after installing all prerequietsed/environment when i am trying to install Repositary Strain Package Showing this error: 'strain': not a valid cloud name. Must be one of ['folsom','folsom-proposed','grizzly','grizzly-proposed', 'havana', 'havana-proposed', 'icehouse', 'icehouse-proposed', 'juno', 'juno-proposed', 'octa', 'octa-proposed', 'newton', 'newton-proposed',
i have try to install that package removing and recreating another VM and follow the instruction page -- openstack, but still same issue their.
i want the points which i need to check which is responsible for giving following error.
Related
I am trying to install the ggpattern package from GitHub (https://www.rdocumentation.org/packages/ggpattern/versions/0.2.0)
I've reinstalled R, followed the all steps according to the site, also tried
remotes::install_github("coolbutuseless/ggpattern", force = TRUE)
But I still get:
Error: Failed to install 'unknown package' from GitHub:
HTTP error 401.
Bad credentials
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I'm working on R version 4.1.2 (newest according to me) on Windows.
Do you have any idea what is the issue here?
You need to check if you have a personal access token set in your environment. For example, when I have a Git project, I set a personal access token. However, I set this in the project environment, so that it isn't any issues outside of that environment.
To see if there is one assigned:
Sys.getenv("GITHUB_PAT")
If there is one set, write it down (you may need that in the future).
To remove it, so you can install the GitHub package:
Sys.unsetenv("GITHUB_PAT")
I am trying to install mlflow in R and im getting this error message saying
mlflow::install_mlflow()
Error in mlflow_conda_bin() :
Unable to find conda binary. Is Anaconda installed?
If you are not using conda, you can set the environment variable MLFLOW_PYTHON_BIN to the path of yourpython executable.
I have tried the following
export MLFLOW_PYTHON_BIN="/usr/bin/python"
source ~/.bashrc
echo $MLFLOW_PYTHON_BIN -> this prints the /usr/bin/python.
or in R,
sys.setenv(MLFLOW_PYTHON_BIN="/usr/bin/python")
sys.getenv() -> prints MLFLOW_PYTHON_BIN is set to /usr/bin/python.
however, it still does not work
I do not want to use conda environment.
how to I get past this error?
The install_mlflow command only works with conda right now, sorry about the confusing message. You can either:
install conda - this is the recommended way of installing and using mlflow
or
install mlflow python package yourself via pip
To install mlflow yourself, pip install correct (matching the the R package) python version of mlflow and set the MLFLOW_PYTHON_BIN environment variable as well as MLFLOW_BIN evn variable: e.g.
library(mlflow)
system(paste("pip install -U mlflow==", mlflow:::mlflow_version(), sep=""))
Sys.setenv(MLFLOW_BIN=system("which mlflow"))
Sys.setenv(MLFLOW_PYTHON_BIN=system("which python"))
Just ran across this, and the accepted answer by #Tomas was very helpful. I added a comment above but, for some additional context, I wanted to create a more thorough response if any other Enterprise Databricks R users run across this post trying to use the MLflow package for R on Databricks.
The Databricks MLflow quickstart guide will tell you that you need to run the following:
library(mlflow)
install_mlflow()
However, for Enterprise Databricks users, the install_mlflow() function will fail if your cluster doesn't have outside connectivity privileges (as most probably don't) and can't connect to the Anaconda repo to download the necessary packages. You'll likely get an error like this:
CondaHTTPError: HTTP 000 CONNECTION FAILED for url https://conda.anaconda.org/conda-forge/linux-64/current_repodata.js
The good news is that MLflow should already be installed on your Databricks runtime. So you can reference that install instead, and then as #Tomas mentioned, use it to set your R environment variables for MLFLOW_BIN and MLFLOW_PYTHON_BIN. From there, the R MLflow API works as specified (in my experience, but ymmv).
The only catch from the above solution is that when you use the system()function in R, you need to set intern=TRUE in order capture the output of the command. The default behavior of the system() function is intern=FALSE. Thus if you do not explicitly set intern=TRUE, then the exit code 0 will be returned from your system() call (or perhaps another exit code upon an error) and Sys.setenv() will set the environment variable to 0!
