Is it possible to create a hub module from existing checkpoints without chaining the training code?
Yes, absolutely. You need a session with (1) a Module and (2) the proper values in its variables. It doesn't matter if those come from actual training or merely restoring a checkpoint. Given a Python library for model building that knows nothing about TensorFlow Hub, you can have a tool on the side for export to a Hub Module that looks like:
import tensorflow_hub as hub
import your_library as build_model_body
def module_fn():
inputs = tf.placeholder(...)
logits = build_model_body(inputs)
hub.add_signature(inputs=inputs, outputs=logits)
def main(_):
spec = hub.create_module_spec(module_fn)
# Supply a checkpoint trained on a model from the same Python code.
checkpoint_path = "..."
# Output will be written here:
export_path = "..."
with tf.Graph().as_default():
module = hub.Module(spec)
init_fn = tf.contrib.framework.assign_from_checkpoint_fn(
checkpoint_path, module.variable_map)
with tf.Session() as session:
init_fn(session)
module.export(export_path, session=session)
Fine points to note:
build_model_body() should transform inputs to outputs (say, pixels to feature vectors) as suitable for a Hub module, but not include data reading, or loss and optimizers. For transfer learning, these are best left to the consumer of the module. Some refactoring may be required.
Supplying the module.variable_map is essential, to translate from plain variable names as created by running build_model_body() by itself to the variable names created by instantiating the Module, live in scope module/state.
Couple of questions about this
For occasions when I'd like to do something like the following in Tensorflow (assume I'm creating training examples by loading WAV files):
import tensorflow as tf
def _some_audio_preprocessing_func(filename):
# ... some logic here which mostly uses Tensorflow ops ...
with tf.Session(graph=tf.Graph()) as sess:
wav_filename_placeholder = tf.placeholder(tf.string, [])
wav_loader = io_ops.read_file(wav_filename_placeholder)
wav_decoder = contrib_audio.decode_wav(wav_loader, desired_channels=1)
data = sess.run(
[wav_decoder],
feed_dict={wav_filename_placeholder: filename})
return data
dataset = tf.data.Dataset.list_files('*.wav')
dataset = dataset.map(_some_preprocessing_func)
If I have a parse_image() function that uses tensor ops - should
this be part of the main Graph? Following the example set in Google's own audio TF tutorial, it looks like they create a separate graph! Doesn't this ruin the point of using Tensorflow to make things faster?
Do I use tf.py_func() any time any single line isn't from the tensorflow library? Again, I wonder what the performance implications are and when I should use this...
Thanks!
When you use Dataset.map(map_func), TensorFlow defines a subgraph for all the ops created in the function map_func, and arranges to execute it efficiently in the same session as the rest of your graph. There is almost never any need to create a tf.Graph or tf.Session inside map_func: if your parsing function is made up of TensorFlow ops, these ops can be embedded directly in the graph that defines the input pipeline.
The modified version of the code using tf.data would look like this:
import tensorflow as tf
from tensorflow.contrib.framework.python.ops import audio_ops as contrib_audio
def _some_audio_preprocessing_func(filename):
wav_loader = tf.read_file(filename)
return contrib_audio.decode_wav(wav_loader, desired_channels=1)
dataset = tf.data.Dataset.list_files('*.wav')
dataset = dataset.map(_some_preprocessing_func)
If your map_func contains non-TensorFlow operations that you want to apply to each element, you should wrap them in a tf.py_func() (or Dataset.from_generator(), if the data generation process is defined in Python logic). The main performance implication is that any code running in a tf.py_func() is subject to the Global Interpreter Lock, so I would generally recommend trying to find a native TensorFlow implementation for anything that is performance critical.
I am new to tensorflow and after going through some basics from different sources I am completely confused about the graphs and their execution.Here is a 6 line code :
x = tf.constant([35, 40, 45], name='x')
y = tf.Variable(x + 5, name='y')
model = tf.global_variables_initializer()
with tf.Session() as session:
session.run(model)
print(session.run(y))
1. Line 1 and 2 creates a constant and a variable , at this point a graph is created ?
2.Is the graph created when I run the 'model' through session i.e variable initialization? and at what point the graph is executed ?
3.When the graph is executed why do we need to run the variable i.e 'session.run(y)' to print its value ?
Edited :
Here is a line by line graph representation , is it correct ? I know 2(a) is wrong that is why i created 2(b) graph . So this is what happens to graph when I run these statements ?
