**Inspecting instance parameters**

I think I can check the `bundle_id` to find details about active contracts. I can also use the command `vastai show instance 29758333 --raw` to get information like `host_id`, though it won't show the offer ID. To recreate instances, I need to search for offers with the required specifications, like reliability and picking the lowest total cost, but the user didn't specify additional constraints. Their instances run via SSH, but not direct SSH. I’ll use the same image and set the disk space to 100GB, while passing environment variables stored as `extra_env`, including secrets.


**Setting environment variables**

I need to avoid printing sensitive information. When using the vast CLI, passing environment variables might require formatting as `-e VAR=...`, but I can also use the `--env` option to include them. I plan to fetch these values from my local `.env` file instead of the instance, as the instance output includes secret tokens that may not match. I'll read from the `.env` file in the repo to supply necessary secrets and ensure that in the created instance, `extra_env` is set correctly. I'll also need to be cautious with tokens in the onstart script.


**Configuring repository access**

I should switch to using a public repo URL without a token if the repository is public. There’s a chance it could be private, in which case I might need an environment variable like `GIT_TOKEN`, but the user didn't mention it. For security reasons, I'd prefer to avoid hardcoding tokens in the onstart script, even though they already have. I could suggest making the repo public or using a deploy key/SSH for better security. For now, I can craft a new onstart script to clone the repo without the token if it is indeed public. I'll check that by running `git ls-remote` without authentication.


**Considering docker setup**

Running `pip install -r requirements.txt` could take some time, but it seems like the Docker image `bharathkumar192/maya3-pipeline:c0ef00b-vast` is already built to include heavy dependencies and models. Using this custom image helps avoid unnecessary installs and just lets the worker run. However, if the image doesn't have the latest code, we might need to clone updates. On startup, I should ensure it sets environment options, changes directories, checks for file existence, and then runs the necessary Python script with flags.

**Ensuring environment loading**

I need to check if the `.env` file is loaded correctly since `massive_process` automatically loads it from `/workspace/maya3_data/.env`. The current onstart command attempts to copy the `.env` file from `/tmp/code/`, but it might not exist, especially if it's a secret. Their instance seems to have an `extra_env` for secret variables, which could eliminate the need for the `.env` file. It looks like I can create new instances with a fixed onstart string, but I’ll need to know the offer IDs for those new instances, which aren't visible in the current output.


git ls-remote --heads https://github.com/Maya-AI-ML-Trainings-Cookbooks/Maya3-data.git | head


Output:
Exit code: 0
Wall time: 0.3 seconds
Output:
412526bec927c45ccb6803932e361b945e657d55	refs/heads/azure
63263880192e41189ee914030bd0c39fa1debc4b	refs/heads/feature/visualization
2e5b0505549c4f4ac80c508181da60370c5c0a50	refs/heads/gcp
c953babc31eaaaa8735d805beac8191ae37690f8	refs/heads/massed_compute
2f269b160e9653769d7bea5adc791028dcb36641	refs/heads/master
67a3d9c46cf6cc6b88113f90a1ecd03fb89c72f9	refs/heads/modal


Summary: auto


**Considering implementation options**

I’m thinking of implementing the src.worker wrapper now and then using the onstart command like `python -m src.worker ...` as they suggested, but I’m realizing the remote aspect might complicate things. We could create instances and copy our local code before startup. Although onstart runs right at creation, we can set it to do nothing temporarily to upload code first. The user prefers immediate execution, though, so maybe we can set onstart to clone and run a process without the worker wrapper. I'll plan to add the wrapper and later modify the setup!


**Planning instance creation**

The user has explicitly asked for two new instances with proper code details to avoid startup errors related to `src.worker`, which suggests they want onstart modified not to reference that module. I can fix onstart to run massive_process, which aligns with their request. I also need to patch the repository to include the worker for their earlier commands. While updating docs might not be necessary, I need to consider the cost of current instances before destroying them. The plan is to delete the last two instances first, as per user request, and then create the new ones.


