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How to use _persist_state method in localstack

Best Python code snippet using localstack_python

jobs.py

Source:jobs.py

...70        except botocore.exceptions.ClientError as error:71            if error.response["Error"]["Code"] != "NoSuchKey":72                raise error73        self.state = {key: Job.parse_obj(val) for key, val in read_state.items()}74    def _persist_state(self) -> None:75        """Persist the jobs state to S3"""76        state_dict = {key: val.dict() for key, val in self.state.items()}77        serialized = bytes(json.dumps(state_dict, cls=DateTimeEncoder), "utf-8")78        self.s3_client.put_object(Bucket=BUCKET_NAME, Key=JOBS_STATE_FILE, Body=serialized)79    def _generate_id(self) -> str:80        """Generate a unique Job ID"""81        i = 082        while i < MAX_TRIES:83            job_id = "".join(random.choices(string.ascii_lowercase, k=4))84            if job_id not in self.state:85                return job_id86            i += 187        raise Exception("Unable to generate ID.")88    def _create_batch_jobs(self, job_id: str, start_frame: int, end_frame: int, gpu: bool) -> Dict[int, BatchJob]:89        """Create batch jobs for a render job"""90        batch_jobs = {}91        # Pick job definition and queue92        job_def, job_queue = JOB_DEF_CPU, JOB_QUEUE_CPU93        if gpu:94            job_def, job_queue = JOB_DEF_GPU, JOB_QUEUE_GPU95        # Create a batch job for each frame96        for frame_num in range(start_frame, end_frame + 1):97            name = f"render-job-{job_id}-frame-{frame_num}"98            params = dict(frame=str(frame_num), job=job_id)99            retry_strategy = dict(100                attempts=3,101                evaluateOnExit=[102                    dict(onStatusReason="Host EC2*", action="RETRY"),103                    dict(onReason="*", action="EXIT"),104                ],105            )106            # Make request107            response = self.batch_client.submit_job(108                jobName=name,109                jobQueue=job_queue,110                jobDefinition=job_def,111                parameters=params,112                retryStrategy=retry_strategy,113            )114            # Create pydantic model115            batch_job = BatchJob(batch_id=response["jobId"], name=response["jobName"], frame=frame_num)116            batch_jobs[frame_num] = batch_job117            typer.echo(f"Created batch job {batch_job.name}")118        return batch_jobs119    def create_job(self, blend_path: str, start_frame: int, end_frame: int, gpu: bool = False) -> Job:120        """121        Create a new job. That process composes of several steps:122        1. Generate a unique ID123        2. Upload blend file to S3124        3. Create AWS Batch jobs125        :param blend_path: Path to the blend file to upload.126        """127        # Reload state128        self._load_state()129        # Generate ID130        typer.echo("Generating unique ID...")131        job_id = self._generate_id()132        # Upload blend file to S3133        typer.echo("Uploading blend file to S3...")134        obj_key = f"jobs/{job_id}/main.blend"135        self.s3_client.upload_file(Filename=blend_path, Bucket=BUCKET_NAME, Key=obj_key)136        # Create AWS Batch jobs137        typer.echo("Creating batch jobs...")138        batch_jobs = self._create_batch_jobs(job_id, start_frame, end_frame, gpu)139        # Create pydantic model140        job = Job(141            job_id=job_id,142            creation_date=datetime.now(),143            children=batch_jobs,144            start_frame=start_frame,145            end_frame=end_frame,146            gpu=gpu,147            file_name=Path(blend_path).name,148            status=STATUS_RUNNING,149        )150        self.state[job.job_id] = job151        # Persist state152        self._persist_state()153        typer.echo(f"Created render job with id: {job.job_id}")154        return job155    def list_jobs(self) -> List[Job]:156        """List available jobs in descending order of their creation date"""157        # Reload state158        self._load_state()159        # Refresh job statuses160        for job in self.state.values():161            if job.status not in (STATUS_SUCCEEDED, STATUS_ERROR):162                self.state[job.job_id] = self._refresh_job(job)163        # Persist state164        self._persist_state()165        # Parse jobs166        jobs = list(self.state.values())167        jobs = sorted(jobs, key=lambda job: job.creation_date, reverse=True)168        return jobs169    def _refresh_children(self, children: Dict[int, BatchJob]) -> Dict[int, BatchJob]:170        """Refresh a set of batch jobs by querying the AWS API"""171        job_ids = [job.batch_id for job in children.values()]172        id_maps = {val.batch_id: key for key, val in children.items()}173        # Iterate over chunks of 100 