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overhaul runners, add GCP & K8s stubs #105

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5 changes: 2 additions & 3 deletions content/docs/cml-with-dvc.md
Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@ on: [push]
jobs:
run:
runs-on: [ubuntu-latest]
container: docker://iterativeai/cml:0-dvc2-base1
container: docker://ghcr.io/iterative/cml:0-dvc2-base1
steps:
- uses: actions/checkout@v2
- name: cml_run
Expand Down Expand Up @@ -58,8 +58,7 @@ jobs:
cml-send-comment report.md
```

If you're using DVC with cloud storage, take note of environmental variables for
your storage format.
## Cloud Storage Provider Credentials

<details>

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286 changes: 179 additions & 107 deletions content/docs/self-hosted-runners.md

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2 changes: 1 addition & 1 deletion content/docs/start/github.md
Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ supported CI systems.
jobs:
run:
runs-on: [ubuntu-latest]
container: docker://iterativeai/cml:0-dvc2-base1
container: docker://ghcr.io/iterative/cml:0-dvc2-base1
steps:
- uses: actions/checkout@v2
- name: cml_run
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5 changes: 3 additions & 2 deletions content/docs/start/index.md
Original file line number Diff line number Diff line change
Expand Up @@ -23,8 +23,9 @@ We built CML with these principles in mind:
plots in each Git Pull Request. Rigorous engineering practices help your team
make informed, data-driven decisions.
- **No additional services.** Build your own ML platform using just GitHub or
GitLab and your favorite cloud services: AWS, Azure, GCP. No databases,
services or complex setup needed.
GitLab and your
[favorite cloud services](/doc/self-hosted-runners#runner-credentials): AWS,
Azure, GCP, or Kubernetes. No databases, services or complex setup needed.

_Need help? Just want to chat about continuous integration for ML?
[Visit our Discord channel!](https://discord.gg/bzA6uY7)_
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13 changes: 5 additions & 8 deletions content/docs/usage.md
Original file line number Diff line number Diff line change
Expand Up @@ -29,9 +29,8 @@ on: [push]
jobs:
run:
runs-on: ubuntu-latest
# optionally use a convenient Ubuntu LTS + CUDA + DVC + CML image
# container: docker://iterativeai/cml:0-dvc2-base1-gpu
# container: docker://ghcr.io/iterative/cml:0-dvc2-base1-gpu
# optionally use a convenient Ubuntu LTS + DVC + CML container
# container: docker://ghcr.io/iterative/cml:0-dvc2-base1
steps:
- uses: actions/checkout@v2
# may need to setup NodeJS & Python3 on e.g. self-hosted
Expand All @@ -58,12 +57,10 @@ jobs:

We helpfully provide CML and other useful libraries pre-installed on our
[custom Docker images](/doc/self-hosted-runners#docker-images). In the above
example, uncommenting the field
`container: docker://iterativeai/cml:0-dvc2-base1-gpu` (or
`container: docker://ghcr.io/iterative/cml:0-dvc2-base1-gpu`) will make the
example, uncommenting the
`container: docker://ghcr.io/iterative/cml:0-dvc2-base1` field will make the
runner pull the CML Docker image. The image already has NodeJS, Python 3, DVC
and CML set up on an Ubuntu LTS base with CUDA libraries and
[Terraform](https://www.terraform.io) installed for convenience.
and CML set up on an Ubuntu LTS base for convenience.

## CML Functions

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