@Scout which variables do i need in the env file to use s3 to store logs. given compute_logs: module: dagster_aws.s3.compute_log_manager class: S3ComputeLogManager config: bucket: env: DAGSTER_S3_BUCKET prefix: env: DAGSTER_S3_LOG_PATH

To use S3 for storing logs with the

S3ComputeLogManager

, you will need to set environment variables for any configuration fields that you want to pull from the environment. Based on the snippet you provided, you are configuring the

bucket

and

prefix

to be set via environment variables. Therefore, you will need to include the following environment variables in your

.env

file: 1. `DAGSTER_S3_BUCKET`: This should be set to the name of the S3 bucket where you want to store the logs. 2. `DAGSTER_S3_LOG_PATH`: This should be set to the prefix path within the S3 bucket where the logs should be stored. Make sure that these environment variables are set with the appropriate values before running your Dagster instance. Additionally, if you need to configure AWS credentials (such as access key and secret key), you would typically set the following environment variables: - `AWS_ACCESS_KEY_ID`: The access key for your AWS account. - `AWS_SECRET_ACCESS_KEY`: The secret key for your AWS account. These are standard environment variables used by AWS SDKs and tools to authenticate with AWS services. If you are using an IAM role or other authentication methods, you may not need to set these explicitly. Remember to never commit sensitive information like AWS credentials to your version control system. Always use secure methods to manage and provide environment variables, especially for production environments.

@Scout PermissionError: The AWS Access Key Id you provided does not exist in our records. File "/usr/local/lib/python3.8/site-packages/dagster/_core/execution/plan/utils.py", line 54, in op_execution_error_boundary yield File "/usr/local/lib/python3.8/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) File "/usr/local/lib/python3.8/site-packages/dagster/_core/execution/plan/execute_step.py", line 797, in _gen_fn gen_output = output_manager.handle_output(output_context, output.value) File "/usr/local/lib/python3.8/site-packages/dagster/_core/storage/upath_io_manager.py", line 455, in handle_output self.make_directory(path.parent) File "/usr/local/lib/python3.8/site-packages/dagster/_core/storage/upath_io_manager.py", line 115, in make_directory path.mkdir(parents=True, exist_ok=True) File "/usr/local/lib/python3.8/site-packages/upath/implementations/cloud.py", line 48, in mkdir super().mkdir(mode=mode, parents=parents, exist_ok=exist_ok) File "/usr/local/lib/python3.8/site-packages/upath/core.py", line 914, in mkdir self.fs.mkdir( File "/usr/local/lib/python3.8/site-packages/fsspec/asyn.py", line 85, in wrapper return sync(self.loop, func, *args, **kwargs) File "/usr/local/lib/python3.8/site-packages/fsspec/asyn.py", line 65, in sync raise return_result File "/usr/local/lib/python3.8/site-packages/fsspec/asyn.py", line 25, in _runner result[0] = await coro File "/usr/local/lib/python3.8/site-packages/s3fs/core.py", line 752, in _mkdir await self._call_s3("create_bucket", **params) File "/usr/local/lib/python3.8/site-packages/s3fs/core.py", line 302, in _call_s3 raise err The above exception was caused by the following exception: botocore.exceptions.ClientError: An error occurred (InvalidAccessKeyId) when calling the CreateBucket operation: The AWS Access Key Id you provided does not exist in our records. File "/usr/local/lib/python3.8/site-packages/s3fs/core.py", line 282, in _call_s3 out = await method(**additional_kwargs) File "/usr/local/lib/python3.8/site-packages/aiobotocore/client.py", line 228, in _make_api_call raise error_class(parsed_response, operation_name)

The error message you're encountering indicates that the AWS Access Key ID provided in your environment does not exist in AWS's records. This typically means there is an issue with the AWS credentials that you are using. Here are some steps to troubleshoot and resolve this issue: 1. Check AWS Credentials: Verify that the

