I'm trying to use ImageMagick to read SVG files. This works just fine locally, since I have librsvg (https://github.com/GNOME/librsvg) installed on my machine. However, the package doesn't seem to be available through yum, which is what Elastic Beanstalk uses.
So in my backend (Rails/Postgresql) I have something like:
def save_svg_as_image
file = params[:uploaded_svg]
#svg_image = MiniMagick::Image.read(file)
self.svg = #svg_image
end
Again, this works fine on local thanks to librsvg, but when testing with my EB (64bit Amazon Linux/2.11.4), I get:
MiniMagick::Invalid (`identify /tmp/mini_magick20210205-24760-bygqeh` failed with error:
identify: no decode delegate for this image format `/tmp/mini_magick20210205-24760-bygqeh' # error/constitute.c/ReadImage/544.
):
I've attempted using the following solution: Taking the code from https://gist.github.com/whyvez/1e0212a35da97aa8f1b1 and using it in a packages.config file, but I'm continually getting the following error:
Command failed on instance. Return code: 2 Output: wget: unrecognized option '--prefix=/usr'
My Environment
ArangoDB Version: 3.6.2
Storage Engine: RocksDB
Deployment Mode: Single Server
Deployment Strategy: Manual Start in Docker
Infrastructure: Own
Operating System: Linux version 4.4.0-154-generic (gcc version 5.4.0 (Ubuntu 5.4.0-6ubuntu1~16.04.10) )
Total RAM in your machine: 4GB
Disks in use: HDD
Used Package: Docker-Official Docker library
My Problem:
I have a graph with 60k nodes and 4*60k edges. Whenever I try using 'path' for Filter or Return, the memory limit reaches, arangodb container crashes and it gets restarted. However, if I don't use 'path' and use 'vertex' or 'edge' only for filter or return the query executes and produces result as expected. This issue is seen in version 3.6.2.
However, in arangodb 3.1.18 this issue is not seen and everything is
working fine.
Sample Query:
FOR v, e, p IN 6 OUTBOUND "root_node" GRAPH "my_graph_db"
FILTER (
LENGTH(p.edges) == 6 &&
LIKE (p.edges[3]._from,"Data_level_3%",true) &&
(LIKE (p.edges[3]._to, "Data_level_4%") || LIKE (p.edges[3]._to, "Data_4%")) &&
...................................................................
)
LIMIT 0,10
RETURN {
result: Merge(
{data: v},
{parent_id: p.edge[5]._id}
),
.................
}
Expected result:
The arangodb container should not reach memory limit crash. 'Path' attributes needs to accessed while doing queries.
Please refer to https://github.com/arangodb/arangodb/issues/11277
I am trying to get my Faster R-CNN model into an Container Instance on ACI. For that I need my docker image to posses python version 3.5.*. I specify that in my conda yaml file, but every time I spin an instance up and docker run -it *** /bin/bash into it I see that it only has Python 3.6.7.
https://user-images.githubusercontent.com/21140767/50680590-82b20b80-1008-11e9-9bfe-4a0e71084ce0.png
How can I get my Docker image to have Python version 3.5.*? I already tried conda installing Python version 3.5.2, but that didn't work as eventually it didn't posses 3.5.2, but only 3.6.7. (dfimage lets you see the dockerfile from which the image was created, https://hub.docker.com/r/chenzj/dfimage/).
https://user-images.githubusercontent.com/21140767/50680673-d6245980-1008-11e9-9d48-71a7c150d925.png
My yaml:
name: project_environment
dependencies:
- python=3.5.2
- pip:
- matplotlib
- opencv-python==3.4.3.18
- azureml-core==1.0.6
- numpy
- cntk
- cython
channels:
- anaconda
Notebook cell:
from azureml.core.conda_dependencies import CondaDependencies
svmandss = CondaDependencies.create(python_version="3.5.2", pip_packages=[
"matplotlib",
"opencv-python==3.4.3.18",
"azureml-core",
"numpy",
"cntk",
"cython"], )
svmandss.add_channel('anaconda')
with open("fasterrcnn.yml","w") as f:
f.write(svmandss.serialize_to_string())
Another notebook cell with ContainerImage specifications.
image_config = ContainerImage.image_configuration(execution_script="score_fasterrcnn.py",runtime="python",conda_file="./fasterrcnn.yml",dependencies=listdir("utils"),docker_file="./Dockerfile")
service = Webservice.deploy_from_model(workspace=ws,
name='faster-rcnn',
deployment_config=aciconfig,
models=[Model(workspace=ws, name='Faster-RCNN')],
image_config=image_config)
service.wait_for_deployment(show_output=True)
Note
For better readability see my GitHub issue: (https://github.com/Azure/MachineLearningNotebooks/issues/163).
Currently, the version of Python is fixed to what's in Azure ML's base image, when deploying the web service. We're investigating removing this limitation in future.
