JetBrains Cadence

Run your code on powerful cloud hardware directly from PyCharm in minutes – no complex setup or cloud expertise required.

Why is Cadence the right choice for ML/AI engineers?

• Serverless compute: Access a range of GPUs on demand and pay per second for the resources consumed.
• Run your code as-is: Seamlessly train your model in the cloud without making any changes.
• Engineered for PyCharm: Keep your existing workflow – use the terminal or debugger just like you would locally.
• Simple data management: Say goodbye to manual transfers. Cadence handles data synchronization automatically, allowing you to download your experiment results anytime.
• Reliable and reproducible: Easily review, refine, and rerun experiments with confidence.
• Skip the line: Schedule as many tasks as you need simultaneously, and Cadence will automatically check for available hosts in different regions and zones.

Why should ML/AI teams choose Cadence?

• Scalable GPU solutions: You can instantly adjust resources with Cadence's cloud-agnostic platform and access a diverse range of globally available GPUs.
• Optimized GPU utilization: Cadence manages resource allocation during training, ensuring GPUs are used only when needed.
• Transparent billing: Cadence uses a pay-as-you-go model, offering transparent spending and fine-grained control over resource consumption.
• Enhanced collaboration: Cadence allows you to easily share and reuse experiment results with your colleagues, saving everyone time and allowing you to work more effectively as a team.