ALIEN

Large Language Models

Train and fine-tune models at scale. Distributed across global GPU infrastructure with automatic optimization.

from alien_compute import ComputeClient, LLMKernel

client = ComputeClient("alien://your-node")

kernel = LLMKernel(
    model_type="llama-70b",
    checkpoint="meta-llama/Llama-2-70b-hf",
    training_config={
        "learning_rate": 1e-5,
        "batch_size": 32,
        "epochs": 3,
        "max_length": 2048
    }
)

job = client.submit_job(
    kernel=kernel,
    dataset="s3://your-data",
    resource_requirements={
        "gpu_memory": "80GB",
        "gpu_count": 8
    },
    budget_limit=1000
)

async for update in job.monitor():
    print(f"Loss: {update.loss:.4f}")

use alien_compute::{ComputeKernel, WeatherSimulation};

let kernel = WeatherSimulation::new()
    .with_model("wrf-4.3")
    .with_resolution(1.0)
    .with_domain(Domain::NorthAmerica)
    .with_forecast_hours(72);

let job = client.submit_job(
    kernel,
    JobConfig {
        input_data: "noaa://latest",
        resource_requirements: ResourceRequirements {
            cpu_cores: 1024,
            memory_gb: 2048,
            storage_gb: 5000,
        },
        max_cost: 500,
    }
).await?;

let mut stream = job.stream_results().await?;
while let Some(forecast) = stream.next().await {
    process_forecast(forecast);
}

Scientific Computing

High-resolution weather forecasting and climate modeling. Distributed computation across thousands of cores.

Data Processing

Distributed data processing at scale. MapReduce paradigm with automatic resource allocation.

class DataProcessor(MapReduceKernel):
    
    def map(self, key, value):
        for item in value.process():
            yield (item.key, item.transform())
    
    def reduce(self, key, values):
        yield (key, aggregate(values))

job = client.submit_job(
    kernel=DataProcessor(),
    input_data="ipfs://QmDataHash",
    num_mappers=100,
    num_reducers=10,
    partitioner=HashPartitioner()
)

results = await job.get_results()
print(f"Processed {results.total_records} records")

Small Language Models running where cloud can't reach.

In satellites monitoring Earth. In hospitals saving lives. In robots helping families. In factories building tomorrow.

Smaller. Faster. Private.

Built for privacy.

Designed for scale.