You love building in Python for Data Science. But training models on your laptop is limiting. Coiled supercharges the Python tools you already use for scale and performance. Move to the cloud with a few clicks—and stay focused on solving important problems.
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import coiled
coiled.create_software_environment(
name="xgboost",
conda={"channels": ["conda-forge"],
"dependencies": ["dask", "xgboost"]},
pip=["s3fs"],
)
Keep data in place and secure.
Move computations to where your data already is to reduce costs and adhere to regulations.
Become cloud agnostic.
Move workloads to AWS and GCP easily to avoid vendor lock-in.
Realize deployment versatility.
Deploy workloads on Kubernetes clusters or move Kubernetes workloads to the cloud.
Stay native with PyData.
Leverage your preferred environment and add GPUs as needed.
Deploy your data science and machine learning applications to private or public cloud.
While moving to the cloud reduces costs initially, uncontrolled usage can spiral costs out of control. With Coiled Cloud, you pay-as-you-go, and the operations dashboard highlights issues from cluster telemetry and gives you the governance to control costs.
Control cluster usage
Set user and team quotas along with GPU access to manage cluster utilization. Stop using idle clusters automatically to keep costs in check.
Keep sensitive information secure
Deploy in your own cloud account or multi-tenant environment and rest assured your data is protected with end-to-end network security.
Get easy access to your data
Built-in credential management for authentication and identity management.
| Cluster Name | Created By | Status | Num Workers | Configuration | Cost (current) | Cost (total) |
| alice-2c0e0f46-0 | alice | running | 4/4 | coiled-examples/pytorch | $4.77/hr | $0.10 |
| Cluster Name | Created By | Status | Num Workers | Configuration | Cost (current) | Cost (total) |
| alice-2c0e0f46-0 | alice | running | 4/40 | coiled-examples/pytorch | $12.13/hr | $0.15 |
“Coiled is amazing technology. We were able to get an initial reduction across our pipelines - from data curation to automated experiments - from 66 hours to 35 minutes and then with additional tuning down to 15 minutes.”
Eyal Toledano
CTO & Co-Founder, Zebra Medical Vision
