Research & Data Science
Need more compute power for your research? Scale the work you're doing locally with Python, R, or Julia to the cloud. Same tools, more resources.
Your Laptop Has Limits
You've got a great workflow on your local machine. Jupyter notebooks, Python scripts, maybe some R. But now your dataset doesn't fit in memory. Training takes overnight. Your fans are screaming.
You could spin up EC2 instances and manage SSH keys, security groups, and storage. Or you could just tell your AI assistant what you need.
From Local to Cloud in Minutes
- 1
Containerize your environment
Package your Python environment, dependencies, and code in a Docker image. Your AI assistant can help.
- 2
Deploy to Nexlayer
Specify the compute resources you need — CPU, memory, and persistent storage for data.
- 3
Run your experiments
Access Jupyter, run scripts, or serve models. Your data persists across sessions.
Built for Research Workflows
Scale Your Compute
Go from running experiments on your laptop to cloud-scale compute. Request the CPU and memory you need.
Persistent Storage
Mount volumes for datasets, model checkpoints, and results. Data persists across runs and restarts.
Right-Size Resources
Allocate exactly what you need. Scale up for training runs, scale down for inference. No wasted resources.
Your Tools, Your Way
Bring your own Docker image with your exact Python environment, CUDA version, and dependencies.
Common Research Use Cases
ML Model Training
Train models on larger datasets than your local machine can handle. Checkpoint to persistent storage and resume anytime.
Data Processing Pipelines
ETL jobs, data cleaning, feature engineering. Run Pandas, Spark, or Dask at scale.
Jupyter Notebooks in the Cloud
Run JupyterLab with GPU access. Connect from anywhere, keep notebooks running.
Model Serving & Inference
Deploy trained models as APIs. Serve predictions without cold starts or timeout limits.
Batch Processing
Process large file collections, run simulations, or generate reports on dedicated compute.
Example: JupyterLab with Persistent Storage
application:
name: research-notebook
pods:
- name: jupyter
image: jupyter/scipy-notebook:latest
path: /
servicePorts: [8888]
resources:
cpu: "4"
memory: "8Gi"
volumes:
- name: notebooks
size: 20Gi
mountPath: /home/jovyan/workScale Your Research
Get cloud compute without the cloud complexity. Your AI assistant handles the infrastructure.