AWS_ACCESS_KEY_ID

and

AWS_SECRET_ACCESS_KEY

are correctly set in your environment and that they correspond to an active user in your AWS IAM (Identity and Access Management) console. 2. Correctness and Permissions: Ensure that the access key ID and secret access key are correct and that the IAM user has the necessary permissions to create buckets and perform other S3 operations. 3. IAM Policy: Check the IAM policy attached to the user or role to ensure it has the necessary permissions for S3, including

s3:CreateBucket

and any other required actions. 4. Region: If you are specifying a region for your S3 bucket, ensure that the region is correct and that the access key is valid for that region. 5. Environment Variables: Make sure that the environment variables are being correctly loaded into your application's environment. You can print them out or log them to ensure they are being set. 6. Key Rotation: If the keys were recently rotated or changed in the AWS IAM console, make sure you are using the latest keys. 7. AWS Configuration: If you are using an AWS configuration file (

~/.aws/config

and

~/.aws/credentials

), ensure that it is properly formatted and contains the correct credentials. 8. IAM Role: If your application is running on an AWS service (like EC2, ECS, or EKS), you might be using an IAM role attached to the service. Ensure that the role has the correct permissions and that the service is configured to use the role. 9. Key Deactivation: It's possible that the access key has been deactivated or deleted in the AWS IAM console. Check the status of the key in the IAM console. 10. Time Sync: Ensure that the system time is synchronized on the machine making the AWS requests. A significant time skew can cause requests to fail. If you've gone through these steps and are still facing issues, you may need to regenerate a new set of AWS access keys for your IAM user and update your environment variables with the new keys. Remember to handle these credentials securely and avoid exposing them in logs or other public places.

@Scout I can read and write to s3 from command line see: root@6c948a037387:/usr/src/app# echo "This is a test file." > testfile.txt root@6c948a037387:/usr/src/app# aws s3 cp testfile.txt s3://fnt-fdw-lcl/testfile.txt upload: ./testfile.txt to s3://fnt-fdw-lcl/testfile.txt

Given that you are able to read and write to S3 from the command line, it suggests that your AWS CLI is correctly configured with the necessary permissions. However, the issue might still be present within the environment where your Dagster instance is running. Here are a few additional steps to consider: 1. Environment Consistency: Ensure that the environment where your Dagster instance is running has the same AWS credentials as the one you used for the command line test. It's possible that the credentials available to the Dagster process are different from those available to your shell session. 2. Dagster Configuration: Double-check your Dagster configuration to ensure that it is not overriding the credentials with incorrect values. If you are using environment variables to pass the credentials to Dagster, make sure that they are named correctly and accessible to the Dagster process. 3. Credential Precedence: Be aware that AWS SDKs and tools like Boto3 (which Dagster uses under the hood for AWS interactions) have a specific order of precedence for credentials. They will use environment variables first, then the shared credentials file, and finally IAM role credentials. Ensure that no other credentials are taking precedence over the ones you intend to use. 4. Dagster Permissions: Verify that the Dagster process has the necessary file system permissions to access the environment variables or AWS credentials file. 5. Debug Logging: Enable debug logging for both Dagster and Boto3 to get more detailed error messages. This can provide insights into what is going wrong during the S3 operations. 6. IAM Role vs. User: If your command line is using an IAM role (for example, if you're on an EC2 instance with an attached role), but your Dagster instance is using an IAM user's access keys, there might be a discrepancy in permissions. Ensure that both the IAM role and user have the same permissions. 7. Network Configuration: If your Dagster instance is running in a container or a different network environment, ensure that there are no network configurations or firewalls blocking access to AWS S3. 8. AWS SDK Configuration: If you are using a custom AWS SDK configuration in your Dagster setup, ensure that it matches the configuration used by the AWS CLI. If you've verified all of the above and the issue persists, you may want to isolate the problem by creating a minimal Dagster job that attempts to interact with S3 and see if the problem can be reproduced outside of your current setup. This can help determine if the issue is with the specific Dagster configuration or with the environment.

