I am trying to access the .csv file which my dockerized python program is making.
Here is my docker file:
# Use an official Python runtime as a parent image
FROM python:3.7
# Set the working directory to /app
WORKDIR /BotCloud
# Copy the current directory contents into the container at /app
ADD . /BotCloud
# Install any needed packages specified in requirements.txt
RUN pip install --trusted-host pypi.python.org -r requirements.txt
RUN wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz && \
tar -xvzf ta-lib-0.4.0-src.tar.gz && \
cd ta-lib/ && \
./configure --prefix=/usr && \
make && \
make install
RUN rm -R ta-lib ta-lib-0.4.0-src.tar.gz
RUN pip install ta-lib
# Run BotFinal.py when the container launches
CMD ["python","-u", "BotLiveSnake.py"]
Here is the code snippet that is in my python file BotSnakeLive.py
def write(string):
with open('outfile.csv','w') as f:
f.write(string)
f.write("\n")
write(str("Starting Time: "+datetime.datetime.utcfromtimestamp(int(df.tail(1)['Open Time'])/10**3).strftime('%Y-%m-%d,%H:%M:%SUTC'))+",Trading:"+str(pairing)+",Starting Money:"+str(money)+",SLpercent:"+str(SLpercent)+",TPpercent,"+str(TPpercent))
Running my python program locally, outfile.csv is created in the same folder as my python program. However, with docker, I'm not sure where this outfile ends up. Any help would be appreciated.
In general, references to file paths that don't start with / are always interpreted relative to the current working directory. Unless you've changed that somehow (os.chdir, an entrypoint script running cd, the docker run -w option) that will be the WORKDIR you declared in the Dockerfile.
So: your file should be in /BotCloud/outfile.csv, in the container's filesystem space.
Note that containers have their own isolated filesystem space that is destroyed when the container is deleted. If the primary way your application communicates is via files, it may be much easier to use a non-Docker mechanism, such as Python virtual environments, to isolate your application from the rest of the system. You can mount a host directory into the container with docker run -v, or docker cp files out. (Note with docker run -v in particular it is helpful if the data is written to someplace that isn't the same directory as your application.)
Related
Helli, I have to build a Docker image for the following bioinformatics tool: https://github.com/CAMI-challenge/CAMISIM. Their dockerfile works but takes a long time to build and I would like to build my own, slightly differently, to learn. I face issues: there are several python script that I should be able to choose to run, not only a main. If I add one script in particular as an ENTRYPOINT then the behavior isn't exactly what I shoud have.
The Dockerfile:
FROM ubuntu:20.04
ENV DEBIAN_FRONTEND=noninteractive
USER root
#COPY ./install_docker.sh ./
#RUN chmod +x ./install_docker.sh && sh ./install_docker.sh
RUN apt-get update && \
apt install -y git python3-pip libxml-simple-perl libncursesw5 && \
git clone https://github.com/CAMI-challenge/CAMISIM.git && \
pip3 install numpy ete3 biom-format biopython matplotlib joblib scikit-learn
ENTRYPOINT ["python3"]
ENV PATH="/CAMISIM/:${PATH}"
This yields :
sudo docker run camisim:latest metagenomesimulation.py --help
python3: can't open file 'metagenomesimulation.py': [Errno 2] No such file or directory
Adding that script as an ENTRYPOINT after python3 allows me to use it with 2 drawbacks: I cannot use another script (I could build a second docker image but that would be a bad solution), and it outputs:
ERROR: 0
usage: python metagenomesimulation.py configuration_file_path
#######################################
# MetagenomeSimulationPipeline #
#######################################
Pipeline for the simulation of a metagenome
optional arguments:
-h, --help show this help message and exit
-silent, --silent Hide unimportant Progress Messages.
-debug, --debug_mode more information, also temporary data will not be deleted
-log LOGFILE, --logfile LOGFILE
output will also be written to this log file
optional config arguments:
-seed SEED seed for random number generators
-s {0,1,2}, --phase {0,1,2}
available options: 0,1,2. Default: 0
0 -> Full run,
1 -> Only Comunity creation,
2 -> Only Readsimulator
-id DATA_SET_ID, --data_set_id DATA_SET_ID
id of the dataset, part of prefix of read/contig sequence ids
-p MAX_PROCESSORS, --max_processors MAX_PROCESSORS
number of available processors
required:
config_file path to the configuration file
You can see there is an error that should'nt be there, it actually does not use the help flag. The original Dockerfile is:
FROM ubuntu:20.04
RUN apt update
RUN apt install -y python3 python3-pip perl libncursesw5
RUN perl -MCPAN -e 'install XML::Simple'
ADD requirements.txt /requirements.txt
RUN cat requirements.txt | xargs -n 1 pip install
ADD *.py /usr/local/bin/
ADD scripts /usr/local/bin/scripts
ADD tools /usr/local/bin/tools
ADD defaults /usr/local/bin/defaults
WORKDIR /usr/local/bin
ENTRYPOINT ["python3"]
It works but shows the error as above, so not so much. Said error is not present when using the tool outside of docker. Last time I made a Docker image I just pulled the git repo and added the main .sh script as an ENTRYPOINT and everything worked despite being more complex (see https://github.com/Louis-MG/Metadbgwas).
