Read below how Metaflow has improved over time.
We take backwards compatibility very seriously. In the vast majority of cases, you can upgrade Metaflow without expecting changes in your existing code. In the rare cases when breaking changes are absolutely necessary, usually, due to bug fixes, you can take a look at minor breaking changes below before you upgrade.
The Metaflow 2.3.6 release is a patch release.
Prior to this release, setting default execution environment to conda through METAFLOW_DEFAULT_ENVIRONMENT would result in a recursion error.
METAFLOW_DEFAULT_ENVIRONMENT=conda python flow.py run
File "/Users/savin/Code/metaflow/metaflow/cli.py", line 868, in start
if e.TYPE == environment][0](ctx.obj.flow)
File "/Users/savin/Code/metaflow/metaflow/plugins/conda/conda_environment.py", line 27, in __init__
if e.TYPE == DEFAULT_ENVIRONMENT][0](self.flow)
File "/Users/savin/Code/metaflow/metaflow/plugins/conda/conda_environment.py", line 27, in __init__
if e.TYPE == DEFAULT_ENVIRONMENT][0](self.flow)
File "/Users/savin/Code/metaflow/metaflow/plugins/conda/conda_environment.py", line 27, in __init__
if e.TYPE == DEFAULT_ENVIRONMENT][0](self.flow)
[Previous line repeated 488 more times]
File "/Users/savin/Code/metaflow/metaflow/plugins/conda/conda_environment.py", line 24, in __init__
from ...plugins import ENVIRONMENTS
RecursionError: maximum recursion depth exceeded
This release fixes this bug.
Dots in volume names - @batch(host_volumes='/path/with/.dot') weren't being santized properly resulting in errors when a Metaflow task launched on AWS Batch. This release fixes this bug.
The Metaflow 2.3.5 release is a patch release.
With this release, you can now mount and access instance host volumes within a Metaflow task running on AWS Batch. To access a host volume, you can add host-volumes argument to your @batch decorator -
@batch(host_volumes=['/home', '/var/log'])
The following flow had a bug where the value for self.input was being imputed to None rather than the dictionary element. This release fixes this issue -
from metaflow import FlowSpec, Parameter, step, JSONType
class ForeachFlow(FlowSpec):
numbers_param = Parameter(
"numbers_param",
type=JSONType,
default='[1,2,3]'
)
@step
def start(self):
# This works, and passes each number to the run_number step:
#
# self.numbers = self.numbers_param
# self.next(self.run_number, foreach='numbers')
# But this doesn't:
self.next(self.run_number, foreach='numbers_param')
@step
def run_number(self):
print(f"number is {self.input}")
self.next(self.join)
@step
def join(self, inputs):
self.next(self.end)
@step
def end(self):
pass
if __name__ == '__main__':
ForeachFlow()The Metaflow 2.3.4 release is a patch release.
PR #607 in Metaflow 2.3.3 introduced a bug with step-functions create command for IncludeFile parameters. This release rolls back that PR. A subsequent release will reintroduce a modified version of PR #607.
The Metaflow 2.3.3 release is a patch release.
Metaflow now supports setting resource tags for AWS Batch jobs and propagating them to the underlying ECS tasks. The following tags are attached to the AWS Batch jobs now -
metaflow.flow_namemetaflow.run_idmetaflow.step_namemetaflow.user/metaflow.ownermetaflow.versionmetaflow.production_token
To enable this feature, set the environment variable (or alternatively in the metaflow config) METAFLOW_BATCH_EMIT_TAGS to True. Keep in mind that the IAM role (MetaflowUserRole, StepFunctionsRole) submitting the jobs to AWS Batch will need to have the Batch:TagResource permission.
Prior to this release, a parameter specification like -
Parameter(name="test_param", type=int, default=None)
will result in an error even though the default has been specified
Flow failed:
The value of parameter test_param is ambiguous. It does not have a default and it is not required.