So Tensorflow runs in two phases,
Creation Phase(Or building phase): Here you define your Variables, Constants and Placeholders, and their relations.(define the mathematical operations on them)
Execution phase: Till now, all your variables and the computations applied (like matmul or adddition etc) are merely defined. Not computed. They are computed in this phase.
So to answer your questions:
Q1: At this point, yes the schema of the graph has been created(or the graph has been built) but it has not been executed.
Q2: The graph is executed(That is the actual initialization is done) when you call the run function on the initializer
Q3: You need to call run on the initializer first because before you do it, as mentioned before, the graph schema has merely been defined. The actual allocations and computations have not been done. When the tensor session is started and the run function called, the graph is executed and during the process the initialization of your variables is done. Before that they are not accessible as they still haven't been initialized even though they have been defined.
The tensorflow getting started guide here offers a great explanation of the same.
Hope this helps!
This is closely related to a lot of questions, including one of my own here: TensorFlow Inference
Every sample in TensorFlow for inference appears to follow this form:
import tensorflow as tf
import CONSTANTS
import Vgg3CIFAR10
import numpy as np
MODEL_PATH = 'models/' + CONSTANTS.MODEL_NAME + '.model'
rand = np.random.rand(1, 32, 32, 3).astype(np.float32)
images = tf.placeholder(tf.float32, shape=(1, 32, 32, 3))
logits = Vgg3CIFAR10.inference(images)
def run_inference():
'''Runs inference against a loaded model'''
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
new_saver = tf.train.import_meta_graph(MODEL_PATH + '.meta')
new_saver.restore(sess, MODEL_PATH)
print(sess.run(logits, feed_dict={images : rand}))
print('done')
run_inference()
Issues:
Restoring the model & graph does just that...except I am creating a parallel graph here where I am possibly adding new parts to the graph. (Except tensorflow graphs are append only; so how does this add to the graph and run just that segment if it is appended; it would want to run the whole thing.
What happens to the queue runners that existed in the loaded graph; all those ops are loaded. By printing out sess.graph.get_operations() you can see all of the old input ops are there.
Does logits = Vgg3CIFAR10.inference(images) not append new items to the graph? If it is because of naming; then does the placeholder input replace the queue runner stuff?
Possible answer for a few items: Because I defined the logits op first; this means that the rest of the graph got appended after that; and via some tensorflow magic sauce the variables from the original graph got restored into the logits portion of the graph?
So I tested this out; and it doesn't even work properly...
It first creates a graph with logits, then it appends to that graph the old graph. So when you call inference; you just get a bunch of garbage back...
[[ 0.09815982 0.09611271 0.10542709 0.10383813 0.0955615 0.10979554
0.12138291 0.09316944 0.08336139 0.09319157]]
[[ 0.10305423 0.092167 0.10572157 0.10368075 0.1043573 0.10057402
0.12435613 0.08916584 0.07929172 0.09763144]]
[[ 0.1068181 0.09361464 0.10377798 0.10060066 0.10110897 0.09462726
0.11688241 0.09941135 0.0869903 0.09616835]]
Here I am expecting node 8 followed by nodes 2 and 2 to be the ones surfaced...obviously its just a bunch of nothing...
So after a ton of review this is what happens...
If you add anything to the graph before restoring the graph; the restored graph is appended to the already created graph.
Restoring variables looks for variable names in your graph at the time of restoring variables with the same name as the variables that are stored in the meta files. If you created a graph with the same variable names, and then restored a graph that also had the same variable names, the test I ran showed the appended stored graph receiving the restoration of the variables and not the initial graph.
So in summary; be careful about what you are doing with your graphs and be very aware of how things get appended/restored. If you were looking at this in hopes to find inference; then look at this S.O. where the answer involves creating a new graph and restoring variables to that new graph which is in fact a subgraph of the original.
TensorFlow Inference
Say I have access to a number of GPUs in a single machine (for the sake of argument assume 8GPUs each with max memory of 8GB each in one single machine with some amount of RAM and disk). I wanted to run in one single script and in one single machine a program that evaluates multiple models (say 50 or 200) in TensorFlow, each with a different hyper parameter setting (say, step-size, decay rate, batch size, epochs/iterations, etc). At the end of training assume we just record its accuracy and get rid of the model (if you want assume the model is being check pointed every so often, so its fine to just throw away the model and start training from scratch. You may also assume some other data may be recorded like the specific hyper params, train, validation, train errors are recorded as we train etc).