Why would I need ADD and moving everything ? I added the git folder to the path, why can't I find the scripts ? How is it different from the Metadbgwas image ?
In your first setup, you start in the image root directory / and run git clone to check out the repository into /CAMISIM. You never change the current directory, though, so when you try to run python3 metagenomesimulation.py --help it's looking in / and not /CAMISIM, hence the "not found" error.
You can fix this just by changing the current directory. At any point after you check out the repository, run
WORKDIR /CAMISIM
You should also delete the ENTRYPOINT line. For each of the scripts you could run as a top-level entry point, check two things:
Is it executable; if you ls -l metagenomesimulation.py are there x in the permission listing? If not, on the host system, run chmod +x metagenomesimulation.py and commit to source control. (Or you could RUN chmod ... in the Dockerfile if you really can't change the repository.)
Does it have a "shebang" line? The very first line of the script should be
#!/usr/bin/env python3
If both of these things are true, then you can just run ./metagenomesimulation.py without explicitly saying python3; since you add the directory to $PATH as well, you can probably run it without specifying the ./... file location.
(Probably deleting the ENTRYPOINT line on its own is enough, given that ENV PATH setting, but your script still might be confused by starting up in the wrong directory.)
The long "help" output just suggests to me that the script is expecting a configuration file name as a parameter and you haven't provided it, or else you've repeated the script name in both the entrypoint and command parts of the container command string.
In the end very little was recquired and the original Dockerfile was correct, the same error is displayed anyway, that is due to the script itself.
What was missing was a link to the interpreter, so I could remove the ENTRYPOINT and actually interpret the script instead of having python look for it in its own path. The Dockerfile:
FROM ubuntu:20.04
ENV DEBIAN_FRONTEND=noninteractive
USER root
RUN ln -s /usr/bin/python3 /usr/bin/python
RUN apt-get update && \
apt install -y git python3-pip libxml-simple-perl libncursesw5 && \
git clone https://github.com/CAMI-challenge/CAMISIM.git && \
pip3 install numpy ete3 biom-format biopython matplotlib joblib scikit-learn
ENV PATH="/CAMISIM:${PATH}"
Trying WORKDIR as suggested instead of the PATH yielded an error.
I can't run 2 containers whereas I can run each one them separately.
I have this 1st container/image related to this DockerFile
FROM debian:latest
RUN apt-get update && apt-get install python3-pip -y && pip3 install requests
ADD test1.py /app/container1/test1.py
WORKDIR /app/
CMD python3 container1/test1.py
I have this 2nd container/image related to this DockerFile
FROM debian:latest
RUN apt-get update && apt-get install python3-pip -y && pip3 install requests
ADD test2.py /app/container2/test2.py
WORKDIR /app/
CMD python3 container2/test2.py
No issues to create images:
docker image build ./authentif -t test1:latest
docker image build ./authoriz -t test2:latest
When I run the 1st container with this command:
docker container run -it --network my_network --name test1_container\
--mount type=volume,src=my_volume,dst=/app -e LOG=1\
--rm test1:latest
it works.
And If i want to check my volume:
sudo ls /var/lib/docker/volumes/my_volume/_data
I can see data in my volume
However when I want run the 2nd container:
docker container run -it --network my_network --name test2_container\
--mount type=volume,src=my_volume,dst=/app -e LOG=1\
--rm test2:latest
I have this error:
python3: can't open file '/app/container2/test2.py': [Errno 2] No such file or directory
If i delete everything and start over : if I start running the 2nd container it works but then id I want to run the 1st container, i have the error again.
why is that?
in my container1, let's assume that my script python writes data in a file, for example :
import os
print("test111111111")
if os.environ.get('LOG') == "1":
print("1111111")
with open('record.log', 'a') as file:
file.write("file11111")
I can't reproduce your issue. When I start 2 containers using
docker run -d --rm -v myvolume:/app --name container1 debian tail -f /dev/null
docker run -d --rm -v myvolume:/app --name container2 debian tail -f /dev/null
and then do
docker exec container1 /bin/sh -c 'echo hello > /app/hello.txt'
docker exec container2 cat /app/hello.txt
it prints out 'hello' as expected.
You are mounting the volume over /app, the directory that contains your application code. That hides the code and replaces it with something else.