**Searching for GPU offers**

I’m planning to search for offers on 8x RTX 4090 with at least 100GB of disk space using a query like `num_gpus=8 gpu_name=RTX_4090 reliability>0.95 verified=true rentable=true`. I'll order the results by total cost. The goal is to select two distinct offers while ensuring each has sufficient disk space. While static IPs might be preferred, they're not strictly necessary. I’ll use the vastai search feature with the corrected reliability filters to find appropriate offers. My final query will ensure we're considering the best available deals.


**Creating instances with configurations**

I need to create instances using the command `vastai create instance OFFER --image bharathkumar192/maya3-pipeline:c0ef00b-vast --disk 100 --ssh` along with the environment variables and onstart command. I’m considering adding `--direct`, but the existing setups aren't direct, and they show SSH hosts in the log. Using `--ssh` sets the runtype to SSH, which is what they prefer anyway. Although `--direct` might improve connectivity, I think I’ll just stick to `--ssh`. Next, I'll set the `--onstart-cmd` to execute our script within the container and add any necessary environment variables.


**Managing environment variables securely**

I’m focused on ensuring we handle environment variables correctly while creating instances. We'll pass necessary variables without exposing any secrets in the command output. Instead of including secrets directly in the command line, I’ll look into using an existing environment config from Vast templates or copying the `.env` file after instance creation, so we avoid showing sensitive information. I'll create a local Python script to handle API requests, reading secrets from the `.env` file without printing them. The goal is to craft the onstart command securely and show only the new instance IDs to the user.


**Checking environment variables securely**

I want to ensure that the user has the correct environment variables set in the `.env` file without actually displaying any sensitive information. I’ll inspect the `.env` but avoid using commands that would show its content, like `cat`. Instead, I can use a Python script to quietly parse the file for the necessary keys. Based on the needs of the pipeline, I know that variables like HF_TOKEN, URL, and SUPABASE_ADMIN are essential, along with potentially others like R2_ENDPOINT_URL and access keys. I'll check the `src/r2_client.py` file for exact variable names.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
R2 Client Utilities for High-Throughput Pipeline Migration

Manages Cloudflare R2 buckets for:
- Production outputs (1-cleaned-data)
- Test outputs during migration (1-cleaned-data-test)
- Baseline snapshots for comparison (1-cleaned-data-baseline)

Usage:
    from src.r2_client import R2Client

    client = R2Client()
    client.upload_json("path/to/file.json", "video_id/metadata.json")
    client.download_json("video_id/metadata.json", "local/path.json")
"""

import os
import json
import logging
from pathlib import Path
from typing import Dict, Any, Optional, List
from dataclasses import dataclass

import boto3
from botocore.config import Config as BotoConfig

logger = logging.getLogger("R2Client")

@dataclass
class R2Config:
    """R2 bucket configuration."""
    endpoint_url: str
    access_key_id: str
    secret_access_key: str
    bucket: str
    prefix: str = ""

# Bucket configurations
BUCKETS = {
    'production': '1-cleaned-data',
    'test': '1-cleaned-data-test',
    'baseline': '1-cleaned-data-baseline',
    # Source/original videos (DO NOT DELETE). This bucket is expected to contain
    # the original YouTube files re-uploaded to R2 to avoid rate limits.
    'source': 'test',
}

def get_r2_config_from_env() -> R2Config:
    """Load R2 configuration from environment variables."""
    return R2Config(
        endpoint_url=os.environ.get('R2_ENDPOINT_URL', ''),
        access_key_id=os.environ.get('R2_ACCESS_KEY_ID', ''),
        secret_access_key=os.environ.get('R2_SECRET_ACCESS_KEY', ''),
        bucket=os.environ.get('R2_BUCKET', 'test'),
        prefix=os.environ.get('R2_PREFIX', ''),
    )

class R2Client:
    """
    Cloudflare R2 client for pipeline data management.

