[Feature] Generate the environment file without installing
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- Dominant language
- Dockerfile
- Stars
- 3
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
Description
At the moment we install the conda environment in the Docker container to primarily get the environment file with all the resolved dependencies. This can also be achieve with a simple dry-run command:
def get_detailed_environment(environment_input_file, environment_output_file):
output_start = subprocess.check_output(
["conda", "env", "create", "-n", "testenv", "-f", environment_input_file, "--dry-run", "--json"],
shell=False,
universal_newlines=True
)
output_start_dict = json.loads(output_start)
output_dict = output_start_dict.copy()
if output_dict["name"] is None:
del output_dict["name"]
output_dict["dependencies"] = list(sorted([
dep.split("::")[-1].replace("==", "=")
for dep in output_dict["dependencies"]
]))
with open(environment_output_file, "w") as f:
f.writelines(yaml.dump(output_dict))
output_extended = subprocess.check_output(
["conda", "env", "create", "-n", "testenv", "-f", environment_output_file, "--dry-run", "--json"],
shell=False,
universal_newlines=True
)
output_extended_dict = json.loads(output_extended)
return output_extended_dict == output_start_dict
So maybe it is possible to first create the conda environment files and then install them in the docker containers.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at the Docker environment-creation flow and locate where the conda environment file is currently produced and installed. Use the proposed get_detailed_environment entry point and conda's dry-run JSON output as the reference, then verify that the environment file is generated without installation and that the Docker build can install it afterward.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- anaconda, docker, python
- Domain
- build-system, devops
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100