Currently I have a (pseudo-)script that looks as follow:
def train_multiple_modles_in_one_script_with_gpu(arg):
'''
trains multiple NN models in one session using GPUs correctly.
arg = some obj/struct with the params for trianing each of the models.
'''
#### try mutliple models
for mdl_id in range(100):
#### define/create graph
graph = tf.Graph()
with graph.as_default():
### get mdl
x = tf.placeholder(float_type, get_x_shape(arg), name='x-input')
y_ = tf.placeholder(float_type, get_y_shape(arg))
y = get_mdl(arg,x)
### get loss and accuracy
loss, accuracy = get_accuracy_loss(arg,x,y,y_)
### get optimizer variables
opt = get_optimizer(arg)
train_step = opt.minimize(loss, global_step=global_step)
#### run session
with tf.Session(graph=graph) as sess:
# train
for i in range(nb_iterations):
batch_xs, batch_ys = get_batch_feed(X_train, Y_train, batch_size)
sess.run(fetches=train_step, feed_dict={x: batch_xs, y_: batch_ys})
# check_point mdl
if i % report_error_freq == 0:
sess.run(step.assign(i))
#
train_error = sess.run(fetches=loss, feed_dict={x: X_train, y_: Y_train})
test_error = sess.run(fetches=loss, feed_dict={x: X_test, y_: Y_test})
print( 'step %d, train error: %s test_error %s'%(i,train_error,test_error) )
essentially it tries lots of models in one single run but it builds each model in a separate graph and runs each one in a separate session.
I guess my main worry is that its unclear to me how tensorflow under the hood allocates resources for the GPUs to be used. For example, does it load the (part of the) data set only when a session is ran? When I create a graph and a model, is it brought in the GPU immediately or when is it inserted in the GPU? Do I need to clear/free the GPU each time it tries a new model? I don't actually care too much if the models are ran in parallel in multiple GPU (which can be a nice addition), but I want it to first run everything serially without crashing. Is there anything special I need to do for this to work?
Currently I am getting an error that starts as follow:
I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats:
Limit: 340000768
InUse: 336114944
MaxInUse: 339954944
NumAllocs: 78
MaxAllocSize: 335665152
W tensorflow/core/common_runtime/bfc_allocator.cc:274] ***************************************************xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
W tensorflow/core/common_runtime/bfc_allocator.cc:275] Ran out of memory trying to allocate 160.22MiB. See logs for memory state.
W tensorflow/core/framework/op_kernel.cc:975] Resource exhausted: OOM when allocating tensor with shape[60000,700]
and further down the line it says:
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[60000,700]
[[Node: standardNN/NNLayer1/Z1/add = Add[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](standardNN/NNLayer1/Z1/MatMul, b1/read)]]
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla P100-SXM2-16GB, pci bus id: 0000:06:00.0)
however further down the output file (where it prints) it seems to print fine the errors/messages that should show as training proceeds. Does this mean that it didn't run out of resources? Or was it actually able to use the GPU? If it was able to use the CPU instead of the CPU, when why is this an error only happening when GPU are about to be used?
The weird thing is that the data set is really not that big (all 60K points are 24.5M) and when I run a single model locally in my own computer it seems that the process uses less than 5GB. The GPUs have at least 8GB and the computer with them has plenty of RAM and disk (at least 16GB). Thus, the errors that tensorflow is throwing at me are quite puzzling. What is it trying to do and why are they occurring? Any ideas?
After reading the answer that suggests to use the multiprocessing library I came up with the following script:
def train_mdl(args):
train(mdl,args)
if __name__ == '__main__':
for mdl_id in range(100):
# train one model with some specific hyperparms (assume they are chosen randomly inside the funciton bellow or read from a config file or they could just be passed or something)
p = Process(target=train_mdl, args=(args,))
p.start()
p.join()
print('Done training all models!')
honestly I am not sure why his answer suggests to use pool, or why there are weird tuple brackets but this is what would make sense for me. Would the resources for tensorflow be re-allocated every time a new process is created in the above loop?