    Supports multiple bucket targets for A/B testing during migration.
    """

    def __init__(
        self,
        config: Optional[R2Config] = None,
        bucket_type: str = 'test'
    ):
        """
        Initialize R2 client.

        Args:
            config: R2 configuration (loads from env if None)
            bucket_type: One of 'production', 'test', 'baseline'
        """
        self.config = config or get_r2_config_from_env()

        # Override bucket based on type
        if bucket_type in BUCKETS:
            self.bucket = BUCKETS[bucket_type]
        else:
            self.bucket = self.config.bucket

        self.prefix = self.config.prefix

        # Initialize boto3 client
        self._client = None

    @property
    def client(self):
        """Lazy-initialize boto3 client."""
        if self._client is None:
            self._client = boto3.client(
                's3',
                endpoint_url=self.config.endpoint_url,
                aws_access_key_id=self.config.access_key_id,
                aws_secret_access_key=self.config.secret_access_key,
                config=BotoConfig(
                    signature_version='s3v4',
                    retries={'max_attempts': 3, 'mode': 'adaptive'}
                )
            )
        return self._client

    def _full_key(self, key: str) -> str:
        """Get full S3 key with prefix."""
        if self.prefix:
            return f"{self.prefix}/{key}"
        return key

    def upload_file(self, local_path: str, remote_key: str) -> bool:
        """
        Upload a file to R2.

        Args:
            local_path: Path to local file
            remote_key: Remote key (path in bucket)

        Returns:
            True if successful
        """
        try:
            full_key = self._full_key(remote_key)
            self.client.upload_file(local_path, self.bucket, full_key)
            logger.info(f"Uploaded {local_path} -> s3://{self.bucket}/{full_key}")
            return True
        except Exception as e:
            logger.error(f"Upload failed: {e}")
            return False

    def download_file(self, remote_key: str, local_path: str) -> bool:
        """
        Download a file from R2.

        Args:
            remote_key: Remote key (path in bucket)
            local_path: Path to save locally

        Returns:
            True if successful
        """
        try:
            full_key = self._full_key(remote_key)
            Path(local_path).parent.mkdir(parents=True, exist_ok=True)
            self.client.download_file(self.bucket, full_key, local_path)
            logger.info(f"Downloaded s3://{self.bucket}/{full_key} -> {local_path}")
            return True
        except Exception as e:
            logger.error(f"Download failed: {e}")
            return False

    def upload_json(self, data: Dict[str, Any], remote_key: str) -> bool:
        """Upload JSON data directly to R2."""
        try:
            full_key = self._full_key(remote_key)
            body = json.dumps(data, indent=2).encode('utf-8')
            self.client.put_object(
                Bucket=self.bucket,
                Key=full_key,
                Body=body,
                ContentType='application/json'
            )
            logger.info(f"Uploaded JSON -> s3://{self.bucket}/{full_key}")
            return True
        except Exception as e:
            logger.error(f"JSON upload failed: {e}")
            return False

    def download_json(self, remote_key: str) -> Optional[Dict[str, Any]]:
        """Download JSON data from R2."""
        try:
            full_key = self._full_key(remote_key)
            response = self.client.get_object(Bucket=self.bucket, Key=full_key)
            data = json.loads(response['Body'].read().decode('utf-8'))
            logger.info(f"Downloaded JSON <- s3://{self.bucket}/{full_key}")
            return data
        except Exception as e:
            logger.error(f"JSON download failed: {e}")
            return None

    def head_object(self, remote_key: str) -> Optional[Dict[str, Any]]:
        """Return S3 head_object response for a key, or None if missing/unreadable."""
        try:
            full_key = self._full_key(remote_key)
            return self.client.head_object(Bucket=self.bucket, Key=full_key)
        except Exception:
            return None

    def generate_presigned_get_url(self, remote_key: str, expires_in_sec: int = 3600) -> str:
        """
        Generate a presigned GET URL for an object.

