I want to deploy my GANs model on a web-based UI for this I need to convert my model's checkpoints into js files to be called by web code. There are functions for saved_model and Keras to convert into pb files but none for js
my main concern is that I am confused about how to dump a session or variable weights in js files
You can save a keras model from python. There is a full tutorial here but basically it amounts to calling this after training:
tfjs.converters.save_keras_model(model, tfjs_target_dir)
then hosting the result somewhere publicly accessible (or on the same server as your web UI) then you can load your model into tensorflow.js as follows:
import * as tf from '#tensorflow/tfjs';
const model = await tf.loadLayersModel('https://foo.bar/tfjs_artifacts/model.json');
Related
How to download hugging face sentiment-analysis pipeline to use it offline? I'm unable to use hugging face sentiment analysis pipeline without internet. How to download that pipeline?
The basic code for sentiment analysis using hugging face is
from transformers import pipeline
classifier = pipeline('sentiment-analysis') #This code will download the pipeline
classifier('We are very happy to show you the 🤗 Transformers library.')
And the output is
[{'label': 'POSITIVE', 'score': 0.9997795224189758}]
Use the save_pretrained() method to save the configs, model weights and vocabulary:
classifier.save_pretrained('/some/directory')
and load it by specifying the tokenizer and model parameter:
from transformers import pipeline
c2 = pipeline(task = 'sentiment-analysis', model='/some/directory', tokenizer='/some/directory')
I am aware that the in Python you can serialize a ML model using the pickle module; however, is there a method to do something similar in the tidymodel space? My goal would be to be able to save a trained model to be deployed later.
In R, you can use saveRDS & readRDS to save/load any R object, just like Python's pickle. Those functions are not specific to Tidymodels, they are basic R functions that can be used to serialize any object.
Usage
saveRDS(any_r_object, "filename.rds")
object_name <- readRDS("filename.rds")
There is also the save() & load() functions, they serve the same function are are mostly similar to saveRDS() & readRDS(). There are many online discussions/blogs comparing the two approaches.
Following up after a good while:
Some model fits in R, e.g. those fitted with the "xgboost" or "keras" engine in tidymodels, require native serialization methods to be saved and reloaded in a new R session properly. saveRDS and readRDS will do the trick most of the time, though fall short for model objects from packages that require native serialization.
The folks from the tidymodels team put together a new package, bundle, to provide a consistent interface for native serialization of model objects. The bundle() verb prepares a model object for serialization, and then you can safely saveRDS() + readRDS() and pass between R sessions as you wish, and then unbundle() in the new session:
mod_bundle <- bundle(mod)
saveRDS(mod_bundle, file = "path/to/file.rds")
# in a new R session:
mod_bundle <- readRDS("path/to/file.rds")
mod_new <- unbundle(mod_bundle)
I am trying to run some example python3 code
https://docs.databricks.com/applications/deep-learning/distributed-training/horovod-runner.html
on databricks GPU cluster (with 1 driver and 2 workers).
Databricks environment:
ML 6.6, scala 2.11, Spark 2.4.5, GPU
It is for distributed deep learning model training.
I just tried a very simple example at first:
from sparkdl import HorovodRunner
hr = HorovodRunner(np=2)
def train():
print('in train')
import tensorflow as tf
print('after import tf')
hvd.init()
print('done')
hr.run(train)
But, the command is alway running without any progress.
HorovodRunner will stream all training logs to notebook cell output. If there are too many
logs, you
can adjust the log level in your train method. Or you can set driver_log_verbosity to
'log_callback_only' and use a HorovodRunner log callback on the first worker to get concise
progress updates.
The global names read or written to by the pickled function are {'print', 'hvd'}.
The pickled object size is 1444 bytes.
### How to enable Horovod Timeline? ###
HorovodRunner has the ability to record the timeline of its activity with Horovod Timeline.
To
record a Horovod Timeline, set the `HOROVOD_TIMELINE` environment variable to the location
of the
timeline file to be created. You can then open the timeline file using the chrome://tracing
facility of the Chrome browser.