This release fixes this behavior by allowing the flow to execute as it would locally.
The IncludeFile parameter would return JSONified metadata about the file rather than the file contents when accessed through the Metaflow Client. This release fixes that behavior by returning instead the file contents, just like any other Metaflow data artifact.
The Metaflow 2.3.2 release is a minor release.
- Features
step-functions triggercommand now supports--run-id-fileoption
step-functions trigger command now supports --run-id-file option
Similar to run , you can now pass --run-id-file option to step-function trigger. Metaflow then will write the triggered run id to the specified file. This is useful if you have additional scripts that require the run id to examine the run or wait until it finishes.
The Metaflow 2.3.1 release is a minor release.
****Performance optimizations for merge_artifacts****
Prior to this release, FlowSpec.merge_artifacts was loading all of the merged artifacts into memory after doing all of the consistency checks with hashes. This release now avoids the memory and compute costs of decompressing, de-pickling, re-pickling, and recompressing each merged artifact - resulting in improved performance of merge_artifacts.
The Metaflow 2.3.0 release is a minor release.
- Features
It's not uncommon for multiple people to work on the same workflow simultaneously. Metaflow makes it possible by keeping executions isolated through independently stored artifacts and namespaces. However, by default, all AWS Step Functions deployments are bound to the name of the workflow. If multiple people call step-functions create independently, each deployment will overwrite the previous one. In the early stages of a project, this simple model is convenient but as the project grows, it is desirable that multiple people can test their own AWS Step Functions deployments without interference. Or, as a single developer, you may want to experiment with multiple independent AWS Step Functions deployments of their workflow. This release introduces a @project decorator to address this need. The @project decorator is used at the FlowSpec-level to bind a Flow to a specific project. All flows with the same project name belong to the same project.
from metaflow import FlowSpec, step, project, current
@project(name='example_project')
class ProjectFlow(FlowSpec):
@step
def start(self):
print('project name:', current.project_name)
print('project branch:', current.branch_name)
print('is this a production run?', current.is_production)
self.next(self.end)
@step
def end(self):
pass
if __name__ == '__main__':
ProjectFlow()
python flow.py run
The flow works exactly as before when executed outside AWS Step Functions and introduces project_name, branch_name & is_production in the current object.
On AWS Step Functions, however, step-functions create will create a new workflow example_project.user.username.ProjectFlow (where username is your user name) with a user-specific isolated namespace and a separate production token.
For deploying experimental (test) versions that can run in parallel with production, you can deploy custom branches with --branch
python flow.py --branch foo step-functions create
To deploy a production version, you can deploy with --production flag (or pair it up with --branch if you want to run multiple variants in production)
python project_flow.py --production step-functions create
Note that the isolated namespaces offered by @project work best when your code is designed to respect these boundaries. For instance, when writing results to a table, you can use current.branch_name to choose the table to write to or you can disable writes outside production by checking current.is_production.
Prior to this release, hyphenated parameters in AWS Step Functions weren't supported through CLI.
from metaflow import FlowSpec, Parameter, step
class ParameterFlow(FlowSpec):
foo_bar = Parameter('foo-bar',
help='Learning rate',
default=0.01)
@step
def start(self):
print('foo_bar is %f' % self.foo_bar)
self.next(self.end)
@step
def end(self):
print('foo_bar is still %f' % self.foo_bar)
if __name__ == '__main__':
ParameterFlow()
Now, users can create their flows as usual on AWS Step Functions (with step-functions create) and trigger the deployed flows through CLI with hyphenated parameters -
python flow.py step-functions trigger --foo-bar 42
Metaflow now logs State Machine execution history in AWS CloudWatch Logs for deployed Metaflow flows. You can enable it by specifying --log-execution-history flag while creating the state machine
python flow.py step-functions create --log-execution-history
Note that you would need to set the environment variable (or alternatively in your Metaflow config) METAFLOW_SFN_EXECUTION_LOG_GROUP_ARN to your AWS CloudWatch Logs Log Group ARN to pipe the execution history logs to AWS CloudWatch Logs
The Metaflow 2.2.13 release is a minor patch release.
