I am very new to docker and Jupyter notebook. I pulled the image from docker, it was able to direct me to the relevant Jupyter notebook. Problem is, whatever plots I am making in the notebook, I am not able to find the file in the system. A file with the name settings.cmnd should be made on my system. I am using Windows 10 home version. I am using the following command
docker run -it -v "//c/Users/AB/project":"//c/program files/Docker Toolbox" -p 8888:8888/tcp CONTAINER NAME
It is running fine as I am able to access the jupyter notebook but the file is still missing on my system.
Here the folder in which I want to save file is project
Kindly help.
I did not find an image called electronioncollider/pythiatutorial, so I'm assuming you meant electronioncollider/pythia-eic-tutorial.
Default working directory for that image is /code so the command on Windows should look like:
docker run --rm -v //c/Users/AB/project://code -p 8888:8888 electronioncollider/pythia-eic-tutorial:latest
Working dierctory can be changed with -w, so the following should work as well:
docker run --rm -w //whatever -v //c/Users/AB/project://whatever -p 8888:8888 electronioncollider/pythia-eic-tutorial:latest
Edit:
electronioncollider/pythia-eic-tutorial:latest image has only one version - one that is meant to run on linux/amd64. This means it's meant to run on 64-bit Linux installed on a computer with Intel or AMD processor.
You're not running it on Windows, but on a Linux VM that runs on your Windows host. Docker can access C:\Users\AB\project, because it's mounted inside the VM as c/Users/AB/project (although most likely it's actuall C:\Users that's mounted as /c/Users). Therein lies the problem - Windows and Linux permission models are incompatible, so the Windows directory is mounted with fixed permissions that allows all Linux users access. Docker then mounts that directory inside the container with the same permissions. Unfortunately Jupyter wants some of the files it creates to have a very specific set of permissions (for security reasons). Since the permissions are fixed to a specific value, Jupyter cannot change them and breaks.
There are two possible solutions
Get inside whatever VM the Docker is running inside, change directory to one not mounted from Windows, and run the container from there using the command from the tutorial/README:
docker run --rm -u `id -u $USER` -v $PWD:$PWD -w $PWD -p 8888:8888 electronioncollider/pythia-eic-tutorial:latest
and the files will appear in the directory that the command is run from.
Use the modified image I created:
docker run --rm -v //c/Users/AB/project://code -p 8888:8888 forinil/pythia-eic-tutorial:latest
You can find the image on Docker Hub here. The source code is available on GitHub here.
Edit:
Due to changes in my version of the image the proper command for it would be:
docker run -it --rm -v //c/Users/AB/project://code --entrypoint rivet forinil/pythia-eic-tutorial
I release a new version, so if you run docker pull forinil/pythia-eic-tutorial:latest, you'll be able to use both the command above, as well as:
docker run -it --rm -v //c/Users/AB/project://code forinil/pythia-eic-tutorial rivet
That being said I did not receive any permission errors while testing either the old or the new versions of the image.
I hope you understand that due to how Docker Toolbox works, you won't be able to use aliases the way the tutorial says you would on Linux.
For one thing, you'll only have access to files inside directory C:\Users\AB\project, for another file path inside the container will be different than outside the container, eg. file C:\Users\AB\project\notebooks\pythiaRivet.ipynb will be available inside the container as /code/notebooks/pythiaRivet.ipynb
Note on asking questions:
You've got banned from asking questions, because your questions are low quality. Please read the guidelines before asking any more.
Related
I have been trying to connect Spyder to a docker container running on a remote server and failing time and again. Here is a quick diagram of what I am trying to achieve:
Currently I am launching the docker container on the remote machine through ssh with
docker run --runtime=nvidia -it --rm --shm-size=2g -v /home/timo/storage:/storage -v /etc/passwd:/etc/passwd -v /etc/group:/etc/group --ulimit memlock=-1 -p 8888:8888 --ipc=host ufoym/deepo:all-jupyter
so I am forwarding on port 8888. Then inside the docker container I am running
jupyter notebook --no-browser --ip=0.0.0.0 --port=8888 --allow-root --notebook-dir='/storage'
OK, now for the Spyder part - As per the instructions here, I go to ~/.local/share/jupyter/runtime, where I find the following files:
kernel-ada17ae4-e8c3-4e17-9f8f-1c029c56b4f0.json nbserver-11-open.html nbserver-21-open.html notebook_cookie_secret
kernel-e81bc397-05b5-4710-89b6-2aa2adab5f9c.json nbserver-11.json nbserver-21.json
Not knowing which one to take, I copy them all to my local machine.