jobs maximum (limited by AWS)174        cur_chunk, chunk_size = 0, 100175        while cur_chunk < (len(children) // chunk_size) + 1:176            chunk_start, chunk_end = (177                cur_chunk * chunk_size,178                (cur_chunk + 1) * chunk_size,179            )180            # Request batch of jobs181            response = self.batch_client.describe_jobs(jobs=job_ids[chunk_start:chunk_end])182            # Parse results183            for obj in response["jobs"]:184                children[id_maps[obj["jobId"]]].status = obj["status"]185                # Check if startedAt is set186                if "startedAt" in obj:187                    unix_ts = int(str(obj["startedAt"])[0:-3])188                    children[id_maps[obj["jobId"]]].started_at = datetime.fromtimestamp(unix_ts)189                if "stoppedAt" in obj:190                    unix_ts = int(str(obj["stoppedAt"])[0:-3])191                    children[id_maps[obj["jobId"]]].stopped_at = datetime.fromtimestamp(unix_ts)192            cur_chunk += 1193        return children194    def _refresh_job(self, job: Job) -> Job:195        """Refresh a job by querying its children and checking it's status"""196        # Refresh children197        job.children = self._refresh_children(job.children)198        # Determine parent job's status199        completion_date = None200        running = False201        ran_into_error = False202        for batch_job in job.children.values():203            # Check if error encountered204            if batch_job.status == "FAILED":205                ran_into_error = True206            # Check if job is still running207            if batch_job.status in (208                "SUBMITTED",209                "PENDING",210                "RUNNABLE",211                "STARTING",212                "RUNNING",213            ):214                running = True215            # Set completion time216            if batch_job.stopped_at is not None:217                if completion_date is None or completion_date < batch_job.stopped_at:218                    completion_date = batch_job.stopped_at219        if not running and not ran_into_error:220            job.status = STATUS_SUCCEEDED221        elif not running and ran_into_error:222            job.status = STATUS_ERROR223        else:224            job.status = STATUS_RUNNING225        job.completion_date = completion_date226        return job227    def get_job(self, job_id: str) -> Optional[Job]:228        """Fetch a job from state by its job ID"""229        # Refresh state230        self._load_state()231        # Return None if not found232        if job_id not in self.state:233            return None234        job = self.state[job_id]235        # Update job236        job = self._refresh_job(job)237        # Persist state238        self.state[job_id] = job239        self._persist_state()240        return job241    def delete_job(self, job: Job) -> None:242        """Cancel a job and remove it from the state"""243        # Refresh state244        self._load_state()245        # Cancel children246        typer.echo("Cancelling batch jobs...")247        for batch_job in job.children.values():248            self.batch_client.cancel_job(jobId=batch_job.batch_id, reason="Canceled by user.")249        # Remove S3 directory250        typer.echo("Removing artifacts...")251        self.bucket.objects.filter(Prefix=f"jobs/{job.job_id}").delete()252        # Remove job from state253        typer.echo("Delete job from state...")254        del self.state[job.job_id]255        # Persist state256        self._persist_state()257    def sync_files(self, job: Job, output_path: str) -> None:258        """Sync a job's files to local disk."""259        # Fetch all objects belonging to a specific job260        job_path = f"jobs/{job.job_id}"261        job_objects = self.bucket.objects.filter(Prefix=job_path)262        # Download each non-blend object263        for obj in job_objects:264            if obj.key.split(".")[-1] != "blend":265                # Determine output path266                obj_subpath = obj.key.split(job_path + "/")[1]267                output_file = Path(output_path) / Path(obj_subpath)268                print(output_file)269                # Create directory if it does not exist270                if not os.path.exists(str(output_file.parent)):...

state.py

Source:state.py

...12                self._state = self._load_state()13        return self._state14    def save(self):15        with self.lock:16            self._persist_state()17            18    def _load_state(self):19        if not os.path.exists(self.filename):20            return {}21        with open(self.filename, 'rb') as f:22            return pickle.load(f)23        24    def _persist_state(self):25        with open(self.filename, 'wb') as f:26            pickle.dump(self._state, f)...

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