joblib | Computing with Python functions | Architecture library
kandi X-RAY | joblib Summary
kandi X-RAY | joblib Summary
Computing with Python functions.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Benchmark examples
- Print a benchmark summary
- Load a pickled file
- Generate random dictionary
- Generate random list
- Process worker worker worker
- Sends a result back to the result queue
- Put obj to the pipe
- Shut down Python interpreter
- Cache a function
- Register a new compressor
- Format the outer frame
- Set the state of the object
- Map a function over an iterable
- Compress a dataset
- Read from unpickler
- Save a Python object to disk
- Launch process
- Wrapper for pickling
- Set pickler
- Store a numpy array
- Fills the function with the given arguments
- Compute the batch size
- Feed data into pipe
- Load a pickle file
- Prepare process
- Returns the number of CPU cores
joblib Key Features
joblib Examples and Code Snippets
Lines of Code : 23License : Strong Copyleft (CC BY-SA 4.0)
Copyimport joblib
model = pickle.load(open('model.pkl', "rb"), encoding="latin1")
joblib.dump(model.tree_.get_arrays()[0], "training_data.pkl")
import joblib
from sklearn.neighbors import KernelDensity
data = joblib.l
FROM openjdk:8
RUN apt-get update && apt-get install -y python3 python3-pip
RUN apt-get -y install python3-pydot python3-pydot-ng graphviz
RUN apt-get -y install python3-tk
RUN apt-get -y install zip unzip
RUN apt-get -y installfrom pyspark.sql.functions import udf
@udf('integer')
def predict_udf(*cols):
return int(braodcast_model.value.predict((cols,)))
list_of_columns = df.columns
df_prediction = df.withColumn('prediction', predict_udf(*list_of_columns))
import joblib
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=(5,)),
tf.keras.layers.Dense(units=16, activation='elu'),
tf.keras.layers.Dense(units=8, activation='elu')python is /opt/anaconda3/bin/python
python is /usr/local/bin/python
python is /usr/bin/python
def my_function(dfx):
# return dfx['abc'] = dfx['def'] + 1
# the above returns the result of assignment
# we need to separate the assignment and return statements
dfx['abc'] = dfx['def'] + 1
return dfx
df = dd.read_pardef my_function(dfx):
dfx['abc'] = dfx['def'] + 1
return dfx
df2 = df.map_partitions(my_function)
out = df2.compute()
f = client.compute(df2)
# Model
from sklearn.ensemble import IsolationForest
# Saving file
import joblib
# Data
import numpy as np
# Create a new model
model = IsolationForest()
# Generate some old data
df1 = np.random.randint(1,100,(100,10))
# Train the modedef Exec_ShowImgGrid(ObjTensor, ch=1, size=(28,28), num=16):
#tensor: 128(pictures at the time ) * 784 (28*28)
Objdata= ObjTensor.detach().cpu().view(-1,ch,*size) #128 *1 *28*28
Objgrid= make_grid(Objdata[:num],nrow=4).permute├── WebApp/
│ └── app.py
└── Untitled.ipynb
from WebApp.app import GensimWord2VecVectorizer
GensimWord2VecVectorizer.__module__ = 'app'
import sys
sys.modules['app'] = sys.modules['WebApp.app']
Community Discussions
Trending Discussions on joblib
QUESTION
I have a dask architecture implemented with five docker containers: a client, a scheduler, and three workers. I also have a large dask dataframe stored in parquet format in a docker volume. The dataframe was created with 3 partitions, so there are 3 files (one file per partition).
I need to run a function on the dataframe with map_partitions, where each worker will take one partition to process.
My attempt:
...ANSWER
Answered 2022-Mar-11 at 13:27The python snippet does not appear to use the dask API efficiently. It might be that your actual function is a bit more complex, so map_partitions cannot be avoided, but let's take a look at the simple case first:
QUESTION
I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:
AttributeError: module
'__main__'has no attribute 'GensimWord2VecVectorizer'
I am creating the model on Google Colab.
Code in Jupyter Notebook:
...ANSWER
Answered 2022-Feb-24 at 11:48Import GensimWord2VecVectorizer in your Flask Web app python file.
QUESTION
I am trying to limit the number of CPUs' usage when I fit a model using sklearn RandomizedSearchCV, but somehow I keep using all CPUs. Following an answer from Python scikit learn n_jobs I have seen that in scikit-learn, we can use n_jobs to control the number of CPU-cores used.
n_jobsis an integer, specifying the maximum number of concurrently running workers. If 1 is given, nojoblibparallelism is used at all, which is useful for debugging. If set to -1, all CPUs are used.
Forn_jobsbelow -1,(n_cpus + 1 + n_jobs)are used. For example withn_jobs=-2, all CPUs but one are used.
But when setting n_jobs to -5 still all CPUs continue to run to 100%. I looked into joblib library to use Parallel and delayed. But still all my CPUs continue to be used. Here what I tried:
ANSWER
Answered 2022-Feb-21 at 10:15Q : " What is going wrong? "
A :
There is not a single thing that we can say that it "goes wrong", the code-execution eco-system is so multi-layered, that it is not as trivial as we might wish to enjoy & there are several (different, some hidden) places, where configurations decide, how many CPU-cores will actually bear the overall processing-load.
Situation is also version-dependent & configuration-specific ( both Scikit, Numpy, Scipy have mutual dependencies & underlying dependencies on respective compilation options for numerical packages used )
Experimentto prove -or- refute a just assumed syntax (d)effect :
Given a documented feature of interpretation of negative numbers in top-level n_jobs parameter in RandomizedSearchCV(...) methods, submit the very same task, yet configured so that it has got explicit amount of permitted (top-level) n_jobs = CPU_cores_allowed_to_load and observe, when & how many cores do actually get loaded during the whole flow of processing.