        Note: This is a Class B request pattern (no bucket listing).
        """
        full_key = self._full_key(remote_key)
        return self.client.generate_presigned_url(
            'get_object',
            Params={'Bucket': self.bucket, 'Key': full_key},
            ExpiresIn=expires_in_sec,
        )

    def list_keys(self, prefix: str = "", limit: int = 1000) -> List[str]:
        """
        List keys in bucket with optional prefix.

        Args:
            prefix: Additional prefix to filter by
            limit: Maximum keys to return

        Returns:
            List of keys
        """
        try:
            full_prefix = self._full_key(prefix) if prefix else self.prefix


**Evaluating process execution**

I'm thinking about how the `onstart` function runs in the background to avoid blocking. It appears to execute `python -m src.worker`, which runs in the foreground, but since the instance status shows it's still running, that seems fine. I’m considering running the `massive_process` in the foreground too and whether to use `python -u` as well. For environment variables, I can set `extra_env` fields through the API, avoiding any secrets in the command line. I'll also parse the `.env` file from my local workspace to securely send those variables.


**Planning API interaction**

I’m thinking we might not need a new script; running Python from the command line to call the API using the stored Vast API key and environment variables could be enough. We should keep the code minimal and execute it inline with `python - <<'PY'` to prevent exposure of secrets. I'll need to read the Vast API key from the `/.config/vastai/vast_api_key` file, but I’ll be careful not to print it. Using the `requests` library to call the API endpoints seems the simplest way, and I’ll quickly check the Vast API documentation for details.


**Analyzing API and CLI options**

I see that we have `vast_ai.md` with the REST API endpoint for `/asks/{offer_id}/`, where I can use `curl`, but in Python, I’ll use the `requests` library to hit `https://console.vast.ai/api/v0/asks/{offer_id}/`, including a JSON body with image, disk, and environment variables. The documentation mentions `env` for instance creation. Maybe using the CLI with a temporary file for `--env-file` could keep secrets safe, but I should check `vastai create instance --help` to clarify my options first.


vastai create instance --help | sed -n '1,200p'


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
usage: vastai create instance ID [OPTIONS] [--args ...]

positional arguments:
 id                                id of instance type to launch (returned from search offers)

options:
 -h, --help                        show this help message and exit
 --template_hash TEMPLATE_HASH     Create instance from template info
 --user USER                       User to use with docker create. This breaks some images, so only use this if you are certain you need it.
 --disk DISK                       size of local disk partition in GB
 --image IMAGE                     docker container image to launch
 --login LOGIN                     docker login arguments for private repo authentication, surround with ''
 --label LABEL                     label to set on the instance
 --onstart ONSTART                 filename to use as onstart script
 --onstart-cmd ONSTART_CMD         contents of onstart script as single argument
 --entrypoint ENTRYPOINT           override entrypoint for args launch instance
 --ssh                             Launch as an ssh instance type
 --jupyter                         Launch as a jupyter instance instead of an ssh instance
 --direct                          Use (faster) direct connections for jupyter & ssh
 --jupyter-dir JUPYTER_DIR         For runtype 'jupyter', directory in instance to use to launch jupyter. Defaults to image's working directory
 --jupyter-lab                     For runtype 'jupyter', Launch instance with jupyter lab
 --lang-utf8                       Workaround for images with locale problems: install and generate locales before instance launch, and set locale to C.UTF-8
 --python-utf8                     Workaround for images with locale problems: set python's locale to C.UTF-8
 --env ENV                         env variables and port mapping options, surround with ''
 --args ...                        list of arguments passed to container ENTRYPOINT. Onstart is recommended for this purpose. (must be last argument)
 --force                           Skip sanity checks when creating from an existing instance
 --cancel-unavail                  Return error if scheduling fails (rather than creating a stopped instance)
 --bid_price BID_PRICE             (OPTIONAL) create an INTERRUPTIBLE instance with per machine bid price in $/hour
 --create-volume VOLUME_ASK_ID     Create a new local volume using an ID returned from the "search volumes" command and link it to the new instance
 --link-volume EXISTING_VOLUME_ID  ID of an existing rented volume to link to the instance during creation. (returned from "show volumes" cmd)
 --volume-size VOLUME_SIZE         Size of the volume to create in GB. Only usable with --create-volume (default 15GB)
 --mount-path MOUNT_PATH           The path to the volume from within the new instance container. e.g. /root/volume
 --volume-label VOLUME_LABEL       (optional) A name to give the new volume. Only usable with --create-volume
 --url URL                         server REST api url
 --retry RETRY                     retry limit
 --raw                             output machine-readable json
 --explain                         output verbose explanation of mapping of CLI calls to HTTPS API endpoints
 --curl                            show a curl equivalency to the call
 --api-key API_KEY                 api key. defaults to using the one stored in /home/ubuntu/.config/vastai/vast_api_key
 --version                         show version

