​
concurrency_limit_reached or cpu quota exceeded

We offer three pricing tiers and each has its own CPU and GPU quotas.
PlanCPU QuotaGPU Quota
Free Trial105
Developer105
Team1,00020
Growth10,000+100+
If you get this message, make sure you’ve added a payment method to your account and selected the pay-as-you-go developer plan on this page.

​
Unable to connect to gateway

Make sure you’re on the latest version of the beam-client CLI. 
pip install --upgrade beam-client
Run this command to validate your version of the CLI: 
beam --version
You can see the latest CLI releases here.

​
No space left on device

This error typically occurs when your app runs out of disk space. For example, if you’re downloading a 30Gi file and your app only has 8Gi of memory, you might see this error. For more information on configuring RAM for your apps, read more on this page.

​
cannot import name 'App' from 'beam'

If you’re seeing this error, it’s because you’re trying to use Beam V2 with a V1 app. There is no App class in Beam V2. For more information on using Beam V2, read more on this page.

​
Unable to locate config file

This typically happens when there are multiple Python environments on your computer.
If you are using Conda, we recommend exiting Conda and using a standard Python Virtual Environment instead: python3 -m virtualenv .venv && source .venv/bin/activate
The most common way of solving this is by running which python and installing beam-client to that specific path. For example: 
$ which python

python: aliased to /usr/bin/python3 # gotcha!

$ /usr/bin/python3 -m virtualenv .venv && source .venv/bin/activate

$ (.venv) /usr/bin/python3 -m pip install --upgrade beam-client

​
Tensorflow Can’t Find GPUs

If you’re using Tensorflow, you might run into an issue when tf doesn’t recognize the available GPUs on the device.
Make sure to install tensorflow[and-cuda], otherwise the regular version of tf won’t have access to the GPU device.
from beam import Image, endpoint, env

if env.is_remote():
    import tensorflow as tf


@endpoint(
    name="tensorflow-gpu",
    cpu=1,
    memory="4Gi",
    gpu="A10G",
    # Make sure to use `tensorflow[and-cuda]` in order to access GPU resources
    image=Image().add_python_packages(["tensorflow[and-cuda]"]),
)
def predict():
    # Show available GPUs
    gpus = tf.config.list_physical_devices("GPU")

    try:
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
    except RuntimeError as e:
        print(e)

    print("🚧 Is built with CUDA:", tf.test.is_built_with_cuda())
    print("🚧 Is GPU available:", tf.test.is_gpu_available())
    print("🚧 GPUs available:", tf.config.list_physical_devices("GPU"))