Next we need to go ahead and install the Python dependencies to be able to use the boto3 library. You can do this by running the pip tool as shown below. Keep in mind make sure your virtual environment is activated before you run this step.
If you wish to use it without having a virtual environment which I do not recommend you can go ahead and simply install it globally in your user account. Now that we have setup our system we need to verify the library is installed properly and it works.
You can do this by simply checking in a python shell using the following command shown below, if you encounter an error please delete your virtual environment and try again. If the problem still persists please drop me a line below and I will try to help you. As you can see above the boto3 library got loaded successfully and the version is 1.
This is as of Late so this may be different in your system based on when you install it. The first thing we need to do is click on create bucket and just fill in the details as shown below. For now these options are not very important we just want to get started and programmatically interact with our setup. For now you can leave the rest of the options default for example for me the following settings were default at the time of this writing:. Once you verify that go ahead and create your first bucket.
For me this looks something like this:. Now that we have our bucket created we need to proceed further into setting up a way to interact with it programmatically. Those are necessary for the platform to know you are authorized to perform actions in a programmatic way rather than logging in the web interface and accessing the features via the console. So our next task is to find where and how those keys are configured and what is needed to set them up on our local computer to start talking to Amazon AWS S3.
First we need to talk about how to add an AWS user. If you do not have a user setup with AWS S3 full permissions then I will walk you through on how to get this done in a simple step by step guide. In the next steps you can use the defaults except the part that is asking you to set the permissions. In this tab you want to expand below and type in the search S3. Once you do that a bunch of permissions will be loaded for you to select from, for now you can simply select the Full permissions for S3 as shown in the screenshot below.
You can skip the tags and proceed to add the user, the final screen summary should look like this. The final confirmation screen should show you the access key and the secret key. The number of physical GPUs to reserve for the container.
The number of GPUs reserved for all containers in a job shouldn't exceed the number of available GPUs on the compute resource that the job is launched on. The memory hard limit in MiB present to the container. This parameter is supported for jobs that are running on EC2 resources. If your container attempts to exceed the memory specified, the container is terminated.
You must specify at least 4 MiB of memory for a job. This is required but can be specified in several places for multi-node parallel MNP jobs. If you're trying to maximize your resource utilization by providing your jobs as much memory as possible for a particular instance type, see Memory Management in the Batch User Guide.
For jobs that are running on Fargate resources, then value is the hard limit in MiB , and must match one of the supported values and the VCPU values must be one of the values supported for that memory value. The number of vCPUs reserved for the container. This is required but can be specified in several places; it must be specified for each node at least once. The supported values are 0.
The type of resource to assign to a container. Linux-specific modifications that are applied to the container, such as details for device mappings. Any host devices to expose to the container. This object isn't applicable to jobs that are running on Fargate resources and shouldn't be provided. The path inside the container that's used to expose the host device.
By default, the hostPath value is used. The explicit permissions to provide to the container for the device. By default, the container has permissions for read , write , and mknod for the device. If true, run an init process inside the container that forwards signals and reaps processes. This parameter maps to the --init option to docker run.
This parameter requires version 1. To check the Docker Remote API version on your container instance, log into your container instance and run the following command: sudo docker version grep "Server API version". This parameter maps to the --shm-size option to docker run. The container path, mount options, and size in MiB of the tmpfs mount. This parameter maps to the --tmpfs option to docker run. The total amount of swap memory in MiB a container can use.
This parameter is translated to the --memory-swap option to docker run where the value is the sum of the container memory plus the maxSwap value. If a maxSwap value of 0 is specified, the container doesn't use swap. Accepted values are 0 or any positive integer.
If the maxSwap parameter is omitted, the container doesn't use the swap configuration for the container instance it is running on. A maxSwap value must be set for the swappiness parameter to be used. This allows you to tune a container's memory swappiness behavior.
A swappiness value of 0 causes swapping not to happen unless absolutely necessary. A swappiness value of causes pages to be swapped very aggressively. Accepted values are whole numbers between 0 and If the swappiness parameter isn't specified, a default value of 60 is used.