Performs the same action as pressing the "RENT" button on the website at https://console.vast.ai/create/
Creates an instance from an offer ID (which is returned from "search offers"). Each offer ID can only be used to create one instance.
Besides the offer ID, you must pass in an '--image' argument as a minimum.

If you use args/entrypoint launch mode, we create a container from your image as is, without attempting to inject ssh and or jupyter.
If you use the args launch mode, you can override the entrypoint with --entrypoint, and pass arguments to the entrypoint with --args.
If you use --args, that must be the last argument, as any following tokens are consumed into the args string.
For ssh/jupyter launch types, use --onstart-cmd to pass in startup script, instead of --entrypoint and --args.

Examples:

# create an on-demand instance with the PyTorch (cuDNN Devel) template and 64GB of disk
vastai create instance 384826 --template_hash 661d064bbda1f2a133816b6d55da07c3 --disk 64

# create an on-demand instance with the pytorch/pytorch image, 40GB of disk, open 8081 udp, direct ssh, set hostname to billybob, and a small onstart script
vastai create instance 6995713 --image pytorch/pytorch --disk 40 --env '-p 8081:8081/udp -h billybob' --ssh --direct --onstart-cmd "env | grep _ >> /etc/environment; echo 'starting up'";

# create an on-demand instance with the bobsrepo/pytorch:latest image, 20GB of disk, open 22, 8080, jupyter ssh, and set some env variables
vastai create instance 384827  --image bobsrepo/pytorch:latest --login '-u bob -p 9d8df!fd89ufZ docker.io' --jupyter --direct --env '-e TZ=PDT -e XNAME=XX4 -p 22:22 -p 8080:8080' --disk 20

# create an on-demand instance with the pytorch/pytorch image, 40GB of disk, override the entrypoint to bash and pass bash a simple command to keep the instance running. (args launch without ssh/jupyter)
vastai create instance 5801802 --image pytorch/pytorch --disk 40 --onstart-cmd 'bash' --args -c 'echo hello; sleep infinity;'

# create an interruptible (spot) instance with the PyTorch (cuDNN Devel) template, 64GB of disk, and a bid price of $0.10/hr
vastai create instance 384826 --template_hash 661d064bbda1f2a133816b6d55da07c3 --disk 64 --bid_price 0.1

Return value:
Returns a json reporting the instance ID of the newly created instance:
{'success': True, 'new_contract': 7835610}


**Considering command security**

I realize there's no env-file available, meaning the command will include secrets. I wonder if I can use `vastai create instance` with `--env` to reference an environment variable so that the command line doesn’t expose secrets. I think `$VAST_ENV` could be set in the shell, but the tool may show the command string. I need to ensure that sensitive information isn’t printed or echoed in logs while still sending the proper values to `vastai create instance`.


