Python SDK

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}")

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}%")

client = ChamberClient(
    token="ch.your-token",
    api_url="https://custom.api.example.com/v1"
)