Miscellaneous Tools

There are many handy tools implemented in ADCME for analysis, benchmarking, input/output, etc.

Debugging and Printing

Add the following line before Session and change tf.Session to see verbose printing (such as GPU/CPU information)

tf.debugging.set_log_device_placement(true)

tf.print can be used for printing tensor values. It must be binded with an executive operator.

# a, b are tensors, and b is executive
op = tf.print(a)
b = bind(b, op)

Debugging Python Codes

If the error comes from Python (through PyCall), we can print out the Python trace with the following commands

import traceback
try:
    # Your codes here 
except Exception:
    print(traceback.format_exc())

This Python script can be inserted to Julia and use interpolation to invoke Julia functions (in the comment line).

Profiling

Profiling can be done with the help of run_profile and save_profile

a = normal(2000, 5000)
b = normal(5000, 1000)
res = a*b 
run_profile(sess, res)
save_profile("test.json")

• Open Chrome and navigate to chrome://tracing
• Load the timeline file

Below shows an example of profiling results.

Save and Load Python Object

psave(o::PyObject, file::String)

pload(file::String)

Save and Load Diary

We can use TensorBoard to track a scalar value easily

d = Diary("test")
p = placeholder(1.0, dtype=Float64)
b = constant(1.0)+p
s = scalar(b, "variable")
for i = 1:100
    write(d, i, run(sess, s, Dict(p=>Float64(i))))
end
activate(d)