I now go to Consoles->Connect to an Existing Kernel, which gives me the "Connect to an Existing Kernel" window which I fill out as so (of course using my actual remote IP address):
(here I have chosen the first of the json files for Connection info:). I hit enter and Spyder goes dark and crashes.
This happens regardless of which connection info file I choose. So, my questions are:
1: Am I doing all of this correctly? I have found lots of instructions for how to connect to remote servers, but not so far for specifically connecting to a jupyter notebook on a docker on a remote server.
2: If yes, then what else can I do to troubleshoot the issues I am encountering?
I should also note that I have no problems connecting to the Jupyter Notebook through the browser on my local machine. It's just that I would prefer to be working with Spyder as my IDE.
Many thanks in advance!
This isn't a solution so much as a work around, but sshfs might be of help
Use sshfs to mount the remote machine's home directory on a local directory, then your local copy of Spyder can edit the file as if it were a local file.
sshfs remotehost.com:/home/user/ ./remote-host/
It typically takes about half a second to upload the changes to an AWS host when you I hit save in Spyder, which is an acceptable delay for me. When it's time to run the code, ssh into the remote machine, and run the code from an IPython shell. It's not elegant, but it does work.
I'm not expecting this to be the best answer, but maybe you can use it as a stopgap solution.
I have the same problem with you. I got it working, maybe a bit clumsy as I am totally new to docker. Here are my steps and notes on where we differ, hope this helps:
Launch docker conatiner in remote machine:
docker run --gpus all --rm -ti --net=host -v /my_storage/data:/home/data -v /my_storage/JSON:/root/.local/share/jupyter/runtime repo/tensorflow:20.03-tf2-py3
I use a second volume mount, in order to get kernel.json file to my local computer. I couldn't manage to access directly from docker via ssh, as it is in /root/ folder in docker container, and with root-only access. If you know how to read from there directly, I'll be happy to learn. My workaround is:
On remote machine, create a JSON/ directory, and map it to the "jupyter --runtime-dir" in container. Once the kernel is created, access the kernel-xxx.json file through this volume mount, copy to local machine and chmod.
Launch ipython kernel in container:
ipython kernel
You are launching jupyter notebook. I suspect this is the reason for your problem. I am not sure if spyder works on notebooks, but it works on iPython kernels. Probably, it works better on spyder-kernels.
copy kernel.json file from /remote_machine/JSON to local machine, chmod for accessing.
launch spyder, use local kernel.json and ssh settings. This part is same as yours.
Not enough reputation... to add comment but to chime on #asim's solution. I was able to have my locally installed Spyder to connect to a kernel running from a container on a remote machine. There is bit of manual work but I am okay with this since I can get much more done with Spyder than with other IDEs.
docker run --rm -it --net=host -v /project_directory_remote_machine:/container_project_directory image_id bash
from container
python -m spyder_kernels.console - matplotlib=’inline’ --ip=127.0.0.1 -f=/container_project_directory/connection_file.json
from remote machine, chmod connection_file.json to open then open and copy/paste content to a file on a local machine :) Use the json file to connect to a remote kernel following steps in the sources below
https://medium.com/#halmubarak/connecting-spyder-ide-to-a-remote-ipython-kernel-25a322f2b2be
https://mazzine.medium.com/how-to-connect-your-spyder-ide-to-an-external-ipython-kernel-with-ssh-putty-tunnel-e1c679e44154
I followed the standard Odoo container instructions on Docker to start the required postgres and odoo servers, and tried to pass host directories as persistent data storage for both as indicated in those instructions:
sudo mkdir /tmp/postgres /tmp/odoo
sudo docker run -d -v /tmp/postgres:/var/lib/postgresql/data/pgdata -e POSTGRES_USER=odoo -e POSTGRES_PASSWORD=odoo -e POSTGRES_DB=postgres --name db postgres:10
sudo docker run -v /tmp/odoo:/var/lib/odoo -p 8069:8069 --name odoo --link db:db -t odoo
The Odoo container shows messages that it starts up fine, but when I point my web browser at http://localhost:8069 I get no response from the server. By contrast, if I omit the -v argument from the Odoo docker run command, my web browser connects to the Odoo server fine, and everything works great.
