SDK Reference
Environment
Image
Defines a custom container image that your code will run in.
An Image object encapsulates the configuration of a custom container image that will be used as the runtime environment for executing tasks.
from beta9 import endpoint, Image
image = (
Image(
python_version="python3.9",
python_packages=[
"transformers",
"torch",
],
commands=["apt-get update -y && apt-get install ffmpeg -y"],
base_image="docker.io/nvidia/cuda:12.3.1-runtime-ubuntu20.04",
),
)
@endpoint(image=image)
def handler():
return {}
The Python version to be used in the image. Defaults to Python 3.8.
A list of Python packages to install in the container image. Alternatively, a string containing a path to a requirements.txt can be provided. Default is [].
A list of shell commands to run when building your container image. These commands can be used for setting up the environment, installing dependencies, etc. Default is [].
A custom base image to replace the default ubuntu20.04 image used in your container. For example: docker.io/library/ubuntu:20.04 This image must contain a valid python executable that matches the version specified in python_version (i.e. python3.8, python3.9, etc) Default is None.
Callables
Function
Decorator for defining a remote function.
This method allows you to run the decorated function in a remote container.
from beta9 import Image, Function
@function(
cpu=1.0,
memory=128,
gpu="T4",
image=Image(python_packages=["torch"]),
keep_warm_seconds=1000,
)
def transcribe(filename: str):
print(filename)
return "some_result"
# Call a function in a remote container
function.remote("some_file.mp4")
# Map the function over several inputs
# Each of these inputs will be routed to remote containers
for result in function.map(["file1.mp4", "file2.mp4"]):
print(result)
The number of CPU cores allocated to the container.
The amount of memory allocated to the container. It should be specified in megabytes (e.g., 128 for 128 megabytes).
The type or name of the GPU device to be used for GPU-accelerated tasks. If not applicable or no GPU required, leave it empty.
The container image used for the task execution..
The maximum number of seconds a task can run before it times out. Default is 3600. Set it to -1 to disable the timeout.
The number of concurrent tasks to handle per container. Modifying this parameter can improve throughput for certain workloads. Workers will share the CPU, Memory, and GPU defined. You may need to increase these values to increase concurrency.
The maximum number of containers that the task queue will autoscale to. If the number of tasks in the queue goes over the concurrency value, the task queue will automatically add containers to handle the load.
The duration in seconds to keep the task queue warm even if there are no pending tasks. Keeping the queue warm helps to reduce the latency when new tasks arrive. Default is 10s.
The maximum number of tasks that can be pending in the queue. If the number of pending tasks exceeds this value, the task queue will stop accepting new tasks.
An optional URL to send a callback to when a task is completed, timed out, or cancelled.
The maximum number of times a task will be retried if the container crashes.
A list of volumes to be mounted to the container.
Remote
You can run any function remotely by using the .remote() method:
from beta9 import function
@function(cpu=8)
def square(i: int):
return i**2
if __name__ == "__main__":
# Run the `square` function remotely on Beam
result = square.remote(i=12)
print(result)
The code above is invoked by running python example.py:
(.venv) user@MacBook % python example.py
=> Building image
=> Using cached image
=> Syncing files
=> Files synced
=> Running function: <example:square>
=> Function complete <908c76b1-ee68-4b33-ac3a-026ae646625f>
144
Map
You can scale out workloads to many containers using the .map() method. You might use this for parallelizing computational-heavy tasks, such as batch inference or data processing jobs.
from beta9 import function
@function(cpu=8)
def square(i: int):
return i**2
def main():
numbers = list(range(10))
squared = []
# Run a remote container for every item in list
for result in square.map(numbers):
squared.append(result)
Endpoint
Decorator used for deploying a web endpoint.
from beta9 import endpoint, Image
@endpoint(
cpu=1.0,
memory=128,
gpu="T4",
image=Image(python_packages=["torch"]),
keep_warm_seconds=1000,
)
def multiply(**inputs):
result = inputs["x"] * 2
return {"result": result}
The number of CPU cores allocated to the container.
The amount of memory allocated to the container. It should be specified in megabytes (e.g., 128 for 128 megabytes).
The type or name of the GPU device to be used for GPU-accelerated tasks. If not applicable or no GPU required, leave it empty.
The container image used for the task execution..
The maximum number of seconds a task can run before it times out. Default is 3600. Set it to -1 to disable the timeout.
The number of concurrent tasks to handle per container. Modifying this parameter can improve throughput for certain workloads. Workers will share the CPU, Memory, and GPU defined. You may need to increase these values to increase concurrency.
The maximum number of containers that the task queue will autoscale to. If the number of tasks in the queue goes over the concurrency value, the task queue will automatically add containers to handle the load.
The duration in seconds to keep the task queue warm even if there are no pending tasks. Keeping the queue warm helps to reduce the latency when new tasks arrive. Default is 10s.
