|
|
@@ -18,24 +18,24 @@ __[WIP]__ - this branch is in progress of updating. If you're interested in supp
|
|
|
* Running on Windows natively is not supported, please use vm or docker;
|
|
|
|
|
|
## How do I run it?
|
|
|
-1. Clone or download this repo. `cd` to its root directory.
|
|
|
-2. Grab or build a working python enviromnent. [Anaconda](https://www.anaconda.com/) works fine.
|
|
|
-3. Install packages from `requirements.txt`
|
|
|
-4. Go to [./experiments](./experiments) and follow the README.md from there
|
|
|
+Currently, there isn't any way to do it easily. There are some tests (you can look into CI logs and/or config) and we want to expand them, but if you want to do something complex with it, you're on your own.
|
|
|
|
|
|
|
|
|
## tesseract quick tour
|
|
|
|
|
|
__Trainer process:__
|
|
|
- * __`RemoteExpert`__(`lib/client/remote_expert.py`) behaves like a pytorch module with autograd support but actually sends request to a remote runtime.
|
|
|
- * __`GatingFunction`__(`lib/client/gating_function.py`) finds best experts for a given input and either returns them as `RemoteExpert` or applies them right away.
|
|
|
+ * __`RemoteExpert`__(`tesseract/client/remote_expert.py`) behaves like a pytorch module with autograd support but actually sends request to a remote runtime.
|
|
|
+ * __`GatingFunction`__(`tesseract/client/gating_function.py`) finds best experts for a given input and either returns them as `RemoteExpert` or applies them right away.
|
|
|
|
|
|
__Runtime process:__
|
|
|
- * __`TesseractRuntime`__ (`lib/runtime/__init__.py`) aggregates batches and performs inference/training of experts according to their priority.
|
|
|
- * __`TesseractServer`__ (`lib/server/__init__.py`) wraps runtime and periodically uploads experts into `TesseractNetwork`.
|
|
|
+ * __`TesseractRuntime`__ (`tesseract/runtime/__init__.py`) aggregates batches and performs inference/training of experts according to their priority.
|
|
|
+ * __`TesseractServer`__ (`tesseract/server/__init__.py`) wraps runtime and periodically uploads experts into `TesseractNetwork`.
|
|
|
|
|
|
__DHT:__
|
|
|
- * __`TesseractNetwork`__(`lib/network/__init__.py`) is a node of Kademlia-based DHT that stores metadata used by trainer and runtime.
|
|
|
+ * __`TesseractNetwork`__(`tesseract/network/__init__.py`) is a node of Kademlia-based DHT that stores metadata used by trainer and runtime.
|
|
|
|
|
|
## Limitations
|
|
|
-WIP
|
|
|
+__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.
|
Anton Gusev