I searched and see other people also struggling with getting the details of persistent data volumes working, e.g. Odoo development on Docker, Encountered errors while bringing up the project
This seems like a significant gap in Docker's standard use-case that users need better info on how to debug:
How to debug why the host volume mounting doesn't work for the odoo container, whereas it clearly does work for the postgres container? I'm not getting any insight from the log messages.
In particular, how to debug whether the container requires the host data volume to be pre-configured in some specific way, in order to work? For example, the fact that I can get the container to work without the -v option seems like it ought to be helpful, but also rather opaque. How can I use that success to inspect what those requirements actually are?
Docker is supposed to help you get a useful service running without needing to know the guts of its internals, e.g. how to set up its internal data directory. Mounting a persistent data volume from the host is a key part of that, e.g. so that users can snapshot, backup and restore their data using tools they already know.
I figured out some good debugging methods that both solved this problem and seem generally useful for figuring out Docker persistent data volume issues.
Test 1: can the container work with an empty Docker volume?
This is a really easy test: just create a new Docker volume and pass that in your -v argument (instead of a host directory absolute path):
sudo docker volume create hello
sudo docker run -v hello:/var/lib/odoo -p 8069:8069 --name odoo --link db:db -t odoo
The odoo container immediately worked successfully this way (i.e. my web browswer was able to connect to the Odoo server). This showed that it could work fine with an (initially) empty data directory. The obvious question then is why it didn't work with an empty host-directory volume. I had read that Docker containers can be persnickety about UID/GID ownership, so my next question was how do I figure out what it expects.
Test 2: inspect the running container's file system
I used docker exec to get an interactive bash shell in the running container:
sudo docker exec -ti odoo bash
Inside this shell I then looked at the data directory ownership, to get numeric UID and GID values:
ls -dn /var/lib/odoo
This showed me the UID/GID values were 101:101. (You can exit from this shell by just typing Control-D)
Test 3: re-run container with matching host-directory UID:GID
I then changed the ownership of my host directory to 101:101 and re-ran the odoo container with my host-directory mount:
sudo chown 101:101 /tmp/odoo
sudo docker stop odoo
sudo docker rm odoo
sudo docker run -v /tmp/odoo:/var/lib/odoo -p 8069:8069 --name odoo --link db:db -t odoo
Success! Finally the odoo container worked properly with a host-directory mount. While it's annoying the Odoo docker docs don't mention anything about this, it's easy to debug if you know how to use these basic tests.
Not 100% sure this is the right place but let's try.
I'm using on my Windows laptop the Docker Quickstart Terminal (docker toolbox) to get access to a Linux env with Google AppEngine, python, mysql...
Well, that seems to work and when I type docker run -i -t appengine /bin/bash I get access to this env.
Now I'd like to have access to some of my local (host) files so I can edit them with my Windows editors but run them into the docker instance.
I've seen a -v option but cannot make it work.
What I do
docker run -v /d/workspace:/home/root/workspace:rw -i -t appengine /bin/bash
But workspace stays empty in the Docker instance...
Any help appreciated
(I've read this before to post: https://github.com/rocker-org/rocker/wiki/Sharing-files-with-host-machine#windows)
You have to enable Shared Drives , you can follow this Blog
I've installed the tensorflow docker container on an ubuntu machine. The tensorflow docker setup instructions specify:
docker run -it b.gcr.io/tensorflow/tensorflow
This puts me into the docker container terminal, and I can run python and execute the Hello World example. I can also manually run .\run_jupyter.sh to start the jupyter notebook. However, I can't reach the notebook from host.
How do I start the jupyter notebook such that I can use the notebook from the host machine? Ideally I would like to use docker to launch the container and start jupyter in a single command.
For a Linux host Robert Graves answer will work, but for Mac OS X or Windows there is more to be done because docker runs in a virtual machine.