Certain docker images override the entrypoint by executing eval on the user-supplied command. The 2.2.10 release impacted these docker images where we modified the entrypoint to support datastore based logging. This release fixes that regression.
The Metaflow 2.2.12 release is a minor patch release.
- Features
- Bug Fixes
Add capability to override AWS Step Functions state machine name while deploying flows to AWS Step Functions
Prior to this release, the State Machines created by Metaflow while deploying flows to AWS Step Functions had the same name as that of the flow. With this release, Metaflow users can now override the name of the State Machine created by passing in a --name argument : python flow.py step-functions --name foo create or python flow.py step-functions --name foo trigger.
Metaflow now registers heartbeats at the run level and the task level for all flow executions (with the exception of flows running on AWS Step Functions where only task-level heartbeats are captured). This provides the necessary metadata to ascertain if a run/task has been lost. Subsequent releases of Metaflow will expose this information through the client.
The latest release of Click (8.0.0) broke certain idempotency assumptions in Metaflow which PR #526 addresses.
The Metaflow 2.2.11 release is a minor patch release.
- Bug Fixes
- Fix regression that broke compatibility with Python 2.7
shlex.quote, introduced in #493, is not compatible with Python 2.7. pipes.quote is now used for Python 2.7.
The Metaflow 2.2.10 release is a minor patch release.
- Features
- AWS Logs Group, Region and Stream are now available in metadata for tasks executed on AWS Batch
- Execution logs are now available for all tasks in Metaflow universe
- Bug Fixes
- Fix regression with
ping/endpoint for Metadata service - Fix the behaviour of
--namespace=CLI args when executing a flow
- Fix regression with
For tasks that execute on AWS Batch, Metaflow now records the location where the AWS Batch instance writes the container logs in AWS Logs. This can be handy in locating the logs through the client API -
Step('Flow/42/a').task.metadata_dict['aws-batch-awslogs-group']
Step('Flow/42/a').task.metadata_dict['aws-batch-awslogs-region']
Step('Flow/42/a').task.metadata_dict['aws-batch-awslogs-stream']
All Metaflow runtime/task logs are now published via a sidecar process to the datastore. The user-visible logs on the console are streamed directly from the datastore. For Metaflow's integrations with the cloud (AWS at the moment), the compute tasks logs (AWS Batch) are directly written by Metaflow into the datastore (Amazon S3) independent of where the flow is launched from (User's laptop or AWS Step Functions). This has multiple benefits
- Metaflow no longer relies on AWS Cloud Watch for fetching the AWS Batch execution logs to the console - AWS Cloud Watch has rather low global API limits which have caused multiple issues in the past for our users
- Logs for AWS Step Functions executions are now also available in Amazon S3 and can be easily fetched by simply doing
python flow.py logs 42/startorStep('Flow/42/start').task.stdout.
Fix a regression introduced in v2.2.9 where the endpoint responsible for ascertaining the version of the deployed Metadata service was erroneously moved to ping/ from ping
python flow.py run --namespace= now correctly makes the global namespace visible within the flow execution.
The Metaflow 2.2.9 release is a minor patch release.
- Bug Fixes
- Remove pinned pylint dependency
- Improve handling of
/in image parameter for batch - List custom FlowSpec parameters in the intended order
Pylint dependency was unpinned and made floating. See PR #462.
You are now able to specify docker images of the form foo/bar/baz:tag in the batch decorator. See PR #466.
The order in which parameters are specified by the user in the FlowSpec is now preserved when displaying them with --help. See PR #456.
The Metaflow 2.2.8 release is a minor patch release.
