I found issue where stutter and nvidia-smi took long time to print, which is unusual. After digging more, apparently this is caused by service of persistence mode.
To solve this, you can disable by comment /lib/systemd/system/nvidia-persistenced.service
sudo vim /lib/systemd/system/nvidia-persistenced.servicesystemctl edit nvidia-persistenced.serviceTo run persistence on your session
sudo nvidia-smi -pm 1 When using Flameshot in Ubuntu Oracular Oriole, its return error : “Unable to capture the screen” Flameshot error. This is persistent whether you are using APT, snap or flatpak.
The quick solution is to modify the launcher and add a simple command. I’m using APT, so I will edit the launcher from ehre
sudo vim /usr/share/applications/org.flameshot.Flameshot.desktopThen edit the execution
[Desktop Entry]
...
Keywords[es]=flameshot;screenshot;capture;shutter;captura;
Keywords[de]=flameshot;screenshot;Bildschirmfoto;Aufnahme;
Keywords[pt_BR]=flameshot;screenshot;captura de tela;captura;shutter;
Exec=flameshot
Icon=org.flameshot.Flameshot
Terminal=false
Type=Application
....Change the execution to be
Exec=sh -c -- "flameshot"This shall fix your problem without any need to disable Wayland and others things. Enjoy!
After installing ASDF in Ubuntu 24.04 and execute gem install, I got error message that no version for ruby, despite I already have it installed.
~/ ruby --version
No version is set for command ruby
Consider adding one of the following versions in your config file at
ruby 3.0.4
The quick solution is to set the global version of ruby or NodeJS (if you have similar problem). Check with asdf current
asdf current
nodejs ______ No version is set. Run "asdf <global|shell|local> nodejs <version>"
ruby ______ No version is set. Run "asdf <global|shell|local> ruby <version>"
Then you can fix this problem by assign asdf global to your installed ruby version
asdf global ruby 3.0.4Mounting Obsidian Vault in google drive using GNOME online account option will not working. The reason because the files in Nautilus cryptic with hash names (literal how Google Drive store our files) without context translation (this part is missing). Also, using this approach the files usually being stream rather persist.
Alternatively, mount GDrive using google-drive-ocamlfuse` solved this problem. Here are 4 quicksteps to do it.
google-drive-ocamlfusesudo add-apt-repository ppa:alessandro-strada/ppa
sudo apt-get update
sudo apt-get install google-drive-ocamlfuse2. Enable your Google Drive API (important!)
Go to https://console.cloud.google.com/apis/api/drive.googleapis.com and enable API.
When running app engine in Google using `dev_appserver.py app.yaml` and I got error
/usr/lib/google-cloud-sdk/platform/google_appengine/google/protobuf/internal/api_implementation.py:100: UserWarning: Selected implementation upb is not available. Falling back to the python implementation.
warnings.warn('Selected implementation upb is not available. '
INFO 2024-07-05 15:09:52,906 <string>:234] Using Cloud Datastore Emulator.
We are gradually rolling out the emulator as the default datastore implementation of dev_appserver.
If broken, you can temporarily disable it by --support_datastore_emulator=False
Read the documentation: https://cloud.google.com/appengine/docs/standard/python/tools/migrate-cloud-datastore-emulator
Help us validate that the feature is ready by taking this survey: https://goo.gl/forms/UArIcs8K9CUSCm733
Report issues at: https://issuetracker.google.com/issues/new?component=187272
INFO 2024-07-05 15:09:52,909 <string>:316] Skipping SDK update check.
WARNING 2024-07-05 15:09:52,910 <string>:325] The default encoding of your local Python interpreter is set to 'utf-8' while App Engine's production environment uses 'ascii'; as a result your code may behave differently when deployed.
