TrainML offers a platform that simplifies machine learning development by automating infrastructure management. The platform allows users to quickly deploy and train models on GPUs without managing servers, SSH, or library dependencies. This enables data scientists to focus on model development while reducing infrastructure costs.
Funding
Funding not disclosed
Founders
Product
Problem
Machine learning developers face challenges in managing infrastructure, including server management, SSH tunneling, and library dependencies, which distract from core model development tasks. Cloud GPU instances offer a solution but can be expensive due to setup, teardown, and the risk of forgetting to stop instances, leading to unnecessary costs.
Solution
TrainML provides a platform that simplifies machine learning development by automating infrastructure management, allowing users to focus on model development. The platform offers on-demand, serverless GPUs, eliminating the need for instance management, reservations, or time restrictions. Users can launch GPU-enabled Jupyter Notebooks or run parallel model training experiments with a few clicks, leveraging pre-built conda environments optimized for deep learning frameworks. TrainML's job system prevents unnecessary usage fees by automatically stopping jobs when training is complete, and its GPUs offer a cost-effective alternative to other cloud providers.
Target Audience
The primary users are machine learning developers and data scientists who need hassle-free, cost-effective GPU resources for model training and inference.
Features
- On-demand GPU instances without instance management, reservations, or time restrictions
- Pre-built conda environments with the latest Tensorflow, PyTorch, MXNet, and other deep learning frameworks pre-installed
- Notebooks: Full instances of JupyterLab running on up to 4 dedicated GPUs
- Training Jobs: Effortlessly run parallel model training experiments across dozens of GPUs by providing the model's git repository and data location
- Inference Jobs: Run new data through trained models without managing, scaling, or descaling server clusters
- Endpoints: Deploy models as REST APIs without managing servers, certificates, networking, or web development
- Datasets: Persistent datasets allow reusing training data across multiple notebooks or training jobs, populated from local computers or cloud providers
- Models: Store an immutable version of model code and artifacts for reuse in other jobs
- Dynamic instance type changes: Scale GPU resources up or down as needed without compromising time