- Bug Fixes
Metaflow was incorrectly handling environment variables passed through the @environment decorator in some specific instances. When @environment decorator is specified over multiple steps, the actual environment that's available to any step is the union of attributes of all the @environment decorators; which is incorrect behavior. For example, in the following workflow -
from metaflow import FlowSpec, step, batch, environment
import os
class LinearFlow(FlowSpec):
@environment(vars={'var':os.getenv('var_1')})
@step
def start(self):
print(os.getenv('var'))
self.next(self.a)
@environment(vars={'var':os.getenv('var_2')})
@step
def a(self):
print(os.getenv('var'))
self.next(self.end)
@step
def end(self):
pass
if __name__ == '__main__':
LinearFlow()
var_1=foo var_2=bar python flow.py run
will result in
Metaflow 2.2.7.post10+gitb7d4c48 executing LinearFlow for user:savin
Validating your flow...
The graph looks good!
Running pylint...
Pylint is happy!
2021-03-12 20:46:04.161 Workflow starting (run-id 6810):
2021-03-12 20:46:04.614 [6810/start/86638 (pid 10997)] Task is starting.
2021-03-12 20:46:06.783 [6810/start/86638 (pid 10997)] foo
2021-03-12 20:46:07.815 [6810/start/86638 (pid 10997)] Task finished successfully.
2021-03-12 20:46:08.390 [6810/a/86639 (pid 11003)] Task is starting.
2021-03-12 20:46:10.649 [6810/a/86639 (pid 11003)] foo
2021-03-12 20:46:11.550 [6810/a/86639 (pid 11003)] Task finished successfully.
2021-03-12 20:46:12.145 [6810/end/86640 (pid 11009)] Task is starting.
2021-03-12 20:46:15.382 [6810/end/86640 (pid 11009)] Task finished successfully.
2021-03-12 20:46:15.563 Done!
Note the output for the step a which should have been bar. PR #452 fixes the issue.
Using @environment would often result in an error from pylint - E1102: environment is not callable (not-callable). Users were getting around this issue by launching their flows with --no-pylint. PR #451 fixes this issue.
The Metaflow 2.2.7 release is a minor patch release.
Workflows orchestrated by AWS Step Functions were failing to properly execute for-each steps on AWS Fargate. The culprit was lack of access to instance metadata for ECS. Metaflow instantiates a connection to Amazon DynamoDB to keep track of for-each cardinality. This connection requires knowledge of the region that the job executes in and is made available via instance metadata on EC2; which unfortunately is not available on ECS (for AWS Fargate). This fix introduces the necessary checks for inferring the region correctly for tasks executing on AWS Fargate. Note that after the recent changes to Amazon S3's consistency model, the Amazon DynamoDB dependency is no longer needed and will be done away in a subsequent release. PR: #436
The Metaflow 2.2.6 release is a minor patch release.
- Features
- Support AWS Fargate as compute backend for Metaflow tasks launched on AWS Batch
- Support
shared_memory,max_swap,swappinessattributes for Metaflow tasks launched on AWS Batch - Support wider very-wide workflows on top of AWS Step Functions
- Bug Fixes
- Assign tags to
Runobjects generated through AWS Step Functions executions - Pipe all workflow set-up logs to
stderr - Handle null assignment to
IncludeFileproperly
- Assign tags to
At AWS re:invent 2020, AWS announced support for AWS Fargate as a compute backend (in addition to EC2) for AWS Batch. With this feature, Metaflow users can now submit their Metaflow jobs to AWS Batch Job Queues which are connected to AWS Fargate Compute Environments as well. By setting the environment variable - METAFLOW_ECS_FARGATE_EXECUTION_ROLE, users can configure the ecsTaskExecutionRole for the AWS Batch container and AWS Fargate agent.
The @batch decorator now supports shared_memory, max_swap, swappiness attributes for Metaflow tasks launched on AWS Batch to provide a greater degree of control for memory management.
The tag metaflow_version: and runtime: is now available for all packaged executions and remote executions as well. This ensures that every run logged by Metaflow will have metaflow_version and runtime system tags available.