INFO 2024-07-05 15:09:52,961 datastore_emulator.py:152] Starting Cloud Datastore emulator at: http://localhost:36569
Traceback (most recent call last):
File "/usr/lib/google-cloud-sdk/platform/google_appengine/dev_appserver.py", line 103, in <module>
_run_file(__file__, globals())
File "/usr/lib/google-cloud-sdk/platform/google_appengine/dev_appserver.py", line 99, in _run_file
_execfile(_PATHS.script_file(script_name), globals_)
File "/usr/lib/google-cloud-sdk/platform/google_appengine/dev_appserver.py", line 81, in _execfile
exec(open(fn).read(), scope)
File "<string>", line 638, in <module>
File "<string>", line 626, in main
File "<string>", line 393, in start
File "<string>", line 746, in create_api_server
File "/usr/lib/google-cloud-sdk/platform/google_appengine/google/appengine/tools/devappserver2/stub_util.py", line 166, in setup_stubs
datastore_grpc_stub_class(os.environ['DATASTORE_EMULATOR_HOST']))
File "/usr/lib/google-cloud-sdk/platform/google_appengine/google/appengine/tools/devappserver2/datastore_grpc_stub.py", line 83, in __init__
raise RuntimeError('The DatastoreGrpcStub requires a local gRPC '
RuntimeError: The DatastoreGrpcStub requires a local gRPC installation, which is not found.
INFO 2024-07-05 15:09:55,335 datastore_emulator.py:158] Cloud Datastore emulator responded after 2.374175 seconds
Exception ignored in: <function DatastoreEmulator.__del__ at 0x79fb5032ecb0>
Traceback (most recent call last):
File "/usr/lib/google-cloud-sdk/platform/google_appengine/google/appengine/tools/devappserver2/cloud_emulators/datastore/datastore_emulator.py", line 207, in __del__
AttributeError: 'NoneType' object has no attribute 'warning'To solve this problem, simply passing argument to ignore datastore by
dev_appserver.py app.yaml --support_datastore_emulator=FalseWhen install python packages using pip in Google Cloud VM, I got error
error: externally-managed-environment
× This environment is externally managed
╰─> To install Python packages system-wide, try apt install
python3-xyz, where xyz is the package you are trying to
install.
If you wish to install a non-Debian-packaged Python package,
create a virtual environment using python3 -m venv path/to/venv.
Then use path/to/venv/bin/python and path/to/venv/bin/pip. Make
sure you have python3-full installed.
If you wish to install a non-Debian packaged Python application,
it may be easiest to use pipx install xyz, which will manage a
virtual environment for you. Make sure you have pipx installed.
See /usr/share/doc/python3.11/README.venv for more information.
note: If you believe this is a mistake, please contact your Python installation or OS distribution provider. You can override this, at the risk of breaking your Python installation or OS, by passing --break-system-packages.
hint: See PEP 668 for the detailed specification.
To solve this problem, simply as
sudo rm /usr/lib/python3.11/EXTERNALLY-MANAGEDWhen running a new Zeppelin, I got error like :
org.apache.zeppelin.interpreter.InterpreterException: java.io.IOException: Fail to detect scala version, the reason is:Cannot run program "null/bin/spark-submit": error=2, No such file or directory at org.apache.zeppelin.interpreter.remote.RemoteInterpreter.open(RemoteInterpreter.java:128) at org.apache.zeppelin.interpreter.remote.RemoteInterpreter.getFormType(RemoteInterpreter.java:270) at org.apache.zeppelin.notebook.Paragraph.jobRun(Paragraph.java:428) at org.apache.zeppelin.notebook.Paragraph.jobRun(Paragraph.java:68) at org.apache.zeppelin.scheduler.Job.run(Job.java:186) at org.apache.zeppelin.scheduler.AbstractScheduler.runJob(AbstractScheduler.java:135) at org.apache.zeppelin.scheduler.RemoteScheduler$JobRunner.run(RemoteScheduler.java:186) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:750) Caused by: java.io.IOException: Fail to detect scala version, the reason is:Cannot run program "null/bin/spark-submit": error=2, No such file or directory at org.apache.zeppelin.interpreter.launcher.SparkInterpreterLauncher.buildEnvFromProperties(SparkInterpreterLauncher.java:139) at org.apache.zeppelin.interpreter.launcher.StandardInterpreterLauncher.launchDirectly(StandardInterpreterLauncher.java:76) at org.apache.zeppelin.interpreter.launcher.InterpreterLauncher.launch(InterpreterLauncher.java:106) at org.apache.zeppelin.interpreter.InterpreterSetting.createInterpreterProcess(InterpreterSetting.java:856) at org.apache.zeppelin.interpreter.ManagedInterpreterGroup.getOrCreateInterpreterProcess(ManagedInterpreterGroup.java:66) at org.apache.zeppelin.interpreter.remote.RemoteInterpreter.getOrCreateInterpreterProcess(RemoteInterpreter.java:103) at org.apache.zeppelin.interpreter.remote.RemoteInterpreter.internal_create(RemoteInterpreter.java:153) at org.apache.zeppelin.interpreter.remote.RemoteInterpreter.open(RemoteInterpreter.java:125)To solve this. First, delete the zeppelin/conf/interpreter.json . Dont worry, it will generated after starting zeppelin-daemon.sh
Next, running the zeppelin, go to interpreter and configure SPARK_HOME path on both spark and spark-submit settings. Don’t forget to setup the python path as well. Hope this solve your problem!