If a value isn't specified for maxSwap , then this parameter is ignored. If maxSwap is set to 0, the container doesn't use swap. This parameter maps to the --memory-swappiness option to docker run. You must enable swap on the instance to use this feature. By default, containers use the same logging driver that the Docker daemon uses. However the container might use a different logging driver than the Docker daemon by specifying a log driver with this parameter in the container definition.
To use a different logging driver for a container, the log system must be configured properly on the container instance or on a different log server for remote logging options. For more information on the options for different supported log drivers, see Configure logging drivers in the Docker documentation. Batch currently supports a subset of the logging drivers available to the Docker daemon shown in the LogConfiguration data type. The log driver to use for the container. The valid values listed for this parameter are log drivers that the Amazon ECS container agent can communicate with by default.
The supported log drivers are awslogs , fluentd , gelf , json-file , journald , logentries , syslog , and splunk. Jobs that are running on Fargate resources are restricted to the awslogs and splunk log drivers.
Specifies the Amazon CloudWatch Logs logging driver. Specifies the Fluentd logging driver. For more information, including usage and options, see Fluentd logging driver in the Docker documentation.
For more information, including usage and options, see Graylog Extended Format logging driver in the Docker documentation. Specifies the journald logging driver. For more information, including usage and options, see Journald logging driver in the Docker documentation.
Specifies the JSON file logging driver. Specifies the Splunk logging driver. For more information, including usage and options, see Splunk logging driver in the Docker documentation. Specifies the syslog logging driver. For more information, including usage and options, see Syslog logging driver in the Docker documentation. If you have a custom driver that's not listed earlier that you want to work with the Amazon ECS container agent, you can fork the Amazon ECS container agent project that's available on GitHub and customize it to work with that driver.
We encourage you to submit pull requests for changes that you want to have included. However, Amazon Web Services doesn't currently support running modified copies of this software. The configuration options to send to the log driver. The secrets to pass to the log configuration. An object representing the secret to expose to your container.
Secrets can be exposed to a container in the following ways:. For more information, see Specifying sensitive data in the Batch User Guide. The secret to expose to the container.
If the parameter exists in a different Region, then the full ARN must be specified. The secrets for the container. The network configuration for jobs that are running on Fargate resources. Jobs that are running on EC2 resources must not specify this parameter. Indicates whether the job should have a public IP address.
For a job that is running on Fargate resources in a private subnet to send outbound traffic to the internet for example, to pull container images , the private subnet requires a NAT gateway be attached to route requests to the internet. For more information, see Amazon ECS task networking.
The platform configuration for jobs that are running on Fargate resources. The Fargate platform version where the jobs are running. A platform version is specified only for jobs that are running on Fargate resources.
This uses a recent, approved version of the Fargate platform for compute resources. The timeout configuration for jobs that are submitted with this job definition. You can specify a timeout duration after which Batch terminates your jobs if they haven't finished. The time duration in seconds measured from the job attempt's startedAt timestamp after which Batch terminates your jobs if they have not finished. The minimum value for the timeout is 60 seconds.
If the job runs on Fargate resources, then you must not specify nodeProperties ; use containerProperties instead. Specifies the node index for the main node of a multi-node parallel job. This node index value must be fewer than the number of nodes. The range of nodes, using node index values. A range of indicates nodes with index values of 0 through 3.
If the starting range value is omitted :n , then 0 is used to start the range. If the ending range value is omitted n: , then the highest possible node index is used to end the range. Your accumulative node ranges must account for all nodes 0:n. You can nest node ranges, for example and , in which case the range properties override the properties.
Specifies whether to propagate the tags from the job or job definition to the corresponding Amazon ECS task. If no value is specified, the tags aren't propagated. Tags can only be propagated to the tasks during task creation. For tags with the same name, job tags are given priority over job definitions tags. If the total number of combined tags from the job and job definition is over 50, the job is moved to the FAILED state.
The platform capabilities required by the job definition. If no value is specified, it defaults to EC2.
The nextToken value to include in a future DescribeJobDefinitions request. When the results of a DescribeJobDefinitions request exceed maxResults , this value can be used to retrieve the next page of results. The nextToken value returned from a previous paginated DescribeJobQueues request where maxResults was used and the results exceeded the value of that parameter.