Run objects generated by flows executed on top of AWS Step Functions were missing the tags assigned to the flow; even though the tags were correctly persisted to tasks. This release fixes and brings inline the tagging behavior as observed with local flow executions.
Execution set-up logs for @conda and IncludeFile were being piped to stdout which made manipulating the output of commands like python flow.py step-functions create --only-json a bit difficult. This release moves the workflow set-up logs to stderr.
A workflow executed without a required IncludeFile parameter would fail when the parameter was referenced inside the flow. This release fixes the issue by assigning a null value to the parameter in such cases.
The Metaflow 2.2.5 release is a minor patch release.
- Features
- Log
metaflow_version:andruntime:tag for all executions
- Log
- Bug Fixes
- Handle inconsistently cased file system issue when creating @conda environments on macOS for linux-64
The tag metaflow_version: and runtime: is now available for all packaged executions and remote executions as well. This ensures that every run logged by Metaflow will have metaflow_version and runtime system tags available.
Handle inconsistently cased file system issue when creating @conda environments on macOS for linux-64
Conda fails to correctly set up environments for linux-64 packages on macOS at times due to inconsistently cased filesystems. Environment creation is needed to collect the necessary metadata for correctly setting up the conda environment on AWS Batch. This fix simply ignores the error-checks that conda throws while setting up the environments on macOS when the intended destination is AWS Batch.
The Metaflow 2.2.4 release is a minor patch release.
- Features
- Metaflow is now compliant with AWS GovCloud & AWS CN regions
- Bug Fixes
- Address a bug with overriding the default value for IncludeFile
- Port AWS region check for AWS DynamoDb from
curltorequests
AWS GovCloud & AWS CN users can now enjoy all the features of Metaflow within their region partition with no change on their end. PR: #364
Address a bug with overriding the default value for IncludeFile
Metaflow v2.1.0 introduced a bug in IncludeFile functionality which prevented users from overriding the default value specified.
Metaflow's AWS Step Functions' integration relies on AWS DynamoDb to manage foreach constructs. Metaflow was leveraging curl at runtime to detect the region for AWS DynamoDb. Some docker images don't have curl installed by default; moving to requests (a metaflow dependency) fixes the issue.
The Metaflow 2.2.3 release is a minor patch release.
- Bug Fixes
- Fix issue #305 : Default 'help' for parameters was not handled properly.
- Pin the conda library versions for Metaflow default dependencies based on the Python version.
- Add conda bin path to the PATH environment variable during Metaflow step execution.
- Fix a typo in metaflow/debug.py
Fix issue #305 : Default 'help' for parameters was not handled properly
Fix the issue where default help for parameters was not handled properly. Issue #305: flow fails because IncludeFile's default value for the help argument is None. PR: #318
The previously pinned library version does not work with python 3.8. Now we have two sets of different version combinations which should work for python 2.7, 3.5, 3.6, 3.7, and 3.8. PR: #308
Previously the executable installed in conda environment was not visible inside Metaflow steps. Fixing this issue by appending conda bin path to the PATH environment variable. PR: #307
PRs: #307, #308, #310, #314, #317, #318
The Metaflow 2.2.2 release is a minor patch release.
- Bug Fixes
- Fix a regression introduced in 2.2.1 related to Conda environments
- Clarify Pandas requirements for Tutorial Episode 04
- Fix an issue with the metadata service
Metaflow 2.2.1 included a commit which was merged too early and broke the use of Conda. This release reverses this patch.
Recent versions of Pandas are not backward compatible with the one used in the tutorial; a small comment was added to warn of this fact.
In some cases, the metadata service would not properly create runs or tasks.
The Metaflow 2.2.1 release is a minor patch release.
- Features
- Add
includeparameter tomerge_artifacts.
- Add
- Bug Fixes
- Fix a regression introduced in 2.1 related to S3 datatools
- Fix an issue where Conda execution would fail if the Conda environment was not writeable
- Fix the behavior of uploading artifacts to the S3 datastore in case of retries
You can now specify the artifacts to be merged explicitly by the merge_artifacts method as opposed to just specifying the ones that should not be merged.