To install the latest Zeppelin (I recommend 0.11.0, since 0.11.1 have bugs) and Spark 3.5.1, we need to do several steps
sudo apt-get install openjdk-11-jdk build-essential Or you can install using previous java version if encountered with error
sudo apt-get install openjdk-8-jdk-headless2. Spark installation
wget -c https://dlcdn.apache.org/spark/spark-3.5.1/spark-3.5.1-bin-hadoop3.tgz
sudo tar -xvvf spark-3.5.1-bin-hadoop3.tgz -C /opt/
sudo mv /opt/spark-3.5.1-bin-hadoop3 /opt/spark3. Zeppelin installation
wget -c https://downloads.apache.org/zeppelin/zeppelin-0.11.0/zeppelin-0.11.0-bin-all.tgz
sudo tar -xvvf zeppelin-0.11.0-bin-all.tgz -C /opt/
sudo mv /opt/zeppelin-0.11.0-bin-all /opt/zeppelin4. Install MambaForge
wget -c https://github.com/conda-forge/miniforge/releases/download/24.1.2-0/Mambaforge-24.1.2-0-Linux-x86_64.sh
bash Mambaforge-24.1.2-0-Linux-x86_64.sh -b -p ~/anaconda3
~/anaconda3/bin/mamba init
source ~/.bashrc
mamba create -n test python=3.105. Run and Configure Zeppelin
sudo ./opt/zeppelin/bin/zeppelin-daemon.sh startThen go to Interpreter settings (on top right menu) and search for Spark. Make adjustment
/opt/spark/home/dev/anaconda3/envs/test/bin/python/home/dev/anaconda3/envs/test/bin/pythonNow you done and enjoy using Zeppelin in your ubuntu!
When installing Zeppelin 0.11.1 and Spark 3.3.3 over Docker in my github ( https://github.com/yodiaditya/docker-rapids-spark-zeppelin ) I receive error
Caused by: org.apache.zeppelin.interpreter.InterpreterException: Fail to open SparkInterpreter
at org.apache.zeppelin.spark.SparkInterpreter.open(SparkInterpreter.java:140)
at org.apache.zeppelin.interpreter.LazyOpenInterpreter.open(LazyOpenInterpreter.java:70)
... 12 more
Caused by: scala.reflect.internal.FatalError: Error accessing /opt/zeppelin/interpreter/spark/._spark-interpreter-0.11.1.jar
at scala.tools.nsc.classpath.AggregateClassPath.$anonfun$list$3(AggregateClassPath.scala:113)Apparently, the solution for this is very simple.
All you need just delete the file that cause the problem. In my case
rm /opt/zeppelin/interpreter/spark/._spark-interpreter-0.11.1.jar
rm /opt/zeppelin/interpreter/spark/scala-2.12/._spark-scala-2.12-0.11.1.jarWhen running toPandas() or another operation, I received this error
usr/lib/spark/python/pyspark/sql/pandas/conversion.py:186: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
df[column_name] = series
/usr/lib/spark/python/pyspark/sql/pandas/conversion.py:186: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
df[column_name] = series
/usr/lib/spark/python/pyspark/sql/pandas/conversion.py:186: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
df[column_name] = series
/usr/lib/spark/python/pyspark/sql/pandas/conversion.py:186: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
df[column_name] = series
/usr/lib/spark/python/pyspark/sql/pandas/conversion.py:186: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
df[column_name] = series
/usr/lib/spark/python/pyspark/sql/pandas/conversion.py:186: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
df[column_name] = series
/usr/lib/spark/python/pyspark/sql/pandas/conversion.py:186: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
df[column_name] = series
/usr/lib/spark/python/pyspark/sql/pandas/conversion.py:186: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
df[column_name] = seriesThe quick solution
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
# spark.conf.set("spark.sql.execution.arrow.enabled", "true")