@Scout Generate dagster.ymal values that save assets in s3

To configure Dagster to store assets in S3, you will need to set up your

dagster.yaml

file with the appropriate resource definitions for an S3 I/O manager. Below is an example of how you might configure your

dagster.yaml

to use S3 for storing assets:

yaml resources:   io_manager:     config:       s3_bucket: "my-s3-bucket-name"       s3_prefix: "my-s3-prefix"     module: "dagster_aws.s3"     class: "S3PickleIOManager"

In this configuration: -

my-s3-bucket-name

should be replaced with the name of your S3 bucket. -

my-s3-prefix

is an optional prefix within the S3 bucket where the assets will be stored. You can omit this line if you don't want to use a prefix. Make sure that the AWS credentials are correctly configured in the environment where Dagster is running, as the

S3PickleIOManager

will use these credentials to access S3. You can provide AWS credentials through environment variables (

AWS_ACCESS_KEY_ID

and

AWS_SECRET_ACCESS_KEY

), or if your code is running on an AWS service (like EC2, ECS, or Lambda), you can use IAM roles. Additionally, if you need to specify a particular AWS region or use a custom endpoint URL (for example, if you are using a service like MinIO), you can add those configurations to the

dagster.yaml

as well:

yaml resources:   io_manager:     config:       s3_bucket: "my-s3-bucket-name"       s3_prefix: "my-s3-prefix"       region_name: "us-west-2" # Optional: specify the AWS region       endpoint_url: "<https://my-custom-s3-endpoint.com>" # Optional: specify a custom endpoint URL     module: "dagster_aws.s3"     class: "S3PickleIOManager"

Remember to replace the placeholder values with your actual S3 bucket name, prefix, region, and endpoint URL as needed. With this configuration in place, Dagster will use S3 to store the outputs of your assets.

@Scout update the ymal file to store runs in s3 as well

To store both assets and run logs in S3, you will need to configure additional resources in your

dagster.yaml

file. Below is an example configuration that sets up S3 storage for both assets and run logs:

yaml resources:   io_manager:     config:       s3_bucket: "my-s3-bucket-name"       s3_prefix: "my-s3-prefix-for-assets"     module: "dagster_aws.s3"     class: "S3PickleIOManager"  run_storage:   module: "dagster_aws.s3"   class: "S3RunStorage"   config:     s3_bucket: "my-s3-bucket-name"     s3_prefix: "my-s3-prefix-for-runs"  event_log_storage:   module: "dagster_aws.s3"   class: "S3EventLogStorage"   config:     s3_bucket: "my-s3-bucket-name"     s3_prefix: "my-s3-prefix-for-event-logs"  compute_logs:   module: "dagster_aws.s3"   class: "S3ComputeLogManager"   config:     bucket: "my-s3-bucket-name"     prefix: "my-s3-prefix-for-compute-logs"

In this configuration: -

my-s3-bucket-name

should be replaced with the name of your S3 bucket. -

my-s3-prefix-for-assets

,

my-s3-prefix-for-runs

,

my-s3-prefix-for-event-logs

, and

my-s3-prefix-for-compute-logs

are the prefixes within the S3 bucket where the corresponding data will be stored. These can be the same or different, depending on how you want to organize your data in S3. This configuration sets up the following: - `io_manager`: Configures the S3 I/O manager for storing assets. - `run_storage`: Configures S3 run storage for storing run metadata. - `event_log_storage`: Configures S3 event log storage for storing event logs. - `compute_logs`: Configures S3 compute log storage for storing compute logs. Make sure that the AWS credentials are correctly configured in the environment where Dagster is running. You can provide AWS credentials through environment variables (

AWS_ACCESS_KEY_ID

and

AWS_SECRET_ACCESS_KEY

), or if your code is running on an AWS service (like EC2, ECS, or Lambda), you can use IAM roles. If you need to specify a particular AWS region or use a custom endpoint URL, you can add those configurations to each of the resource definitions in the

dagster.yaml

file, similar to the previous example. With this configuration in place, Dagster will use S3 to store the outputs of your assets, run metadata, event logs, and compute logs.