I think that running all models in one single script can be bad practice in the long term (see my suggestion below for a better alternative). However, if you would like to do it, here is a solution: You can encapsulate your TF session into a process with the multiprocessing module, this will make sure TF releases the session memory once the process is done. Here is a code snippet:
from multiprocessing import Pool
import contextlib
def my_model((param1, param2, param3)): # Note the extra (), required by the pool syntax
< your code >
num_pool_worker=1 # can be bigger than 1, to enable parallel execution
with contextlib.closing(Pool(num_pool_workers)) as po: # This ensures that the processes get closed once they are done
pool_results = po.map_async(my_model,
((param1, param2, param3)
for param1, param2, param3 in params_list))
results_list = pool_results.get()
Note from OP: The random number generator seed does not reset automatically with the multi-processing library if you choose to use it. Details here: Using python multiprocessing with different random seed for each process
About TF resource allocation: Usually TF allocates much more resources than it needs. Many times you can restrict each process to use a fraction of the total GPU memory, and discover through trial and error the fraction your script requires.
You can do it with the following snippet
gpu_memory_fraction = 0.3 # Choose this number through trial and error
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction,)
session_config = tf.ConfigProto(gpu_options=gpu_options)
sess = tf.Session(config=session_config, graph=graph)
Note that sometimes TF increases the memory usage in order to accelerate the execution. Therefore, reducing the memory usage might make your model run slower.
Answers to the new questions in your edit/comments:
Yes, Tensorflow will be re-allocated every time a new process is created, and cleared once a process ends.
The for-loop in your edit should also do the job. I suggest to use Pool instead, because it will enable you to run several models concurrently on a single GPU. See my notes about setting gpu_memory_fraction and "choosing the maximal number of processes". Also note that: (1) The Pool map runs the loop for you, so you don't need an outer for-loop once you use it. (2) In your example, you should have something like mdl=get_model(args) before calling train()
Weird tuple parenthesis: Pool only accepts a single argument, therefore we use a tuple to pass multiple arguments. See multiprocessing.pool.map and function with two arguments for more details. As suggested in one answer, you can make it more readable with
def train_mdl(params):
(x,y)=params
< your code >
As #Seven suggested, you can use CUDA_VISIBLE_DEVICES environment variable to choose which GPU to use for your process. You can do it from within your python script using the following on the beginning of the process function (train_mdl).
import os # the import can be on the top of the python script
os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(gpu_id)
A better practice for executing your experiments would be to isolate your training/evaluation code from the hyper parameters/ model search code.
E.g. have a script named train.py, which accepts a specific combination of hyper parameters and references to your data as arguments, and executes training for a single model.
Then, to iterate through the all the possible combinations of parameters you can use a simple task (jobs) queue, and submit all the possible combinations of hyper-parametrs as separate jobs. The task queue will feed your jobs one at a time to your machine. Usually, you can also set the queue to execute number of processes concurrently (see details below).
Specifically, I use task spooler, which is super easy to install and handful (doesn't requires admin privileges, details below).
Basic usage is (see notes below about task spooler usage):
ts <your-command>
In practice, I have a separate python script that manages my experiments, set all the arguments per specific experiment and send the jobs to the ts queue.
Here are some relevant snippets of python code from my experiments manager:
run_bash executes a bash command
def run_bash(cmd):
p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, executable='/bin/bash')
out = p.stdout.read().strip()
return out # This is the stdout from the shell command
The next snippet sets the number of concurrent processes to be run (see note below about choosing the maximal number of processes):
max_job_num_per_gpu = 2
run_bash('ts -S %d'%max_job_num_per_gpu)
The next snippet iterates through a list of all combinations of hyper params / model params. Each element of the list is a dictionary, where the keys are the command line arguments for the train.py script
for combination_dict in combinations_list:
job_cmd = 'python train.py ' + ' '.join(
['--{}={}'.format(flag, value) for flag, value in combination_dict.iteritems()])
submit_cmd = "ts bash -c '%s'" % job_cmd
run_bash(submit_cmd)
A note about about choosing the maximal number of processes:
If you are short on GPUs, you can use gpu_memory_fraction you found, to set the number of processes as max_job_num_per_gpu=int(1/gpu_memory_fraction)
Notes about task spooler (ts):
You could set the number of concurrent processes to run ("slots") with:
ts -S <number-of-slots>
Installing ts doesn't requires admin privileges. You can download and compile it from source with a simple make, add it to your path and you're done.
You can set up multiple queues (I use it for multiple GPUs), with
TS_SOCKET=<path_to_queue_name> ts <your-command>
e.g.
