AMD equivalent to NvOptimusEnablement

According to extern “C” { __declspec(dllexport) int AmdPowerXpressRequestHighPerformance = 1; } This will select the high performance GPU as long as no profile exists that assigns the application to another GPU. Please make sure to use a 13.35 or newer driver. Older drivers do not support this.

How to make Jupyter Notebook to run on GPU?

I am answering my own question. Easiest way to do is use connect to Local Runtime ( then select hardware accelerator as GPU as shown in (

Is there a way of determining how much GPU memory is in use by TensorFlow?

(1) There is some limited support with Timeline for logging memory allocations. Here is an example for its usage: run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() summary, _ =[merged, train_step], feed_dict=feed_dict(True), options=run_options, run_metadata=run_metadata) train_writer.add_run_metadata(run_metadata, ‘step%03d’ % i) train_writer.add_summary(summary, i) print(‘Adding run metadata for’, i) tl = timeline.Timeline(run_metadata.step_stats) print(tl.generate_chrome_trace_format(show_memory=True)) trace_file = tf.gfile.Open(name=”timeline”, mode=”w”) trace_file.write(tl.generate_chrome_trace_format(show_memory=True)) You can … Read more