So to begin launch the docker shell (or any shell if you are using Linux) and run the following command to launch a new TensorFlow container:
docker run -p 8888:8888 -p 6006:6006 b.gcr.io/tensorflow/tensorflow ./run_jupyter.sh
Then for Mac OS X and Windows you need to do the following only once:
Open VirtualBox
Click on the docker vm (mine was automatically named "default")
Open the settings by clicking settings
In the network settings open the port forwarding dialog
Click the + symbol to add another port and connect a port from your mac to the VM by filling in the dialog as shown below. In this example I chose port 8810 because I run other notebooks using port 8888.
then open a browser and connect to http://localhost:8810 (or whichever port you set in the host port section
Make your fancy pants machine learning app!
My simple yet efficient workflow:
TL;DR version:
Open Docker Quickstart Terminal. If it is already open, run $ cd
Run this once: $ docker run -it -p 8888:8888 -p 6006:6006 -v /$(pwd)/tensorflow:/notebooks --name tf b.gcr.io/tensorflow/tensorflow
To start every time: $ docker start -i tf
If you are not on windows, you should probably change /$(pwd) to $(pwd)
You will get an empty folder named tensorflow in your home directory for use as a persistent storage of project files such as Ipython Notebooks and datasets.
Explanation:
cd for making sure you are in your home directory.
params:
-it stands for interactive, so you can interact with the container in the terminal environment.
-v host_folder:container_folder enables sharing a folder between the host and the container. The host folder should be inside your home directory. /$(pwd) translates to //c/Users/YOUR_USER_DIR in Windows 10. This folder is seen as notebooks directory in the container which is used by Ipython/Jupyter Notebook.
--name tf assigns the name tf to the container.
-p 8888:8888 -p 6006:6006 mapping ports of container to host, first pair for Jupyter notebook, the second one for Tensorboard
-i stands for interactive
Running TensorFlow on the cloud
After further reading of docker documentation I have a solution that works for me:
docker run -p 8888:8888 -p 6006:6006 b.gcr.io/tensorflow/tensorflow ./run_jupyter.sh
The -p 8888:8888 and -p 6006:6006 expose the container ports to the host on the same port number. If you just use -p 8888, a random port on the host will be assigned.
The ./run_jupyter.sh tells docker what to execute within the container.
With this command, I can use a browser on the host machine to connect to http://localhost:8888/ and access the jupyter notebook.
UPDATE:
After wrestling with docker on windows I switched back to a Ubuntu machine with docker. My notebook was being erased between docker sessions which makes sense after reading more docker documentation. Here is an updated command which also mounts a host directory within the container and starts jupyter pointing to that mounted directory. Now my notebook is saved on the host and will be available next time start up tensorflow.
docker run -p 8888:8888 -p 6006:6006 -v /home/rob/notebook:/notebook b.gcr.io/tensorflow/tensorflow sh -c "jupyter notebook /notebook"
Jupyter now has a ready to run Docker image for TensorFlow:
docker run -d -v $(pwd):/home/jovyan/work -p 8888:8888 jupyter/tensorflow-notebook
These steps worked for me if you are a total docker noob using a windows machine.
Versions: Windows 8.1, docker 1.10.3, tensorflow r0.7
Run Docker Quickstart Terminal
After it is loaded, note the ip address. If you can't find it use this docker-machine ip and make a note. Lets call it 'ip address'. Will look something like this: 192.168.99.104 (I made up this ip address)
Paste this command on the docker terminal:
docker run -p 8888:8888 -p 6006:6006 b.gcr.io/tensorflow/tensorflow.
If you are running this for the first time, it will download and install the image on this light weight vm. Then it should say 'The Jupyter notebook is running at ....' -> This is a good sign!
Open your browser at: <your ip address (see above)>:8888. Eg. 192.168.99.104:8888/
Hopefully you can see your ipython files.
To get this to run under hyper-v. Perform the following steps:
1) Create a docker virtual machine using https://blogs.msdn.microsoft.com/scicoria/2014/10/09/getting-docker-running-on-hyper-v-8-1-2012-r2/ this will get you a working docker container. You can connect to it via the console or via ssh. I'd put at least 8gb of memory since I'm sure this will use a lot of memory.
2) run "ifconfig" to determine the IP address of the Docker VM
3) On the docker shell prompt type:
docker run -p 8888:8888 -p 6006:6006 -it b.gcr.io/tensorflow/tensorflow
4) Connect to the Jupyter Workbench using http:/[ifconfig address]:8888/
To tidy up the things a little bit, I want to give some additional explanations because I also suffered a lot setting up docker with tensorflow. For this I refer to this video which is unfortunately not selfexplanatory in all cases.
