Skip to content

Latest commit

 

History

History
64 lines (46 loc) · 2.24 KB

README.md

File metadata and controls

64 lines (46 loc) · 2.24 KB
Raw

interpreta

Interpreta is a GKE-powered managed notebook environment for mechanistic interpretability research. Born from the resource limitations of online notebook environments, it provides researchers complete control over dependencies and the compute resources they want for running large-scale experiments.

logo

Features

  • Python and jupyter notebook environment are already setup
  • Mech interp packages are setup
  • Whatever machine you want within the Google ecosystem is setup. Currently it is an n1-standard-1.

Packages

Python environment: python 3.11.6

    "torch==2.5.1" \
    "einops==0.8.0" \
    "jaxtyping==0.2.36" \
    "tqdm==4.67.1" \
    "numpy==1.26.4" \
    "transformer-lens==2.9.1" \
    "sae-lens==5.2.0" \
    "plotly==5.24.0" \
    "transformers==4.47.0" \
    "huggingface_hub==0.27.0"

Stack

terraform, helm, docker

Setup instructions

FYI, I aliased kubectl with k.

  1. Assuming you've set up your terraform backend within GCP, run a terraform apply within infrastructure/environments/dev. This will spin up your cluster.

  2. Run k get nodes. You should see two nodes.

    NAME                                                  STATUS   ROLES    AGE     VERSION
gke-interpreta-gke-interpreta-node-po-756e7c1c-l47x   Ready    <none>   20h     v1.29.7-gke.1104000
gke-interpreta-gke-interpreta-node-po-756e7c1c-v159   Ready    <none>   9m20s   v1.29.7-gke.1104000

  3. To get the jupyter environment running: cd helm and then make install

  4. After that completes, you should be able to see the services: k get svc --namespace jhub

    You should hopefully see a few different services that are not exposed to the internet:

    NAME           TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)    AGE
hub            ClusterIP   34.118.226.209   <none>        8081/TCP   99s
proxy-api      ClusterIP   34.118.229.142   <none>        8001/TCP   99s
proxy-public   ClusterIP   34.118.235.32    <none>        80/TCP     99s

  5. To access the jupyter login page, you need to port-forward the traffic to the proxy-public service.

    make proxy

    Then you should be able to access the jupyter login page on http://localhost:8080