Spring cleaning

.github/workflows/edge.yml → .github/workflows/fxnc.yml

@@ -10,16 +10,16 @@ jobs:
 
     - uses: actions/setup-python@v2
       with:
-        python-version: '3.8'
+        python-version: "3.11"
 
     - name: Install dependencies
       run: |
         python3 -m pip install --upgrade pip
         python3 -m pip install build twine
         python3 -m pip install -r requirements.txt
 
-    - name: Pull EdgeFunction
-      run: python3 edgefxn.py
+    - name: Pull Function C
+      run: python3 fxnc.py
 
     - name: Build Function
       run: python3 -m build

.github/workflows/pypi.yml

@@ -21,8 +21,8 @@ jobs:
         python3 -m pip install build twine
         python3 -m pip install -r requirements.txt
 
-    - name: Pull EdgeFunction
-      run: python3 edgefxn.py
+    - name: Pull Function C
+      run: python3 fxnc.py
 
     - name: Build Function
       run: python3 -m build

Changelog.md

@@ -1,6 +1,28 @@
 ## 0.0.36
++ Added `Acceleration.Default` enumeration constant.
++ Added `Acceleration.GPU` enumeration constant for running predictions on the GPU.
++ Added `Acceleration.NPU` enumeration constant forn running predictions on the neural processor.
 + Fixed crash when using `PIL.Image` values returned by edge predictors.
-+ Updated to Function C 0.0.23.
++ Updated to Function C 0.0.26.
++ Removed `Value` type.
++ Removed `PredictorType` enumeration.
++ Removed `fxn.predictors.create` method for creating predictors. [Apply](https://fxn.ai/waitlist) for early access to the new experience.
++ Removed `fxn.predictions.to_object` method.
++ Removed `fxn.predictions.to_value` method.
++ Removed `Predictor.type` field.
++ Removed `Predictor.acceleration` field.
++ Removed `Prediction.type` field.
++ Removed `Acceleration.A40` enumeration constant.
++ Removed `Acceleration.A100` enumeration constant.
++ Removed `fxn create` CLI function.
++ Removed `fxn delete` CLI function.
++ Removed `fxn list` CLI function.
++ Removed `fxn search` CLI function.
++ Removed `fxn retrieve` CLI function.
++ Removed `fxn archive` CLI function.
++ Removed `fxn env` CLI function group.
++ Removed `--raw-outputs` option from `fxn predict` CLI function.
++ Function now requires Python 3.10+.
 
 ## 0.0.35
 + Updated to Function C 0.0.18.

README.md

@@ -4,7 +4,7 @@
 
 [![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fy5vwgXkz2f%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Function%20community)](https://fxn.ai/community)
 
-Run AI prediction functions (a.k.a "predictors") in your Python apps. With Function, you can build AI-powered apps by creating and composing GPU-accelerated predictors that run in the cloud. In a few steps:
+Run prediction functions (a.k.a "predictors") locally in your Python apps, with full GPU acceleration and zero dependencies. In a few steps:
 
 ## Installing Function
 Function is distributed on PyPi. This distribution contains both the Python client and the command line interface (CLI). To install, open a terminal and run the following command:
@@ -22,27 +22,24 @@ Head over to [fxn.ai](https://fxn.ai) to create an account by logging in. Once y
 ![generate access key](https://raw.githubusercontent.com/fxnai/.github/main/access_key.gif)
 
 ## Making a Prediction
-Let's run the [`@samples/stable-diffusion`](https://fxn.ai/@samplefxn/stable-diffusion) predictor which accepts a text `prompt` and generates a corresponding image. Run the following Python script:
+Let's run the [`@fxn/greeting`](https://fxn.ai/@fxn/greeting) predictor which accepts a `name` and returns a congenial greeting. Run the following Python script:
 ```py
 from fxn import Function
 
 # Create the Function client
-fxn = Function(access_key="<ACCESS KEY>")
+fxn = Function(access_key="<Function access key>")
 # Create a prediction
 prediction = fxn.predictions.create(
-    tag="@samples/stable-diffusion",
-    inputs={
-        "prompt": "An astronaut riding a horse on Mars"
-    }
+    tag="@fxn/greeting",
+    inputs={ "name": "Peter" }
 )
-# Show the generated image
-image = prediction.results[0]
-image.show()
+# Print the returned greeting
+print(prediction.results[0])
 ```
 