I assume you already installed docker. The really interesting general part of the video starts at minute 0:44 where he finally started docker. Until there he only downloads the tensorflow repo into the folder, that he then mounts into the container. You can of course put anything else into the container and access it later in the docker VM.
First he runs the long docker command docker run –dit -v /c/Users/Jay/:/media/disk –p 8000 –p 8888 –p 6006 b.gcr.io/tensorflow/tensorflow. The “run” command starts containers. In this case it starts the container “b.gcr.io/tensorflow/tensorflow”, whose address is provided within the tensorflow docker installation tutorial. The container will be downloaded by docker if not already locally available.
Then he gives two additional kinds of arguments: He mounts a folder of the hostsystem at the given path to the container. DO NOT forget to give the partition in the beginning (eg. "/c/").
Additionally he declares ports being available later from the host machine with the params -p.
From all this command you get back the [CONTAINER_ID] of this container execution!
You can always see the currently running containers by running “docker ps” in the docker console. Your container created above should appear in this list with the same id.
Next Step: With your container running, you now want to execute something in it. In our case jupyter notebook or tensorflow or whatever: To do this you make docker execute the bash on the newly created container: docker exec –ti [CONTAINER_ID] bash. This command now starts a bash shell on your container. You see this because the “$” now changed to root#[CONTAINER_ID]:. From here is no way back. If you want to get back to the docker terminal, you have to start another fresh docker console like he is doing in minute 1:10. Now with a bash shell running in the container you can do whatever you want and execute Jupiter or tensorflow or whatever. The folder of the host system, you gave in the run command, should be available now under “/media/disk”.
Last step accessing the VM output. It still did not want to work out for me and I could not access my notebook. You still have to find the correct IP and Port to access the launched notebook, tensorboard session or whatever. First find out the main IP by using docker-machine –ls. In this list you get the URL. (If it is your only container it is called default.) You can leave away the port given here. Then from docker ps you get the list of forwarded ports. When there is written 0.0.0.32776->6006/tcp in the list, you can access it from the hostmachine by using the port given in the first place (Awkyard). So in my case the executed tensorboard in the container said “launched on port 6006”. Then from my hostmachine I needed to enter http://192.168.99.100:32776/ to access it.
-> And that’s it! It ran for me like this!
It gives you the terminal prompt:
FOR /f "tokens=*" %i IN ('docker-machine env --shell cmd vdocker') DO %i
docker run -it tensorflow/tensorflow:r0.9-devel
or
FOR /f "tokens=*" %i IN ('docker-machine env --shell cmd vdocker') DO %i
docker run -it b.gcr.io/tensorflow/tensorflow:latest-devel
You should have 'vdocker' or change vdocker to 'default'.
For some reason I ran into one additional problem that I needed to overcome beyond the examples provided, using the --ip flag:
nvidia-docker run --rm \
-p 8888:8888 -p 6006:6006 \
-v `pwd`:/root \
-it tensorflow/tensorflow:latest-devel-gpu-py3 sh -c "jupyter notebook --ip 0.0.0.0 ."
And then I can access via http://localhost:8888 from my machine. In some ways this makes sense; within the container you bind to 0.0.0.0 which represents all available addresses. But whether I need to do this seems to vary (e.g I've started notebooks using jupyter/scipy-notebook without having to do this).
In any case, the above command works for me, might be of use to others.
As an alternative to the official TensorFlow image, you can also use the ML Workspace Docker image. The ML Workspace is an open-source web IDE that combines Jupyter, VS Code, TensorFlow, and many other tools & libraries into one convenient Docker image. Deploying a single workspace instance is as simple as:
docker run -p 8080:8080 mltooling/ml-workspace:latest
All tools are accessible from the same port and integrated into the Jupyter UI. You can find the documentation here.
How can I share a folder between my Windows files and a docker container, by mounting a volume with simple --volume command using Docker Toolbox on?
I'm using "Docker Quickstart Terminal" and when I try this:
winpty docker run -it --rm --volume /C/Users/myuser:/myuser ubuntu
I have this error:
Invalid value "C:\\Users\\myuser\\:\\myuser" for flag --volume: bad mount mode specified : \myuser
See 'docker run --help'.