The absolute best approach here, if you can handle it, is to avoid sharing files at all. Keep the data somewhere like a relational database (which may be stateful). Don't mount anything on to your containers. Especially if you're looking forward to a clustered environment like Kubernetes, sharing files can be surprisingly tricky.
If you can't get rid of the shared directory, then put it somewhere other than /app. You might need to configure the alternate directory using an environment variable.
docker container run ... \
--mount type=volume,src=my_volume,dst=/data \ # /data, not /app
...
What's actually happening in your setup is that Docker has a feature to copy the contents of the image into an empty named volume on first use. This only happens if the volume is completely empty, this only happens with a named Docker volume and not bind mounts, and this doesn't happen on other container systems like Kubernetes. (I'd discourage actually relying on this behavior.)
So when you run the first container, it sees that my_volume is empty and copies the test1 image into it; then the container sees the code it expects it in /app and it apparently works fine. The second container sees my_volume is non-empty, and so the volume contents (with the first image's code) hide what was in the image (the second image's code). I'd expect, if you started from scratch, whichever of the two containers you started first would work, but not the other, and if you change the code in the working image, a new container won't see that change (it will use the code out of the volume).
The image stores the application source code in /app. When running a container off the image without volume mapping, it works just fine.
If I setup a mount point for /app:/opt/test then I get the following error:
python: can't open file 'run.py': [Errno 2] No such file or directory
I can't seem to figure out what exactly is the problem. Can the application source code not be directly setup in a volume? I need to be able to mount the /app directory to the host and still run code inside /app, or some alternative.
Dockerfile:
FROM python:3.8-slim-buster
RUN mkdir /app
# Install SCIP requirements
RUN apt-get update && apt-get install -y wget libgfortran4 libblas3 liblapack3 libtbb-dev libgsl-dev libboost-all-dev build-essential g++ python-dev autotools-dev libicu-dev build-essential libbz2-dev libgmp3-dev libreadline-dev
RUN wget https://www.scipopt.org/download/release/SCIPOptSuite-7.0.1-Linux.sh -O scip.sh && chmod +x scip.sh && ./scip.sh --skip-license && mv bin/scip /app/scip
VOLUME ["/app"]
WORKDIR /app
# Install pip requirements
ADD requirements.txt .
RUN python -m pip install -r requirements.txt
ADD . /app
# Switching to a non-root user, please refer to https://aka.ms/vscode-docker-python-user-rights
RUN useradd appuser && chown -R appuser /app
USER appuser
# During debugging, this entry point will be overridden. For more information, please refer to https://aka.ms/vscode-docker-python-debug
ENTRYPOINT ["python", "run.py"]
# Keeps Python from generating .pyc files in the container
ENV PYTHONDONTWRITEBYTECODE 1
# Turns off buffering for easier container logging
ENV PYTHONUNBUFFERED 1
# Hypixel API key
ENV API_KEY key
# Bot Discord token
ENV DISCORD_TOKEN token
How to solve it:
Just remove from your Dockerfile
VOLUME ["/app"]
Explanation:
You're creating an unnamed-volume in your Dockerfile before copying it. So, your files added with ADD . /app are not being saved in your image, but in your volume.
When you create a VOLUME in a Dockerfile (not with docker create volume), it's unnamed. It means that docker assign an arbitrary name (in the following example, 69e64d18f338whatever) for it and save data in /var/lib/docker/volumes/69e64d18f338whatever/_data
So, if you create container docker run without mounting these data you can't find it in your image.
Some good practices.
A good practices is use VOLUMES in Dockerfile for logs and volatile info.
If you want to use config, VOLUMES in Dockerfile is not recommended either. For configuration, it's better use named volumes.
For binaries used as entrypoint or command, you should set them directly in the docker image and never in a docker volume.
I think your mountpoint is reversed.
The correct syntax is host_folder:container_folder not the other way around.
Try mounting /opt/test:/app.
FROM python:3
WORKDIR /Users/vaibmish/Documents/new/graph-report
RUN pip install graphreport==1.2.1
CMD [ cd /Users/vaibmish/Documents/new/graph-report/graphreport_metrics ]
CMD [ graphreport ]
THIS IS PART OF DCOKERFIILE
i wish to remove cd volumes from tha file and have a command like -v there so that whoever runs that can give his or her own volume path in same
The line
CMD [ cd /Users/vaibmish/Documents/new/graph-report/graphreport_metrics ]
is wrong. You achieve the same with WORKDIR:
WORKDIR /Users/vaibmish/Documents/new/graph-report/graphreport_metrics
WORKDIR creates the path if it doesn't exist and then changes the current directory to that path (same as mkdir -p /path/new && cd /path/new)
You can also declare the path as a volume and instruct who runs the container to provide their own path (docker run -v host_path:container_path ...)
