TensorFlow Federated does not use GPU despite being detected

Question

I'm running a TensorFlow Federated (TFF) script and attempting to utilize my GPU for federated learning simulations. While TensorFlow itself detects my GPU and can use it without issues, TFF outputs the following message, preventing GPU utilization:

Using GPU...
Enabled GPU(s): 1
I tensorflow/core/grappler/devices.cc:66] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0

My Setup: GPU: NVIDIA GeForce MX250 (Compute Capability 6.1, 2GB VRAM) CUDA and cuDNN: Compatible versions installed TensorFlow: 2.14.1 TensorFlow Federated: 0.87.0 OS: Ubuntu 22.04 Python: 3.9.20

My GPU Configuration Code:

import os
import tensorflow as tf
import tensorflow_federated as tff

os.environ['TF_CUDNN_USE_AUTOTUNE'] = "1"
os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'

if tf.config.list_physical_devices('GPU'):
    print("Using GPU...")
else:
    print("Using CPU...")

gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        print(f"Enabled GPU(s): {len(gpus)}")
    except RuntimeError as e:
        print(f"Error setting GPU memory growth: {e}")
else:
    print("No GPUs found, running on CPU.")

What Works:

TensorFlow can use the GPU for normal operations, including model training.
The GPU is correctly detected and initialized by TensorFlow.

What Doesn't Work:

TensorFlow Federated does not utilize the GPU and instead shows the message: java

Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0

This happens despite the GPU being available and meeting the minimum compute capability (6.1).

What I've Tried:

Setting various os.environ flags to optimize GPU usage:

TF_FORCE_GPU_ALLOW_GROWTH
TF_XLA_FLAGS
TF_GPU_THREAD_MODE

Using tff.backends.native.set_sync_local_cpp_execution_context() to configure TFF's execution context.
Explicitly setting loop_implementation=tff.learning.LoopImplementation.DATASET_ITERATE in my federated learning algorithm.
Verifying CUDA, cuDNN, and TensorFlow installations with deviceQuery and TensorFlow's diagnostic tools.

Question:

How can I force TensorFlow Federated to utilize my GPU for federated learning simulations despite the "eligible GPUs" limitation?
Is there a way to bypass the core count check or manually override TFF's GPU eligibility criteria?
Are there specific configurations or patches for TFF to support GPUs with fewer cores or limited compute capability?

Any insights or suggestions would be greatly appreciated. Thank you!

Have you seen this resource tensorflow.org/federated/tutorials/… if that solves you can write an answer based on what you find. — VRehnberg
– VRehnberg, Commented Apr 30 at 11:47

Sagar · Accepted Answer · 2025-10-13 06:48:01Z

0

The issue is caused by TensorFlow's Grappler optimizer incorrectly filtering your GPU, as it mandates a minimum of 8 Streaming Multiprocessors(SMs). Set the environment variable TF_MIN_GPU_MULTIPROCESSOR_COUNT equal to '1' to enable TFF to utilize your device before importing TensorFlow. This standard workaround effectively bypasses the core check without needing TFF patches.

import os
os.environ['TF_MIN_GPU_MULTIPROCESSOR_COUNT'] = '1'
import tensorflow as tf
import tensorflow_federated as tff

answered Oct 13 at 6:48

Sagar

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