Results:
if and only if that very number of "permitted" CPU-cores was loaded, the top-level call did correctly "propagate" the parameter settings to each & every method or procedure used alongside the flow of processing
In case your observation proves the settings were not "obeyed", we can only review the whole scope of all source-code verticals to decide, who is to be blamed for such dis-obedience of not keeping the work compliant with the top-level set ceiling for the n_jobs. While O/S tools for CPU-core affinity mappings may give us some chances to "externally" restrict the number of such cores used, some other adverse effects ( the add-on management costs being the least performance-punishing ones ) will arise - thermal-management introduced CPU-core "hopping", being the disallowed by affinity maps, will on contemporary processors cause a more and more reduced clock-frequency (as cores get indeed hot in numerically intensive processing), thus prolonging the overall task processing times, as there are "cooler" (thus faster) CPU-cores in the system (those, that were prevented from being used by the affinity-mapping), yet these are very the same CPU-cores, that the affinity-mappings disallowed from being used for temporally placing our task processing (while the hot ones, from which the flow of the processing was reallocated due to reached thermal-ceilings, got some time to cold down and re-gain the chances to run at not decreased CPU-clock-rates)
Top-level call might have set an n_jobs-parameter, yet any lower-level component might have "obeyed" that one value ( without knowing, how many other, concurrently working peers did the same - as in joblib.Parallel() and similar constructors do, not mentioning the other, inherently deployed, GIL-evading multithreading libraries - as that happen to lack any mutual coordination so as to keep the top-level set n_jobs-ceiling )
QUESTION
I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:
...ANSWER
Answered 2022-Feb-07 at 09:19It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason
QUESTION
I have a local python project called jive that I would like to use in an another project. My current method of using jive in other projects is to activate the conda env for the project, then move to my jive directory and use python setup.py install. This works fine, and when I use conda list, I see everything installed in the env including jive, with a note that jive was installed using pip.
But what I really want is to do this with full conda. When I want to use jive in another project, I want to just put jive in that projects environment.yml.
So I did the following:
- write a simple
meta.yamlso I could use conda-build to buildjivelocally - build jive with
conda build . - I looked at the tarball that was produced and it does indeed contain the
jivesource as expected - In my other project, add jive to the dependencies in
environment.yml, and add 'local' to the list of channels. - create a conda env using that environment.yml.
When I activate the environment and use conda list, it lists all the dependencies including jive, as desired. But when I open python interpreter, I cannot import jive, it says there is no such package. (If use python setup.py install, I can import it.)
How can I fix the build/install so that this works?
Here is the meta.yaml, which lives in the jive project top level directory:
ANSWER
Answered 2022-Feb-05 at 04:16The immediate error is that the build is generating a Python 3.10 version, but when testing Conda doesn't recognize any constraint on the Python version, and creates a Python 3.9 environment.
I think the main issue is that python >=3.5 is only a valid constraint when doing noarch builds, which this is not. That is, once a package builds with a given Python version, the version must be constrained to exactly that version (up through minor). So, in this case, the package is built with Python 3.10, but it reports in its metadata that it is compatible with all versions of Python 3.5+, which simply isn't true because Conda Python packages install the modules into Python-version-specific site-packages (e.g., lib/python-3.10/site-packages/jive).
Typically, Python versions are controlled by either the --python argument given to conda-build or a matrix supplied by the conda_build_config.yaml file (see documentation on "Build variants").
Try adjusting the meta.yaml to something like
QUESTION
I noticed my Sagemaker (Amazon aws) jupyter notebook has an outdated version of the sklearn library.
when I run ! pip freeze I get:
ANSWER
Answered 2022-Jan-01 at 11:24I managed to update sklearn to version 0.24.2 via the following command:
QUESTION
I have the following code that runs two TensorFlow trainings in parallel using Dask workers implemented in Docker containers.
I need to launch two processes, using the same dask client, where each will train their respective models with N workers.
To that end, I do the following:
- I use
joblib.delayedto spawn the two processes. - Within each process I run
with joblib.parallel_backend('dask'):to execute the fit/training logic. Each training process triggers N dask workers.
The problem is that I don't know if the entire process is thread safe, are there any concurrency elements that I'm missing?
...ANSWER
Answered 2021-Dec-24 at 05:12This is pure speculation, but one potential concurrency issue is due to if client is None: part, where two processes could race to create a Client.
If this is resolved (e.g. by explicitly creating a client in advance), then dask scheduler will rely on time of submission to prioritize task (unless priority is clearly assigned) and also the graph (DAG) structure, there are further details available in docs.
QUESTION
I am working with a simple ML model with streamlit. It runs fine on my local machine inside conda environment, but it shows Error installing requirements when I try to deploy it on share.streamlit.io.
The error message is the following:
ANSWER
Answered 2021-Dec-25 at 14:42Streamlit share runs the app in a linux environment meaning there is no pywin32 because this is for windows.
Delete the pywin32 from the requirements file and also the pywinpty==1.1.6 for the same reason.
After deleting these requirements re-deploy your app and it will work.
QUESTION
My Initial import looks like this and this code block runs fine.
...ANSWER
Answered 2021-Nov-30 at 14:20For the second part you can do this to fix it, I copied the rest of your code as well, and added the bottom part.
QUESTION
I'm having trouble installing the following packages in a new python 3.9.7 virtual environment on Arch Linux.
My requirements.txt file:
...ANSWER
Answered 2021-Nov-27 at 17:57The ruamel.yaml documentation states that it should be installed using:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install joblib
You can use joblib like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
