Choose Anycloud when your training code is already in a published container and you want a Job command with bucket sync, logs, and cost tracking. Choose SkyPilot when its YAML tasks, managed pipelines, or multi-node workflows fit your training setup. Both require application checkpoints to recover useful progress.
Compare the training workflow
SkyPilot managed jobs provision resources, recover from preemptions and other failures, and clean up when finished. Its documented workflow uses YAML tasks and supports managed pipelines and multi-node Jobs.
Anycloud Jobs run published containers with a command, hardware target, and optional buckets. A VM-host Job creates one host; use it when the training process fits on that machine. Anycloud also has Kubernetes Deployments for targeted Jobs on an existing container.
Compare the number of GPUs per host, cloud quota, checkpoint storage, and startup time. Neither tool makes training resumable unless the application saves and reloads its state.
Run the published training image with Anycloud
Assuming the CLI and a named cloud credential are configured, choose one credential and an available H100 target. Replace the placeholders before running:
anycloud job ghcr.io/example/training:latest \
--credentials <aws-or-gcp-credential> \
--gpu-type h100 --gpus all --spot \
--output-bucket <unique-results-bucket> \
-- python train.py
anycloud logs <job-id> --follow
anycloud cost <job-id>--gpus all uses every GPU on the selected host, which may be more than one. The training code must write checkpoints to /mnt/checkpoint and load them on restart. Sync runs about every 60 seconds, so the latest unsynced work can be lost. An automatically created checkpoint bucket is removed when the Job finishes; use a pre-existing checkpoint bucket to retain checkpoints between Jobs.
A named compute credential selects one cloud identity. Compare AWS and GCP availability and prices before selecting it; repeating --credentials is rejected.