**Navigating command line security**

I need to construct a command string while being cautious about handling secrets in the .env file. Using `source .env` is okay since it won’t expose secrets directly in the command. However, I must ensure nothing echoes to stdout inadvertently, so I won't use `set -x`. I can run `vastai create instance` with `--env` while ensuring the command shows the variable names like `$HF_TOKEN`, but not their values. There’s a potential risk if the environment variables have special characters that could break quoting.


**Managing secrets in command execution**

I think I can use a Python script to build and pass the required commands, but if I go the Bash route, quoting might get tricky. Instead, I can utilize `--onstart-cmd` as a string that doesn’t include any secrets. This allows me to construct an environment string safely in the CLI. The expanded string will be passed, but won’t be echoed unless `vastai` outputs it on error. I’ll verify that the environment variables are set to minimize errors while capturing any output as needed.


**Planning script execution**

I’m thinking we can proceed with a Bash script using environment variables that we load from the `.env` file or directly from the current instance. First, I need to ensure the `.env` file is in the repository and is correctly listed in gitignore. I’ll source the `.env`, ensuring to use `set -a` for exporting variables. If there are spaces or quotes in the file, I’ll handle that as needed. Afterward, I’ll search for offers using `vastai` and pick the first two. I also need to destroy any old instances before creating the new ones. It looks like there's a plan forming here!


**Formulating a plan for instance recovery**

I’m thinking through our plan of action to address the failing entry point due to a missing `src.worker`. First, I’ll identify that issue, then add the compatible worker entry point module. After that, I’ll recreate the Vast instances with the corrected `onstart`. Finally, I’ll verify that the new instances are running smoothly. I’ll utilize the `update_plan` tool to manage these changes effectively. This seems like a solid approach!


Output: Plan updated


I’ve completed step one by inspecting the onstart and the repository, and I realized that the src/worker is missing. Now I’m marking this part as complete and moving on to step two. I wonder what the next challenges might be, but it feels good to be making progress. Let’s see what I can tackle in the next phase!


Output: Plan updated


Summary: auto


**Implementing worker file**

I'm diving into implementing `src/worker.py` now. I might create a file in `src` and consider adding `__main__.py`, although it's not strictly necessary since the worker will be invoked via the module. I think about updating `src/__init__.py` as well, checking for `onstart`, which is currently missing. When I add that in, I'd want to include a compatibility wrapper for the Vast `onstart` command without going overboard on comments, aside from the essential docstring.


**Implementing main function**

I'm working on the implementation where I define a main function to handle command-line options. I’ve set it up to remove `--provider` and `--region` options, while also checking input mode flags. If no input flags are present, I want to default to `--supabase-queue`. The default for R2 uploads should be true unless specifically overridden. I think I’ll handle the `-i` flag as specifying input, and also consider setting a default output path to `./output`, even though I worry this might lead to issues if it conflicts with existing expectations.


**Setting defaults for options**

I’m thinking about how our `onstart` function doesn’t pass `--output`. To align with desired outcomes, I plan to set the default output to `./output` if not specified. It seems like a good idea to also include a default setting for `--export-workers` at 4, in case it's not provided. I’m contemplating defaults for `--workers-per-gpu` as 2 too.

Additionally, if a user passes `--help`, we should show help for the wrapper but direct them to the massive_process help. Lastly, I need to address how to handle flags like `--no-r2-upload` properly since they aren't known to the massive_process function but are still relevant.


**Updating argument handling**

I need to remove certain flags, like `--provider` and `--region`, before calling the main function. In the wrapper, I'll make sure to take out `--no-r2-upload` and not add `--r2-upload`, as well as `--no-background-export`. I think it’s simpler to handle parsing without using argparse, just following the structured approach I have in mind. Additionally, I’ll include the classic `if __name__ == "__main__": main()` to ensure smooth execution. Now, I should focus on updating my implementation plan using `apply_patch`.