Since this is one of the top Google answers when searching for "azureml python version" I'm posting the answer here. The documentation is not very clear when it comes to this, but the following will work:
from azureml.core import Workspace
from azureml.core.runconfig import RunConfiguration
from azureml.core.conda_dependencies import CondaDependencies
ws = Workspace.from_config()
# This is the important part
conda_dep = CondaDependencies(conda_dependencies_file_path="pipeline/environment.yml")
aml_run_config = RunConfiguration(conda_dependencies=conda_dep)
# Define compute target - must be preconfigured in th workspace
compute_target = ws.compute_targets['my-azureml-target']
aml_run_config.target = compute_target
from azureml.pipeline.steps import PythonScriptStep
script_source_dir = "./pipeline"
step_1_script = "test.py"
step_1 = PythonScriptStep(
script_name=step_1_script,
source_directory=script_source_dir,
compute_target=compute_target,
runconfig=aml_run_config,
allow_reuse=True
)
from azureml.pipeline.core import Pipeline
# Build the pipeline
pipeline1 = Pipeline(workspace=ws, steps=[step_1])
from azureml.core import Experiment
# Submit the pipeline to be run
pipeline_run1 = Experiment(ws, 'Test-pipeline').submit(pipeline1)
pipeline_run1.wait_for_completion(show_output=True)
This assumes the following directory structure:
root/
create_pipeline.py
pipeline/
test.py
environment.yml
where create_pipeline.py is the file above, test.py is the script you would like to run and environment.yml is the conda environment file - including the python version.
I was able to change the Python version by registering the environment in Azure ML Workspace:
from azureml.core.environment import Environment, Workspace
environment = Environment.from_conda_specification(name='myenv', file_path='environment.yml')
environment.python.user_managed_dependencies = False
workspace = Workspace.from_config()
environment = environment.register(workspace=workspace)
env_build = environment.build(workspace=workspace)
Then, configure the endpoint for publishing as follows:
from azureml.core.model import InferenceConfig
environment = Environment.get(workspace=workspace, name='myenv')
inference_config = InferenceConfig(
entry_script='inference.py',
source_directory='.',
environment=environment
)
This is using Azure ML SDK 1.29.0. Perhaps this has already been fixed and the original method works as well, but I didn't test that.
EDIT:
This is no longer an issue for me. I found another way to get my code to work with python version 3.6.7.
This is however still an issue if you ask me. If in the future I do need python version 3.5 then there will not be a solution as of now.
You can still post an answer if you would like.
Q: How to you change/update the character set on Microsoft R Server?
Issue: I am trying to read a CSV that is delimited with '§' but the R Server is not able to interperet the '§' character when I work remotely. Similarly for other characters like 'ø' , 'æ' and 'å'. When I work locally it's not an issue.
For example:
This works fine:
> x <- '§'
> x
[1] "§"
But when i login remotely to the server the following happens:
REMOTE> x <- '§'
REMOTE> x
[1] "?"
Setup: I am running Microsoft R Server 9.0.1 on Windows Server 2012 R2
Detailed sessionInfo:
REMOTE> sessionInfo() R version 3.3.2 (2016-10-31) Platform:
x86_64-w64-mingw32/x64 (64-bit) Running under: Windows Server >= 2012
x64 (build 9200)
locale: [1] LC_COLLATE=Norwegian (Bokm�l)_Norway.1252 [2]
LC_CTYPE=Norwegian (Bokm�l)_Norway.1252 [3] LC_MONETARY=Norwegian
(Bokm�l)_Norway.1252 [4] LC_NUMERIC=C
[5] LC_TIME=Norwegian (Bokm�l)_Norway.1252
attached base packages: [1] stats graphics grDevices utils
datasets methods base
other attached packages: [1] RevoUtilsMath_10.0.0 RevoUtils_10.0.2
RevoMods_10.0.0 [4] RevoScaleR_9.0.1 lattice_0.20-34
rpart_4.1-10
loaded via a namespace (and not attached): [1] R6_2.2.0
tools_3.3.2 CompatibilityAPI_1.1.0 [4] codetools_0.2-15
grid_3.3.2 iterators_1.0.8 [7] foreach_1.4.3
mrupdate_1.0.0 jsonlite_1.1
In addition to installing version 9.1 of Microsoft R Server I also had to make the following change for the server to work correctly with remote login:
Stop the service 'RServe9.0.0.0'
and go to C:\Program Files\Microsoft\R Server\R_SERVER\o16n\RServe\RScripts\source.R on the compute nodes
and change
```
#add more here if necessary......
```
to
```
#add more here if necessary......
options(encoding = "UTF-8")
```
and then start that service again, you should be able to use §.
Thanks to Microsoft for providing this fix.
This is a known bug, and has been patched in Microsoft R Server 9.1, please upgrade to solve your issue.
I've setup RStudio server and pointed it to use an existing R (2.13) installation. ROracle works fine when accessed from R, but the same does not work from RStudio web-interface.
> library(ROracle)
Loading required package: DBI
> drv <- dbDriver("Oracle")
Error in .oci.Driver() : ROracle internal error [rociDrvInit, 1, -1]
I installed RStudio server with --nodeps and later pointed it to an existing installation of R
by setting the values in /etc/rstudio/rserver.conf file.
Tried getting help from RStudio support, but was pointed toward "Stack Overflow".
http://support.rstudio.org/help/discussions/problems/1879-rstudio-roracle-internal-error
Thanks in advance,
Sai.
Finally got it working with help from Denis Mukhin on the Oracle forums. In particular, ORACLE_HOME and OREACLE_SID were missing in the RStudio environment. Adding the following lines to ~/.Renviron fixed it:
ORACLE_HOME=/u01/app/oracle/product/11.1.0/db_1
ORACLE_SID=<your sid (the default is usually orcl)>
export ORACLE_HOME ORACLE_SID