TS_SOCKET=/tmp/socket-ts.gpu_queue_1 ts <your-command>
TS_SOCKET=/tmp/socket-ts.gpu_queue_2 ts <your-command>
See here for further usage example
A note about automatically setting the path names and file names:
Once you separate your main code from the experiment manager, you will need an efficient way to generate file names and directory names, given the hyper-params. I usually keep my important hyper params in a dictionary and use the following function to generate a single chained string from the dictionary key-value pairs.
Here are the functions I use for doing it:
def build_string_from_dict(d, sep='%'):
"""
Builds a string from a dictionary.
Mainly used for formatting hyper-params to file names.
Key-value pairs are sorted by the key name.
Args:
d: dictionary
Returns: string
:param d: input dictionary
:param sep: key-value separator
"""
return sep.join(['{}={}'.format(k, _value2str(d[k])) for k in sorted(d.keys())])
def _value2str(val):
if isinstance(val, float):
# %g means: "Floating point format.
# Uses lowercase exponential format if exponent is less than -4 or not less than precision,
# decimal format otherwise."
val = '%g' % val
else:
val = '{}'.format(val)
val = re.sub('\.', '_', val)
return val
As I understand, firstly tensorflow constructs a symbolic graph and infers the derivatives based on chain rule. Then allocates memory for all (necessary) tensors, including some inputs and outputs of layers for efficiency. When running a session, data will be loaded into the graph but in general, memory use will not change any more.
The error you met, I guess, may be caused by constructing several models in one GPU.
Isolating your training/evaluation code from the hyper parameters is a good choice, as #user2476373 proposed. But I am using bash script directly, not task spooler (may be it's more convenient), e.g.
CUDA_VISIBLE_DEVICES=0 python train.py --lrn_rate 0.01 --weight_decay_rate 0.001 --momentum 0.9 --batch_size 8 --max_iter 60000 --snapshot 5000
CUDA_VISIBLE_DEVICES=0 python eval.py
Or you can write a 'for' loop in the bash script, not necessarily in python script. Noting that I used CUDA_VISIBLE_DEVICES=0 at beginning of the script (the index could be 7 if you have 8 GPUs in one machine). Because based on my experience, I've found that tensorflow uses all GPUs in one machine if I didn't specify operations use which GPU with the code like this
with tf.device('/gpu:0'):
If you want to try multi-GPU implementation, there is some example.
Hope this could help you.
An easy solution: Give each model a unique session and graph.
It works for this platform: TensorFlow 1.12.0, Keras 2.1.6-tf, Python 3.6.7, Jupyter Notebook.
Key code:
with session.as_default():
with session.graph.as_default():
# do something about an ANN model
Full code:
import tensorflow as tf
from tensorflow import keras
import gc
def limit_memory():
""" Release unused memory resources. Force garbage collection """
keras.backend.clear_session()
keras.backend.get_session().close()
tf.reset_default_graph()
gc.collect()
#cfg = tf.ConfigProto()
#cfg.gpu_options.allow_growth = True
#keras.backend.set_session(tf.Session(config=cfg))
keras.backend.set_session(tf.Session())
gc.collect()
def create_and_train_ANN_model(hyper_parameter):
print('create and train my ANN model')
info = { 'result about this ANN model' }
return info
for i in range(10):
limit_memory()
session = tf.Session()
keras.backend.set_session(session)
with session.as_default():
with session.graph.as_default():
hyper_parameter = { 'A set of hyper-parameters' }
info = create_and_train_ANN_model(hyper_parameter)
limit_memory()
Inspired by this link: Keras (Tensorflow backend) Error - Tensor input_1:0, specified in either feed_devices or fetch_devices was not found in the Graph
I have the same issue. My solution is to run from another script doing the following as many times and in as many hyperparameter configurations as you want.
cmd = "python3 ./model_train.py hyperparameters"
os.system(cmd)
You probably don't want to do this.
If you run thousands and thousands of models on your data, and pick the one that evaluates best, you are not doing machine learning; instead you are memorizing your data set, and there is no guarantee that the model you pick will perform at all outside that data set.
In other words, that approach is similar to having a single model, which has thousands of degrees of liberty. Having a model with such high order of complexity is problematic, since it will be able to fit your data better than is actually warranted; such a model is annoyingly able to memorize any noise (outliers, measurement errors, and such) in your training data, which causes the model to perform poorly when the noise is even slightly different.
(Apologies for posting this as an answer, the site wouldn't let me add a comment.)