Following this, I also tried
winpty docker run -it --rm --volume "//C/Users/myuser:/myuser" ubuntu
and got
Invalid value "\\\\C:\\Users\\myuser\\:\\myuser" for flag --volume: \myuser is not an absolute path
See 'docker run --help'.
This is an improvement of the selected answer because that answer is limited to c:\Users folder. If you want to create a volume using a directory outside of c:\Users this is an extension.
In windows 7, I used docker toolbox. It used Virtual Box.
Open virtual box
Select the machine (in my case default).
Right clicked and select settings option
Go to Shared Folders
Include a new machine folder.
For example, in my case I have included:
**Name**: c:\dev
**Path**: c/dev
Click and close
Open "Docker Quickstart Terminal" and restart the docker machine.
Use this command:
$ docker-machine restart
To verify that it worked, following these steps:
SSH to the docker machine.
Using this command:
$ docker-machine ssh
Go to the folder that you have shared/mounted.
In my case, I use this command
$ cd /c/dev
Check the user owner of the folder. You could use "ls -all" and verify that the owner will be "docker"
You will see something like this:
docker#default:/c/dev$ ls -all
total 92
drwxrwxrwx 1 docker staff 4096 Feb 23 14:16 ./
drwxr-xr-x 4 root root 80 Feb 24 09:01 ../
drwxrwxrwx 1 docker staff 4096 Jan 16 09:28 my_folder/
In that case, you will be able to create a volume for that folder.
You can use these commands:
docker create -v /c/dev/:/app/dev --name dev image
docker run -d -it --volumes-from dev image
or
docker run -d -it -v /c/dev/:/app/dev image
Both commands work for me. I hope this will be useful.
This is actually an issue of the project and there are 2 working workarounds:
Creating a data volume:
docker create -v //c/Users/myuser:/myuser --name data hello-world
winpty docker run -it --rm --volumes-from data ubuntu
SSHing directly in the docker host:
docker-machine ssh default
And from there doing a classic:
docker run -it --rm --volume /c/Users/myuser:/myuser ubuntu
If you are looking for the solution that will resolve all the Windows issues and make it work on the Windows OS in the same way as on Linux, then see below. I tested this and it works in all cases. I’m showing also how I get it (the steps and thinking process). I've also wrote an article about using Docker and dealing with with docker issues here.
Solution 1: Use VirtualBox (if you think it's not good idea see Solution 2 below)
Open VirtualBox (you have it already installed along with the docker tools)
Create virtual machine
(This is optional, you can skip it and forward ports from the VM) Create second ethernet card - bridged, this way it will receive IP address from your network (it will have IP like docker machine)
Install Ubuntu LTS which is older than 1 year
Install docker
Add shared directories to the virtual machine and automount your project directories (this way you have access to the project directory from Ubuntu) but still can work in Windows
Done
Bonus:
Everything is working the same way as on Linux
Pause/Unpause the dockerized environment whenever you want
Solution 2: Use VirtualBox (this is very similar to the solution 1 but it shows also the thinking process, which might be usefull when solving similar issues)
Read that somebody move the folders to /C/Users/Public and that works https://forums.docker.com/t/sharing-a-volume-on-windows-with-docker-toolbox/4953/2
Try it, realize that it doesn’t have much sense in your case.
Read entire page here https://github.com/docker/toolbox/issues/607 and try all solutions listed on page
Find this page (the one you are reading now) and try all the solutions from other comments
Find somewhere information that setting COMPOSE_CONVERT_WINDOWS_PATHS=1 environment variable might solve the issue.
Stop looking for the solution for few months
Go back and check the same links again
Cry deeply
Feel the enlightenment moment
Open VirtualBox (you have it already installed along with the docker tools)
Create virtual machine with second ethernet card - bridged, this way it will receive IP address from your network (it will have IP like docker machine)
Install Ubuntu LTS which is very recent (not older than few months)
Notice that the automounting is not really working and the integration is broken (like clipboard sharing etc.)
