Quick start [nothing here yet]

This will eventually become a tutorial on how to host a hivemind node or connect to an existing node.

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What do I need to run it?

• One or several computers, each equipped with at least one GPU
• Each computer should have at least two open ports (if not, consider ssh port forwarding)
• Some popular Linux x64 distribution
  • Tested on Ubuntu16.04, should work fine on any popular linux64 and even MacOS;
  • Running on Windows natively is not supported, please use vm or docker;

How do I run it?

Currently, there is no way to do it easily. There are some tests (you can check ./tests/benchmark_throughput.py or look into CI logs) and we want to expand them. If you want to do something complex with it, please contact us by opening an issue (less preferred: telegram).

hivemind quick tour

Trainer process:

• RemoteExpert(hivemind/client/remote_expert.py) behaves like a pytorch module with autograd support but actually sends request to a remote runtime.
• RemoteMixtureOfExperts(hivemind/client/remote_moe.py) finds best experts for a given input and either returns them as RemoteExpert or applies them right away.

Runtime process:

• Runtime (hivemind/runtime/__init__.py) aggregates batches and performs inference/training of experts according to their priority.
• Server (hivemind/server/__init__.py) wraps runtime and periodically uploads experts into DHT.

DHT:

• DHT(hivemind/dht/__init__.py) is a node of Kademlia-based DHT that stores metadata used by trainer and runtime.

Limitations

DHT:

• DHT functionality is severely limited by its inability to traverse NAT.
• Because of this all the features that require DHT are in deep pre-alpha state and cannot be used without special setup.

Runtime:

• You can achieve 4x less network load by passing quantized uint8 activations across experts. Implement your own quantization or wait for hivemind v0.8.
• Currently runtime can form batches that exceed maximal batch_size by task_size - 1. We will fix that in the nearest patch.