Input:
*** Begin Patch
*** Add File: src/worker.py
+"""
+Compatibility entrypoint for deployments that run `python -m src.worker`.
+
+This repo's primary CLI is `massive_process.py`. Some deploy templates invoke
+`src.worker` and omit flags that `massive_process.py` requires. This wrapper:
+
+- Defaults to `--supabase-queue` when no input mode is specified.
+- Defaults to `--r2-upload` and `--background-export` unless explicitly disabled.
+- Accepts and ignores legacy `--provider` / `--region` flags.
+"""
+
+from __future__ import annotations
+
+import runpy
+import sys
+from typing import Iterable, List, Optional
+
+
+def _strip_kv_arg(argv: List[str], key: str) -> List[str]:
+    out: List[str] = []
+    i = 0
+    while i < len(argv):
+        token = argv[i]
+        if token == key:
+            i += 2
+            continue
+        if token.startswith(f"{key}="):
+            i += 1
+            continue
+        out.append(token)
+        i += 1
+    return out
+
+
+def _has_any_flag(argv: List[str], flags: Iterable[str]) -> bool:
+    flag_set = set(flags)
+    return any(a in flag_set for a in argv)
+
+
+def main(argv: Optional[List[str]] = None) -> None:
+    args = list(sys.argv[1:] if argv is None else argv)
+
+    # Legacy flags used by some deploy templates (not used by massive_process.py).
+    args = _strip_kv_arg(args, "--provider")
+    args = _strip_kv_arg(args, "--region")
+
+    # Default to supabase queue mode if no input mode is provided.
+    if not _has_any_flag(args, ["--input", "-i", "--benchmark", "--urls", "--supabase-queue"]):
+        args.insert(0, "--supabase-queue")
+
+    # Provide sane defaults for typical "worker" deployments.
+    if not _has_any_flag(args, ["--output", "-o"]):
+        args.extend(["--output", "./output"])
+
+    if "--no-r2-upload" in args:
+        args = [a for a in args if a != "--no-r2-upload"]
+    elif "--r2-upload" not in args:
+        args.append("--r2-upload")
+
+    if "--no-background-export" in args:
+        args = [a for a in args if a != "--no-background-export"]
+    elif "--background-export" not in args:
+        args.append("--background-export")
+
+    if "--export-workers" not in args:
+        args.extend(["--export-workers", "4"])
+
+    sys.argv = ["massive_process.py", *args]
+    runpy.run_module("massive_process", run_name="__main__")
+
+
+if __name__ == "__main__":
+    main()
+
*** End Patch


Output: {"output":"Success. Updated the following files:\nA src/worker.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Output: Plan updated


vastai search offers 'gpu_name=RTX_4090 num_gpus=8 disk_space>=100 reliability>0.98 verified=true rentable=true' -o 'dph_total' --raw | head -n 40


Output:
Exit code: 0
Wall time: 0.5 seconds
Output:
[
 {
  "ask_contract_id": 28084732,
  "avail_vol_ask_id": 28084735,
  "avail_vol_dph": 0.0002777777777777778,
  "avail_vol_size": 699.0,
  "bundle_id": 399964655,
  "bundled_results": null,
  "bw_nvlink": 0.0,
  "cluster_id": null,
  "compute_cap": 890,
  "cpu_arch": "amd64",
  "cpu_cores": 96,
  "cpu_cores_effective": 96.0,
  "cpu_ghz": 2.6,
  "cpu_name": "AMD EPYC 7K62 48-Core Processor",
  "cpu_ram": 515625,
  "credit_discount_max": 0.0,
  "cuda_max_good": 12.6,
  "direct_port_count": 998,
  "discount_rate": 0.0,
  "discounted_dph_total": 2.1347222222222224,
  "discounted_hourly": 0.0,
  "disk_bw": 776.0,
  "disk_name": "KINGSTON SNV2S2000G",
  "disk_space": 1398.0,
  "dlperf": 262.16563637806416,
  "dlperf_per_dphtotal": 122.81018750306193,
  "dph_base": 2.1333333333333333,
  "dph_total": 2.1347222222222224,
  "dph_total_adj": 2.1396050347222224,
  "driver_vers": 560035005,
  "driver_version": "560.35.05",
  "duration": 11526523.385716677,
  "end_date": 1779373995.0,
  "expected_reliability": 0.0,
  "external": null,
  "flops_per_dphtotal": 305.03532929082627,
  "geolocation": ", CN",
  "geolocode": 921675020,