The maximum number of tasks that can be pending in the queue. If the number of pending tasks exceeds this value, the task queue will stop accepting new tasks.
A function that runs when the container first starts. The return values of the
on_start function can be retrieved by passing a context argument to your
handler function.
A list of volumes to be mounted to the container.
Serve
beta9 serve functions like an Ngrok server that live-reloads as you work. Beta9 monitors changes in your local file system and forwards the remote container logs to your local shell.
Serve is great for prototyping. You can develop in a containerized cloud environment in real-time, with adjustable CPU, memory, GPU resources.
It’s also great for testing an app before deploying it. Served functions are orchestrated identically to deployments, which means you can test your workflow end-to-end before deploying.
To start an ephemeral serve session, you’ll use the serve command:
beta9 serve app.py
By default, Beta9 will sync all the files in your working directory to the
remote container. This allows you to use the files you have locally while
developing. If you want to prevent some files from getting uploaded, you can
create a .beamignore.
Task Queue
Decorator for defining a task queue.
This method allows you to create a task queue out of the decorated function.
The tasks are executed asynchronously. You can interact with the task queue either through an API (when deployed), or directly in Python through the .put() method.
from beta9 import Image, task_queue
# Define the task queue
@task_queue(
cpu=1.0,
memory=128,
gpu="T4",
image=Image(python_packages=["torch"]),
keep_warm_seconds=1000,
)
def transcribe(filename: str):
return {}
transcribe.put("some_file.mp4")
The number of CPU cores allocated to the container.
The amount of memory allocated to the container. It should be specified in megabytes (e.g., 128 for 128 megabytes).
The type or name of the GPU device to be used for GPU-accelerated tasks. If not applicable or no GPU required, leave it empty.
The container image used for the task execution..
The maximum number of seconds a task can run before it times out. Default is 3600. Set it to -1 to disable the timeout.
The number of concurrent tasks to handle per container. Modifying this parameter can improve throughput for certain workloads. Workers will share the CPU, Memory, and GPU defined. You may need to increase these values to increase concurrency.
The maximum number of containers that the task queue will autoscale to. If the number of tasks in the queue goes over the concurrency value, the task queue will automatically add containers to handle the load.
The duration in seconds to keep the task queue warm even if there are no pending tasks. Keeping the queue warm helps to reduce the latency when new tasks arrive. Default is 10s.
The maximum number of tasks that can be pending in the queue. If the number of pending tasks exceeds this value, the task queue will stop accepting new tasks.
An optional URL to send a callback to when a task is completed, timed out, or cancelled.
The maximum number of times a task will be retried if the container crashes.
A list of volumes to be mounted to the container.
Serve
beta9 serve functions like an Ngrok server that live-reloads as you work. Beta9 monitors changes in your local file system and forwards the remote container logs to your local shell.
Serve is great for prototyping. You can develop in a containerized cloud environment in real-time, with adjustable CPU, memory, GPU resources.
It’s also great for testing an app before deploying it. Served functions are orchestrated identically to deployments, which means you can test your workflow end-to-end before deploying.
To start an ephemeral serve session, you’ll use the serve command:
beta9 serve app.py
By default, Beta9 will sync all the files in your working directory to the
remote container. This allows you to use the files you have locally while
developing. If you want to prevent some files from getting uploaded, you can
create a .beamignore.
Data Structures
Simple Queue
Creates a Queue instance.
Use this a concurrency safe distributed queue, accessible both locally and within remote containers.
Serialization is done using cloudpickle, so any object that supported by that should work here. The interface is that of a standard python queue.
Because this is backed by a distributed queue, it will persist between runs.
from beta9 import Queue
val = [1, 2, 3]
# Initialize the Queue
q = Queue(name="myqueue")
for i in range(100):
# Insert something to the queue
q.put(val)
while not q.empty():
# Remove something from the queue
val = q.pop()
print(val)
The name of the queue (any arbitrary string).
Map
Creates a Map Instance.
Use this a concurrency safe key/value store, accessible both locally and within remote containers.
Serialization is done using cloudpickle, so any object that supported by that should work here. The interface is that of a standard python dictionary.
Because this is backed by a distributed dictionary, it will persist between runs.
from beta9 import Map
# Name the map
m = Map(name="test")
# Set a key
m["some_key"] = True
# Delete a key
del m["some_key"]
# Iterate through the map
for k, v in m.items():
print("key: ", k)
print("value: ", v)
The name of the map (any arbitrary string).
Storage
Volume
Creates a Volume instance.
When your container runs, your volume will be available at ./{name} and /volumes/{name}.
from beta9 import function, Volume
VOLUME_PATH = "./model_weights"
@function(
volumes=[Volume(name="model-weights", mount_path=VOLUME_PATH)],
)
def load_model():
from transformers import AutoModel
# Load model from cloud storage cache
AutoModel.from_pretrained(VOLUME_PATH)
The name of the volume, a descriptive identifier for the data volume.
The path where the volume is mounted within the container environment.
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