Release 0.6.2

.github/workflows/commit_new_release_to_main.yml

@@ -0,0 +1,31 @@
+name: Merge release into main
+
+on:
+  push:
+    branches:
+      - release
+
+jobs:
+  merge-to-main:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Check out code
+      uses: actions/checkout@v2
+
+    - name: Configure Git user as basetenbot
+      run: |
+        git config user.name "basetenbot"
+        git config user.email "96544894+basetenbot@users.noreply.github.com"
+
+    - name: Fetch all branches
+      run: |
+        git fetch --all
+
+    - name: Merge release into main with priority on main changes
+      run: |
+        git checkout main
+        git merge --strategy-option=ours release -m "Merge release into main prioritizing main changes"
+        git push origin main
+      env:
+        GH_TOKEN: ${{ secrets.BASETENBOT_GITHUB_TOKEN }}

.github/workflows/create_release_pr.yml

@@ -0,0 +1,53 @@
+name: Create Release PR
+
+on:
+  workflow_dispatch:
+    inputs:
+      version:
+        description: 'Version to bump to'
+        required: true
+
+jobs:
+  bump_version:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - name: Install poetry
+      run: |
+        curl -sSL https://install.python-poetry.org | python3 -
+
+    - name: Bump version in pyproject.toml
+      run: |
+        poetry version $INPUT_VERSION
+      env:
+        INPUT_VERSION: ${{ github.event.inputs.version }}
+
+
+    - name: Commit changes
+      run: |
+        git config --local user.email "96544894+basetenbot@users.noreply.github.com"
+        git config --local user.name "basetenbot"
+        git add pyproject.toml
+        git commit -m "Bump version to $INPUT_VERSION"
+      env:
+        INPUT_VERSION: ${{ github.event.inputs.version }}
+
+    # TODO: Also push changes to main
+    - name: Push changes to new branch
+      run: |
+        git push origin HEAD:refs/heads/bump-version-${{ github.event.inputs.version }}
+      env:
+        GH_TOKEN: ${{ secrets.BASETENBOT_GITHUB_TOKEN }}
+
+    - name: Make PR
+      run: |
+        CURR_VERSION=$(curl https://pypi.org/pypi/truss/json | jq ".info.version")
+        PR_BODY="Updating Truss from [$CURR_VERSION](https://pypi.org/pypi/truss/json) to $INPUT_VERSION."
+        PR_URL=$(gh pr create --base release --head refs/heads/bump-version-$INPUT_VERSION --title "Release $INPUT_VERSION" --body "$PR_BODY")
+        gh pr merge $PR_URL --auto --merge
+      env:
+        INPUT_VERSION: ${{ github.event.inputs.version }}
+        GH_TOKEN: ${{ secrets.BASETENBOT_GITHUB_TOKEN }}

README.md

@@ -13,10 +13,9 @@
 
 See Trusses for popular models including:
 
-* 🦅 [Falcon 40B](https://github.com/basetenlabs/falcon-40b-truss)
-* 🧙 [WizardLM](https://github.com/basetenlabs/wizardlm-truss)
-* 🎨 [Stable Diffusion](https://github.com/basetenlabs/stable-diffusion-truss)
-* 🗣 [Whisper](https://github.com/basetenlabs/whisper-truss)
+* 🦙 [Llama 2 7B](https://github.com/basetenlabs/truss-examples/tree/main/llama-2-7b-chat) ([13B](https://github.com/basetenlabs/truss-examples/tree/main/llama-2-13b-chat)) ([70B](https://github.com/basetenlabs/truss-examples/tree/main/llama-2-70b-chat))
+* 🎨 [Stable Diffusion XL](https://github.com/basetenlabs/truss-examples/tree/main/stable-diffusion-xl-1.0)
+* 🗣 [Whisper](https://github.com/basetenlabs/truss-examples/tree/main/whisper-truss)
 
 and [dozens more examples](examples/).
 
@@ -36,61 +35,100 @@ As a quick example, we'll package a [text classification pipeline](https://huggi
 
 To get started, create a Truss with the following terminal command:
 
-```
+```sh
 truss init text-classification
 ```
 
-This will create an empty Truss at `./text-classification`.
+When prompted, give your Truss a name like `Text classification`.
+
+Then, navigate to the newly created directory:
+
+```sh
+cd text-classification
+```
 
 ### Implement the model
 
-The model serving code goes in `./text-classification/model/model.py` in your newly created Truss.
+One of the two essential files in a Truss is `model/model.py`. In this file, you write a `Model` class: an interface between the ML model that you're packaging and the model server that you're running it on.
+
+There are two member functions that you must implement in the `Model` class:
+
+* `load()` loads the model onto the model server. It runs exactly once when the model server is spun up or patched.
+* `predict()` handles model inference. It runs every time the model server is called.
+
+Here's the complete `model/model.py` for the text classification model:
 
 ```python
-from typing import List
 from transformers import pipeline
 
 
 class Model:
-    def __init__(self, **kwargs) -> None:
+    def __init__(self, **kwargs):
         self._model = None
 
     def load(self):
         self._model = pipeline("text-classification")
 
-    def predict(self, model_input: str) -> List:
+    def predict(self, model_input):
         return self._model(model_input)
 ```
 
-There are two functions to implement:
-
-* `load()` runs once when the model is spun up and is responsible for initializing `self._model`
-* `predict()` runs each time the model is invoked and handles the inference. It can use any JSON-serializable type as input and output.
-
 ### Add model dependencies
 
-The pipeline model relies on Transformers and PyTorch. These dependencies must be specified in the Truss config.
+The other essential file in a Truss is `config.yaml`, which configures the model serving environment. For a complete list of the config options, see [the config reference](https://truss.baseten.co/reference/config).
 
-In `./text-classification/config.yaml`, find the line `requirements`. Replace the empty list with:
+The pipeline model relies on [Transformers](https://huggingface.co/docs/transformers/index) and [PyTorch](https://pytorch.org/). These dependencies must be specified in the Truss config.
+
+In `config.yaml`, find the line `requirements`. Replace the empty list with:
 
 ```yaml
 requirements:
   - torch==2.0.1
   - transformers==4.30.0
 ```
 
-No other configuration needs to be changed.
+No other configuration is needed.
 
 ## Deployment
 
-You can deploy a Truss to your [Baseten](https://baseten.co) account with:
+Truss is maintained by [Baseten](https://baseten.co), which provides infrastructure for running ML models in production. We'll use Baseten as the remote host for your model.
 
-```
-cd ./text-classification
+Other remotes are coming soon, starting with AWS SageMaker.
+
+### Get an API key
+
+To set up the Baseten remote, you'll need a [Baseten API key](https://app.baseten.co/settings/account/api_keys). If you don't have a Baseten account, no worries, just [sign up for an account](https://app.baseten.co/signup/) and you'll be issued plenty of free credits to get you started.
+
+### Run `truss push`
+
+With your Baseten API key ready to paste when prompted, you can deploy your model:
+
+```sh
 truss push
 ```
 
-Truss will support other remotes soon, starting with AWS SageMaker.
+You can monitor your model deployment from [your model dashboard on Baseten](https://app.baseten.co/models/).
+
+### Invoke the model
+
+After the model has finished deploying, you can invoke it from the terminal.
+
+**Invocation**
+
+```sh
+truss predict -d '"Truss is awesome!"'
+```
+
+**Response**
+
+```json
+[
+  {
+    "label": "POSITIVE",
+    "score": 0.999873161315918
+  }
+]
+```
 
 ## Truss contributors