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+# SkyPilot Roadmap
+
+This doc lists general directions of interest to facilitate community contributions. 
+
+Note that
+- This list is not meant to be comprehensive (i.e., new work items of interest may pop up)
+- Even though listed under a specific version, not all items need to be completed before we ship that version (i.e., some items can go into future versions)
+
+## v0.3
+
+### Managed Spot
+- Minimize the cost of the controller
+  - Support running spot controller on an existing/local cluster
+  - Reducing the fixed cost of the controller (e.g., allow setting controller VM type)
+- Supporting a higher number of pending/concurrent jobs
+- Framework-specific guides to add checkpointing/reloading using SkyPilot Storage
+
+### Smarter Optimizer
+- Fine-grained optimizer: pick by cheapest zone order
+- Better consider data egress time/cost
+  - Consider buckets/Storage objects in file_mounts
+- Optimizing the data placement for SkyPilot Storage local uploads
+  - Use the optimizer to decide the bucket location
+
+### Programmatic API
+- Refactor/extend the current API to *make it easy to programmatically use SkyPilot*
+- Expose core classes in docs
+
+### Support more clouds
+- Refactoring of interfaces to ease adding new clouds
+- IBM Cloud
+- Explore support for low-cost clouds (e.g., lambda labs/runpod/jarvis labs)
+
+### On-prem
+- Robustify the on-prem feature
+- Design for switching between cloud and on-prem
+- Explore/design of "local mode" to run SkyPilot tasks locally
+
+### Faster launching speed
+- Consider a more minimal image
+- Azure speed investigation
+
+### k8s support
+- Ray-on-k8s backend
+  - To figure out: Launch a new k8s cluster? Launch SkyPilot Tasks to an existing k8s cluster?
+
+### Cost: Optimization, Tracking, and Reporting
+- Track and show costs related to a job/cluster
+- For managed spot jobs, track and show %savings vs. on-demand
+- Optimizer: take into account disk costs
+
+### Serverless
+- Design and prototype of a "serverless jobs" submission API and CLI
+  - Initial use case: hundreds of hyperparameter tuning trials
+
+### Backend
+- Support heterogeneous node types in a cluster (e.g., in RL, CPU actor(s) and GPU learner(s) in the same cluster)
+- Support CPUs as resource requirements
+- General robustness/UX improvements