Do I miss something or need to set up something to make it work ?
Thanks
your code does no actual training within it.. you might have better luck with the better example code
https://docs.databricks.com/applications/machine-learning/train-model/distributed-training/mnist-pytorch.html
I want to deploy sklearn model in sagemaker. I created a training script.
scripPath=' sklearn.py'
sklearn=SKLearn(entry_point=scripPath,
train_instance_type='ml.m5.xlarge',
role=role, output_path='s3://{}/{}/output'.format(bucket,prefix), sagemaker_session=session)
sklearn.fit({"train-dir' : train_input})
When I deploy it
predictor=sklearn.deploy(initial_count=1,instance_type='ml.m5.xlarge')
It throws,
Clienterror: An error occured when calling the CreateModel operation:Could not find model data at s3://tree/sklearn/output/model.tar.gz
Can anyone say how to solve this issue?
When deploying models, SageMaker looks up S3 to find your trained model artifact. It seems that there is no trained model artifact at s3://tree/sklearn/output/model.tar.gz. Make sure to persist your model artifact in your training script at the appropriate local location in docker which is /opt/ml/model.
for example, in your training script this could look like:
joblib.dump(model, /opt/ml/model/mymodel.joblib)
After training, SageMaker will copy the content of /opt/ml/model to s3 at the output_path location.
If you deploy in the same session a model.deploy() will map automatically to the artifact path. If you want to deploy a model that you trained elsewhere, possibly during a different session or in a different hardware, you need to explicitly instantiate a model before deploying
from sagemaker.sklearn.model import SKLearnModel
model = SKLearnModel(
model_data='s3://...model.tar.gz', # your artifact
role=get_execution_role(),
entry_point='script.py') # script containing inference functions
model.deploy(
instance_type='ml.m5.xlarge',
initial_instance_count=1,
endpoint_name='your_endpoint_name')
See more about Sklearn in SageMaker here https://sagemaker.readthedocs.io/en/stable/using_sklearn.html
I am trying to deploy a tf.keras image classification model to Google CloudML Engine. Do I have to include code to create serving graph separately from training to get it to serve my models in a web app? I already have my model in SavedModel format (saved_model.pb & variable files), so I'm not sure if I need to do this extra step to get it to work.
e.g. this is code directly from GCP Tensorflow Deploying models documentation
def json_serving_input_fn():
"""Build the serving inputs."""
inputs = {}
for feat in INPUT_COLUMNS:
inputs[feat.name] = tf.placeholder(shape=[None], dtype=feat.dtype)
return tf.estimator.export.ServingInputReceiver(inputs, inputs)
You are probably training your model with actual image files, while it is best to send images as encoded byte-string to a model hosted on CloudML. Therefore you'll need to specify a ServingInputReceiver function when exporting the model, as you mention. Some boilerplate code to do this for a Keras model:
# Convert keras model to TF estimator
tf_files_path = './tf'
estimator =\
tf.keras.estimator.model_to_estimator(keras_model=model,
model_dir=tf_files_path)
# Your serving input function will accept a string
# And decode it into an image
def serving_input_receiver_fn():
def prepare_image(image_str_tensor):
image = tf.image.decode_png(image_str_tensor,
channels=3)
return image # apply additional processing if necessary
# Ensure model is batchable
# https://stackoverflow.com/questions/52303403/
input_ph = tf.placeholder(tf.string, shape=[None])
images_tensor = tf.map_fn(
prepare_image, input_ph, back_prop=False, dtype=tf.float32)
return tf.estimator.export.ServingInputReceiver(
{model.input_names[0]: images_tensor},
{'image_bytes': input_ph})
# Export the estimator - deploy it to CloudML afterwards
export_path = './export'
estimator.export_savedmodel(
export_path,
serving_input_receiver_fn=serving_input_receiver_fn)
You can refer to this very helpful answer for a more complete reference and other options for exporting your model.
Edit: If this approach throws a ValueError: Couldn't find trained model at ./tf. error, you can try it the workaround solution that I documented in this answer.