Describes the ability of the queue to accept new jobs. A short, human-readable string to provide additional details about the current status of the job queue. Priority is determined in descending order, for example, a job queue with a priority value of 10 is given scheduling preference over a job queue with a priority value of 1. The compute environments that are attached to the job queue and the order that job placement is preferred. Compute environments are selected for job placement in ascending order.
The tags applied to the job queue. The nextToken value to include in a future DescribeJobQueues request. When the results of a DescribeJobQueues request exceed maxResults , this value can be used to retrieve the next page of results. The scheduling policy of the job definition. A short max characters human-readable string to provide additional details about a running or stopped container. The name of the CloudWatch Logs log stream associated with the container.
A short, human-readable string to provide additional details about the current status of the job attempt. A short, human-readable string to provide additional details about the current status of the job. The Unix timestamp in milliseconds for when the job was created.
This parameter isn't provided for child jobs of array jobs or multi-node parallel jobs. Additional parameters passed to the job that replace parameter substitution placeholders or override any corresponding parameter defaults from the job definition.
For jobs that run on EC2 resources, you can specify the vCPU requirement for the job using resourceRequirements , but you can't specify the vCPU requirements in both the vcpus and resourceRequirements object. You must specify at least one vCPU. This is required but can be specified in several places. This parameter isn't applicable to jobs that run on Fargate resources. For jobs that run on Fargate resources, you must specify the vCPU requirement for the job using resourceRequirements. For jobs run on EC2 resources that didn't specify memory requirements using resourceRequirements , the number of MiB of memory reserved for the job.
For other jobs, including all run on Fargate resources, see resourceRequirements. A list of ulimit values to set in the container. However, the container might use a different logging driver than the Docker daemon by specifying a log driver with this parameter in the container definition.
To use a different logging driver for a container, the log system must be configured properly on the container instance. Or, alternatively, it must be configured on a different log server for remote logging options. Additional log drivers might be available in future releases of the Amazon ECS container agent.
The secrets to pass to the container. Show 4 more comments. Joe Haddad 6 6 silver badges 12 12 bronze badges. Tushar Niras Tushar Niras 2, 2 2 gold badges 19 19 silver badges 21 21 bronze badges. Clean and simple, any reason why not to use this? It's much more understandable than all the other solutions. Collections seem to do a lot of things for you in the background.
I guess you should first create all subfolders in order to have this working properly. This code will put everything in the top-level output directory regardless of how deeply nested it is in S3. And if multiple files have the same name in different directories, it will stomp on one with another.
I think you need one more line: os. It is a flat file structure. Alexis Wilke John Rotenstein John Rotenstein k 17 17 gold badges silver badges bronze badges. But i needed the folder to be created, automatically just like aws s3 sync. Is it possible in boto3. You would have to include the creation of a directory as part of your Python code.
It is not an automatic capability of boto. Here, the content of the S3 bucket is dynamic, so i have to check s3. Ben Please start a new Question rather than asking a question as a comment on an old question.
Show 1 more comment. I'm currently achieving the task, by using the following! If not it created them. Got KeyError: 'Contents'. Adding if 'Contents' not in result: continue should solve the problem but I would check the use-case prior to making that change. Install awscli as python lib: pip install awscli Then define this function: from awscli. UTF' os. Times reduced from minutes almost 1h to literally seconds — acaruci.
I'm using this code but have an issue where all the debug logs are showing. I have this declared globally: logging. Any ideas? ThreadPoolExecutor as executor: futures. Utkarsh Dalal Utkarsh Dalal 5 5 bronze badges. Alex B Alex B 1, 1 1 gold badge 23 23 silver badges 30 30 bronze badges. It is a very bad idea to get all files in one go, you should rather get it in batches. Then download the file actually. You cannot download folder from S3 using Boto3 using a clean implementation. Instead you can download all files from a directory using the previous section.
Its the clean implementation. Refer the tutorial to learn How to Run Python File in terminal. Save my name, email, and website in this browser for the next time I comment. Notify me via e-mail if anyone answers my comment.
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