Fixes the regression described in #285.
In some cases, Conda is installed system wide and the user cannot write to its installation directory. This was causing issues when trying to use the Conda environment. Fixes #179.
Retries were not properly handled when uploading artifacts to the S3 datastore. This fix addresses this issue.
PRs #282, #286, #287, #288, #289, #290, #291
The Metaflow 2.2.0 release is a minor release and introduces Metaflow's support for R lang.
- Features
- Support for R lang.
This release provides an idiomatic API to access Metaflow in R lang. It piggybacks on the Pythonic implementation as the backend providing most of the functionality previously accessible to the Python community. With this release, R users can structure their code as a metaflow flow. Metaflow will snapshot the code, data, and dependencies automatically in a content-addressed datastore allowing for resuming of workflows, reproducing past results, and inspecting anything about the workflow e.g. in a notebook or RStudio IDE. Additionally, without any changes to their workflows, users can now execute code on AWS Batch and interact with Amazon S3 seamlessly.
The Metaflow 2.1.1 release is a minor patch release.
- Bug Fixes
- Handle race condition for
/stependpoint of metadata service.
- Handle race condition for
The foreach step in AWS Step Functions launches multiple AWS Batch tasks, each of which tries to register the step metadata if it already doesn't exist. This can result in a race condition and cause the task to fail. This patch properly handles the 409 response from the service.
The Metaflow 2.1.0 release is a minor release and introduces Metaflow's integration with AWS Step Functions.
- Features
- Add capability to schedule Metaflow flows with AWS Step Functions.
- Improvements
- Fix log indenting in Metaflow.
- Throw exception properly if fetching code package from Amazon S3 on AWS Batch fails.
- Remove millisecond information from timestamps returned by Metaflow client.
- Handle CloudWatchLogs resource creation delay gracefully.
Netflix uses an internal DAG scheduler to orchestrate most machine learning and ETL pipelines in production. Metaflow users at Netflix can seamlessly deploy and schedule their flows to this scheduler. Now, with this release, we are introducing a similar integration with AWS Step Functions where Metaflow users can easily deploy & schedule their flows by simply executing
python myflow.py step-functions create
which will create an AWS Step Functions state machine for them. With this feature, Metaflow users can now enjoy all the features of Metaflow along with a highly available, scalable, maintenance-free production scheduler without any changes in their existing code.
We are also introducing a new decorator - @schedule, which allows Metaflow users to instrument time-based triggers via Amazon EventBridge for their flows deployed on AWS Step Functions.
With this integration, Metaflow users can inspect their flows deployed on AWS Step Functions as before and debug and reproduce results from AWS Step Functions on their local laptop or within a notebook.
Documentation
Launch Blog Post
Metaflow was inadvertently removing leading whitespace from user-visible logs on the console. Now Metaflow presents user-visible logs with the correct formatting.
Due to malformed permissions, AWS Batch might not be able to fetch the code package from Amazon S3 for user code execution. In such scenarios, it wasn't apparent to the user, where the code package was being pulled from, making triaging any permission issue a bit difficult. Now, the Amazon S3 file location is part of the exception stack trace.
Metaflow uses time to store the created_at and finished_at information for the Run object returned by Metaflow client. time unfortunately does not support the %f directive, making it difficult to parse these fields by datetime or time. Since Metaflow doesn't expose timings at millisecond grain, this PR drops the %f directive.
When launching jobs on AWS Batch, the CloudWatchLogStream might not be immediately created (and may never be created if say we fail to pull the docker image for any reason whatsoever). Metaflow will now simply retry again next time.
PR #209.
The Metaflow 2.0.5 release is a minor patch release.
- ****Improvements****
- Fix logging of prefixes in
datatools.S3._read_many_files. - Increase retry count for AWS Batch logs streaming.