### intern=True missing ###
Sys.setenv(MLFLOW_BIN=system("which mlflow"))
Sys.setenv(MLFLOW_PYTHON_BIN=system("which python"))
Example output (you can see the the environment variables did not get set correctly):
s <- Sys.getenv()
s[grep("MLFLOW", names(s))]
MLFLOW_BIN 0
MLFLOW_CONDA_HOME /databricks/conda
MLFLOW_PYTHON_BIN 0
MLFLOW_PYTHON_EXECUTABLE
/databricks/python/bin/python
MLFLOW_TRACKING_URI databricks
However, when intern=TRUE, you'll get the correct environment variables:
### intern=True set ###
Sys.setenv(MLFLOW_BIN=system("which mlflow", intern=TRUE))
Sys.setenv(MLFLOW_PYTHON_BIN=system("which python", intern=TRUE))
Example output:
s <- Sys.getenv()
s[grep("MLFLOW", names(s))]
MLFLOW_BIN /databricks/python3/bin/mlflow
MLFLOW_CONDA_HOME /databricks/conda
MLFLOW_PYTHON_BIN /databricks/python3/bin/python
MLFLOW_PYTHON_EXECUTABLE
/databricks/python/bin/python
MLFLOW_TRACKING_URI databricks
Note: This was using Databricks runtime 9.1 LTS ML. This may or may not work on other Databricks runtime configurations.
I am struggling with installing a package from a GitLab repository on a Windows computer.
I found different hints but still have problems to install my package from GitLab. First of all, I generated a public and private key with puttygen.exe. The files need to be changed afterwards, I had to remove comments and stuff so they look like my the file on my Unix system. So now, both public and private key files have just a single line.
I tried to install my package via devtools::install_git which takes very long and I get the error message
Error: Failed to install 'unknown package' from Git:
Error in 'git2r_remote_ls': Failed to authenticate SSH session: Unable to send userauth-publickey request
And with devtools::install_gitlab I get a different error message and I somehow have the feeling, the link which gets generated doesn't fit to my GitLab server.
Error: Failed to install 'unknown package' from GitLab:
cannot open URL 'https://gitlab.rlp.net/api/v4/projects/madejung%2FMQqueue.git/repository/files/DESCRIPTION/raw?ref=master'
My complete code to test at the moment is
creds <- git2r::cred_ssh_key(publickey="~/.ssh/id_rsa_gitlab.pub",
privatekey="~/.ssh/id_rsa_gitlab")
devtools::install_git(
url='git#gitlab.rlp.net:madejung/MQqueue.git',
quiet=FALSE,
credentials=creds)
devtools::install_gitlab(
repo='madejung/MQqueue.git',
host='gitlab.rlp.net',
quiet=FALSE,
credentials=creds
)
My id_rsa_gitlab.pub file looks like this and is just a single line:
ssh-rsa AAAA....fiwbw== rsa-key-20200121
The id_rsa_gitlab file has just the code:
AAABA.....3WNSIAGE=
Update
On my Mac system it works as expected after installing the libssh2 library via homebrew and and recompiling git2r with install.packages("git2r", type = "source").
So the working code on my machine is:
creds <- git2r::cred_ssh_key(publickey="~/.ssh/id_rsa_gitlab.rlp.net.pub",
privatekey="~/.ssh/id_rsa_gitlab.rlp.net")
devtools::install_git(
url='git#gitlab.rlp.net:madejung/MQqueue.git',
quiet=FALSE,
credentials=creds
)
For some strange reason, the devtools::install_git call needs about a minute to fail in the end. I have no idea where the problem here is.
After struggling for almost a day, I found a solution I can live with...
I first created a PAT (Personal Access Token) in my gitlab account and granted full API access. For some reason the read_only access didn't worked and I am now tired to figure out what the problem is.
After this I had still problems to install my package and for some reason, the wininet setting for downloading doesn't work.
I used the command capabilities("libcurl") to check if libcurl is available on my windows, which was and tried to overwrite wininet to libcurl by using method='libcurl' in the install function. Somehow, this was not enough so I overwrote the options variable download.file.method directly.
options("download.file.method"='libcurl')
devtools::install_gitlab(
repo='madejung/MQqueue',
auth_token='Ho...SOMETHING...xugzb',
host='gitlab.rlp.net',
quiet=FALSE, force=TRUE
)
I've been trying to install and run keras in RStudio (Windows) in vain.
i installed keras package using normal package "keras"
(didn't use github)
I've installed latest python (3.6) and Anaconda.
then i use
> library(keras)
> install.keras()
and i get this error:
Creating r-tensorflow conda environment for TensorFlow installation...