Delete virtual machine
Go out and have a drink
Rent expensive car and go with high speed on highway
Destroy the car and die
Respawn in front of your PC
Install Ubuntu LTS which is older than 1 year
Try to run docker
Notice it’s not installed
Install docker by apt-get install docker
Install suggested docker.io
Try to run docker-compose
Notice it’s not installed
apt get install docker-compose
Try to run your project with docker-compose
Notice that it’s old version
Check your power level (it should be over 9000)
Search how to install latest version of docker and find the official guide https://docs.docker.com/install/linux/docker-ce/ubuntu/
Uninstall the current docker-compose and docker.io
Install docker using the official guide https://docs.docker.com/install/linux/docker-ce/ubuntu/
Add shared directories to the virtual machine and automount your project directories (this way you have access to the project directory from Ubuntu, so you can run any docker command)
Done
As of August 2016 Docker for windows now uses hyper-v directly instead of virtualbox, so I think it is a little different. First share the drive in settings then use the C: drive letter format, but use forward slashes. For instance I created an H:\t\REDIS directory and was able to see it mounted on /data in the container with this command:
docker run -it --rm -v h:/t/REDIS:/data redis sh
The same format, using drive letter and a colon then forward slashes for the path separator worked both from windows command prompt and from git bash.
I found this question googling to find an answer, but I couldn't find anything that worked. Things would seem to work with no errors being thrown, but I just couldn't see the data on the host (or vice-versa). Finally I checked out the settings closely and tried the format they show:
So first, you have to share the whole drive to the docker vm in settings here, I think that gives the 'docker-machine' vm running in hyper-v access to that drive. Then you have to use the format shown there, which seems to only exist in this one image and in no documentation or questions I could find on the web:
docker run --rm -v c:/Users:/data alpine ls /data
Simply using double leading slashes worked for me on Windows 7:
docker run --rm -v //c/Users:/data alpine ls /data/
Taken from here: https://github.com/moby/moby/issues/12590
Try this:
Open Docker Quickstart Terminal. If it is already open, run $ cd ~ to make sure you are in Windows user directory.
$ docker run -it -v /$(pwd)/ubuntu:/windows ubuntu
It will work if the error is due to typo. You will get an empty folder named ubuntu in your user directory. You will see this folder with the name windows in your ubuntu container.
For those using Virtual Box who prefer command-line approach
1) Make sure the docker-machine is not running
Docker Quickstart Terminal:
docker-machine stop
2) Create the sharing Windows <-> docker-machine
Windows command prompt:
(Modify following to fit your scenario. I feed my Apache httpd container from directory synced via Dropbox.)
set VBOX=D:\Program Files\Oracle\VirtualBox\VBoxManage.exe
set VM_NAME=default
set NAME=c/htdocs
set HOSTPATH=%DROPBOX%\htdocs
"%VBOX%" sharedfolder add "%VM_NAME%" --name "%NAME%" --hostpath "%HOSTPATH%" --automount
3) Start the docker-machine and mount the volume in a new container
Docker Quickstart Terminal:
(Again, I am starting an Apache httpd container, hence that port exposing.)
docker-machine start
docker run -d --name my-apache-container-0 -p 80:80 -v /c/htdocs:/usr/local/apache2/htdocs my-apache-image:1.0
share folders virtualBox toolbox and windows 7 and nodejs image container
using...
Docker Quickstart Terminal [QST]
Windows Explorer [WE]
lets start...
[QST] open Docker Quickstart Terminal
[QST] stop virtual-machine
$ docker-machine stop
[WE] open a windows explorer
[WE] go to the virtualBox installation dir
[WE] open a cmd and execute...
C:\Program Files\Oracle\VirtualBox>VBoxManage sharedfolder add "default" --name
"/d/SVN_FOLDERS/X2R2_WP6/nodejs" --hostpath "\?\d:\SVN_FOLDERS\X2R2_WP6\nodejs" --automount
check in the oracle virtual machine, that the new shared folder has appeared
[QST] start virtual-machine
$ docker-machine start
[QST] run container nodejs
docker stop nodejs
docker rm nodejs
docker run -d -it --rm --name nodejs -v /d/SVN_FOLDERS/X2R2_WP6/nodejs:/usr/src/app -w /usr/src/app node2
[QST] open bash to the container
docker exec -i -t nodejs /bin/bash
[QST] execute dir and you will see the shared files
I solved it!
Add a volume:
docker run -d -v my-named-volume:C:\MyNamedVolume testimage:latest
Mount a host directory:
docker run -d -v C:\Temp\123:C:\My\Shared\Dir testimage:latest