 > [!TIP]
 > Explore public predictors [on Function](https://fxn.ai/explore) or [create your own](https://fxn.ai/waitlist).
-
+r
 ## Using the Function CLI
 Open up a terminal and run the following command:
 
@@ -51,14 +48,9 @@ Open up a terminal and run the following command:
 fxn auth login <ACCESS KEY>
 
 # Make a prediction using the Function CLI
-fxn predict @samplefxn/stable-diffusion \
-  --prompt "An astronaut riding a horse on the moon"
+fxn predict @fxn/greeting --name Peter
 ```
 
-Within a few seconds, you should see a creepy-looking image pop up 😅:
-
-![prediction](https://raw.githubusercontent.com/fxnai/.github/main/predict.gif)
-
 ___
 
 ## Useful Links

fxn/cli/__init__.py

@@ -9,7 +9,7 @@
 from .env import app as env_app
 from .misc import cli_options
 from .predict import predict
-from .predictors import archive_predictor, create_predictor, delete_predictor, list_predictors, retrieve_predictor, search_predictors
+from .predictors import archive_predictor, delete_predictor, list_predictors, retrieve_predictor, search_predictors
 from ..version import __version__
 
 # Define CLI
@@ -26,16 +26,16 @@
 
 # Add subcommands
 app.add_typer(auth_app, name="auth", help="Login, logout, and check your authentication status.")
-app.add_typer(env_app, name="env", help="Manage predictor environment variables.")
+#app.add_typer(env_app, name="env", help="Manage predictor environment variables.")
 
 # Add top-level commands
-app.command(name="create", help="Create a predictor.")(create_predictor)
-app.command(name="delete", help="Delete a predictor.")(delete_predictor)
+#app.command(name="create", help="Create a predictor.")(create_predictor)
+#app.command(name="delete", help="Delete a predictor.")(delete_predictor)
 app.command(name="predict", help="Make a prediction.", context_settings={ "allow_extra_args": True, "ignore_unknown_options": True })(predict)
-app.command(name="list", help="List predictors.")(list_predictors)
-app.command(name="search", help="Search predictors.")(search_predictors)
-app.command(name="retrieve", help="Retrieve a predictor.")(retrieve_predictor)
-app.command(name="archive", help="Archive a predictor.")(archive_predictor)
+#app.command(name="list", help="List predictors.")(list_predictors)
+#app.command(name="search", help="Search predictors.")(search_predictors)
+#app.command(name="retrieve", help="Retrieve a predictor.")(retrieve_predictor)
+#app.command(name="archive", help="Archive a predictor.")(archive_predictor)
 
 # Run
 if __name__ == "__main__":

fxn/cli/auth.py

@@ -15,7 +15,7 @@
 def login (
     access_key: str=Argument(..., help="Function access key.", envvar="FXN_ACCESS_KEY")
 ):
-    fxn = Function(access_key)
+    fxn = Function(access_key=access_key)
     user = fxn.users.retrieve()
     user = user.model_dump() if user else None
     _set_access_key(access_key if user is not None else None)

fxn/cli/predict.py

@@ -18,12 +18,11 @@
 
 def predict (
     tag: str = Argument(..., help="Predictor tag."),
-    raw_outputs: bool = Option(False, "--raw-outputs", help="Output raw Function values instead of converting into plain Python values."),
     context: Context = 0
 ):
-    run_async(_predict_async(tag, context=context, raw_outputs=raw_outputs))
+    run_async(_predict_async(tag, context=context))
 
-async def _predict_async (tag: str, context: Context, raw_outputs: bool):
+async def _predict_async (tag: str, context: Context):
     with Progress(
         SpinnerColumn(spinner_name="dots"),
         TextColumn("[progress.description]{task.description}"),
@@ -34,7 +33,7 @@ async def _predict_async (tag: str, context: Context, raw_outputs: bool):
         inputs = { context.args[i].replace("-", ""): _parse_value(context.args[i+1]) for i in range(0, len(context.args), 2) }
         # Stream
         fxn = Function(get_access_key())
-        async for prediction in fxn.predictions.stream(tag, inputs=inputs, raw_outputs=raw_outputs, return_binary_path=True):
+        async for prediction in fxn.predictions.stream(tag, inputs=inputs):
             # Parse results
             images = [value for value in prediction.results or [] if isinstance(value, Image.Image)]
             prediction.results = [_serialize_value(value) for value in prediction.results] if prediction.results is not None else None

fxn/cli/predictors.py

