concurrency_limit_reached or cpu quota exceeded
We offer three pricing tiers and each has its own CPU and GPU quotas.
| Plan | CPU Quota | GPU Quota |
|---|
| Free Trial | 10 | 5 |
| Developer | 10 | 5 |
| Team | 1,000 | 20 |
| Growth | 10,000+ | 100+ |
Unable to connect to gateway
Make sure you’re on the latest version of the beam-client CLI.
pip install --upgrade beam-client
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
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"))