VOLUME /Users/vaibmish/Documents/new/graph-report
A final note: It looks like these paths are from the host. Remember that the paths in the Dockerfile are not host paths. They are paths inside the container.
Typical practice here is to pick some fixed path inside the Docker container. It should be a different path from where your application is installed; it does not need to match any particular host path at all.
FROM python:3
RUN pip3 install graphreport==1.2.1
WORKDIR /data
CMD ["graphreport"]
docker build -t me/graphreport:1.2.1 .
docker run --rm \
-v /Users/vaibmish/Documents/new/graph-report:/data \
me/graphreport:1.2.1
(Remember that only the last CMD has an effect, and if it's not a well-formed JSON array, Docker will interpret it as a shell command. What you show in the question would run the test(1) command and not the program you're installing.)
If you're trying to install a single package from PyPI and just run it on local files, a Python virtual environment will be much easier to set up than anything based on Docker, and will essentially work as you expect:
python3 -m venv graphreport
. graphreport/bin/activate
pip3 install graphreport==1.2.1
cd /Users/vaibmish/Documents/new/graph-report
graphreport
deactivate # switch back to system Python/pip
All of the installed Python code is inside the graphreport virtual environment directory, and if you don't need this application again, you can just delete the directory tree.
I am trying to set up a very basic data processing project where I use docker to create an ubuntu environment on an EC2, install python, take an input csv, perform some simple data manipulation, then output the data to a new csv in the folder where the input was. I have been able to successfully run my python code locally, as well as on the ec2, but when I run it with the docker container, the data appears to be processed (my script prints out the data), but the results not saved at the end of the run. Is there a command I am missing from my dockerfile that is causing the results not to be saved? Alternatively, is there a way I can save the output directly to an S3 bucket?
EDIT: The the path to the input files is "/home/ec2-user/docker_test/data" and the path to the code is "/home/ec2-user/docker_test/code". After the data is processed, I want the result to be written as a new file in the "/home/ec2-user/docker_test/data" directory on the host.
Dockerfile:
FROM ubuntu:latest
RUN apt-get update \
&& apt-get install -y --no-install-recommends software-properties-common \
&& add-apt-repository -y ppa:deadsnakes/ppa \
&& apt-get update \
&& apt-get install -q -y --no-install-recommends python3.6 python3.6-dev python3-pip python3-setuptools \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
VOLUME /home/ec2-user/docker_test/data
VOLUME /home/ec2-user/docker_test/code
WORKDIR /home/ec2-user/docker_test/
COPY requirements.txt ./
RUN cat requirements.txt | xargs -n 1 -L 1 python3.6 -m pip install --no-cache-dir
COPY . .
ENV LC_ALL C.UTF-8
ENV LANG=C.UTF-8
CMD python3.6 main.py
Python Script:
import pandas as pd
import os
from code import processing
path = os.getcwd()
def main():
df = pd.read_csv(path + '/data/table.csv')
print('input df: \n{}'.format(df))
df_out = processing.processing(df)
df_out.to_csv(path + '/data/updated_table.csv', index = False)
print('\noutput df: \n{}'.format(df_out))
if __name__ == '__main__':
main()
EDIT: I have been running the dockerfile with "docker run docker_test"
Ok, gotcha, with the edit about expectations of the CSV being output to the host, we do have a problem with how this is set up.
You've got two VOLUMEs declared in your Dockerfile, which is fine. These are named volumes, which are great for persisting data between containers going up and down on a single host, but you aren't able to easily just go in like it's a normal file system from your host.
If you want the file to show up on your host, you can create a bind mounted volume at runtime, which maps a path in your host filesystem to a path in the Docker container's filesystem.
docker run -v $(pwd):/home/ec2-user/docker_test/data docker_test will do this. $(pwd) is an expression that evaluates to your current working directory if you're on a *nix system, where you're running the command. Take care with that and adjust as needed (like if you're using Windows as your host).
With a volume set up this way, when the CSV is created in the container file system at the location you intend, it will be accessible on your host in the location relative to however you've mapped it.
Read up on volumes. They're vital to using Docker, not hard to grasp at first glance, but there a some gotchas in the details.
Regarding uploading to S3, I would recommend using the boto3 library and doing it in your Python script. You could also use something like s3cmd if you find that simpler.
You could use S3FS Fuse to mount the S3 bucket as a drive in your docker container. This basically creates a folder on your filesystem that is actually the S3 bucket. Anything that you save/modify in that folder will be reflected in the S3 bucket.
If you delete the docker container or unmount the drive you still have your S3 bucket intact, so you don't need to worry too much about erasing files in the S3 bucket through normal docker use.