@@ -5,12 +5,10 @@
 
 from rich import print_json
 from rich.progress import Progress, SpinnerColumn, TextColumn
-from pathlib import Path
 from typer import Argument, Option
-from typing import List
 
 from ..function import Function
-from ..types import Acceleration, AccessMode, PredictorStatus, PredictorType
+from ..types import PredictorStatus
 from .auth import get_access_key
 
 def retrieve_predictor (
@@ -47,43 +45,6 @@ def search_predictors (
     predictors = [predictor.model_dump() for predictor in predictors]
     print_json(data=predictors)
 
-def create_predictor (
-    tag: str=Argument(..., help="Predictor tag."),
-    notebook: Path=Argument(..., help="Path to predictor notebook."),
-    type: PredictorType=Option(None, case_sensitive=False, help="Predictor type. This defaults to `cloud`."),
-    edge: bool=Option(False, "--edge", is_flag=True, help="Shorthand for `--type edge`."),
-    cloud: bool=Option(False, "--cloud", is_flag=True, help="Shorthand for `--type cloud`."),
-    access: AccessMode=Option(None, case_sensitive=False, help="Predictor access mode. This defaults to `private`."),
-    description: str=Option(None, help="Predictor description. This must be less than 200 characters long."),
-    media: Path=Option(None, help="Predictor image path."),
-    acceleration: Acceleration=Option(None, case_sensitive=False, help="Cloud predictor acceleration. This defaults to `cpu`."),
-    license: str=Option(None, help="Predictor license URL."),
-    env: List[str]=Option([], help="Specify a predictor environment variable."),
-    overwrite: bool=Option(None, "--overwrite", help="Overwrite any existing predictor with the same tag.")
-):
-    with Progress(
-        SpinnerColumn(spinner_name="dots"),
-        TextColumn("[progress.description]{task.description}"),
-        transient=True
-    ) as progress:
-        progress.add_task(description="Analyzing Function...", total=None)
-        fxn = Function(get_access_key())
-        type = PredictorType.Cloud if cloud else PredictorType.Edge if edge else type
-        environment = { e.split("=")[0].strip(): e.split("=")[1].strip() for e in env }
-        predictor = fxn.predictors.create(
-            tag=tag,
-            notebook=notebook,
-            type=type,
-            access=access,
-            description=description,
-            media=media,
-            acceleration=acceleration,
-            environment=environment,
-            license=license,
-            overwrite=overwrite
-        )
-        print_json(data=predictor.model_dump())
-
 def delete_predictor (
     tag: str=Argument(..., help="Predictor tag.")
 ):

fxn/function.py

@@ -17,8 +17,6 @@ class Function:
         users (UserService): Manage users.
         predictors (PredictorService): Manage predictors.
         predictions (PredictionService): Manage predictions.
-        environment_variables (EnvironmentVariableService): Manage predictor environment variables.
-        storage (StorageService): Upload and download files.
 
     Constructor:
         access_key (str): Function access key.
@@ -28,15 +26,16 @@ class Function:
     users: UserService
     predictors: PredictorService
     predictions: PredictionService
-    environment_variables: EnvironmentVariableService
-    storage: StorageService
+    #environment_variables: EnvironmentVariableService
+    #storage: StorageService
 
     def __init__ (self, access_key: str=None, api_url: str=None):
         access_key = access_key or environ.get("FXN_ACCESS_KEY", None)
         api_url = api_url or environ.get("FXN_API_URL", "https://api.fxn.ai")
-        self.client = GraphClient(access_key, api_url)
-        self.users = UserService(self.client)
-        self.storage = StorageService(self.client)
-        self.predictors = PredictorService(self.client, self.storage)
-        self.predictions = PredictionService(self.client, self.storage)
-        self.environment_variables = EnvironmentVariableService(self.client)
+        client = GraphClient(access_key, api_url)
+        storage = StorageService(client)
+        self.client = client
+        self.users = UserService(client)
+        self.predictors = PredictorService(client, storage)
+        self.predictions = PredictionService(client)
+        #self.environment_variables = EnvironmentVariableService(self.client)