry by name
job = client.run("./my-project", gpus=4, team="ml-research", registry="dev")
print(f"Submitted: {job.id}")
```
Install with `pip install chamber-sdk[run]` to use this feature. See the [Auto-Containerize & Run guide](/sdk/python/run) for details.
### Standard: Full Control
For production workloads where you need complete control:
```python theme={null}
from chamber_sdk import ChamberClient, JobClass
# Initialize client
client = ChamberClient.from_config()
# Submit a training workload
job = client.submit_job(
name="llm-fine-tuning",
initiative_id="team-ml",
gpu_type="H100",
requested_gpus=8,
job_class=JobClass.RESERVED,
tags={"experiment": "gpt-finetune-v1"}
)
print(f"Submitted workload: {job.id}")
# Wait for completion
result = client.wait_for_completion(job.id, timeout=3600)
print(f"Workload finished with status: {result.status}")
# Get metrics
if result.status.value == "COMPLETED":
metrics = client.get_workload_metrics(job.id)
print(f"GPU utilization: {metrics.gpu_utilization.avg:.1f}%")
```
## API Endpoint
The SDK connects to the Chamber API at `https://api.usechamber.io/v1` by default. You can override this for custom deployments:
```python theme={null}
client = ChamberClient(
token="ch.your-token",
api_url="https://custom.api.example.com/v1"
)
```
## Requirements
* Python 3.8 or higher
* `requests` library (installed automatically)
## Security
The SDK is designed with security as a priority:
* **Input Validation** - All user inputs are validated before being passed to external commands or APIs
* **Credential Protection** - Tokens and credentials are never logged or exposed in error messages
* **Safe Command Execution** - Shell injection is prevented through strict input sanitization
* **Path Traversal Protection** - File operations are restricted to intended directories
Credentials loaded from `~/.chamber/token.json` or environment variables are handled securely and never included in debug output or exception messages.
## Supported Container Registries
Push container images to your preferred registry with automatic authentication:
| Registry | Auto-Auth | Auto-Create Repo |
| ---------------------------- | ---------------- | ---------------- |
| **Google Artifact Registry** | ✅ via gcloud CLI | ✅ |
| **AWS ECR** | ✅ via AWS CLI | ✅ |
Just provide your registry URL—the SDK automatically detects the type and handles authentication. No manual `docker login` required.
## What's New
* **Seamless Multi-Cloud Registries** - Full support for Google Artifact Registry and AWS ECR with auto-authentication
* **Named Registries** - Configure registries once, use by name (e.g., `registry="prod"`)
* **Auto-Containerize & Run** - Submit workloads in one line without Docker/K8s expertise
* **Framework Detection** - Auto-detect PyTorch, TensorFlow, JAX and select optimal base images
* **Distributed Training** - Auto-detect DeepSpeed, Accelerate, Ray and configure appropriately
* **Workload Search** - Advanced filtering with full-text search
* **Aggregations** - Get workload counts by status, GPU type, team, and more
* **Teams API** - List, create, and manage teams
* **Templates** - Use predefined workload templates
## Next Steps
Install the SDK via pip
One-liner workload submission
Configure your API credentials
Complete method and class documentation