Rixi - Remote Execution for Compute-Intensive Workloads
Remote execution for compute-intensive workloads with auditable job tracking
Rixi handles distributed training, batch inference, and data processing at scale. Built for regulated environments where you need full control and transparency.
Built for Scale and Compliance
Remote GPU/CPU Execution
Submit jobs to powerful remote clusters with automatic resource allocation and isolation.
Auditable Job Tracking
Complete audit trail for every job with timestamped logs, resource usage, and compliance metadata.
Secure by Default
End-to-end encryption, authentication, and authorization for enterprise security requirements.
Reproducible Environments
Containerized execution ensures consistent results across different infrastructure environments.
Resource Management
Intelligent scheduling and resource optimization to maximize cluster utilization and minimize costs.
Open Source
No vendor lock-in. Full source code access with enterprise support available.
Use Cases
Distributed AI/ML Training
- Distributed PyTorch and TensorFlow training
- Hyperparameter optimization at scale
- Model evaluation pipelines
- Automated model versioning
# Submit distributed training job
rixi submit \
--script train_model.py \
--gpus 8 \
--nodes 2 \
--framework pytorch \
--requirements requirements.txt
# Monitor training progress
rixi logs training-job-xyz --follow
# Download trained model artifacts
rixi download training-job-xyz ./models/Large-Scale Batch Processing
- Large-scale data transformation
- ETL pipelines for analytics
- Scientific computing workloads
- Parallel processing jobs
# Process large dataset in parallel
rixi submit \
--script process_data.py \
--cpus 32 \
--memory 128GB \
--input s3://data-bucket/raw/ \
--output s3://data-bucket/processed/
# Schedule recurring batch job
rixi schedule \
--cron "0 2 * * *" \
--script daily_etl.pyScalable Model Inference
- Batch inference for large datasets
- Model serving with auto-scaling
- A/B testing infrastructure
- Real-time prediction pipelines
# Deploy model for batch inference
rixi deploy \
--model ./model.pkl \
--script inference.py \
--input-format parquet \
--batch-size 1000
# Scale inference based on queue depth
rixi scale inference-service \
--min-replicas 2 \
--max-replicas 20 \
--target-queue-depth 100Ready to Scale Your Compute Workloads?
Get started with Rixi today and see how remote execution can accelerate your AI/ML and data processing pipelines.