Shale - Decentralized Compute

Shale is aiming to bring cloud computing to Filecoin and allow people to lease computing units from Storage Providers (SPs) just like on AWS or Google Cloud.

With Shale, SPs are able to earn SHALE or FIL by leasing their computing severs (CPU/RAM/GPU servers) together with their sealed data.

Shale is targeting on native high-performance computing such as AI/ML training/inference, usually against large/open datasets. Any traditional tasks are possible, such as C/C++ compiling, short-time web serving, general data-processing etc..

Design

The idea is "data not move, but programs", i.e. sending programs to where the data locate (i.e. the data center where storage deals live), and utilizing those Filecoin+ deals by LAN disk mouting.

Programs here refer to off-chain native executables like C/C++, CUDA, TensorFlow.

SSH-payment-tunnel

Just like normal SSH, but equipped with a local cryptographic payment tunnel, which accumulates fractional payments and aggregates them to ZK proofs in the end. The ZK proofs will be posted to Shale layer-2 by SPs.

Both parties could terminate the lease anytime.

The design is to accommodate future plugins for in-place monitoring/benchmark of session quality to catch potential cheating behaviors.

Shale Layer-2 Marketplace

There will be smart contracts on FVM for coordinating orders between clients and SPs, where:

• Clients create orders with their SSH pub-keys, prices, time and resource requirements
• SPs accept orders, confirming by clients with collateral
• SPs upload payment-tunnel ZKPs to claim rewards
• Clients post ratings/reviews based on their time/usage.

High-performance native computing

Programs are running natively in container with access to local accelerators (GPU).

All traditional web1/web2 software ecosystems (unix, apt-get, gcc, python, nodejs, etc.) become available, thanks to container technology.

Examples: C/C++ compiling, ML training/inference, general data pipeline/processing, short-time serving etc..

Implementation

The first implementation is a terminal command-line tool (CLI) that focused on demonstrating the using experiences, which includes:

• Client lists SPs with available datasets
• Client sends request to particular SP
• SP spawns docker containers on server with required accelerators(e.g., GPUs)
• SP auto-provisions SSH server and client's pub-key inside container
• Mock SSH-payment-tunnel UI
• Client runs a ML training task remotely in the container

Client-side

For those who have Shales(FILs) and want to rent computing resources.

List all SPs

Request a container

shale give-me-container \
  --miner=f01946720 \
  --price=0.01fil/min \
  --num_cpus=10 \
  --num_rams=12Gi \
  --num_gpus=1 \
  --bandwidth=1Mi \
  --image=cuda-ubuntu18.04 \
  --mnt=<data_cid_1>:/mnt/mnist.zip \
  --mnt=<data_cid_2>:/mnt/cifar-100.zip \
  --ssh_pubkey=~/.ssh/id_rsa.pub

Server-side

For those storage providers who want to lease their server.

Start a container to lease (low-level command)

shale lease-container ...

Start Daemon

Daemon that accepts requests and start containers automatically.

TBD.