> ## Documentation Index
> Fetch the complete documentation index at: https://docs.beam.cloud/llms.txt
> Use this file to discover all available pages before exploring further.

# FAQ

> This is an ongoing list of issues people sometimes encounter while using Beam. If you're having an issue, check this list first.

## `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+      |

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](https://platform.beam.cloud/settings/plans).

## `Unable to connect to gateway`

Make sure you're on the latest version of the `beam-client` CLI.

```bash theme={null}
uv tool upgrade beam-client
```

Run this command to validate your version of the CLI:

```bash theme={null}
beam --version
```

<Tip>
  [You can see the latest CLI releases
  here](https://github.com/beam-cloud/beam-client/releases).
</Tip>

## `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](/v2/environment/gpu#configuring-cpu-and-memory).

## `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](/v2/releases/v2-upgrade).

## `Unable to locate config file`

This typically happens when there are multiple Python environments on your computer.

<Warning>
  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`
</Warning>

The most common way of solving this is by running `which python` and installing `beam-client` to that specific path.

For example:

```bash theme={null}
$ 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.

<Warning>
  Make sure to install `tensorflow[and-cuda]`, otherwise the regular version of
  `tf` won't have access to the GPU device.
</Warning>

```python theme={null}
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"))
```