Fetching package metadata ... CondaHTTPError: HTTP 000 CONNECTION
FAILED for url
https://repo.continuum.io/pkgs/main/win-64/repodata.json.bz2
Elapsed: -
An HTTP error occurred when trying to retrieve this URL. HTTP errors
are often intermittent, and a simple retry will get you on your way.
ConnectTimeout(MaxRetryError("HTTPSConnectionPool(host='repo.continuum.io',
port=443): Max retries exceeded with url:
/pkgs/main/win-64/repodata.json.bz2 (Caused by
ConnectTimeoutError(, 'Connection to repo.continuum.io timed out.
(connect timeout=9.15)'))",),)
Error: Error 1 occurred creating conda environment r-tensorflow In
addition: Warning message: running command
'"C:\PROGRA~3\ANACON~1\Scripts\conda.exe" "create" "--yes" "--name"
"r-tensorflow" "python=3.6"' had status 1
I've looked up everywhere on the web and can't figure out how to install keras and tensorflow properly. Using latest version of R (3.4.2)
Every method fails somewhere.
just to add to misery, i've also tried:
> devtools::install_github("rstudio/keras")
and i get this error:
Installation failed: Timeout was reached: Connection timed out after
10015 milliseconds
I am not behind any authenticated proxies. So, after multiple failure, i just downloaded the zip file from github and manually installed it using the zip file.
i also tried install.packages("keras") and that didn't give me any error either.
when i call the library i don't get any errors (as shown above)
UPDATE: I was able to install and use the package very easily on another computer that doesn't have python/anaconda installed on it already.
UPDATE 2: my proxy does not need authentication and there is no https_proxy either.
OK,, FINALLY found a solution.
Turns out RStudio uses a lot of default proxy settings, so i needed to change all that and set up my own proxy settings.
First step:
Rstudio --> Tools --> Global Options --> packages --> uncheck both "Use secure download method for HTTP" and "Use Internet Explorer librayr/proxy for HTTP"
Second step, in RStudio type:
> file.edit('./.Renviron')
Either an empty file or some file with already existing proxy settings will open. (Mine was empty). Then I included the following two:
http_proxy=http://myusename:password#proxy.server.com:port/
https_proxy=http://myusename:password#proxy.server.com:port/
(a few notes: I didn't have a https_proxy setting but I still needed to use the http_proxy details for my https_proxy setting. This was one of the culprits for my issue. Also, I needed to include the username:password even though my proxy doesn't need secure authentication. Same thing goes with the port. Port number had to be included, otherwise it wouldn't work.
Step 3:
Saved the new changes in .Renviron file and restarted RStudio.
I checked my proxy settings in RStudio after restart by typing:
> Sys.getenv("http_proxy")
> Sys.getenv("https_proxy")
The first few times i did this i realised that the proxy settings were not being changed in RStudio because i was editing the wrong .Renviron file. So, it's best to use file.edit('~/.Renviron') in step 2 to make sure it's the right file.
After all this, when i ran install.keras(), it installed successfully, including installing Tensorflow. Again, initially i had skipped step 1 so keras started being installed but it failed at installing tensorflow.
It was only going through all the steps that i was able to install both keras and tensorflow successfully over a proxy. Hope this helps.
Uninstalling Anaconda3 and installing Anaconda2 (i.e. Python 2.7) did the trick for me: https://www.anaconda.com/download/
I am new to Julia's package manager, and I am having trouble installing GLPK and LinProgGLPK.
I have already run Pkg.Init() and have successfully installed Curl using Pkg.add("Curl"). However, when I try to install GLPK or LinProgGLPK (using Pkg.add("GLPK") and Pkg.add("LinProgGLPK")), I get the following message:
MESSAGE: Installing BinDeps v0.0.0
ERROR: Path BinDeps already exists! Please remove to allow installation.
in _resolve at pkg.jl:345
in anonymous at no file:163
in cd at file.jl:26
in cd_pkgdir at pkg.jl:42
in add at pkg.jl:143
in add at pkg.jl:175
I seem to get the same message for other packages that are dependent on BinDeps (including Winston).
I have tried calling Pkg.rm("BinDeps"), Pkg.add("BinDeps"), Pkg.update(), and Pkg.resolve(), and then returned to trying to add GLPK, but the same message persists. I have also entered ~/.julia to remove the BinDeps folder, but that did not work either. What am I missing?
P.S. I am running julia in Linux Ubuntu.
Honestly, when something gets screwed up, it's best just to wipe ~/.julia and re-add the packages.
Just make sure you back up your local changes!