- Upper-bound
pylintversion to< 2.5.0for compatibility issues.
- Fix logging of prefixes in
The Metaflow 2.0.5 release is a minor patch release.
Avoid a cryptic error message when datatools.S3._read_many_files is unsuccessful by converting prefixes from a generator to a list.
Modify the retry behavior for log fetching on AWS Batch by adding jitters to exponential backoffs as well as reset the retry counter for every successful request.
Additionally, fail the Metaflow task when we fail to stream the task logs back to the user's terminal even if AWS Batch task succeeds.
pylint version 2.5.0 would mark Metaflow's self.next() syntax as an error. As a result, python helloworld.py run would fail at the pylint check step unless we run with --no-pylint. This version upper-bound is supposed to automatically downgrade pylint during metaflow installation if pylint==2.5.0 has been installed.
The Metaflow 2.0.4 release is a minor patch release.
- ****Improvements****
- Expose
retry_countinCurrent - Mute superfluous
ThrottleExceptionsin AWS Batch job logs
- Expose
- ****Bug Fixes
- Set proper thresholds for retrying
DescribeJobsAPI for AWS Batch - Explicitly override
PYTHONNOUSERSITEforcondaenvironments - Preempt AWS Batch job log collection when the job fails to get into a
RUNNINGstate
- Set proper thresholds for retrying
You can now use the current singleton to access the retry_count of your task. The first attempt of the task will have retry_count as 0 and subsequent retries will increment the retry_count. As an example:
@retry
@step
def my_step(self):
from metaflow import current
print("retry_count: %s" % current.retry_count)
self.next(self.a)The AWS Logs API for get_log_events has a global hard limit on 10 requests per sec. While we have retry logic in place to respect this limit, some of the ThrottleExceptions usually end up in the job logs causing confusion to the end-user. This release addresses this issue (also documented in #184).
The AWS Batch API for describe_jobs throws ThrottleExceptions when managing a flow with a very wide for-each step. This release adds retry behavior with backoffs to add proper resiliency (addresses #138).
In certain user environments, to properly isolate conda environments, we have to explicitly override PYTHONNOUSERSITE rather than simply relying on python -s (addresses #178).
Fixes a bug where if the AWS Batch job crashes before entering the RUNNING state (often due to incorrect IAM perms), the previous log collection behavior would fail to print the correct error message making it harder to debug the issue (addresses #185).
The Metaflow 2.0.3 release is a minor patch release.
- ****Improvements****
- Parameter listing
- Ability to specify S3 endpoint
- Usability improvements
- ****Performance****
- Conda
- Bug Fixes****
- Executing on AWS Batch
You can now use the current singleton (documented here) to access the names of the parameters passed into your flow. As an example:
for var in current.parameter_names:
print("Parameter %s has value %s" % (var, getattr(self, var))This addresses #137.
A few issues were addressed to improve the usability of Metaflow. In particular, show now properly respects indentation making the description of steps and flows more readable. This addresses #92. Superfluous print messages were also suppressed when executing on AWS batch with the local metadata provider (#152).
A smaller, newer and standalone Conda installer is now used resulting in faster and more reliable Conda bootstrapping (#123).
We now check for the command line --datastore-root prior to using the environment variable METAFLOW_DATASTORE_SYSROOT_S3 when determining the S3 root (#134). This release also fixes an issue where using the local Metadata provider with AWS batch resulted in incorrect directory structure in the .metaflow directory (#141).
Bug Fixes
Enhancements
- Introduce
metaflow configure [import|export]for importing/exporting Metaflow configurations. - Revamp
metaflow configure awscommand to address usability concerns. - Handle keyboard interrupts for Batch jobs more gracefully for large fan-outs.
Bug Fixes
- Fix a docker registry parsing bug in AWS Batch.
- Fix various typos in Metaflow tutorials.
- First Open Source Release.
- Read the blogpost announcing the release