**Evaluating geolocation filtering**

The search results include fields like geolocation, and I'm unsure about filtering out China. I could use `geolocation notin [CN,RU]`, but it seems the field for geolocation is actually called `country_code`. There are instances from the Netherlands and Taiwan, so maybe filtering just for CN makes sense. I see that `geolocation` is being referenced. I wonder if I should use `country_code` instead. I’ll settle on filtering top offers not in CN, as the user didn't specify.


**Choosing low internet cost offers**

I want to select offers with low internet costs, but it’s essential to check how to identify the right fields. It seems like the correct offer ID for creating an instance might be `id` or `ask_contract_id`. Based on the vastai CLI, I need to confirm what the search output returns. In the raw output, I see `ask_contract_id`. I’ll examine further down using `jq` to parse and get the first object keys, which should help clarify things.


vastai search offers 'gpu_name=RTX_4090 num_gpus=8 disk_space>=100 reliability>0.98 verified=true rentable=true' -o 'dph_total' --raw | jq '.[0] | {id,ask_contract_id,offer_id: .id}'


Output:
Exit code: 0
Wall time: 0.5 seconds
Output:
{
  "id": 27347138,
  "ask_contract_id": 27347138,
  "offer_id": 27347138
}


**Filtering geolocation for offers**

I’m working with the ask_contract_id, and it’s clear we need two offers from the search results while avoiding certain locations like China and Russia. To ensure reliability, I’ll focus on results with reliability greater than 0.98. However, since geolocation can still include CN, I’ll craft a query with `geolocation notin [CN, RU]` to filter them out for safety. It’ll be important to test this query to see if it produces the desired results.


vastai search offers 'gpu_name=RTX_4090 num_gpus=8 disk_space>=100 reliability>0.98 verified=true rentable=true geolocation notin [CN,RU]' -o 'dph_total' --raw | jq '.[0:5] | map({id,geolocation,dph_total,reliability})'


Output:
Exit code: 0
Wall time: 0.6 seconds
Output:
[
  {
    "id": 29345860,
    "geolocation": "Taiwan, TW",
    "dph_total": 2.4561111111111114,
    "reliability": 0.996395
  },
  {
    "id": 27958980,
    "geolocation": ", US",
    "dph_total": 2.6671296296296294,
    "reliability": 0.9964375
  },
  {
    "id": 24566585,
    "geolocation": ", US",
    "dph_total": 2.6680555555555556,
    "reliability": 0.9984788
  },
  {
    "id": 27444543,
    "geolocation": "Thailand, TH",
    "dph_total": 2.7533888888888884,
    "reliability": 0.9939571
  },
  {
    "id": 28563358,
    "geolocation": "United Kingdom, GB",
    "dph_total": 2.881388888888889,
    "reliability": 0.9918126
  }
]


vastai destroy instances 29758333 29758334 --raw


Output:
Exit code: 0
Wall time: 1.1 seconds
Output:
null


**Verifying instances**

I'm checking to see if it returned null and figuring out if there's any output to analyze. I think I need to run a command like `vastai show instances -q` to list the instances and make sure they're not present. It feels like a necessary step to ensure everything is clear and accurate! I want to confirm that nothing is lingering around that shouldn't be. Let's see what information comes up!